Upload folder using huggingface_hub

1_Pooling/config.json

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+{
+  "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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+---
+base_model: sentence-transformers/all-mpnet-base-v2
+language:
+- en
+library_name: sentence-transformers
+license: apache-2.0
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1363306
+- loss:CoSENTLoss
+widget:
+- source_sentence: labneh
+  sentences:
+  - iftar
+  - bathing suit
+  - coffee cup
+- source_sentence: Velvet flock Veil
+  sentences:
+  - mermaid purse
+  - veil
+  - mobile bag
+- source_sentence: Red lipstick
+  sentences:
+  - chemise dress
+  - tote
+  - rouge
+- source_sentence: Unisex Travel bag
+  sentences:
+  - spf
+  - basic vega ring
+  - travel backpack
+- source_sentence: jeremy hush book
+  sentences:
+  - chinese jumper
+  - perfume
+  - home automation device
+---
+
+# all-mpnet-base-v2-pair_score
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
+- **Maximum Sequence Length:** 384 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (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})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'jeremy hush book',
+    'chinese jumper',
+    'perfume',
+]
+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 Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 2
+- `warmup_ratio`: 0.1
+- `fp16`: 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`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 2
+- `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`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: 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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss | loss   |
+|:------:|:-----:|:-------------:|:------:|
+| 0.0094 | 100   | 16.0755       | -      |
+| 0.0188 | 200   | 13.0643       | -      |
+| 0.0282 | 300   | 9.3474        | -      |
+| 0.0376 | 400   | 8.2606        | -      |
+| 0.0469 | 500   | 8.084         | -      |
+| 0.0563 | 600   | 8.0581        | -      |
+| 0.0657 | 700   | 8.0175        | -      |
+| 0.0751 | 800   | 8.0285        | -      |
+| 0.0845 | 900   | 8.0024        | -      |
+| 0.0939 | 1000  | 8.0161        | -      |
+| 0.1033 | 1100  | 7.9941        | -      |
+| 0.1127 | 1200  | 8.0233        | -      |
+| 0.1221 | 1300  | 8.0141        | -      |
+| 0.1314 | 1400  | 7.9644        | -      |
+| 0.1408 | 1500  | 8.0311        | -      |
+| 0.1502 | 1600  | 8.0306        | -      |
+| 0.1596 | 1700  | 7.989         | -      |
+| 0.1690 | 1800  | 8.0034        | -      |
+| 0.1784 | 1900  | 8.0107        | -      |
+| 0.1878 | 2000  | 7.9737        | -      |
+| 0.1972 | 2100  | 7.9827        | -      |
+| 0.2066 | 2200  | 8.0389        | -      |
+| 0.2159 | 2300  | 7.973         | -      |
+| 0.2253 | 2400  | 7.9669        | -      |
+| 0.2347 | 2500  | 8.0296        | -      |
+| 0.2441 | 2600  | 7.9984        | -      |
+| 0.2535 | 2700  | 7.9772        | -      |
+| 0.2629 | 2800  | 7.9838        | -      |
+| 0.2723 | 2900  | 7.9816        | -      |
+| 0.2817 | 3000  | 8.0021        | -      |
+| 0.2911 | 3100  | 7.9715        | -      |
+| 0.3004 | 3200  | 7.9809        | -      |
+| 0.3098 | 3300  | 7.9849        | -      |
+| 0.3192 | 3400  | 7.9463        | -      |
+| 0.3286 | 3500  | 8.0067        | -      |
+| 0.3380 | 3600  | 7.9431        | -      |
+| 0.3474 | 3700  | 7.9877        | -      |
+| 0.3568 | 3800  | 7.9494        | -      |
+| 0.3662 | 3900  | 7.9466        | -      |
+| 0.3756 | 4000  | 7.9708        | -      |
+| 0.3849 | 4100  | 7.9525        | -      |
+| 0.3943 | 4200  | 7.9322        | -      |
+| 0.4037 | 4300  | 7.9415        | -      |
+| 0.4131 | 4400  | 7.9932        | -      |
+| 0.4225 | 4500  | 7.9481        | -      |
+| 0.4319 | 4600  | 7.976         | -      |
+| 0.4413 | 4700  | 7.971         | -      |
+| 0.4507 | 4800  | 7.9647        | -      |
+| 0.4601 | 4900  | 7.9217        | -      |
+| 0.4694 | 5000  | 7.9374        | 7.9518 |
+| 0.4788 | 5100  | 7.9026        | -      |
+| 0.4882 | 5200  | 7.9304        | -      |
+| 0.4976 | 5300  | 7.9148        | -      |
+| 0.5070 | 5400  | 7.9538        | -      |
+| 0.5164 | 5500  | 8.0002        | -      |
+| 0.5258 | 5600  | 7.9571        | -      |
+| 0.5352 | 5700  | 7.932         | -      |
+| 0.5445 | 5800  | 7.9047        | -      |
+| 0.5539 | 5900  | 7.9353        | -      |
+| 0.5633 | 6000  | 7.9203        | -      |
+| 0.5727 | 6100  | 7.8967        | -      |
+| 0.5821 | 6200  | 7.9414        | -      |
+| 0.5915 | 6300  | 7.9631        | -      |
+| 0.6009 | 6400  | 7.9606        | -      |
+| 0.6103 | 6500  | 7.9377        | -      |
+| 0.6197 | 6600  | 7.9108        | -      |
+| 0.6290 | 6700  | 7.9225        | -      |
+| 0.6384 | 6800  | 7.9154        | -      |
+| 0.6478 | 6900  | 7.9191        | -      |
+| 0.6572 | 7000  | 7.8903        | -      |
+| 0.6666 | 7100  | 7.9213        | -      |
+| 0.6760 | 7200  | 7.9202        | -      |
+| 0.6854 | 7300  | 7.8998        | -      |
+| 0.6948 | 7400  | 7.9153        | -      |
+| 0.7042 | 7500  | 7.9037        | -      |
+| 0.7135 | 7600  | 7.9146        | -      |
+| 0.7229 | 7700  | 7.8972        | -      |
+| 0.7323 | 7800  | 7.9374        | -      |
+| 0.7417 | 7900  | 7.8647        | -      |
+| 0.7511 | 8000  | 7.8915        | -      |
+| 0.7605 | 8100  | 7.8846        | -      |
+| 0.7699 | 8200  | 7.8988        | -      |
+| 0.7793 | 8300  | 7.8702        | -      |
+| 0.7887 | 8400  | 7.923         | -      |
+| 0.7980 | 8500  | 7.891         | -      |
+| 0.8074 | 8600  | 7.8832        | -      |
+| 0.8168 | 8700  | 7.8726        | -      |
+| 0.8262 | 8800  | 7.8813        | -      |
+| 0.8356 | 8900  | 7.8986        | -      |
+| 0.8450 | 9000  | 7.8743        | -      |
+| 0.8544 | 9100  | 7.8791        | -      |
+| 0.8638 | 9200  | 7.8783        | -      |
+| 0.8732 | 9300  | 7.8528        | -      |
+| 0.8825 | 9400  | 7.8864        | -      |
+| 0.8919 | 9500  | 7.8989        | -      |
+| 0.9013 | 9600  | 7.8617        | -      |
+| 0.9107 | 9700  | 7.8371        | -      |
+| 0.9201 | 9800  | 7.8566        | -      |
+| 0.9295 | 9900  | 7.8776        | -      |
+| 0.9389 | 10000 | 7.8558        | 7.8492 |
+| 0.9483 | 10100 | 7.848         | -      |
+| 0.9577 | 10200 | 7.8227        | -      |
+| 0.9670 | 10300 | 7.8311        | -      |
+| 0.9764 | 10400 | 7.8437        | -      |
+| 0.9858 | 10500 | 7.8454        | -      |
+| 0.9952 | 10600 | 7.8362        | -      |
+| 1.0046 | 10700 | 7.8681        | -      |
+| 1.0140 | 10800 | 7.8745        | -      |
+| 1.0234 | 10900 | 7.8339        | -      |
+| 1.0328 | 11000 | 7.8458        | -      |
+| 1.0422 | 11100 | 7.8493        | -      |
+| 1.0515 | 11200 | 7.8317        | -      |
+| 1.0609 | 11300 | 7.841         | -      |
+| 1.0703 | 11400 | 7.8292        | -      |
+| 1.0797 | 11500 | 7.8121        | -      |
+| 1.0891 | 11600 | 7.8165        | -      |
+| 1.0985 | 11700 | 7.8259        | -      |
+| 1.1079 | 11800 | 7.8303        | -      |
+| 1.1173 | 11900 | 7.809         | -      |
+| 1.1267 | 12000 | 7.818         | -      |
+| 1.1360 | 12100 | 7.8071        | -      |
+| 1.1454 | 12200 | 7.801         | -      |
+| 1.1548 | 12300 | 7.8123        | -      |
+| 1.1642 | 12400 | 7.8203        | -      |
+| 1.1736 | 12500 | 7.8609        | -      |
+| 1.1830 | 12600 | 7.7782        | -      |
+| 1.1924 | 12700 | 7.8092        | -      |
+| 1.2018 | 12800 | 7.815         | -      |
+| 1.2112 | 12900 | 7.8196        | -      |
+| 1.2205 | 13000 | 7.8206        | -      |
+| 1.2299 | 13100 | 7.8022        | -      |
+| 1.2393 | 13200 | 7.8043        | -      |
+| 1.2487 | 13300 | 7.7823        | -      |
+| 1.2581 | 13400 | 7.8061        | -      |
+| 1.2675 | 13500 | 7.8016        | -      |
+| 1.2769 | 13600 | 7.8076        | -      |
+| 1.2863 | 13700 | 7.7996        | -      |
+| 1.2957 | 13800 | 7.8035        | -      |
+| 1.3050 | 13900 | 7.8092        | -      |
+| 1.3144 | 14000 | 7.7902        | -      |
+| 1.3238 | 14100 | 7.8114        | -      |
+| 1.3332 | 14200 | 7.8112        | -      |
+| 1.3426 | 14300 | 7.8036        | -      |
+| 1.3520 | 14400 | 7.8178        | -      |
+| 1.3614 | 14500 | 7.8391        | -      |
+| 1.3708 | 14600 | 7.8151        | -      |
+| 1.3802 | 14700 | 7.7957        | -      |
+| 1.3895 | 14800 | 7.7833        | -      |
+| 1.3989 | 14900 | 7.8049        | -      |
+| 1.4083 | 15000 | 7.8163        | 7.8078 |
+| 1.4177 | 15100 | 7.7864        | -      |
+| 1.4271 | 15200 | 7.8241        | -      |
+| 1.4365 | 15300 | 7.7694        | -      |
+| 1.4459 | 15400 | 7.7784        | -      |
+| 1.4553 | 15500 | 7.7628        | -      |
+| 1.4647 | 15600 | 7.8044        | -      |
+| 1.4740 | 15700 | 7.7871        | -      |
+| 1.4834 | 15800 | 7.809         | -      |
+| 1.4928 | 15900 | 7.7955        | -      |
+| 1.5022 | 16000 | 7.8056        | -      |
+| 1.5116 | 16100 | 7.774         | -      |
+| 1.5210 | 16200 | 7.7874        | -      |
+| 1.5304 | 16300 | 7.7918        | -      |
+| 1.5398 | 16400 | 7.7787        | -      |
+| 1.5492 | 16500 | 7.7881        | -      |
+| 1.5585 | 16600 | 7.7723        | -      |
+| 1.5679 | 16700 | 7.7809        | -      |
+| 1.5773 | 16800 | 7.8096        | -      |
+| 1.5867 | 16900 | 7.7559        | -      |
+| 1.5961 | 17000 | 7.8063        | -      |
+| 1.6055 | 17100 | 7.8137        | -      |
+| 1.6149 | 17200 | 7.761         | -      |
+| 1.6243 | 17300 | 7.7672        | -      |
+| 1.6336 | 17400 | 7.7939        | -      |
+| 1.6430 | 17500 | 7.8052        | -      |
+| 1.6524 | 17600 | 7.7519        | -      |
+| 1.6618 | 17700 | 7.7643        | -      |
+| 1.6712 | 17800 | 7.7823        | -      |
+| 1.6806 | 17900 | 7.7507        | -      |
+| 1.6900 | 18000 | 7.777         | -      |
+| 1.6994 | 18100 | 7.786         | -      |
+| 1.7088 | 18200 | 7.8097        | -      |
+| 1.7181 | 18300 | 7.7749        | -      |
+| 1.7275 | 18400 | 7.7626        | -      |
+| 1.7369 | 18500 | 7.7783        | -      |
+| 1.7463 | 18600 | 7.7552        | -      |
+| 1.7557 | 18700 | 7.7837        | -      |
+| 1.7651 | 18800 | 7.7583        | -      |
+| 1.7745 | 18900 | 7.7617        | -      |
+| 1.7839 | 19000 | 7.7649        | -      |
+| 1.7933 | 19100 | 7.7767        | -      |
+| 1.8026 | 19200 | 7.7565        | -      |
+| 1.8120 | 19300 | 7.7702        | -      |
+| 1.8214 | 19400 | 7.7552        | -      |
+| 1.8308 | 19500 | 7.7511        | -      |
+| 1.8402 | 19600 | 7.7818        | -      |
+| 1.8496 | 19700 | 7.7704        | -      |
+| 1.8590 | 19800 | 7.7824        | -      |
+| 1.8684 | 19900 | 7.751         | -      |
+| 1.8778 | 20000 | 7.7868        | 7.7942 |
+| 1.8871 | 20100 | 7.7981        | -      |
+| 1.8965 | 20200 | 7.7673        | -      |
+| 1.9059 | 20300 | 7.7695        | -      |
+| 1.9153 | 20400 | 7.7587        | -      |
+| 1.9247 | 20500 | 7.7444        | -      |
+| 1.9341 | 20600 | 7.7736        | -      |
+| 1.9435 | 20700 | 7.7655        | -      |
+| 1.9529 | 20800 | 7.7686        | -      |
+| 1.9623 | 20900 | 7.7731        | -      |
+| 1.9716 | 21000 | 7.7527        | -      |
+| 1.9810 | 21100 | 7.7962        | -      |
+| 1.9904 | 21200 | 7.7676        | -      |
+| 1.9998 | 21300 | 7.7641        | -      |
+
+</details>
+
+### Framework Versions
+- Python: 3.8.10
+- Sentence Transformers: 3.1.1
+- Transformers: 4.45.2
+- PyTorch: 2.4.1+cu118
+- Accelerate: 1.0.1
+- Datasets: 3.0.1
+- Tokenizers: 0.20.3
+
+## 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 +1,24 @@
+{
+  "_name_or_path": "sentence-transformers/all-mpnet-base-v2",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
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+}

config_sentence_transformers.json

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+    "transformers": "4.45.2",
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+  "similarity_fn_name": null
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model.safetensors

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modules.json

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+    "name": "0",
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+    "type": "sentence_transformers.models.Transformer"
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+    "type": "sentence_transformers.models.Normalize"
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sentence_bert_config.json

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+{
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+  "do_lower_case": false
+}

special_tokens_map.json

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tokenizer_config.json

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+}