Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:478146
+- loss:CoSENTLoss
+widget:
+- source_sentence: However, its underutilization is mainly due to the absence of a
+    concrete and coherent dissemination strategy.
+  sentences:
+  - At the same time, they need to understand that living in Europe brings great responsibilities
+    in addition to great benefits.
+  - 'The mainstay of any intelligent and patriotic mineral policy can be summed up
+    in the following postulate: "since minerals are exhaustible, they should only
+    be exploited with the maximum return for the economy of the country where they
+    are mined".'
+  - We must move quickly to a shared sustainable energy supply, sustainable transportation
+    and clean air.
+- source_sentence: Their track record shows they do not support Australia<92>s traditional
+    industries because they are constantly pandering to the Greens.
+  sentences:
+  - An economic dynamic based on the sustainable development of national potential,
+    equitable access to the means of production, social justice, environmental conservation,
+    the incorporation of added value, the promotion of competitiveness and self-management,
+  - the cry "El campo no aguanta más" (The countryside can't take it anymore), of
+    the peasant movement and its proclamation of "Salvemos al Campo para salvar a
+    México" (Let's save the countryside to save Mexico);
+  - On the other hand, increasing defence capacity is directly related to the involvement
+    of all citizens in appropriate programmes, which, together with the acquisition
+    of skills, experience and organisation, also contribute to forging a spirit of
+    militancy and collectivity.
+- source_sentence: We will prepare the proposals of the United Nations Declaration
+    on the Rights of the Child in line with the commitments made.
+  sentences:
+  - For the presentation of Czech culture, we will also use the upcoming major anniversaries
+    (100 years of the founding of Czechoslovakia, the 30th anniversary of the canonization
+    of Agnes of Bohemia, 600 years since the birth of George of Poděbrady, etc.).
+  - Separate prison units for young people should be established, and special rehabilitation
+    measures should be introduced in these units.
+  - Austrian citizenship is a valuable asset and should not become accessible to those
+    who do not abide by the laws of our state.
+- source_sentence: Third, CD&V wants to strengthen the social sustainability of our
+    agriculture and horticulture sector.
+  sentences:
+  - We will take a farm-level approach where possible so that low-emissions farmers
+    are rewarded with a lower cost through the ETS, rather than the current approach
+    that assumes each cow, for instance, has the same emissions on every farm.
+  - In addition, 20 billion euros in tax revenues are fraudulently evaded every year
+    (the equivalent of the healthcare budget).
+  - 87 percent of arrested undocumented migrants are released sooner or later, but
+    without papers, in a lawless situation.
+- source_sentence: This incites social hatred, threatens economic and social stability,
+    and undermines trust in the authorities.
+  sentences:
+  - ' The conditions for a healthy entrepreneurship, where the most innovative and
+    creative win and where the source of enrichment cannot be property speculation
+    or guilds and networks.   '
+  - According to statistics from the Attorney General's Office, since February 2005,
+    when the implementation of the PSD was announced, the rate of violent deaths per
+    100,000 inhabitants has dropped from 26.41 in December 2005 to 18.43 in December
+    2007.
+  - As a result, the profits of the oligarchs are more than 400 times what our entire
+    country gets from the exploitation of natural resources.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - json
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("LequeuISIR/final-DPR-8e-05")
+# Run inference
+sentences = [
+    'This incites social hatred, threatens economic and social stability, and undermines trust in the authorities.',
+    '\xa0The conditions for a healthy entrepreneurship, where the most innovative and creative win and where the source of enrichment cannot be property speculation or guilds and networks.   ',
+    'As a result, the profits of the oligarchs are more than 400 times what our entire country gets from the exploitation of natural resources.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### 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.*
+-->
+
+<!--
+## 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
+
+#### json
+
+* Dataset: json
+* Size: 478,146 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                           | sentence2                                                                           | label                                                             |
+  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                              | int                                                               |
+  | details | <ul><li>min: 17 tokens</li><li>mean: 33.73 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 33.84 tokens</li><li>max: 101 tokens</li></ul> | <ul><li>0: ~57.50%</li><li>1: ~4.10%</li><li>2: ~38.40%</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                         | sentence2                                                                                                                                                                                                                  | label          |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>There have also been other important structural changes in the countryside, which have come together to form this new, as yet unknown, country.</code>                      | <code>Meanwhile, investment, which is the way to increase production, employment capacity and competitiveness of the economy, fell from 20% of output in 1974 to only 11.8% on average between 1984 and 1988.</code>       | <code>0</code> |
+  | <code>Introduce new visa categories so we can be responsive to humanitarian needs and incentivise greater investment in our domestic infrastructure and regional economies</code> | <code>The purpose of the project is to design and implement public policies aimed at achieving greater and faster inclusion of immigrants.</code>                                                                          | <code>2</code> |
+  | <code>and economic crimes that seriously and generally affect the fundamental rights of individuals and the international community as a whole.</code>                            | <code>For the first time in the history, not only of Ecuador, but of the entire world, a government promoted a public audit process of the foreign debt and declared some of its tranches illegitimate and immoral.</code> | <code>0</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### json
+
+* Dataset: json
+* Size: 478,146 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                           | sentence2                                                                           | label                                                             |
+  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                              | int                                                               |
+  | details | <ul><li>min: 17 tokens</li><li>mean: 33.62 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 34.48 tokens</li><li>max: 111 tokens</li></ul> | <ul><li>0: ~57.30%</li><li>1: ~2.90%</li><li>2: ~39.80%</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                           | sentence2                                                                                                                                                                             | label          |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>The anchoring of the Slovak Republic in the European Union allows citizens to feel: secure politically, secure economically, secure socially.</code>                          | <code>Radikale Venstre wants Denmark to participate fully and firmly in EU cooperation on immigration, asylum and cross-border crime.</code>                                          | <code>2</code> |
+  | <code>Portugal's participation in the Community's negotiation of the next financial perspective should also be geared in the same direction.</code>                                 | <code>Given the dynamic international framework, safeguarding the national interest requires adjustments to each of these vectors.</code>                                             | <code>2</code> |
+  | <code>On asylum, the Green Party will: Dismantle the direct provision system and replace it with an efficient and humane system for determining the status of asylum seekers</code> | <code>The crisis in the coal sector subsequently forced these immigrant workers to move into other economic sectors such as metallurgy, chemicals, construction and transport.</code> | <code>2</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `learning_rate`: 8e-05
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.05
+- `bf16`: True
+- `batch_sampler`: no_duplicates
+
+#### 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`: 64
+- `per_device_eval_batch_size`: 64
+- `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`: 8e-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`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `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`: 42
+- `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`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `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`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | Validation Loss |
+|:------:|:-----:|:-------------:|:---------------:|
+| 0.0837 | 500   | 0.7889        | 9.5828          |
+| 0.1673 | 1000  | 1.2158        | 9.3274          |
+| 0.2510 | 1500  | 1.8215        | 9.4274          |
+| 0.3346 | 2000  | 2.3548        | 8.2583          |
+| 0.4183 | 2500  | 2.7493        | 8.1446          |
+| 0.5019 | 3000  | 2.8998        | 7.9046          |
+| 0.5856 | 3500  | 2.9298        | 8.0640          |
+| 0.6692 | 4000  | 2.9053        | 7.2746          |
+| 0.7529 | 4500  | 3.0905        | 7.5099          |
+| 0.8365 | 5000  | 3.1864        | 7.3883          |
+| 0.9202 | 5500  | 3.2322        | 6.9968          |
+| 1.0038 | 6000  | 3.1194        | 7.4682          |
+| 1.0875 | 6500  | 3.0122        | 7.7295          |
+| 1.1712 | 7000  | 3.0453        | 7.1696          |
+| 1.2548 | 7500  | 2.9439        | 7.2775          |
+| 1.3385 | 8000  | 3.1108        | 7.4838          |
+| 1.4221 | 8500  | 2.8512        | 7.5204          |
+| 1.5058 | 9000  | 2.9865        | 7.4528          |
+| 1.5894 | 9500  | 2.9995        | 8.0682          |
+| 1.6731 | 10000 | 3.1073        | 7.5344          |
+| 1.7567 | 10500 | 3.0631        | 7.4572          |
+| 1.8404 | 11000 | 2.9915        | 7.4961          |
+| 1.9240 | 11500 | 3.0445        | 7.3575          |
+| 2.0077 | 12000 | 2.9501        | 7.9786          |
+| 2.0914 | 12500 | 2.3377        | 8.6208          |
+| 2.1750 | 13000 | 2.2833        | 8.8356          |
+| 2.2587 | 13500 | 2.2785        | 8.8709          |
+| 2.3423 | 14000 | 2.3012        | 8.6250          |
+| 2.4260 | 14500 | 2.3488        | 8.1099          |
+| 2.5096 | 15000 | 2.095         | 9.2305          |
+| 2.5933 | 15500 | 2.4123        | 8.6405          |
+| 2.6769 | 16000 | 2.2236        | 8.7805          |
+| 2.7606 | 16500 | 2.3367        | 8.7110          |
+| 2.8442 | 17000 | 2.1159        | 8.6447          |
+| 2.9279 | 17500 | 2.1622        | 8.7123          |
+| 3.0115 | 18000 | 2.1916        | 9.0314          |
+| 3.0952 | 18500 | 1.604         | 9.3373          |
+| 3.1789 | 19000 | 1.4116        | 9.6509          |
+| 3.2625 | 19500 | 1.4036        | 9.9127          |
+| 3.3462 | 20000 | 1.5392        | 9.8093          |
+| 3.4298 | 20500 | 1.5791        | 9.8325          |
+| 3.5135 | 21000 | 1.5343        | 9.7822          |
+| 3.5971 | 21500 | 1.3913        | 9.6243          |
+| 3.6808 | 22000 | 1.5151        | 9.9644          |
+| 3.7644 | 22500 | 1.3922        | 9.7816          |
+| 3.8481 | 23000 | 1.3361        | 9.5338          |
+| 3.9317 | 23500 | 1.3363        | 9.8282          |
+| 4.0154 | 24000 | 1.2234        | 10.2117         |
+| 4.0990 | 24500 | 0.5927        | 10.4107         |
+| 4.1827 | 25000 | 0.6879        | 10.4405         |
+| 4.2664 | 25500 | 0.6832        | 10.5138         |
+| 4.3500 | 26000 | 0.6514        | 10.2798         |
+| 4.4337 | 26500 | 0.7396        | 10.3250         |
+| 4.5173 | 27000 | 0.6813        | 10.4115         |
+| 4.6010 | 27500 | 0.765         | 10.1365         |
+| 4.6846 | 28000 | 0.5915        | 10.2402         |
+| 4.7683 | 28500 | 0.5028        | 10.3197         |
+| 4.8519 | 29000 | 0.5306        | 10.3270         |
+| 4.9356 | 29500 | 0.5886        | 10.3543         |
+
+
+### Framework Versions
+- Python: 3.9.21
+- Sentence Transformers: 3.4.0
+- Transformers: 4.48.1
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## 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",
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+<!--
+## 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 +1,47 @@
+{
+  "_name_or_path": "output/ModernBERT-base/ModernBERT-base-DPR-8e-05/final",
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "reference_compile": true,
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.1",
+  "vocab_size": 50368
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.0",
+    "transformers": "4.48.1",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8404364bbab77e7a7129bb7c94d3e28892cac3f48c7e125920d01b04af278fc8
+size 596070136

modules.json

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

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 8192,
+  "do_lower_case": false
+}

special_tokens_map.json

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

tokenizer_config.json

@@ -0,0 +1,952 @@
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