| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:4122 |
| - loss:MultipleNegativesRankingLoss |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| widget: |
| - source_sentence: Environment Minister Greg Hunt the Coalition's emissions reduction |
| fund, at $13.95 per tonne of carbon, is around 1 per cent of the cost of reducing |
| carbon under the former Labor government's carbon pricing scheme, which he cost |
| $1,300 a tonne. |
| sentences: |
| - Sirius's heliacal rising, just before the start of the Nile flood, gave Sopdet |
| a close connection with the flood and the resulting growth of plants. |
| - The proposal would have set an emissions price of NZ$15 per tonne of CO2-equivalent. |
| - '"More recently, evaporation over lakes has steadily been increasing, largely |
| due to increases in water surface temperature," Gronewold said.' |
| - source_sentence: “In 2013 the level of U.S. farm output was about 2.7 times its |
| 1948 level, and productivity was growing at an average annual rate of 1.52%. |
| sentences: |
| - As the concentration of carbon dioxide increases in the atmosphere, the increased |
| uptake of carbon dioxide into the oceans is causing a measurable decrease in the |
| pH of the oceans, which is referred to as ocean acidification. |
| - The IPCC was tasked with reviewing peer-reviewed scientific literature and other |
| relevant publications to provide information on the state of knowledge about climate |
| change. |
| - Private sector productivity growth, measured as real output per hour of all persons, |
| increased at an average rate of 1.9% during Reagan's eight years, compared to |
| an average 1.3% during the preceding eight years. |
| - source_sentence: '''Phil Jones said that for the past 15 years there has been no |
| "statistically significant" warming.' |
| sentences: |
| - From this, he concluded that "The post-1980 global warming trend from surface |
| thermometers is not credible. |
| - Fox News has widely been described as a major platform for climate change denial. |
| - In comparison to the extended record, the sea-ice extent in the polar region by |
| September 2007 was only half the recorded mass that had been estimated to exist |
| within the 1950–1970 period. |
| - source_sentence: '"NASA satellite data from the years 2000 through 2011 show the |
| Earth''s atmosphere is allowing far more heat to be released into space than alarmist |
| computer models have predicted, reports a new study in the peer-reviewed science |
| journal Remote Sensing.' |
| sentences: |
| - The Lamont–Doherty Earth Observatory at Columbia University is one of the world's |
| leading research centers developing fundamental knowledge about the origin, evolution |
| and future of the natural world. |
| - Mann said, "Ten years ago, the availability of data became quite sparse by the |
| time you got back to 1,000 AD, and what we had then was weighted towards tree-ring |
| data; but now you can go back 1,300 years without using tree-ring data at all |
| and still get a verifiable conclusion." |
| - This premature announcement came from a preliminary news release about a study |
| which had not yet been peer reviewed. |
| - source_sentence: '...there [is] anecdotal and other evidence suggesting similar |
| melts from 1938-43 and on other occasions.' |
| sentences: |
| - They were formed by the melting of sulfur deposits at temperatures as low as 113 °C |
| (235 °F). |
| - For example, in the study of the origin of the earth, one can reasonably model |
| earth's mass, temperature, and rate of rotation, as a function of time allowing |
| one to extrapolate forward or backward in time and so predict future or prior |
| events. |
| - Consequently, summers are 2.3 °C (4 °F) warmer in the Northern Hemisphere than |
| in the Southern Hemisphere under similar conditions. |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy@1 |
| - cosine_accuracy@3 |
| - cosine_accuracy@5 |
| - cosine_accuracy@10 |
| - cosine_precision@1 |
| - cosine_precision@3 |
| - cosine_precision@5 |
| - cosine_precision@10 |
| - cosine_recall@1 |
| - cosine_recall@3 |
| - cosine_recall@5 |
| - cosine_recall@10 |
| - cosine_ndcg@10 |
| - cosine_mrr@10 |
| - cosine_map@100 |
| model-index: |
| - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
| results: |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: claims dev |
| type: claims-dev |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.24025974025974026 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.44155844155844154 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.5454545454545454 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.6818181818181818 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.24025974025974026 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.19047619047619044 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.15454545454545457 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.10714285714285714 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.09577922077922078 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.21482683982683978 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.27532467532467536 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.36612554112554113 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.2932326612195408 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.3742553081838797 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.23004915088757852 |
| name: Cosine Map@100 |
| --- |
| |
| # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> |
| - **Maximum Sequence Length:** 256 tokens |
| - **Output Dimensionality:** 384 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Supported Modality:** Text |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'}) |
| (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', 'include_prompt': True}) |
| (2): Normalize({}) |
| ) |
| ``` |
|
|
| ## Usage |
|
|
| ### Direct Usage (Sentence Transformers) |
|
|
| First install the Sentence Transformers library: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
| Then you can load this model and run inference. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("jmroth/my-awesome-model") |
| # Run inference |
| sentences = [ |
| '...there [is] anecdotal and other evidence suggesting similar melts from 1938-43 and on other occasions.', |
| 'They were formed by the melting of sulfur deposits at temperatures as low as 113\xa0°C (235\xa0°F).', |
| 'Consequently, summers are 2.3\xa0°C (4\xa0°F) warmer in the Northern Hemisphere than in the Southern Hemisphere under similar conditions.', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 384] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 0.4966, 0.1535], |
| # [0.4966, 1.0000, 0.3254], |
| # [0.1535, 0.3254, 1.0000]]) |
| ``` |
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Information Retrieval |
|
|
| * Dataset: `claims-dev` |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | cosine_accuracy@1 | 0.2403 | |
| | cosine_accuracy@3 | 0.4416 | |
| | cosine_accuracy@5 | 0.5455 | |
| | cosine_accuracy@10 | 0.6818 | |
| | cosine_precision@1 | 0.2403 | |
| | cosine_precision@3 | 0.1905 | |
| | cosine_precision@5 | 0.1545 | |
| | cosine_precision@10 | 0.1071 | |
| | cosine_recall@1 | 0.0958 | |
| | cosine_recall@3 | 0.2148 | |
| | cosine_recall@5 | 0.2753 | |
| | cosine_recall@10 | 0.3661 | |
| | **cosine_ndcg@10** | **0.2932** | |
| | cosine_mrr@10 | 0.3743 | |
| | cosine_map@100 | 0.23 | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Dataset |
| |
| #### Unnamed Dataset |
| |
| * Size: 4,122 training samples |
| * Columns: <code>anchor</code> and <code>positive</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | |
| |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 8 tokens</li><li>mean: 26.75 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 38.71 tokens</li><li>max: 256 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | |
| |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.</code> | <code>At very high concentrations (100 times atmospheric concentration, or greater), carbon dioxide can be toxic to animal life, so raising the concentration to 10,000 ppm (1%) or higher for several hours will eliminate pests such as whiteflies and spider mites in a greenhouse.</code> | |
| | <code>Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.</code> | <code>Plants can grow as much as 50 percent faster in concentrations of 1,000 ppm CO 2 when compared with ambient conditions, though this assumes no change in climate and no limitation on other nutrients.</code> | |
| | <code>Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.</code> | <code>Higher carbon dioxide concentrations will favourably affect plant growth and demand for water.</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `per_device_train_batch_size`: 32 |
| - `per_device_eval_batch_size`: 128 |
| - `learning_rate`: 2e-05 |
| - `weight_decay`: 0.01 |
| - `warmup_steps`: 0.1 |
| - `fp16`: True |
| - `load_best_model_at_end`: True |
| - `push_to_hub`: True |
| - `hub_model_id`: jmroth/nlp-biencoder-finetuned |
| - `hub_strategy`: end |
| - `batch_sampler`: no_duplicates |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `do_predict`: False |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 32 |
| - `per_device_eval_batch_size`: 128 |
| - `gradient_accumulation_steps`: 1 |
| - `eval_accumulation_steps`: None |
| - `torch_empty_cache_steps`: None |
| - `learning_rate`: 2e-05 |
| - `weight_decay`: 0.01 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1.0 |
| - `num_train_epochs`: 3 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_ratio`: None |
| - `warmup_steps`: 0.1 |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `enable_jit_checkpoint`: False |
| - `save_on_each_node`: False |
| - `save_only_model`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `use_cpu`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `bf16`: False |
| - `fp16`: True |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `local_rank`: -1 |
| - `ddp_backend`: None |
| - `debug`: [] |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 0 |
| - `dataloader_prefetch_factor`: None |
| - `disable_tqdm`: False |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `load_best_model_at_end`: True |
| - `ignore_data_skip`: False |
| - `fsdp`: [] |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| - `parallelism_config`: None |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `group_by_length`: False |
| - `length_column_name`: length |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `skip_memory_metrics`: True |
| - `push_to_hub`: True |
| - `resume_from_checkpoint`: None |
| - `hub_model_id`: jmroth/nlp-biencoder-finetuned |
| - `hub_strategy`: end |
| - `hub_private_repo`: None |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `include_for_metrics`: [] |
| - `eval_do_concat_batches`: True |
| - `auto_find_batch_size`: False |
| - `full_determinism`: False |
| - `ddp_timeout`: 1800 |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `include_num_input_tokens_seen`: no |
| - `neftune_noise_alpha`: None |
| - `optim_target_modules`: None |
| - `batch_eval_metrics`: False |
| - `eval_on_start`: False |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: True |
| - `use_cache`: False |
| - `prompts`: None |
| - `batch_sampler`: no_duplicates |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | claims-dev_cosine_ndcg@10 | |
| |:----------:|:-------:|:-------------:|:-------------------------:| |
| | 0.0775 | 10 | 1.4212 | - | |
| | 0.1550 | 20 | 1.4229 | - | |
| | 0.2326 | 30 | 1.1129 | - | |
| | 0.3101 | 40 | 0.9966 | - | |
| | 0.3876 | 50 | 0.9207 | 0.2829 | |
| | 0.4651 | 60 | 0.8326 | - | |
| | 0.5426 | 70 | 0.8989 | - | |
| | 0.6202 | 80 | 0.9630 | - | |
| | 0.6977 | 90 | 0.8394 | - | |
| | 0.7752 | 100 | 0.8764 | 0.2893 | |
| | 0.8527 | 110 | 0.8208 | - | |
| | 0.9302 | 120 | 0.7684 | - | |
| | 1.0078 | 130 | 0.7049 | - | |
| | 1.0853 | 140 | 0.7378 | - | |
| | 1.1628 | 150 | 0.6265 | 0.2941 | |
| | 1.2403 | 160 | 0.6832 | - | |
| | 1.3178 | 170 | 0.6365 | - | |
| | 1.3953 | 180 | 0.5991 | - | |
| | 1.4729 | 190 | 0.5456 | - | |
| | **1.5504** | **200** | **0.6355** | **0.2943** | |
| | 1.6279 | 210 | 0.5927 | - | |
| | 1.7054 | 220 | 0.7117 | - | |
| | 1.7829 | 230 | 0.5096 | - | |
| | 1.8605 | 240 | 0.6036 | - | |
| | 1.9380 | 250 | 0.6768 | 0.2896 | |
| | 2.0155 | 260 | 0.6589 | - | |
| | 2.0930 | 270 | 0.5436 | - | |
| | 2.1705 | 280 | 0.5173 | - | |
| | 2.2481 | 290 | 0.5544 | - | |
| | 2.3256 | 300 | 0.5583 | 0.2911 | |
| | 2.4031 | 310 | 0.5903 | - | |
| | 2.4806 | 320 | 0.5265 | - | |
| | 2.5581 | 330 | 0.5107 | - | |
| | 2.6357 | 340 | 0.6144 | - | |
| | 2.7132 | 350 | 0.5175 | 0.2932 | |
| | 2.7907 | 360 | 0.5805 | - | |
| | 2.8682 | 370 | 0.5299 | - | |
| | 2.9457 | 380 | 0.5621 | - | |
|
|
| * The bold row denotes the saved checkpoint. |
|
|
| ### Training Time |
| - **Training**: 32.6 minutes |
|
|
| ### Framework Versions |
| - Python: 3.12.13 |
| - Sentence Transformers: 5.4.1 |
| - Transformers: 5.0.0 |
| - PyTorch: 2.10.0+cu128 |
| - Accelerate: 1.13.0 |
| - Datasets: 4.0.0 |
| - Tokenizers: 0.22.2 |
|
|
| ## Citation |
|
|
| ### BibTeX |
|
|
| #### Sentence Transformers |
| ```bibtex |
| @inproceedings{reimers-2019-sentence-bert, |
| title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
| author = "Reimers, Nils and Gurevych, Iryna", |
| booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
| month = "11", |
| year = "2019", |
| publisher = "Association for Computational Linguistics", |
| url = "https://arxiv.org/abs/1908.10084", |
| } |
| ``` |
|
|
| #### MultipleNegativesRankingLoss |
| ```bibtex |
| @misc{oord2019representationlearningcontrastivepredictive, |
| title={Representation Learning with Contrastive Predictive Coding}, |
| author={Aaron van den Oord and Yazhe Li and Oriol Vinyals}, |
| year={2019}, |
| eprint={1807.03748}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG}, |
| url={https://arxiv.org/abs/1807.03748}, |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
