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 sha256:30a1b40c47289459d3087884d9706d2c8a2bab239ecf1b1dd07b3d1781065c21
+size 196696

README.md

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+---
+tags:
+- ColBERT
+- PyLate
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:5679484
+- 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: 0.4773999750614166
+      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': 179, '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-3-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-3-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** | **0.4774** |
+
+<!--
+## 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: 5,679,484 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: 12.97 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 31.84 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 31.64 tokens</li><li>max: 32 tokens</li></ul> |
+* Samples:
+  | question                                                  | answer                                                                                                                                                                                                                                                                        | negative                                                                                                                                                                                                                                                                                                                                     |
+  |:----------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>can i use bluetooth headphones for xbox one?</code> | <code>Headsets cannot be connected to any third party wireless controller. Headsets need to be connected to the Xbox one controller in order to function. The Xbox one console doesn't have a Bluetooth feature. Hence the headsets cannot be connected via Bluetooth.</code> | <code>You can connect Bluetooth headphones to a PS4, but only if they are compatible with the PS4. Most standard Bluetooth headphones are not compatible with the PS4, so you will need to make sure you have Bluetooth headphones that are specifically geared to the PS4.</code>                                                           |
+  | <code>can i use bluetooth headphones for xbox one?</code> | <code>Headsets cannot be connected to any third party wireless controller. Headsets need to be connected to the Xbox one controller in order to function. The Xbox one console doesn't have a Bluetooth feature. Hence the headsets cannot be connected via Bluetooth.</code> | <code>Summary – how to pair Sony Bluetooth headphones Tap and hold the Power button on the headphones for 7 seconds to put your Sony Bluetooth headphones into pairing mode. Tap the Settings icon on your iPhone. Select the Bluetooth option. Select your headphones from the list of devices, then wait for it to say “Connected.”</code> |
+  | <code>can i use bluetooth headphones for xbox one?</code> | <code>Headsets cannot be connected to any third party wireless controller. Headsets need to be connected to the Xbox one controller in order to function. The Xbox one console doesn't have a Bluetooth feature. Hence the headsets cannot be connected via Bluetooth.</code> | <code>You can only pair one Bluetooth headphone or soundbar and one other Bluetooth device to the TV at the same time, but not two Bluetooth headphones or soundbars at the same time.</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: 12.83 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 31.71 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 31.37 tokens</li><li>max: 32 tokens</li></ul> |
+* Samples:
+  | question                                                          | answer                                                                                                                                                                                                                                                                                                   | negative_1                                                                                                                                                                                                                                     |
+  |:------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>what is controlled by the peripheral nervous system?</code> | <code>The efferent nerves of the somatic nervous system of the PNS is responsible for voluntary, conscious control of skeletal muscles (effector organ) using motor (efferent) nerves. The efferent nerves of the autonomic (visceral) nervous system control the visceral functions of the body.</code> | <code>Which of the following is not a part of peripheral nervous system? Explanation: Peripheral nervous system lies outside the brain and spinal cord. Spinal cord is not a part of peripheral nervous system.</code>                         |
+  | <code>is cold water good to drink in the morning?</code>          | <code>This is probably because drinking cold water makes it easier for your body to maintain a lower core temperature. Drinking plain water, no matter the temperature, has been proven to give your body more energy throughout the day.</code>                                                         | <code>What Are Benefits of It Cold? Drinking water cold is beneficial because it tastes better and you are more likely to drink more of it. Cold lemon water tastes delicious and so you are more likely to drink more of it.</code>           |
+  | <code>how to get rid of fungal nail quickly?</code>               | <code>According to a 2016 review, thymol has antifungal and antibacterial properties. To treat toenail fungus, apply oregano oil to the affected nail twice daily with a cotton swab. Some people use oregano oil and tea tree oil together.</code>                                                      | <code>With treatment, many people can get rid of nail fungus. Even when the fungus clears, your nail(s) may look unhealthy until the infected nail grows out. A fingernail grows out in 4 to 6 months and a toenail in 12 to 18 months.</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
+<details><summary>Click to expand</summary>
+
+| Epoch      | Step      | Training Loss | Validation Loss | accuracy |
+|:----------:|:---------:|:-------------:|:---------------:|:--------:|
+| 0          | 0         | -             | -               | 0.3568   |
+| 0.0000     | 1         | 10.0066       | -               | -        |
+| 0.0045     | 200       | 9.562         | -               | -        |
+| 0.0090     | 400       | 8.6749        | -               | -        |
+| 0.0135     | 600       | 6.7475        | -               | -        |
+| 0.0180     | 800       | 4.9203        | -               | -        |
+| 0.0225     | 1000      | 3.4444        | -               | -        |
+| 0.0270     | 1200      | 2.5604        | -               | -        |
+| 0.0316     | 1400      | 2.1878        | -               | -        |
+| 0.0361     | 1600      | 1.9166        | -               | -        |
+| 0.0406     | 1800      | 1.7376        | -               | -        |
+| 0.0451     | 2000      | 1.5786        | -               | -        |
+| 0.0496     | 2200      | 1.4304        | -               | -        |
+| 0.0541     | 2400      | 1.3307        | -               | -        |
+| 0.0586     | 2600      | 1.2409        | -               | -        |
+| 0.0631     | 2800      | 1.1913        | -               | -        |
+| 0.0676     | 3000      | 1.0885        | -               | -        |
+| 0.0721     | 3200      | 1.0439        | -               | -        |
+| 0.0766     | 3400      | 0.9721        | -               | -        |
+| 0.0811     | 3600      | 0.918         | -               | -        |
+| 0.0856     | 3800      | 0.8688        | -               | -        |
+| 0.0901     | 4000      | 0.8269        | -               | -        |
+| 0.0947     | 4200      | 0.7815        | -               | -        |
+| 0.0992     | 4400      | 0.7577        | -               | -        |
+| 0.1037     | 4600      | 0.714         | -               | -        |
+| 0.1082     | 4800      | 0.6923        | -               | -        |
+| 0.1127     | 5000      | 0.6619        | -               | -        |
+| 0.1172     | 5200      | 0.6409        | -               | -        |
+| 0.1217     | 5400      | 0.6142        | -               | -        |
+| 0.1262     | 5600      | 0.6163        | -               | -        |
+| 0.1307     | 5800      | 0.5821        | -               | -        |
+| 0.1352     | 6000      | 0.5822        | -               | -        |
+| 0.1397     | 6200      | 0.5572        | -               | -        |
+| 0.1442     | 6400      | 0.555         | -               | -        |
+| 0.1487     | 6600      | 0.5392        | -               | -        |
+| 0.1533     | 6800      | 0.5326        | -               | -        |
+| 0.1578     | 7000      | 0.5185        | -               | -        |
+| 0.1623     | 7200      | 0.507         | -               | -        |
+| 0.1668     | 7400      | 0.4943        | -               | -        |
+| 0.1713     | 7600      | 0.4915        | -               | -        |
+| 0.1758     | 7800      | 0.4951        | -               | -        |
+| 0.1803     | 8000      | 0.4806        | -               | -        |
+| 0.1848     | 8200      | 0.4782        | -               | -        |
+| 0.1893     | 8400      | 0.4719        | -               | -        |
+| 0.1938     | 8600      | 0.4628        | -               | -        |
+| 0.1983     | 8800      | 0.4615        | -               | -        |
+| 0.2028     | 9000      | 0.4624        | -               | -        |
+| 0.2073     | 9200      | 0.4462        | -               | -        |
+| 0.2119     | 9400      | 0.4571        | -               | -        |
+| 0.2164     | 9600      | 0.452         | -               | -        |
+| 0.2209     | 9800      | 0.4454        | -               | -        |
+| 0.2254     | 10000     | 0.4387        | -               | -        |
+| 0.2299     | 10200     | 0.4247        | -               | -        |
+| 0.2344     | 10400     | 0.4221        | -               | -        |
+| 0.2389     | 10600     | 0.4242        | -               | -        |
+| 0.2434     | 10800     | 0.422         | -               | -        |
+| 0.2479     | 11000     | 0.4252        | -               | -        |
+| 0.2524     | 11200     | 0.416         | -               | -        |
+| 0.2569     | 11400     | 0.4138        | -               | -        |
+| 0.2614     | 11600     | 0.4139        | -               | -        |
+| 0.2659     | 11800     | 0.4168        | -               | -        |
+| 0.2704     | 12000     | 0.4008        | -               | -        |
+| 0.2750     | 12200     | 0.3994        | -               | -        |
+| 0.2795     | 12400     | 0.3973        | -               | -        |
+| 0.2840     | 12600     | 0.393         | -               | -        |
+| 0.2885     | 12800     | 0.3863        | -               | -        |
+| 0.2930     | 13000     | 0.3914        | -               | -        |
+| 0.2975     | 13200     | 0.38          | -               | -        |
+| 0.3020     | 13400     | 0.3805        | -               | -        |
+| 0.3065     | 13600     | 0.3749        | -               | -        |
+| 0.3110     | 13800     | 0.3814        | -               | -        |
+| 0.3155     | 14000     | 0.3783        | -               | -        |
+| 0.3200     | 14200     | 0.3733        | -               | -        |
+| 0.3245     | 14400     | 0.3762        | -               | -        |
+| 0.3290     | 14600     | 0.3797        | -               | -        |
+| 0.3336     | 14800     | 0.3727        | -               | -        |
+| 0.3381     | 15000     | 0.3658        | -               | -        |
+| 0.3426     | 15200     | 0.3655        | -               | -        |
+| 0.3471     | 15400     | 0.3619        | -               | -        |
+| 0.3516     | 15600     | 0.3685        | -               | -        |
+| 0.3561     | 15800     | 0.3608        | -               | -        |
+| 0.3606     | 16000     | 0.3631        | -               | -        |
+| 0.3651     | 16200     | 0.3587        | -               | -        |
+| 0.3696     | 16400     | 0.3536        | -               | -        |
+| 0.3741     | 16600     | 0.3477        | -               | -        |
+| 0.3786     | 16800     | 0.3595        | -               | -        |
+| 0.3831     | 17000     | 0.3558        | -               | -        |
+| 0.3876     | 17200     | 0.3518        | -               | -        |
+| 0.3921     | 17400     | 0.353         | -               | -        |
+| 0.3967     | 17600     | 0.354         | -               | -        |
+| 0.4012     | 17800     | 0.3477        | -               | -        |
+| 0.4057     | 18000     | 0.3457        | -               | -        |
+| 0.4102     | 18200     | 0.346         | -               | -        |
+| 0.4147     | 18400     | 0.3451        | -               | -        |
+| 0.4192     | 18600     | 0.3437        | -               | -        |
+| 0.4237     | 18800     | 0.3401        | -               | -        |
+| 0.4282     | 19000     | 0.342         | -               | -        |
+| 0.4327     | 19200     | 0.3416        | -               | -        |
+| 0.4372     | 19400     | 0.3405        | -               | -        |
+| 0.4417     | 19600     | 0.3331        | -               | -        |
+| 0.4462     | 19800     | 0.3319        | -               | -        |
+| 0.4507     | 20000     | 0.3264        | -               | -        |
+| 0          | 0         | -             | -               | 0.4590   |
+| 0.4507     | 20000     | -             | 1.2902          | -        |
+| 0.4553     | 20200     | 0.3312        | -               | -        |
+| 0.4598     | 20400     | 0.3363        | -               | -        |
+| 0.4643     | 20600     | 0.333         | -               | -        |
+| 0.4688     | 20800     | 0.3341        | -               | -        |
+| 0.4733     | 21000     | 0.3287        | -               | -        |
+| 0.4778     | 21200     | 0.3357        | -               | -        |
+| 0.4823     | 21400     | 0.3325        | -               | -        |
+| 0.4868     | 21600     | 0.3323        | -               | -        |
+| 0.4913     | 21800     | 0.3385        | -               | -        |
+| 0.4958     | 22000     | 0.3244        | -               | -        |
+| 0.5003     | 22200     | 0.3281        | -               | -        |
+| 0.5048     | 22400     | 0.3251        | -               | -        |
+| 0.5093     | 22600     | 0.3271        | -               | -        |
+| 0.5138     | 22800     | 0.3271        | -               | -        |
+| 0.5184     | 23000     | 0.3245        | -               | -        |
+| 0.5229     | 23200     | 0.3185        | -               | -        |
+| 0.5274     | 23400     | 0.3212        | -               | -        |
+| 0.5319     | 23600     | 0.3211        | -               | -        |
+| 0.5364     | 23800     | 0.3205        | -               | -        |
+| 0.5409     | 24000     | 0.3104        | -               | -        |
+| 0.5454     | 24200     | 0.3208        | -               | -        |
+| 0.5499     | 24400     | 0.3218        | -               | -        |
+| 0.5544     | 24600     | 0.3183        | -               | -        |
+| 0.5589     | 24800     | 0.3208        | -               | -        |
+| 0.5634     | 25000     | 0.3151        | -               | -        |
+| 0.5679     | 25200     | 0.3138        | -               | -        |
+| 0.5724     | 25400     | 0.3155        | -               | -        |
+| 0.5770     | 25600     | 0.3201        | -               | -        |
+| 0.5815     | 25800     | 0.3135        | -               | -        |
+| 0.5860     | 26000     | 0.3157        | -               | -        |
+| 0.5905     | 26200     | 0.3051        | -               | -        |
+| 0.5950     | 26400     | 0.3121        | -               | -        |
+| 0.5995     | 26600     | 0.3109        | -               | -        |
+| 0.6040     | 26800     | 0.3103        | -               | -        |
+| 0.6085     | 27000     | 0.316         | -               | -        |
+| 0.6130     | 27200     | 0.3119        | -               | -        |
+| 0.6175     | 27400     | 0.3135        | -               | -        |
+| 0.6220     | 27600     | 0.3007        | -               | -        |
+| 0.6265     | 27800     | 0.304         | -               | -        |
+| 0.6310     | 28000     | 0.3014        | -               | -        |
+| 0.6356     | 28200     | 0.3075        | -               | -        |
+| 0.6401     | 28400     | 0.3074        | -               | -        |
+| 0.6446     | 28600     | 0.3072        | -               | -        |
+| 0.6491     | 28800     | 0.3043        | -               | -        |
+| 0.6536     | 29000     | 0.3059        | -               | -        |
+| 0.6581     | 29200     | 0.3054        | -               | -        |
+| 0.6626     | 29400     | 0.3019        | -               | -        |
+| 0.6671     | 29600     | 0.3108        | -               | -        |
+| 0.6716     | 29800     | 0.3032        | -               | -        |
+| 0.6761     | 30000     | 0.3054        | -               | -        |
+| 0.6806     | 30200     | 0.3034        | -               | -        |
+| 0.6851     | 30400     | 0.3008        | -               | -        |
+| 0.6896     | 30600     | 0.3           | -               | -        |
+| 0.6941     | 30800     | 0.3042        | -               | -        |
+| 0.6987     | 31000     | 0.3018        | -               | -        |
+| 0.7032     | 31200     | 0.3162        | -               | -        |
+| 0.7077     | 31400     | 0.2998        | -               | -        |
+| 0.7122     | 31600     | 0.2975        | -               | -        |
+| 0.7167     | 31800     | 0.3015        | -               | -        |
+| 0.7212     | 32000     | 0.3005        | -               | -        |
+| 0.7257     | 32200     | 0.3028        | -               | -        |
+| 0.7302     | 32400     | 0.3029        | -               | -        |
+| 0.7347     | 32600     | 0.2968        | -               | -        |
+| 0.7392     | 32800     | 0.3066        | -               | -        |
+| 0.7437     | 33000     | 0.2958        | -               | -        |
+| 0.7482     | 33200     | 0.2968        | -               | -        |
+| 0.7527     | 33400     | 0.2963        | -               | -        |
+| 0.7573     | 33600     | 0.3026        | -               | -        |
+| 0.7618     | 33800     | 0.2891        | -               | -        |
+| 0.7663     | 34000     | 0.2991        | -               | -        |
+| 0.7708     | 34200     | 0.2939        | -               | -        |
+| 0.7753     | 34400     | 0.2923        | -               | -        |
+| 0.7798     | 34600     | 0.295         | -               | -        |
+| 0.7843     | 34800     | 0.2901        | -               | -        |
+| 0.7888     | 35000     | 0.294         | -               | -        |
+| 0.7933     | 35200     | 0.2945        | -               | -        |
+| 0.7978     | 35400     | 0.299         | -               | -        |
+| 0.8023     | 35600     | 0.297         | -               | -        |
+| 0.8068     | 35800     | 0.2881        | -               | -        |
+| 0.8113     | 36000     | 0.298         | -               | -        |
+| 0.8158     | 36200     | 0.2925        | -               | -        |
+| 0.8204     | 36400     | 0.2978        | -               | -        |
+| 0.8249     | 36600     | 0.2989        | -               | -        |
+| 0.8294     | 36800     | 0.2914        | -               | -        |
+| 0.8339     | 37000     | 0.2913        | -               | -        |
+| 0.8384     | 37200     | 0.2925        | -               | -        |
+| 0.8429     | 37400     | 0.2991        | -               | -        |
+| 0.8474     | 37600     | 0.291         | -               | -        |
+| 0.8519     | 37800     | 0.2937        | -               | -        |
+| 0.8564     | 38000     | 0.2989        | -               | -        |
+| 0.8609     | 38200     | 0.2854        | -               | -        |
+| 0.8654     | 38400     | 0.2878        | -               | -        |
+| 0.8699     | 38600     | 0.2905        | -               | -        |
+| 0.8744     | 38800     | 0.287         | -               | -        |
+| 0.8790     | 39000     | 0.2869        | -               | -        |
+| 0.8835     | 39200     | 0.2927        | -               | -        |
+| 0.8880     | 39400     | 0.2889        | -               | -        |
+| 0.8925     | 39600     | 0.2912        | -               | -        |
+| 0.8970     | 39800     | 0.2927        | -               | -        |
+| **0.9015** | **40000** | **0.2952**    | **-**           | **-**    |
+| 0          | 0         | -             | -               | 0.4774   |
+| **0.9015** | **40000** | **-**         | **1.2227**      | **-**    |
+| 0.9060     | 40200     | 0.29          | -               | -        |
+| 0.9105     | 40400     | 0.2878        | -               | -        |
+| 0.9150     | 40600     | 0.2924        | -               | -        |
+| 0.9195     | 40800     | 0.2877        | -               | -        |
+| 0.9240     | 41000     | 0.2844        | -               | -        |
+| 0.9285     | 41200     | 0.2951        | -               | -        |
+| 0.9330     | 41400     | 0.291         | -               | -        |
+| 0.9375     | 41600     | 0.292         | -               | -        |
+| 0.9421     | 41800     | 0.2902        | -               | -        |
+| 0.9466     | 42000     | 0.2815        | -               | -        |
+| 0.9511     | 42200     | 0.29          | -               | -        |
+| 0.9556     | 42400     | 0.2872        | -               | -        |
+| 0.9601     | 42600     | 0.2759        | -               | -        |
+| 0.9646     | 42800     | 0.2832        | -               | -        |
+| 0.9691     | 43000     | 0.2886        | -               | -        |
+| 0.9736     | 43200     | 0.2908        | -               | -        |
+| 0.9781     | 43400     | 0.2857        | -               | -        |
+| 0.9826     | 43600     | 0.2833        | -               | -        |
+| 0.9871     | 43800     | 0.2837        | -               | -        |
+| 0.9916     | 44000     | 0.2882        | -               | -        |
+| 0.9961     | 44200     | 0.2919        | -               | -        |
+
+* The bold row denotes the saved checkpoint.
+</details>
+
+### 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": [
+    "!",
+    "\"",
+    "#",
+    "$",
+    "%",
+    "&",
+    "'",
+    "(",
+    ")",
+    "*",
+    "+",
+    ",",
+    "-",
+    ".",
+    "/",
+    ":",
+    ";",
+    "<",
+    "=",
+    ">",
+    "?",
+    "@",
+    "[",
+    "\\",
+    "]",
+    "^",
+    "_",
+    "`",
+    "{",
+    "|",
+    "}",
+    "~"
+  ]
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3342c2e12cb985591d5bde4a820bb466ecf4aac3f668dd8cadfa488c6925967b
+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": 179,
+  "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
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30522": {
+      "content": "[Q] ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "30523": {
+      "content": "[D] ",
+      "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]"
+}