![\"Jina](\"https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp\"){kind=logo}
\n\n\n
\nThe embedding model trained by Jina AI.\n
\n\n\njina-embeddings-v3: Multilingual Embeddings With Task LoRA\n
\n\n## Quick Start\n\n[Blog](https://jina.ai/news/jina-embeddings-v3-a-frontier-multilingual-embedding-model/#parameter-dimensions) | [Azure](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/jinaai.jina-embeddings-v3-vm) | [AWS SageMaker](https://aws.amazon.com/marketplace/pp/prodview-kdi3xkt62lo32) | [API](https://jina.ai/embeddings)\n\n\n## Intended Usage & Model Info\n\n\n`jina-embeddings-v3` is a **multilingual multi-task text embedding model** designed for a variety of NLP applications.\nBased on the [Jina-XLM-RoBERTa architecture](https://huggingface.co/jinaai/xlm-roberta-flash-implementation), \nthis model supports Rotary Position Embeddings to handle long input sequences up to **8192 tokens**.\nAdditionally, it features 5 LoRA adapters to generate task-specific embeddings efficiently.\n\n### Key Features:\n- **Extended Sequence Length:** Supports up to 8192 tokens with RoPE.\n- **Task-Specific Embedding:** Customize embeddings through the `task` argument with the following options:\n - `retrieval.query`: Used for query embeddings in asymmetric retrieval tasks\n - `retrieval.passage`: Used for passage embeddings in asymmetric retrieval tasks\n - `separation`: Used for embeddings in clustering and re-ranking applications\n - `classification`: Used for embeddings in classification tasks\n - `text-matching`: Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks\n- **Matryoshka Embeddings**: Supports flexible embedding sizes (`32, 64, 128, 256, 512, 768, 1024`), allowing for truncating embeddings to fit your application.\n\n### Supported Languages:\nWhile the foundation model supports 100 languages, we've focused our tuning efforts on the following 30 languages: \n**Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek, \nHindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian, \nRussian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu,** and **Vietnamese.**\n\n\n> **\u26a0\ufe0f Important Notice:** \n> We fixed a bug in the `encode` function [#60](https://huggingface.co/jinaai/jina-embeddings-v3/discussions/60) where **Matryoshka embedding truncation** occurred *after normalization*, leading to non-normalized truncated embeddings. This issue has been resolved in the latest code revision. \n> \n> If you have encoded data using the previous version and wish to maintain consistency, please use the specific code revision when loading the model: `AutoModel.from_pretrained('jinaai/jina-embeddings-v3', code_revision='da863dd04a4e5dce6814c6625adfba87b83838aa', ...)`\n\n\n## Usage\n\n**\n\n### Why Use Mean Pooling?\n\nMean pooling takes all token embeddings from the model's output and averages them at the sentence or paragraph level. \nThis approach has been shown to produce high-quality sentence embeddings.\n\nWe provide an `encode` function that handles this for you automatically.\n\nHowever, if you're working with the model directly, outside of the `encode` function, \nyou'll need to apply mean pooling manually. Here's how you can do it:\n\n\n```python\nimport torch\nimport torch.nn.functional as F\nfrom transformers import AutoTokenizer, AutoModel\n\n\ndef mean_pooling(model_output, attention_mask):\n token_embeddings = model_output[0]\n input_mask_expanded = (\n attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()\n )\n return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(\n input_mask_expanded.sum(1), min=1e-9\n )\n\n\nsentences = [\"How is the weather today?\", \"What is the current weather like today?\"]\n\ntokenizer = AutoTokenizer.from_pretrained(\"jinaai/jina-embeddings-v3\")\nmodel = AutoModel.from_pretrained(\"jinaai/jina-embeddings-v3\", trust_remote_code=True)\n\nencoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors=\"pt\")\ntask = 'retrieval.query'\ntask_id = model._adaptation_map[task]\nadapter_mask = torch.full((len(sentences),), task_id, dtype=torch.int32)\nwith torch.no_grad():\n model_output = model(**encoded_input, adapter_mask=adapter_mask)\n\nembeddings = mean_pooling(model_output, encoded_input[\"attention_mask\"])\nembeddings = F.normalize(embeddings, p=2, dim=1)\n\n```\n\n
\n\n\nYou can use ONNX for efficient inference with `jina-embeddings-v3`:\n```python\nimport onnxruntime\nimport numpy as np\nfrom transformers import AutoTokenizer, PretrainedConfig\n\n# Mean pool function\ndef mean_pooling(model_output: np.ndarray, attention_mask: np.ndarray):\n token_embeddings = model_output\n input_mask_expanded = np.expand_dims(attention_mask, axis=-1)\n input_mask_expanded = np.broadcast_to(input_mask_expanded, token_embeddings.shape)\n sum_embeddings = np.sum(token_embeddings * input_mask_expanded, axis=1)\n sum_mask = np.clip(np.sum(input_mask_expanded, axis=1), a_min=1e-9, a_max=None)\n return sum_embeddings / sum_mask\n\n# Load tokenizer and model config\ntokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v3')\nconfig = PretrainedConfig.from_pretrained('jinaai/jina-embeddings-v3')\n\n# Tokenize input\ninput_text = tokenizer('sample text', return_tensors='np')\n\n# ONNX session\nmodel_path = 'jina-embeddings-v3/onnx/model.onnx'\nsession = onnxruntime.InferenceSession(model_path)\n\n# Prepare inputs for ONNX model\ntask_type = 'text-matching'\ntask_id = np.array(config.lora_adaptations.index(task_type), dtype=np.int64)\ninputs = {\n 'input_ids': input_text['input_ids'],\n 'attention_mask': input_text['attention_mask'],\n 'task_id': task_id\n}\n\n# Run model\noutputs = session.run(None, inputs)[0]\n\n# Apply mean pooling and normalization to the model outputs\nembeddings = mean_pooling(outputs, input_text[\"attention_mask\"])\nembeddings = embeddings / np.linalg.norm(embeddings, ord=2, axis=1, keepdims=True)\n```\n\n
\nsentence_0, sentence_1, and sentence_2\n* Approximate statistics based on the first 1000 samples:\n | | sentence_0 | sentence_1 | sentence_2 |\n |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|\n | type | string | string | string |\n | details | how much does it cost to paint a interior house | Interior House Painting Cost Factors. Generally, it will take a minimum of two gallons of paint to cover a room. At the highest end, paint will cost anywhere between $30 and $60 per gallon and come in three different finishes: flat, semi-gloss or high-gloss.Flat finishes are the least shiny and are best suited for areas requiring frequent cleaning.rovide a few details about your project and receive competitive quotes from local pros. The average national cost to paint a home interior is $1,671, with most homeowners spending between $966 and $2,426. | Question DetailsAsked on 3/12/2014. Guest_... How much does it cost per square foot to paint the interior of a house? We just bought roughly a 1500 sg ft townhouse and want to get the entire house, including ceilings painted (including a roughly 400 sq ft finished basement not included in square footage). |\n | when is s corp taxes due | If you form a corporate entity for your small business, regardless of whether it's taxed as a C or S corporation, a tax return must be filed with the Internal Revenue Service on its due date each year. Corporate tax returns are always due on the 15th day of the third month following the close of the tax year. The actual day that the tax return filing deadline falls on, however, isn't the same for every corporation. | Before Jan. 1, 2026 After Dec. 31, 2025 Starting with 2016 tax returns, all. other C corps besides Dec. 31 and. June 30 year-ends (including those with. other fiscal year-ends) will be due on. the 15th of the 4th month after the. |\n | what are disaccharides | Disaccharides are formed when two monosaccharides are joined together and a molecule of water is removed, a process known as dehydration reaction. For example; milk sugar (lactose) is made from glucose and galactose whereas the sugar from sugar cane and sugar beets (sucrose) is made from glucose and fructose.altose, another notable disaccharide, is made up of two glucose molecules. The two monosaccharides are bonded via a dehydration reaction (also called a condensation reaction or dehydration synthesis) that leads to the loss of a molecule of water and formation of a glycosidic bond. | Disaccharides- Another type of carbohydrate. How many sugar units are disaccharides composed of?_____ What elements make up disaccharides? _____ How does the body use disaccharides? _____ There is no chemical test for disaccharides. Table sugar (white granulated sugar) is an example of a disaccharide. List some foods that contain a lot of disaccharides: _____ |\n* Loss: beir.losses.bpr_loss.BPRLoss\n\n### Training Hyperparameters\n#### Non-Default Hyperparameters\n\n- `eval_strategy`: steps\n- `per_device_train_batch_size`: 32\n- `per_device_eval_batch_size`: 32\n- `num_train_epochs`: 8\n- `multi_dataset_batch_sampler`: round_robin\n\n#### All Hyperparameters\nEmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)\n\n| Metric | Value |\n|:--------------------|:-----------|\n| pearson_cosine | 0.4774 |\n| **spearman_cosine** | **0.4969** |\n\n\n\n\n\n## Training Details\n\n### Training Dataset\n\n#### Unnamed Dataset\n\n\n* Size: 63,802 training samples\n* Columns: doc, candidate, and label\n* Approximate statistics based on the first 1000 samples:\n | | doc | candidate | label |\n |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|\n | type | string | string | int |\n | details | \u043c\u0430\u0441\u0441\u0430\u0436\u0435\u0440 xiaomi massage gun eu bhr5608eu | \u043f\u0435\u0440\u043a\u0443\u0441\u0441\u0438\u043e\u043d\u043d\u044b\u0439 \u043c\u0430\u0441\u0441\u0430\u0436\u0435\u0440 xiaomi massage gun mini bhr6083gl | 0 |\n | \u0431\u0435\u0437\u0443\u0434\u0430\u0440\u043d\u0430\u044f \u0434\u0440\u0435\u043b\u044c ingco ed50028 | \u0443\u0434\u0430\u0440\u043d\u0430\u044f \u0434\u0440\u0435\u043b\u044c ingco id211002 | 0 |\n | \u0436\u0438\u0434\u043a\u043e\u0441\u0442\u044c old smuggler 30\u043c\u043b 20\u043c\u0433 | \u0436\u0438\u0434\u043a\u043e\u0441\u0442\u044c old smuggler salt 30ml marlboro 20mg | 0 |\n* Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:\n ```json\n {\n \"scale\": 20.0,\n \"similarity_fct\": \"pairwise_cos_sim\"\n }\n ```\n\n### Evaluation Dataset\n\n#### Unnamed Dataset\n\n\n* Size: 7,090 evaluation samples\n* Columns: doc, candidate, and label\n* Approximate statistics based on the first 1000 samples:\n | | doc | candidate | label |\n |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|\n | type | string | string | int |\n | details | \u043a\u0440\u0443\u0433\u043b\u043e\u0435 \u043f\u043b\u044f\u0436\u043d\u043e\u0435 \u043f\u0430\u0440\u0435\u043e \u0441\u0435\u043b\u0444\u0438 \u043a\u043e\u0432\u0440\u0438\u043a \u043f\u043b\u044f\u0436\u043d\u0430\u044f \u043f\u043e\u0434\u0441\u0442\u0438\u043b\u043a\u0430 \u043f\u043b\u044f\u0436\u043d\u043e\u0435 \u043f\u043e\u043a\u0440\u044b\u0432\u0430\u043b\u043e \u043f\u043b\u044f\u0436\u043d\u044b\u0439 \u043a\u043e\u0432\u0440\u0438\u043a \u043f\u0438\u0440\u043e\u0436\u0435\u043d\u043a\u043e | \u043a\u0440\u0443\u0433\u043b\u043e\u0435 \u043f\u043b\u044f\u0436\u043d\u043e\u0435 \u043f\u0430\u0440\u0435\u043e \u0441\u0435\u043b\u0444\u0438 \u043a\u043e\u0432\u0440\u0438\u043a \u043f\u043b\u044f\u0436\u043d\u0430\u044f \u043f\u043e\u0434\u0441\u0442\u0438\u043b\u043a\u0430 \u043f\u043b\u044f\u0436\u043d\u043e\u0435 \u043f\u043e\u043a\u0440\u044b\u0432\u0430\u043b\u043e \u043f\u043b\u044f\u0436\u043d\u044b\u0439 \u043a\u043e\u0432\u0440\u0438\u043a \u043a\u043b\u0443\u0431\u043d\u0438\u043a\u0430 | 0 |\n | \u0430\u043a\u043a\u0443\u043c\u0443\u043b\u044f\u0442\u043e\u0440 \u0431\u0430\u0442\u0430\u0440\u0435\u044f \u0434\u043b\u044f \u043d\u043e\u0443\u0442\u0431\u0443\u043a\u0430 asus g751 | \u0430\u043a\u043a\u0443\u043c\u0443\u043b\u044f\u0442\u043e\u0440 \u0431\u0430\u0442\u0430\u0440\u0435\u044f \u0434\u043b\u044f \u043d\u043e\u0443\u0442\u0431\u0443\u043a\u0430 asus g75 series | 0 |\n | \u043c\u0438\u043a\u0441\u0435\u0440 bosch mfq3520 mfq 3520 | \u043c\u0438\u043a\u0441\u0435\u0440 bosch mfq 4020 | 0 |\n* Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:\n ```json\n {\n \"scale\": 20.0,\n \"similarity_fct\": \"pairwise_cos_sim\"\n }\n ```\n\n### Training Hyperparameters\n#### Non-Default Hyperparameters\n\n- `eval_strategy`: steps\n- `per_device_train_batch_size`: 16\n- `per_device_eval_batch_size`: 16\n- `num_train_epochs`: 2\n- `lr_scheduler_type`: cosine\n- `warmup_ratio`: 0.1\n- `load_best_model_at_end`: True\n- `batch_sampler`: no_duplicates\n\n#### All Hyperparameters\nsentence1, sentence2, and label\n* Approximate statistics based on the first 1000 samples:\n | | sentence1 | sentence2 | label |\n |:--------|:--------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|\n | type | string | string | float |\n | details | i rent i be curious yellow from my video store because of all the controversy that surround it when it be first release in 1967. i also hear that at first it be seize by u. s. custom if it ever try to enter this country and therefore be a fan of film consider controversial i really have to see this for myself . the plot be center around a young swedish drama student name lena who want to learn everything she can about life . in particular she want to focus her attention to make some sort of documentary on what the average swede think about certain political issue such a the vietnam war and race issue in the united state . in between ask politician and ordinary denizen of stockholm about their opinion on politics and she have sex with her drama teacher and classmate and and marry men . what kill me about i be curious yellow be that 40 year ago and this be consider pornographic . really and the sex and nudity scene be few and far between and even then it not shot like some cheaply make porno . while my countryman mind find it shock and in reality sex and nudity be a major staple in swedish cinema . even ingmar bergman and arguably their answer to good old boy john ford and have sex scene in his film . i do commend the filmmaker for the fact that any sex show in the film be show for artistic purpose rather than just to shock people and make money to be show in pornographic theater in america . i be curious yellow be a good film for anyone want to study the meat and potato ( no pun intend ) of swedish cinema . but really and this film doesn not have much of a plot . | This is a semantically negative review. | 1.0 |\n | i rent i be curious yellow from my video store because of all the controversy that surround it when it be first release in 1967. i also hear that at first it be seize by u. s. custom if it ever try to enter this country and therefore be a fan of film consider controversial i really have to see this for myself . the plot be center around a young swedish drama student name lena who want to learn everything she can about life . in particular she want to focus her attention to make some sort of documentary on what the average swede think about certain political issue such a the vietnam war and race issue in the united state . in between ask politician and ordinary denizen of stockholm about their opinion on politics and she have sex with her drama teacher and classmate and and marry men . what kill me about i be curious yellow be that 40 year ago and this be consider pornographic . really and the sex and nudity scene be few and far between and even then it not shot like some cheaply make porno . while my countryman mind find it shock and in reality sex and nudity be a major staple in swedish cinema . even ingmar bergman and arguably their answer to good old boy john ford and have sex scene in his film . i do commend the filmmaker for the fact that any sex show in the film be show for artistic purpose rather than just to shock people and make money to be show in pornographic theater in america . i be curious yellow be a good film for anyone want to study the meat and potato ( no pun intend ) of swedish cinema . but really and this film doesn not have much of a plot . | This is a semantically positive review. | 0.0 |\n | i be curious represent yellow be a risible and pretentious steam pile . it doesn not matter what one political view be because this film can hardly be take seriously on any level . a for the claim that frontal male nudity be an automatic nc 17 and that isn not true . i have see r rat film with male nudity . grant and they only offer some fleeting view and but where be the r rat film with gap vulva and flap labium . nowhere and because they do not exist . the same go for those crappy cable show represent schlongs swing in the breeze but not a clitoris in sight . and those pretentious indie movie like the brown bunny and in which be treat to the site of vincent gallo throb johnson and but not a trace of pink visible on chloe sevigny . before cry ( or imply ) double standard in matter of nudity and the mentally obtuse should take into account one unavoidably obvious anatomical difference between men and woman represent there be no genitals on display when actresses appear nude and and the same can not be say for a man . in fact and you generally would not see female genitals in an american film in anything short of porn or explicit erotica . this allege double standard be less a double standard than an admittedly depressing ability to come to term culturally with the inside of woman body . | This is a semantically negative review. | 1.0 |\n* Loss: [OnlineContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)\n\n### Training Hyperparameters\n#### Non-Default Hyperparameters\n\n- `per_device_train_batch_size`: 64\n- `per_device_eval_batch_size`: 64\n\n#### All Hyperparameters\nsentence1, sentence2, and label\n* Approximate statistics based on the first 1000 samples:\n | | sentence1 | sentence2 | label |\n |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:--------------------------------------------------------------|\n | type | string | string | float |\n | details | There are two kinds of 1950s musicals. First you have the glossy MGM productions with big names and great music. And then you have the minor league with a less famous cast, less famous music and second rate directors. 'The Girl Can't Help It' belongs to the latter category. Neither Tom Ewell or Edmond O'Brien became famous and Jayne Mansfield was famous for her... well, never mind. Seems like every decade has its share of Bo Dereks or Pamela Andersons. The plot itself is thin as a razorblade and one can't help suspect that it is mostly an attempt to sell records for Fats Domino, Little Richard or others of the 1950s rock acts that appear in the movie. If that music appeals to you this is worth watching. If not, don't bother. | negative negative negative negative | 1.0 |\n | There are two kinds of 1950s musicals. First you have the glossy MGM productions with big names and great music. And then you have the minor league with a less famous cast, less famous music and second rate directors. 'The Girl Can't Help It' belongs to the latter category. Neither Tom Ewell or Edmond O'Brien became famous and Jayne Mansfield was famous for her... well, never mind. Seems like every decade has its share of Bo Dereks or Pamela Andersons. The plot itself is thin as a razorblade and one can't help suspect that it is mostly an attempt to sell records for Fats Domino, Little Richard or others of the 1950s rock acts that appear in the movie. If that music appeals to you this is worth watching. If not, don't bother. | positive positive positive positive | 0.0 |\n | Thankfully as a student I have been able to watch \"Diagnosis Murder\" for a number of years now. It is basically about a doctor who solves murders with the help of his LAPD son, a young doctor and a pathologist. DM provided 8 seasons of exceptional entertainment. What made it different from the many other cop shows and worth watching many times over was its cast and quality of writing. The main cast gave good performances and Dick Van Dyke's entertainer roots shone through with the use of magic, dance and humor. The best aspects of DM was the fast pace, witty scripts and of course the toe tapping score. Sadly it has been unfairly compared to \"Murder, She Wrote\". DM is far superior boasting more difficult mysteries to solve and more variety. Now it is gone TV is a worse place. Gone are the days of feelgood, family friendly cop shows. Now there is just depressing 'gritty' ones. | positive positive positive positive | 1.0 |\n* Loss: [OnlineContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)\n\n### Training Hyperparameters\n#### Non-Default Hyperparameters\n\n- `per_device_train_batch_size`: 64\n- `per_device_eval_batch_size`: 64\n\n#### All Hyperparameters\nsentence1, sentence2, and label\n* Approximate statistics based on the first 1000 samples:\n | | sentence1 | sentence2 | label |\n |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:--------------------------------------------------------------|\n | type | string | string | float |\n | details | There are two kinds of 1950s musicals. First you have the glossy MGM productions with big names and great music. And then you have the minor league with a less famous cast, less famous music and second rate directors. 'The Girl Can't Help It' belongs to the latter category. Neither Tom Ewell or Edmond O'Brien became famous and Jayne Mansfield was famous for her... well, never mind. Seems like every decade has its share of Bo Dereks or Pamela Andersons. The plot itself is thin as a razorblade and one can't help suspect that it is mostly an attempt to sell records for Fats Domino, Little Richard or others of the 1950s rock acts that appear in the movie. If that music appeals to you this is worth watching. If not, don't bother. | negative negative negative negative | 1.0 |\n | There are two kinds of 1950s musicals. First you have the glossy MGM productions with big names and great music. And then you have the minor league with a less famous cast, less famous music and second rate directors. 'The Girl Can't Help It' belongs to the latter category. Neither Tom Ewell or Edmond O'Brien became famous and Jayne Mansfield was famous for her... well, never mind. Seems like every decade has its share of Bo Dereks or Pamela Andersons. The plot itself is thin as a razorblade and one can't help suspect that it is mostly an attempt to sell records for Fats Domino, Little Richard or others of the 1950s rock acts that appear in the movie. If that music appeals to you this is worth watching. If not, don't bother. | positive positive positive positive | 0.0 |\n | Thankfully as a student I have been able to watch \"Diagnosis Murder\" for a number of years now. It is basically about a doctor who solves murders with the help of his LAPD son, a young doctor and a pathologist. DM provided 8 seasons of exceptional entertainment. What made it different from the many other cop shows and worth watching many times over was its cast and quality of writing. The main cast gave good performances and Dick Van Dyke's entertainer roots shone through with the use of magic, dance and humor. The best aspects of DM was the fast pace, witty scripts and of course the toe tapping score. Sadly it has been unfairly compared to \"Murder, She Wrote\". DM is far superior boasting more difficult mysteries to solve and more variety. Now it is gone TV is a worse place. Gone are the days of feelgood, family friendly cop shows. Now there is just depressing 'gritty' ones. | positive positive positive positive | 1.0 |\n* Loss: [OnlineContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)\n\n### Training Hyperparameters\n#### Non-Default Hyperparameters\n\n- `per_device_train_batch_size`: 64\n- `per_device_eval_batch_size`: 64\n\n#### All Hyperparameters\nanchor and positive\n* Approximate statistics based on the first 1000 samples:\n | | anchor | positive |\n |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|\n | type | string | string |\n | details | ###Question###:Area of Simple Shapes-Calculate the area of a parallelogram where the dimensions are given in the same units-What is the area of this shape? ![A parallelogram drawn on a square grid in purple with an area of 9 square units. The base is length 3 squares and the perpendicular height is also length 3 squares.]()
###Correct Answer###:\\( 9 \\)
###Misconcepted Incorrect answer###:\\( 12 \\) | Counts half-squares as full squares when calculating area on a square grid |\n | ###Question###:Substitution into Formula-Substitute into simple formulae given in words-A theme park charges \\( \u00a3 8 \\) entry fee and then \\( \u00a3 3 \\) for every ride you go on.
Heena goes on \\( 5 \\) rides.
How much does she pay in total?
###Correct Answer###:\\( \u00a3 23 \\)
###Misconcepted Incorrect answer###:\\( \u00a3 55 \\) | Combines variables with constants when writing a formula from a given situation |\n | ###Question###:Trial and Improvement and Iterative Methods-Use area to write algebraic expressions-The area of the rectangle on the right is \\( 8 \\mathrm{~cm}^{2} \\).
Which of the following equations can we write from the information given? ![A rectangle with the short side labelled \\(x\\) and the opposite side labelled \\(x^2 + 9\\).]()
###Correct Answer###:\\( x^{3}+9 x=8 \\)
###Misconcepted Incorrect answer###:\\( x^{3}+9=8 \\) | Only multiplies the first term in the expansion of a bracket |\n* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:\n ```json\n {\n \"scale\": 20.0,\n \"similarity_fct\": \"cos_sim\"\n }\n ```\n\n### Training Hyperparameters\n#### Non-Default Hyperparameters\n\n- `num_train_epochs`: 10\n- `push_to_hub`: True\n- `batch_sampler`: no_duplicates\n\n#### All Hyperparameters\nanchor, positive, negative_1, negative_2, and negative_3\n* Approximate statistics based on the first 1000 samples:\n | | anchor | positive | negative_1 | negative_2 | negative_3 |\n |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|\n | type | string | string | string | string | string |\n | details | What does the plot of the story revolve around | Respawn points are created when the player accumulates enough blood collected from slain enemies or in-level blood pickups, and idles a certain distance away from immediate level hazards. Plot
The plot follows the events of an unnamed young girl's arrival at the Lafcadio Academy for Troubled Young Ladies. | An really interesting idea behind the story and one that had me unable to put it down some nights! View all my reviews | And everything has such meaning and depth behind it. Nothing is just said casually, and it is all so thoughfully laced with emotion and words to draw you in to the story itself. | It has a terribly implication that this flashback may be lasting more than a chapter. It's not as if we aren't learning anything of importance. I'm just not curious where this is going. I'm wondering when it'll finally be over. Not something you want from your audience as a story teller. In no simple terms. |\n | What type of warranty is offered with the Zhumell Signature 10x42 binoculars | The Signature is also backed by Zhumell's full, 25-year, no-fault warranty, ensuring a lifetime of worry-free viewing. The Zhumell Signature 10x42 binoculars will give you plenty of power - whenever you need it, for as long as you need it! | This item is backed by a Limited Lifetime Warranty. In the event this item should fail due to manufacturing defects during intended use, we will exchange the part free of charge (excludes shipping charges) for the original purchaser. | if you have different ideas or better suggestion ,be free to leave message . Warranty and terms:
-Warranty year is 1 year under normal use,the warranty period is a year from the date of original purchase. | We have more than 55 years of experience designing, manufacturing and refining custom optical lenses for use in a range of industries. Our production staff follows strict ISO 9001 standards and uses state-of-the-art metrology equipment to test finished lenses for quality and performance. |\n | When did he announce his retirement from all professional rugby | He was named in the Pro12 Dream Teams at the end of the 2014/15 and 2016/17 seasons. In April 2021 he announced his retirement from all professional rugby. International career
Qualifying to play internationally for Scotland through his Glasgow-born mother, on 24 October 2012 he was named in the full Scottish national team for the 2012 end-of-year rugby union tests. | After retiring from full-time professional football, he worked as a production controller before becoming a sales administrator for International Computers Limited. He lived in Southampton for the rest of his life and died on 28 January 2014. | On December 15 2018, it was announced that he had left WWE voluntarily. Professional boxing record
{| class=\"wikitable\" style=\"text-align:center;\"
| style=\"text-align:center;\" colspan=\"8\" | 6 Wins (3 knockouts, 3 decisions), 0 Losses, 0 Draws
|- style=\"text-align:center; background:#e3e3e3;\"
| style=\"border-style:none none solid solid;\" | Res. | Since retiring from football he has worked as a journalist for the Professional Footballers' Association. References
English men's footballers
Bristol City F.C. players
Kidderminster Harriers F.C. players
Yeovil Town F.C. |\n* Loss: cachedselfloss2.CachedInfonce with these parameters:\n ```json\n {\n \"scale\": 20.0,\n \"similarity_fct\": \"cos_sim\"\n }\n ```\n\n### Evaluation Dataset\n\n#### hard_negative_merged\n\n* Dataset: hard_negative_merged\n* Size: 589,508 evaluation samples\n* Columns: anchor, positive, negative_1, negative_2, and negative_3\n* Approximate statistics based on the first 1000 samples:\n | | anchor | positive | negative_1 | negative_2 | negative_3 |\n |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|\n | type | string | string | string | string | string |\n | details | What could the term 'Golia' refer to | Golia may refer to:
Golia (surname)
Golia, Ganjam
Golia Monastery
1226 Golia | Gouka may refer to:
9708 Gouka, a main-belt asteroid after the Dutch astronomer Adriaan Gouka
Eric Gouka (born 1970), Dutch cricketer
Gouka, Benin, a town and arrondissement | Gottschelia is a genus of liverworts belonging to the family Cephaloziellaceae. | Agila may refer to:
Agila I (died 554), Visigothic king
Agila II (died 714), Visigothic king
Agila 2, the first Filipino satellite
Agila (album), a 1996 album by Spanish rock band Extremoduro
Agila (film), a 1980 Philippine film directed by Eddie Romero
Agila (TV series), a 1987 Philippine teledrama series
Agila Town, Benue State, Nigeria
Opel Agila or Vauxhall Agila, a city car
See also
Agila division, the 10th Infantry Division of the Philippine Army
Aguila (disambiguation) |\n | What is the timeframe in which Itera plans to potentially make an agreement with a financial institution | As Itera's President Igor Makarov reported at today's meeting of the Russian Gas Society in Moscow, the gas company could make an agreement with a financial institution, which would make the most profitable and optimum offer, in the next two to three months. According to him, they are currently holding negotiations with several financial enterprises, which specialize in introducing companies to the financial market. | The process from receipt of the funding proposal to completion of due diligence is incredibly quick, with a goal of 30 days. After initial evaluation of their proposals, a selected number of start-ups, usually 6 to 8, are asked to make preliminary presentations to the steering committee. | Coinexchange, Cryptopia, YoBit, HitBtc, Binance, Bittrex
Q1 2018 : Partners announced (Debit card & Merchants) We are currently in negotiation with major payment providers to offer you a worldwide usable card. Q1/2 2018 : ETHX Beta Wallet release (Android, Windows, iOS) and debit cart pre-order
Q3 201 : More partnerships Wider range of companies accepting ETHX. First targets are the biggest e-commerce websites. We will release a beta application to collect user reviews and answer to the community. The app is expected to come out in Q1 2018 on Android and later on iOS. We are very sensitive about our community welfare, so we try to do our best to keep our members informed about the latest news. The app will also help us to inform and get suggestions. Ethereum X is community driven. If you are also a cryptography and distributed ledger tech-nology enthusiast and want to support the project, please feel free to contact us. Additional developers as well as community managers for our social... | The project will be floated in the market for solicitation of expression of interest from the potential investors in June 2017. The land slots will be awarded to the successful bidders based on evaluation by the end of August, 2017. The Monitoring and Evaluation (M&E) of forest sites, awarded to successful bidders, will be done in collaboration with the Forestry, Wildlife & Fisheries Department, Government of the Punjab, as per the provisions of PPP Act, 2014, and The Punjab Forest (Amendment) Act, 2016. Revenue sharing will be done in this initiative. The Company in order to effectively reach out to the business community is organizing seminars in collaboration with various Chambers of Commerce & Industry to sensitize business groups to invest in the opportunity. |\n | What role does File History play in the issue being discussed | What has File History got to do with the problem
I don't know but maybe someone at DC does
I post the question..... get lots of ideas and methods to remove the naughty files, but I still don't know why deleting file history worked unless the file history is tacked onto the file somehow
Since then I've been checking more of the \"includes folders\" for more over-long files and trying to figure what to do with them. The files are easy to find once you start paying attention
Open a folder and if it contains extra long files a scroll bar appears at the bottom of the page
Found some more files and started playing. | Newspapers feature stories about lost computers and memory sticks but a more common and longstanding problem is about staff accessing records that they have no right to see. It has always been possible for staff to look at paper records, and in most cases, there is no track of record. | In data vault it is referred to as the record source. Background
The need to identify systems of record can become acute in organizations where management information systems have been built by taking output data from multiple source systems, re-processing this data, and then re-presenting the result for a new business use. | The idea of preservation, in the sense of both immortalization and protection is addressed. How do we decide what to remember from history, and what do we leave out |\n* Loss: cachedselfloss2.CachedInfonce with these parameters:\n ```json\n {\n \"scale\": 20.0,\n \"similarity_fct\": \"cos_sim\"\n }\n ```\n\n### Training Hyperparameters\n#### Non-Default Hyperparameters\n\n- `eval_strategy`: steps\n- `per_device_train_batch_size`: 500\n- `per_device_eval_batch_size`: 500\n- `learning_rate`: 2e-05\n- `num_train_epochs`: 10\n- `warmup_ratio`: 0.1\n- `bf16`: True\n- `batch_sampler`: no_duplicates\n\n#### All Hyperparameters\nInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:\n ```json\n {\n \"truncate_dim\": 1024\n }\n ```\n\n| Metric | Value |\n|:--------------------|:-----------|\n| cosine_accuracy@1 | 0.7803 |\n| cosine_accuracy@3 | 0.8884 |\n| cosine_accuracy@5 | 0.904 |\n| cosine_accuracy@10 | 0.9234 |\n| cosine_precision@1 | 0.7803 |\n| cosine_precision@3 | 0.2961 |\n| cosine_precision@5 | 0.1808 |\n| cosine_precision@10 | 0.0923 |\n| cosine_recall@1 | 0.7803 |\n| cosine_recall@3 | 0.8884 |\n| cosine_recall@5 | 0.904 |\n| cosine_recall@10 | 0.9234 |\n| **cosine_ndcg@10** | **0.8576** |\n| cosine_mrr@10 | 0.8359 |\n| cosine_map@100 | 0.8374 |\n\n#### Information Retrieval\n\n* Dataset: `dim_768`\n* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:\n ```json\n {\n \"truncate_dim\": 768\n }\n ```\n\n| Metric | Value |\n|:--------------------|:-----------|\n| cosine_accuracy@1 | 0.7827 |\n| cosine_accuracy@3 | 0.8877 |\n| cosine_accuracy@5 | 0.9034 |\n| cosine_accuracy@10 | 0.9246 |\n| cosine_precision@1 | 0.7827 |\n| cosine_precision@3 | 0.2959 |\n| cosine_precision@5 | 0.1807 |\n| cosine_precision@10 | 0.0925 |\n| cosine_recall@1 | 0.7827 |\n| cosine_recall@3 | 0.8877 |\n| cosine_recall@5 | 0.9034 |\n| cosine_recall@10 | 0.9246 |\n| **cosine_ndcg@10** | **0.8588** |\n| cosine_mrr@10 | 0.8372 |\n| cosine_map@100 | 0.8385 |\n\n#### Information Retrieval\n\n* Dataset: `dim_512`\n* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:\n ```json\n {\n \"truncate_dim\": 512\n }\n ```\n\n| Metric | Value |\n|:--------------------|:-----------|\n| cosine_accuracy@1 | 0.7797 |\n| cosine_accuracy@3 | 0.8859 |\n| cosine_accuracy@5 | 0.901 |\n| cosine_accuracy@10 | 0.9228 |\n| cosine_precision@1 | 0.7797 |\n| cosine_precision@3 | 0.2953 |\n| cosine_precision@5 | 0.1802 |\n| cosine_precision@10 | 0.0923 |\n| cosine_recall@1 | 0.7797 |\n| cosine_recall@3 | 0.8859 |\n| cosine_recall@5 | 0.901 |\n| cosine_recall@10 | 0.9228 |\n| **cosine_ndcg@10** | **0.8564** |\n| cosine_mrr@10 | 0.8347 |\n| cosine_map@100 | 0.8362 |\n\n#### Information Retrieval\n\n* Dataset: `dim_256`\n* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:\n ```json\n {\n \"truncate_dim\": 256\n }\n ```\n\n| Metric | Value |\n|:--------------------|:-----------|\n| cosine_accuracy@1 | 0.7707 |\n| cosine_accuracy@3 | 0.8823 |\n| cosine_accuracy@5 | 0.9016 |\n| cosine_accuracy@10 | 0.9191 |\n| cosine_precision@1 | 0.7707 |\n| cosine_precision@3 | 0.2941 |\n| cosine_precision@5 | 0.1803 |\n| cosine_precision@10 | 0.0919 |\n| cosine_recall@1 | 0.7707 |\n| cosine_recall@3 | 0.8823 |\n| cosine_recall@5 | 0.9016 |\n| cosine_recall@10 | 0.9191 |\n| **cosine_ndcg@10** | **0.8512** |\n| cosine_mrr@10 | 0.8287 |\n| cosine_map@100 | 0.8303 |\n\n#### Information Retrieval\n\n* Dataset: `dim_128`\n* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:\n ```json\n {\n \"truncate_dim\": 128\n }\n ```\n\n| Metric | Value |\n|:--------------------|:-----------|\n| cosine_accuracy@1 | 0.7604 |\n| cosine_accuracy@3 | 0.869 |\n| cosine_accuracy@5 | 0.8902 |\n| cosine_accuracy@10 | 0.9131 |\n| cosine_precision@1 | 0.7604 |\n| cosine_precision@3 | 0.2897 |\n| cosine_precision@5 | 0.178 |\n| cosine_precision@10 | 0.0913 |\n| cosine_recall@1 | 0.7604 |\n| cosine_recall@3 | 0.869 |\n| cosine_recall@5 | 0.8902 |\n| cosine_recall@10 | 0.9131 |\n| **cosine_ndcg@10** | **0.8415** |\n| cosine_mrr@10 | 0.8181 |\n| cosine_map@100 | 0.82 |\n\n#### Information Retrieval\n\n* Dataset: `dim_64`\n* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:\n ```json\n {\n \"truncate_dim\": 64\n }\n ```\n\n| Metric | Value |\n|:--------------------|:-----------|\n| cosine_accuracy@1 | 0.7248 |\n| cosine_accuracy@3 | 0.8521 |\n| cosine_accuracy@5 | 0.8751 |\n| cosine_accuracy@10 | 0.8974 |\n| cosine_precision@1 | 0.7248 |\n| cosine_precision@3 | 0.284 |\n| cosine_precision@5 | 0.175 |\n| cosine_precision@10 | 0.0897 |\n| cosine_recall@1 | 0.7248 |\n| cosine_recall@3 | 0.8521 |\n| cosine_recall@5 | 0.8751 |\n| cosine_recall@10 | 0.8974 |\n| **cosine_ndcg@10** | **0.8182** |\n| cosine_mrr@10 | 0.792 |\n| cosine_map@100 | 0.7938 |\n\n\n\n\n\n## Training Details\n\n### Training Dataset\n\n#### json\n\n* Dataset: json\n* Size: 14,907 training samples\n* Columns: query and answer\n* Approximate statistics based on the first 1000 samples:\n | | query | answer |\n |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|\n | type | string | string |\n | details | En Valdeconejos, \u00bfcu\u00e1l era la sociedad de agricultores en 1952? | En Valdeconejos, la sociedad de agricultores en 1952 era el P\u00f3sito de Agricultores. |\n | \u00bfQu\u00e9 nombres de capataces se registran en el pueblo de Escucha en el a\u00f1o 1952? | En Escucha, en 1952, los capataces registrados son Peralta (Manuel) y Rodriguez (Gonzalo). |\n | En el contexto de la miner\u00eda, \u00bfqu\u00e9 implica 'despajar'? | 'Despajar' se refiere a cribar a mano material y desechos para obtener las partes de carb\u00f3n que hay en ellos. |\n* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:\n ```json\n {\n \"loss\": \"MultipleNegativesRankingLoss\",\n \"matryoshka_dims\": [\n 1024,\n 768,\n 512,\n 256,\n 128,\n 64\n ],\n \"matryoshka_weights\": [\n 1,\n 1,\n 1,\n 1,\n 1,\n 1\n ],\n \"n_dims_per_step\": -1\n }\n ```\n\n### Training Hyperparameters\n#### Non-Default Hyperparameters\n\n- `eval_strategy`: epoch\n- `per_device_train_batch_size`: 64\n- `per_device_eval_batch_size`: 16\n- `gradient_accumulation_steps`: 32\n- `learning_rate`: 2e-05\n- `num_train_epochs`: 12\n- `lr_scheduler_type`: cosine\n- `warmup_ratio`: 0.1\n- `tf32`: True\n- `load_best_model_at_end`: True\n- `optim`: adamw_torch_fused\n- `batch_sampler`: no_duplicates\n\n#### All Hyperparameters\nInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:\n ```json\n {\n \"truncate_dim\": 1024\n }\n ```\n\n| Metric | Value |\n|:--------------------|:-----------|\n| cosine_accuracy@1 | 0.7701 |\n| cosine_accuracy@3 | 0.8926 |\n| cosine_accuracy@5 | 0.9155 |\n| cosine_accuracy@10 | 0.933 |\n| cosine_precision@1 | 0.7701 |\n| cosine_precision@3 | 0.2975 |\n| cosine_precision@5 | 0.1831 |\n| cosine_precision@10 | 0.0933 |\n| cosine_recall@1 | 0.7701 |\n| cosine_recall@3 | 0.8926 |\n| cosine_recall@5 | 0.9155 |\n| cosine_recall@10 | 0.933 |\n| **cosine_ndcg@10** | **0.8579** |\n| cosine_mrr@10 | 0.8331 |\n| cosine_map@100 | 0.8343 |\n\n#### Information Retrieval\n\n* Dataset: `dim_768`\n* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:\n ```json\n {\n \"truncate_dim\": 768\n }\n ```\n\n| Metric | Value |\n|:--------------------|:-----------|\n| cosine_accuracy@1 | 0.7695 |\n| cosine_accuracy@3 | 0.889 |\n| cosine_accuracy@5 | 0.9125 |\n| cosine_accuracy@10 | 0.933 |\n| cosine_precision@1 | 0.7695 |\n| cosine_precision@3 | 0.2963 |\n| cosine_precision@5 | 0.1825 |\n| cosine_precision@10 | 0.0933 |\n| cosine_recall@1 | 0.7695 |\n| cosine_recall@3 | 0.889 |\n| cosine_recall@5 | 0.9125 |\n| cosine_recall@10 | 0.933 |\n| **cosine_ndcg@10** | **0.8571** |\n| cosine_mrr@10 | 0.8321 |\n| cosine_map@100 | 0.8333 |\n\n#### Information Retrieval\n\n* Dataset: `dim_512`\n* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:\n ```json\n {\n \"truncate_dim\": 512\n }\n ```\n\n| Metric | Value |\n|:--------------------|:-----------|\n| cosine_accuracy@1 | 0.7683 |\n| cosine_accuracy@3 | 0.8865 |\n| cosine_accuracy@5 | 0.9113 |\n| cosine_accuracy@10 | 0.9306 |\n| cosine_precision@1 | 0.7683 |\n| cosine_precision@3 | 0.2955 |\n| cosine_precision@5 | 0.1823 |\n| cosine_precision@10 | 0.0931 |\n| cosine_recall@1 | 0.7683 |\n| cosine_recall@3 | 0.8865 |\n| cosine_recall@5 | 0.9113 |\n| cosine_recall@10 | 0.9306 |\n| **cosine_ndcg@10** | **0.8555** |\n| cosine_mrr@10 | 0.8307 |\n| cosine_map@100 | 0.8321 |\n\n#### Information Retrieval\n\n* Dataset: `dim_256`\n* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:\n ```json\n {\n \"truncate_dim\": 256\n }\n ```\n\n| Metric | Value |\n|:--------------------|:-----------|\n| cosine_accuracy@1 | 0.764 |\n| cosine_accuracy@3 | 0.8902 |\n| cosine_accuracy@5 | 0.9083 |\n| cosine_accuracy@10 | 0.93 |\n| cosine_precision@1 | 0.764 |\n| cosine_precision@3 | 0.2967 |\n| cosine_precision@5 | 0.1817 |\n| cosine_precision@10 | 0.093 |\n| cosine_recall@1 | 0.764 |\n| cosine_recall@3 | 0.8902 |\n| cosine_recall@5 | 0.9083 |\n| cosine_recall@10 | 0.93 |\n| **cosine_ndcg@10** | **0.8535** |\n| cosine_mrr@10 | 0.8283 |\n| cosine_map@100 | 0.8296 |\n\n#### Information Retrieval\n\n* Dataset: `dim_128`\n* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:\n ```json\n {\n \"truncate_dim\": 128\n }\n ```\n\n| Metric | Value |\n|:--------------------|:-----------|\n| cosine_accuracy@1 | 0.7447 |\n| cosine_accuracy@3 | 0.8769 |\n| cosine_accuracy@5 | 0.9028 |\n| cosine_accuracy@10 | 0.9215 |\n| cosine_precision@1 | 0.7447 |\n| cosine_precision@3 | 0.2923 |\n| cosine_precision@5 | 0.1806 |\n| cosine_precision@10 | 0.0922 |\n| cosine_recall@1 | 0.7447 |\n| cosine_recall@3 | 0.8769 |\n| cosine_recall@5 | 0.9028 |\n| cosine_recall@10 | 0.9215 |\n| **cosine_ndcg@10** | **0.8403** |\n| cosine_mrr@10 | 0.8134 |\n| cosine_map@100 | 0.8149 |\n\n#### Information Retrieval\n\n* Dataset: `dim_64`\n* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:\n ```json\n {\n \"truncate_dim\": 64\n }\n ```\n\n| Metric | Value |\n|:--------------------|:-----------|\n| cosine_accuracy@1 | 0.7103 |\n| cosine_accuracy@3 | 0.8491 |\n| cosine_accuracy@5 | 0.8781 |\n| cosine_accuracy@10 | 0.8998 |\n| cosine_precision@1 | 0.7103 |\n| cosine_precision@3 | 0.283 |\n| cosine_precision@5 | 0.1756 |\n| cosine_precision@10 | 0.09 |\n| cosine_recall@1 | 0.7103 |\n| cosine_recall@3 | 0.8491 |\n| cosine_recall@5 | 0.8781 |\n| cosine_recall@10 | 0.8998 |\n| **cosine_ndcg@10** | **0.8119** |\n| cosine_mrr@10 | 0.7829 |\n| cosine_map@100 | 0.7851 |\n\n\n\n\n\n## Training Details\n\n### Training Dataset\n\n#### json\n\n* Dataset: json\n* Size: 14,907 training samples\n* Columns: query and answer\n* Approximate statistics based on the first 1000 samples:\n | | query | answer |\n |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|\n | type | string | string |\n | details | \u00bfQu\u00e9 tipos de palas se utilizan para cargar el carb\u00f3n y el mineral? | Se utiliza una pala convencional y una pala hidr\u00e1ulica, esta \u00faltima descarga sobre un p\u00e1ncer, puede hacerlo lateralmente y se desplaza sobre ruedas u oruga. |\n | Tras el cierre de la tejer\u00eda de Florencio Salvador, \u00bfde d\u00f3nde procedieron finalmente los ladrillos para las doscientas diez viviendas construidas en Utrillas? | Los ladrillos y material para las doscientas diez viviendas construidas en Utrillas procedieron finalmente de Letux, Zaragoza . |\n | \u00bfCu\u00e1l es el formato de los juegos infantiles que se est\u00e1n preparando para el verano en Escucha en 2021? | Los juegos infantiles que se est\u00e1n preparando para el verano en Escucha en 2021 est\u00e1n en formato revista. |\n* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:\n ```json\n {\n \"loss\": \"MultipleNegativesRankingLoss\",\n \"matryoshka_dims\": [\n 1024,\n 768,\n 512,\n 256,\n 128,\n 64\n ],\n \"matryoshka_weights\": [\n 1,\n 1,\n 1,\n 1,\n 1,\n 1\n ],\n \"n_dims_per_step\": -1\n }\n ```\n\n### Training Hyperparameters\n#### Non-Default Hyperparameters\n\n- `eval_strategy`: epoch\n- `per_device_train_batch_size`: 64\n- `per_device_eval_batch_size`: 16\n- `gradient_accumulation_steps`: 32\n- `learning_rate`: 2e-05\n- `num_train_epochs`: 8\n- `lr_scheduler_type`: cosine\n- `warmup_ratio`: 0.1\n- `tf32`: True\n- `load_best_model_at_end`: True\n- `optim`: adamw_torch_fused\n- `batch_sampler`: no_duplicates\n\n#### All Hyperparameters\n\n ![\"Speed](\"https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/3JEnPfgF2BfbN5H81K0XD.png\")
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