Add new CrossEncoder model

README.md

@@ -0,0 +1,474 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- cross-encoder
+- generated_from_trainer
+- dataset_size:36728
+- loss:BinaryCrossEntropyLoss
+base_model: cross-encoder/ms-marco-MiniLM-L6-v2
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- pearson
+- spearman
+- map
+- mrr@10
+- ndcg@10
+model-index:
+- name: ms-marco-MiniLM-L-6-v2 Finetuned on PV211 HomeWork
+  results:
+  - task:
+      type: cross-encoder-correlation
+      name: Cross Encoder Correlation
+    dataset:
+      name: sts dev
+      type: sts_dev
+    metrics:
+    - type: pearson
+      value: 0.8857946136871967
+      name: Pearson
+    - type: spearman
+      value: 0.8182465826410324
+      name: Spearman
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoMSMARCO R100
+      type: NanoMSMARCO_R100
+    metrics:
+    - type: map
+      value: 0.6048
+      name: Map
+    - type: mrr@10
+      value: 0.5974
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.6644
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNFCorpus R100
+      type: NanoNFCorpus_R100
+    metrics:
+    - type: map
+      value: 0.3633
+      name: Map
+    - type: mrr@10
+      value: 0.5961
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.4082
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNQ R100
+      type: NanoNQ_R100
+    metrics:
+    - type: map
+      value: 0.6871
+      name: Map
+    - type: mrr@10
+      value: 0.7117
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.7413
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-nano-beir
+      name: Cross Encoder Nano BEIR
+    dataset:
+      name: NanoBEIR R100 mean
+      type: NanoBEIR_R100_mean
+    metrics:
+    - type: map
+      value: 0.5517
+      name: Map
+    - type: mrr@10
+      value: 0.635
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.6046
+      name: Ndcg@10
+---
+
+# ms-marco-MiniLM-L-6-v2 Finetuned on PV211 HomeWork
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Cross Encoder
+- **Base model:** [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) <!-- at revision ce0834f22110de6d9222af7a7a03628121708969 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Output Labels:** 1 label
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
+
+## 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 CrossEncoder
+
+# Download from the 🤗 Hub
+model = CrossEncoder("maennyn/pv211_beir_cqadupstack_crossencoder2")
+# Get scores for pairs of texts
+pairs = [
+    ['Increase the X length of a tikzpicture', "In recent years I've developed a habit of formatting SQL `SELECT` queries like so:               SELECT         fieldNames     FROM         sources         JOIN tableSource ON col1 = col2         JOIN (             SELECT                 fieldNames             FROM                 otherSources          ) AS subQuery ON subQuery.foo = col2     WHERE         someField = somePredicate      So you see my pattern: each keyword is on its own line and that keyword's fields are indented by 1 tab-stop and the pattern is used recursively for sub- queries. This works well for all of my `SELECT` queries, as it maximizes readability though at the cost of vertical space; but it doesn't work for things like `INSERT` and `UPDATE` which have radically different syntax.               INSERT INTO tableName            (  col1,   col2,   col3,   col4,   col5,   col6,   col7,   col8  )     VALUES ( 'col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8' ),     VALUES ( 'col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8' )          UPDATE tableName     SET         col1 = 'col1',         col2 = 'col2',         col3 = 'col3',         // etc     WHERE         someField = somePredicate      As you can see, they aren't as pretty, and when you're dealing with tables with a lot of columns they quickly become unweildly. Is there a better way to format `INSERT` and `UPDATE`? And what about `CREATE` statements and other operations?"],
+    ['Fillable form: checkbox linked to hide/unhide sections; pushbutton to add/delete rows', "I'd like to create a LaTeX document that when rendered into PDF, has forms that can be filled out using Adobe Reader or other such programs. Then I'd like to be able to extract the data. I deliberately would like to avoid using Acrobat for all the usual reasons (non-free, need different versions for different platforms etc). Can this be done ?"],
+    ['Is there any way to get something like pmatrix with customizable grid lines between cells?', "> **Possible Duplicate:**   >  Highlight elements in the matrix i have a matrix:               \\begin{equation}      \\begin{bmatrix}         1 & 5 & 4 & 2 & 1 \\\\         1 & 5 & 4 & 2 & 1 \\\\         1 & 5 & 4 & 2 & 1 \\\\     \\end{bmatrix}     \\label{e:crop1}     \\end{equation}      and i would like to draw a box around a few of the values to highlight a selection & label it, how would i go about this? I've looked at nodes but havent got a clue. thanks"],
+    ["Difference between 'all' and 'all the'", 'I am not confident about my judgement as to whether or not "the" is required if a relative clause is used in a sentence.   For example, > The data can be collected on all the computers on which the software is > installed. I think it must be "all the computers " and not be "all computers" because "computers" is specified by "on which the software is installed". Please help me confirm that I am right.'],
+    ['Understanding the exclamation mark (!) in bash', "I'm following through a tutorial and it mentions to run this command:               sudo chmod 700 !$      I'm not familiar with `!$`. What does it mean?"],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'Increase the X length of a tikzpicture',
+    [
+        "In recent years I've developed a habit of formatting SQL `SELECT` queries like so:               SELECT         fieldNames     FROM         sources         JOIN tableSource ON col1 = col2         JOIN (             SELECT                 fieldNames             FROM                 otherSources          ) AS subQuery ON subQuery.foo = col2     WHERE         someField = somePredicate      So you see my pattern: each keyword is on its own line and that keyword's fields are indented by 1 tab-stop and the pattern is used recursively for sub- queries. This works well for all of my `SELECT` queries, as it maximizes readability though at the cost of vertical space; but it doesn't work for things like `INSERT` and `UPDATE` which have radically different syntax.               INSERT INTO tableName            (  col1,   col2,   col3,   col4,   col5,   col6,   col7,   col8  )     VALUES ( 'col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8' ),     VALUES ( 'col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8' )          UPDATE tableName     SET         col1 = 'col1',         col2 = 'col2',         col3 = 'col3',         // etc     WHERE         someField = somePredicate      As you can see, they aren't as pretty, and when you're dealing with tables with a lot of columns they quickly become unweildly. Is there a better way to format `INSERT` and `UPDATE`? And what about `CREATE` statements and other operations?",
+        "I'd like to create a LaTeX document that when rendered into PDF, has forms that can be filled out using Adobe Reader or other such programs. Then I'd like to be able to extract the data. I deliberately would like to avoid using Acrobat for all the usual reasons (non-free, need different versions for different platforms etc). Can this be done ?",
+        "> **Possible Duplicate:**   >  Highlight elements in the matrix i have a matrix:               \\begin{equation}      \\begin{bmatrix}         1 & 5 & 4 & 2 & 1 \\\\         1 & 5 & 4 & 2 & 1 \\\\         1 & 5 & 4 & 2 & 1 \\\\     \\end{bmatrix}     \\label{e:crop1}     \\end{equation}      and i would like to draw a box around a few of the values to highlight a selection & label it, how would i go about this? I've looked at nodes but havent got a clue. thanks",
+        'I am not confident about my judgement as to whether or not "the" is required if a relative clause is used in a sentence.   For example, > The data can be collected on all the computers on which the software is > installed. I think it must be "all the computers " and not be "all computers" because "computers" is specified by "on which the software is installed". Please help me confirm that I am right.',
+        "I'm following through a tutorial and it mentions to run this command:               sudo chmod 700 !$      I'm not familiar with `!$`. What does it mean?",
+    ]
+)
+# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
+```
+
+<!--
+### 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
+
+#### Cross Encoder Correlation
+
+* Dataset: `sts_dev`
+* Evaluated with [<code>CrossEncoderCorrelationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderCorrelationEvaluator)
+
+| Metric       | Value      |
+|:-------------|:-----------|
+| pearson      | 0.8858     |
+| **spearman** | **0.8182** |
+
+#### Cross Encoder Reranking
+
+* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
+* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
+  ```json
+  {
+      "at_k": 10,
+      "always_rerank_positives": true
+  }
+  ```
+
+| Metric      | NanoMSMARCO_R100     | NanoNFCorpus_R100    | NanoNQ_R100          |
+|:------------|:---------------------|:---------------------|:---------------------|
+| map         | 0.6048 (+0.1152)     | 0.3633 (+0.1023)     | 0.6871 (+0.2674)     |
+| mrr@10      | 0.5974 (+0.1199)     | 0.5961 (+0.0962)     | 0.7117 (+0.2850)     |
+| **ndcg@10** | **0.6644 (+0.1240)** | **0.4082 (+0.0832)** | **0.7413 (+0.2407)** |
+
+#### Cross Encoder Nano BEIR
+
+* Dataset: `NanoBEIR_R100_mean`
+* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "msmarco",
+          "nfcorpus",
+          "nq"
+      ],
+      "rerank_k": 100,
+      "at_k": 10,
+      "always_rerank_positives": true
+  }
+  ```
+
+| Metric      | Value                |
+|:------------|:---------------------|
+| map         | 0.5517 (+0.1616)     |
+| mrr@10      | 0.6350 (+0.1670)     |
+| **ndcg@10** | **0.6046 (+0.1493)** |
+
+<!--
+## 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: 36,728 training samples
+* Columns: <code>query</code>, <code>document</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                                           | document                                                                                          | label                                           |
+  |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                                          | string                                                                                            | int                                             |
+  | details | <ul><li>min: 15 characters</li><li>mean: 49.89 characters</li><li>max: 128 characters</li></ul> | <ul><li>min: 36 characters</li><li>mean: 718.8 characters</li><li>max: 17541 characters</li></ul> | <ul><li>0: ~48.90%</li><li>1: ~51.10%</li></ul> |
+* Samples:
+  | query                                                                                                   | document                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | label          |
+  |:--------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>Increase the X length of a tikzpicture</code>                                                     | <code>In recent years I've developed a habit of formatting SQL `SELECT` queries like so:               SELECT         fieldNames     FROM         sources         JOIN tableSource ON col1 = col2         JOIN (             SELECT                 fieldNames             FROM                 otherSources          ) AS subQuery ON subQuery.foo = col2     WHERE         someField = somePredicate      So you see my pattern: each keyword is on its own line and that keyword's fields are indented by 1 tab-stop and the pattern is used recursively for sub- queries. This works well for all of my `SELECT` queries, as it maximizes readability though at the cost of vertical space; but it doesn't work for things like `INSERT` and `UPDATE` which have radically different syntax.               INSERT INTO tableName            (  col1,   col2,   col3,   col4,   col5,   col6,   col7,   col8  )     VALUES ( 'col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8' ),     VALUES ( 'col1', 'col2', 'col3', 'col4',...</code> | <code>0</code> |
+  | <code>Fillable form: checkbox linked to hide/unhide sections; pushbutton to add/delete rows</code>      | <code>I'd like to create a LaTeX document that when rendered into PDF, has forms that can be filled out using Adobe Reader or other such programs. Then I'd like to be able to extract the data. I deliberately would like to avoid using Acrobat for all the usual reasons (non-free, need different versions for different platforms etc). Can this be done ?</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | <code>1</code> |
+  | <code>Is there any way to get something like pmatrix with customizable grid lines between cells?</code> | <code>> **Possible Duplicate:**   >  Highlight elements in the matrix i have a matrix:               \begin{equation}      \begin{bmatrix}         1 & 5 & 4 & 2 & 1 \\         1 & 5 & 4 & 2 & 1 \\         1 & 5 & 4 & 2 & 1 \\     \end{bmatrix}     \label{e:crop1}     \end{equation}      and i would like to draw a box around a few of the values to highlight a selection & label it, how would i go about this? I've looked at nodes but havent got a clue. thanks</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | <code>1</code> |
+* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
+  ```json
+  {
+      "activation_fn": "torch.nn.modules.linear.Identity",
+      "pos_weight": null
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `warmup_ratio`: 0.1
+- `save_only_model`: True
+- `fp16`: True
+- `load_best_model_at_end`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `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`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: True
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `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
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch   | Step     | Training Loss | sts_dev_spearman | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10  | NanoBEIR_R100_mean_ndcg@10 |
+|:-------:|:--------:|:-------------:|:----------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
+| -1      | -1       | -             | 0.7222           | 0.6686 (+0.1282)         | 0.3930 (+0.0680)          | 0.7599 (+0.2592)     | 0.6072 (+0.1518)           |
+| 0.4355  | 1000     | 0.4163        | -                | -                        | -                         | -                    | -                          |
+| 0.8711  | 2000     | 0.1632        | -                | -                        | -                         | -                    | -                          |
+| **1.0** | **2296** | **-**         | **0.8182**       | **0.6644 (+0.1240)**     | **0.4082 (+0.0832)**      | **0.7413 (+0.2407)** | **0.6046 (+0.1493)**       |
+| 1.3066  | 3000     | 0.1227        | -                | -                        | -                         | -                    | -                          |
+| 1.7422  | 4000     | 0.1157        | -                | -                        | -                         | -                    | -                          |
+| 2.0     | 4592     | -             | 0.8201           | 0.6266 (+0.0862)         | 0.4096 (+0.0846)          | 0.7032 (+0.2026)     | 0.5798 (+0.1244)           |
+| 2.1777  | 5000     | 0.0964        | -                | -                        | -                         | -                    | -                          |
+| 2.6132  | 6000     | 0.081         | -                | -                        | -                         | -                    | -                          |
+| 3.0     | 6888     | -             | 0.8203           | 0.6241 (+0.0837)         | 0.4068 (+0.0817)          | 0.6931 (+0.1924)     | 0.5747 (+0.1193)           |
+| -1      | -1       | -             | 0.8182           | 0.6644 (+0.1240)         | 0.4082 (+0.0832)          | 0.7413 (+0.2407)     | 0.6046 (+0.1493)           |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 4.1.0
+- Transformers: 4.51.3
+- PyTorch: 2.8.0.dev20250319+cu128
+- Accelerate: 1.6.0
+- Datasets: 3.6.0
+- Tokenizers: 0.21.1
+
+## 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",
+}
+```
+
+<!--
+## 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 +1,35 @@
+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
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+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "id2label": {
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+  "label2id": {
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+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "sentence_transformers": {
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+    "version": "4.1.0"
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+  "type_vocab_size": 2,
+  "use_cache": true,
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+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5512e84b07899ce2a75bf2176d66f2859bbcac7e30ebff8f1afe697dc8187f6
+size 90866412

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": false,
+    "normalized": false,
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}