Add new SentenceTransformer model

1_Pooling/config.json

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+{
+    "embedding_dimension": 384,
+    "pooling_mode": "mean",
+    "include_prompt": true
+}

README.md

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+---
+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.*
+-->

config.json

@@ -0,0 +1,30 @@
+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "is_decoder": false,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "pytorch": "2.10.0+cu128",
+    "sentence_transformers": "5.4.1",
+    "transformers": "5.0.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:298040f67ce76b454d35d3da23791f7f7637c68573d7ffba0a17472a37e72984
+size 90864176

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,10 @@
+{
+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

tokenizer_config.json

@@ -0,0 +1,16 @@
+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}