Add new SentenceTransformer model

1_Dense/config.json

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+{"in_features": 384, "out_features": 128, "bias": false, "activation_function": "torch.nn.modules.linear.Identity"}

1_Dense/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:65f7b7277e45dd150496f6274e5268fcda7c3fef27f4588298fc162b1e9c73a9
+size 196696

README.md

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+---
+tags:
+- ColBERT
+- PyLate
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1893949
+- loss:Contrastive
+base_model: nreimers/MiniLM-L6-H384-uncased
+pipeline_tag: sentence-similarity
+library_name: PyLate
+metrics:
+- accuracy
+model-index:
+- name: PyLate model based on nreimers/MiniLM-L6-H384-uncased
+  results:
+  - task:
+      type: col-berttriplet
+      name: Col BERTTriplet
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: accuracy
+      value: 0.37379997968673706
+      name: Accuracy
+---
+
+# PyLate model based on nreimers/MiniLM-L6-H384-uncased
+
+This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased). It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
+
+## Model Details
+
+### Model Description
+- **Model Type:** PyLate model
+- **Base model:** [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) <!-- at revision 3276f0fac9d818781d7a1327b3ff818fc4e643c0 -->
+- **Document Length:** 180 tokens
+- **Query Length:** 32 tokens
+- **Output Dimensionality:** 128 tokens
+- **Similarity Function:** MaxSim
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
+- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
+- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
+
+### Full Model Architecture
+
+```
+ColBERT(
+  (0): Transformer({'max_seq_length': 31, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Dense({'in_features': 384, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
+)
+```
+
+## Usage
+First install the PyLate library:
+
+```bash
+pip install -U pylate
+```
+
+### Retrieval
+
+PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
+
+#### Indexing documents
+
+First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
+
+```python
+from pylate import indexes, models, retrieve
+
+# Step 1: Load the ColBERT model
+model = models.ColBERT(
+    model_name_or_path=ayushexel/colbert-MiniLM-L6-H384-uncased-1-neg-1-epoch-gooaq-1995000,
+)
+
+# Step 2: Initialize the Voyager index
+index = indexes.Voyager(
+    index_folder="pylate-index",
+    index_name="index",
+    override=True,  # This overwrites the existing index if any
+)
+
+# Step 3: Encode the documents
+documents_ids = ["1", "2", "3"]
+documents = ["document 1 text", "document 2 text", "document 3 text"]
+
+documents_embeddings = model.encode(
+    documents,
+    batch_size=32,
+    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
+    show_progress_bar=True,
+)
+
+# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
+index.add_documents(
+    documents_ids=documents_ids,
+    documents_embeddings=documents_embeddings,
+)
+```
+
+Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
+
+```python
+# To load an index, simply instantiate it with the correct folder/name and without overriding it
+index = indexes.Voyager(
+    index_folder="pylate-index",
+    index_name="index",
+)
+```
+
+#### Retrieving top-k documents for queries
+
+Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
+To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
+
+```python
+# Step 1: Initialize the ColBERT retriever
+retriever = retrieve.ColBERT(index=index)
+
+# Step 2: Encode the queries
+queries_embeddings = model.encode(
+    ["query for document 3", "query for document 1"],
+    batch_size=32,
+    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
+    show_progress_bar=True,
+)
+
+# Step 3: Retrieve top-k documents
+scores = retriever.retrieve(
+    queries_embeddings=queries_embeddings,
+    k=10,  # Retrieve the top 10 matches for each query
+)
+```
+
+### Reranking
+If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
+
+```python
+from pylate import rank, models
+
+queries = [
+    "query A",
+    "query B",
+]
+
+documents = [
+    ["document A", "document B"],
+    ["document 1", "document C", "document B"],
+]
+
+documents_ids = [
+    [1, 2],
+    [1, 3, 2],
+]
+
+model = models.ColBERT(
+    model_name_or_path=ayushexel/colbert-MiniLM-L6-H384-uncased-1-neg-1-epoch-gooaq-1995000,
+)
+
+queries_embeddings = model.encode(
+    queries,
+    is_query=True,
+)
+
+documents_embeddings = model.encode(
+    documents,
+    is_query=False,
+)
+
+reranked_documents = rank.rerank(
+    documents_ids=documents_ids,
+    queries_embeddings=queries_embeddings,
+    documents_embeddings=documents_embeddings,
+)
+```
+
+<!--
+### 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
+
+#### Col BERTTriplet
+
+* Evaluated with <code>pylate.evaluation.colbert_triplet.ColBERTTripletEvaluator</code>
+
+| Metric       | Value      |
+|:-------------|:-----------|
+| **accuracy** | **0.3738** |
+
+<!--
+## 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: 1,893,949 training samples
+* Columns: <code>question</code>, <code>answer</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | question                                                                          | answer                                                                             | negative                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 9 tokens</li><li>mean: 12.73 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 31.78 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 31.7 tokens</li><li>max: 32 tokens</li></ul> |
+* Samples:
+  | question                                                          | answer                                                                                                                                                                                                                                                                                                                                             | negative                                                                                                                                                                                                                                                                                                                                   |
+  |:------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>how do i import photos from iphone onto mac?</code>         | <code>['Open the Photos app.', 'Connect your iPhone to Mac using a USB cable.', 'In the upper menu of the Photos app, choose Import.', 'Here you will see all the photos your iPhone has.', 'To import all photos, click Import all new photos on the upper-right corner of the window.']</code>                                                   | <code>Import to your Mac Connect your iPhone, iPad, or iPod touch to your Mac with a USB cable. Open the Photos app. The Photos app shows an Import screen with all the photos and videos that are on your connected device. If the Import screen doesn't automatically appear, click the device's name in the Photos sidebar.</code>      |
+  | <code>what are hyperplastic colon polyps?</code>                  | <code>A hyperplastic polyp is a growth of extra cells that projects out from tissues inside your body. They occur in areas where your body has repaired damaged tissue, especially along your digestive tract. Hyperplastic colorectal polyps happen in your colon, the lining of your large intestine.</code>                                     | <code>During the colonoscopy, it's hard to differentiate between the benign hyperplastic and the more worrisome adenomatous polyp. Polyps appear as lumps inside the colon. Some are flat and others hang down from a stalk. Each polyp is biopsied and tissue from the polyp is sent to a lab and tested for cancer.</code>               |
+  | <code>what are the flaws of the electoral college quizlet?</code> | <code>['the winner of the popular vote is not guaranteed the presidency. ... ', 'electors are not required to vote in accord with the popular vote. ... ', 'any election might have to be decided in the HOR. ... ', 'small states are overrepresented- they have more electoral votes per a smaller amount of people than larger states.']</code> | <code>In other U.S. elections, candidates are elected directly by popular vote. But the president and vice president are not elected directly by citizens. Instead, they're chosen by “electors” through a process called the Electoral College. ... It was a compromise between a popular vote by citizens and a vote in Congress.</code> |
+* Loss: <code>pylate.losses.contrastive.Contrastive</code>
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 5,000 evaluation samples
+* Columns: <code>question</code>, <code>answer</code>, and <code>negative_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | question                                                                          | answer                                                                             | negative_1                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 9 tokens</li><li>mean: 12.84 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 31.77 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 31.47 tokens</li><li>max: 32 tokens</li></ul> |
+* Samples:
+  | question                                                              | answer                                                                                                                                                                                                                        | negative_1                                                                                                                                                                                                                    |
+  |:----------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>1 cup how many grams of flour?</code>                           | <code>A cup of all-purpose flour weighs 4 1/4 ounces or 120 grams. This chart is a quick reference for volume, ounces, and grams equivalencies for common ingredients.</code>                                                 | <code>Convert 25 grams or g of flour to cups. 25 grams flour equals 1/4 cup.</code>                                                                                                                                           |
+  | <code>is lasker rink owned by trump?</code>                           | <code>Lasker Rink was announced in 1962 and completed in 1966. It has been operated by The Trump Organization since 1987. In 2018, the city announced that the rink would be closed and rebuilt between 2021 and 2024.</code> | <code>Lasker Rink was announced in 1962 and completed in 1966. It has been operated by The Trump Organization since 1987. In 2018, the city announced that the rink would be closed and rebuilt between 2021 and 2024.</code> |
+  | <code>how many litres of water to drink a day for weight loss?</code> | <code>Bottom Line: According to the studies, 1–2 liters of water per day is enough to assist with weight loss, especially when consumed before meals.</code>                                                                  | <code>Based on the studies, drinking 1-2 liters of water per day should be sufficient to help with weight loss. Here's how much water you should drink, in different measurements: Liters: 1–2.</code>                        |
+* Loss: <code>pylate.losses.contrastive.Contrastive</code>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `learning_rate`: 3e-06
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `seed`: 12
+- `bf16`: True
+- `dataloader_num_workers`: 12
+- `load_best_model_at_end`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `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`: 3e-06
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `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`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 12
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `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`: 12
+- `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}
+- `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
+- `dispatch_batches`: None
+- `split_batches`: 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 | accuracy |
+|:------:|:-----:|:-------------:|:--------:|
+| 0      | 0     | -             | 0.3738   |
+| 0.0001 | 1     | 9.8144        | -        |
+| 0.0135 | 200   | 8.6046        | -        |
+| 0.0270 | 400   | 6.3812        | -        |
+| 0.0405 | 600   | 4.0823        | -        |
+| 0.0541 | 800   | 2.3103        | -        |
+| 0.0676 | 1000  | 1.7525        | -        |
+| 0.0811 | 1200  | 1.4658        | -        |
+| 0.0946 | 1400  | 1.2898        | -        |
+| 0.1081 | 1600  | 1.1659        | -        |
+| 0.1216 | 1800  | 1.0575        | -        |
+| 0.1352 | 2000  | 1.0146        | -        |
+| 0.1487 | 2200  | 0.9502        | -        |
+| 0.1622 | 2400  | 0.9233        | -        |
+| 0.1757 | 2600  | 0.8957        | -        |
+| 0.1892 | 2800  | 0.8514        | -        |
+| 0.2027 | 3000  | 0.8499        | -        |
+| 0.2163 | 3200  | 0.8311        | -        |
+| 0.2298 | 3400  | 0.8007        | -        |
+| 0.2433 | 3600  | 0.787         | -        |
+| 0.2568 | 3800  | 0.7648        | -        |
+| 0.2703 | 4000  | 0.7538        | -        |
+| 0.2838 | 4200  | 0.7373        | -        |
+| 0.2974 | 4400  | 0.732         | -        |
+| 0.3109 | 4600  | 0.7335        | -        |
+| 0.3244 | 4800  | 0.7084        | -        |
+| 0.3379 | 5000  | 0.7109        | -        |
+| 0.3514 | 5200  | 0.7091        | -        |
+| 0.3649 | 5400  | 0.691         | -        |
+| 0.3785 | 5600  | 0.6814        | -        |
+| 0.3920 | 5800  | 0.6817        | -        |
+| 0.4055 | 6000  | 0.6694        | -        |
+| 0.4190 | 6200  | 0.6602        | -        |
+| 0.4325 | 6400  | 0.6594        | -        |
+| 0.4460 | 6600  | 0.6526        | -        |
+| 0.4596 | 6800  | 0.6433        | -        |
+| 0.4731 | 7000  | 0.6378        | -        |
+| 0.4866 | 7200  | 0.6362        | -        |
+| 0.5001 | 7400  | 0.6273        | -        |
+| 0.5136 | 7600  | 0.6293        | -        |
+| 0.5271 | 7800  | 0.6198        | -        |
+| 0.5407 | 8000  | 0.6166        | -        |
+| 0.5542 | 8200  | 0.6194        | -        |
+| 0.5677 | 8400  | 0.618         | -        |
+| 0.5812 | 8600  | 0.6109        | -        |
+| 0.5947 | 8800  | 0.6145        | -        |
+| 0.6082 | 9000  | 0.598         | -        |
+| 0.6217 | 9200  | 0.5982        | -        |
+| 0.6353 | 9400  | 0.5989        | -        |
+| 0.6488 | 9600  | 0.5926        | -        |
+| 0.6623 | 9800  | 0.5956        | -        |
+| 0.6758 | 10000 | 0.597         | -        |
+| 0.6893 | 10200 | 0.5803        | -        |
+| 0.7028 | 10400 | 0.5889        | -        |
+| 0.7164 | 10600 | 0.5907        | -        |
+| 0.7299 | 10800 | 0.5904        | -        |
+| 0.7434 | 11000 | 0.5857        | -        |
+| 0.7569 | 11200 | 0.5825        | -        |
+| 0.7704 | 11400 | 0.5825        | -        |
+| 0.7839 | 11600 | 0.5786        | -        |
+| 0.7975 | 11800 | 0.5797        | -        |
+| 0.8110 | 12000 | 0.5746        | -        |
+| 0.8245 | 12200 | 0.577         | -        |
+| 0.8380 | 12400 | 0.5765        | -        |
+| 0.8515 | 12600 | 0.5803        | -        |
+| 0.8650 | 12800 | 0.5671        | -        |
+| 0.8786 | 13000 | 0.5716        | -        |
+| 0.8921 | 13200 | 0.5822        | -        |
+| 0.9056 | 13400 | 0.5806        | -        |
+| 0.9191 | 13600 | 0.5734        | -        |
+| 0.9326 | 13800 | 0.578         | -        |
+| 0.9461 | 14000 | 0.569         | -        |
+| 0.9597 | 14200 | 0.5637        | -        |
+| 0.9732 | 14400 | 0.5777        | -        |
+| 0.9867 | 14600 | 0.5653        | -        |
+
+
+### Framework Versions
+- Python: 3.11.0
+- Sentence Transformers: 4.0.1
+- PyLate: 1.1.7
+- Transformers: 4.48.2
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- Datasets: 3.5.0
+- 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"
+}
+```
+
+#### PyLate
+```bibtex
+@misc{PyLate,
+title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
+author={Chaffin, Antoine and Sourty, Raphaël},
+url={https://github.com/lightonai/pylate},
+year={2024}
+}
+```
+
+<!--
+## 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.*
+-->

added_tokens.json

@@ -0,0 +1,4 @@
+{
+  "[D] ": 30523,
+  "[Q] ": 30522
+}

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "nreimers/MiniLM-L6-H384-uncased",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30524
+}

config_sentence_transformers.json

@@ -0,0 +1,49 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.0.1",
+    "transformers": "4.48.2",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "MaxSim",
+  "query_prefix": "[Q] ",
+  "document_prefix": "[D] ",
+  "query_length": 32,
+  "document_length": 180,
+  "attend_to_expansion_tokens": false,
+  "skiplist_words": [
+    "!",
+    "\"",
+    "#",
+    "$",
+    "%",
+    "&",
+    "'",
+    "(",
+    ")",
+    "*",
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+    "-",
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+    "@",
+    "[",
+    "\\",
+    "]",
+    "^",
+    "_",
+    "`",
+    "{",
+    "|",
+    "}",
+    "~"
+  ]
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a9ef92adeba87216234823cd4cd5c9919e7ea80a7a5cc5b18f02a17ddc266bda
+size 90867264

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Dense",
+    "type": "pylate.models.Dense.Dense"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 31,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[MASK]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,74 @@
+{
+  "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,
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+      "single_word": false,
+      "special": true
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "single_word": false,
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+      "lstrip": false,
+      "normalized": true,
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+      "single_word": false,
+      "special": false
+    },
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+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "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": "[MASK]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}