Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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

@@ -0,0 +1,461 @@
+---
+language:
+- tr
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:482091
+- loss:MultipleNegativesRankingLoss
+base_model: Alibaba-NLP/gte-multilingual-base
+widget:
+- source_sentence: Ya da dışarı çıkıp yürü ya da biraz koşun. Bunu düzenli olarak
+    yapmıyorum ama Washington bunu yapmak için harika bir yer.
+  sentences:
+  - “Washington's yürüyüş ya da koşu için harika bir yer.”
+  - H-2A uzaylılar Amerika Birleşik Devletleri'nde zaman kısa süreleri var.
+  - “Washington'da düzenli olarak yürüyüşe ya da koşuya çıkıyorum.”
+- source_sentence: Orta yaylalar ve güney kıyıları arasındaki kontrast daha belirgin
+    olamazdı.
+  sentences:
+  - İşitme Yardımı Uyumluluğu Müzakere Kuralları Komitesi, Federal İletişim Komisyonu'nun
+    bir ürünüdür.
+  - Dağlık ve sahil arasındaki kontrast kolayca işaretlendi.
+  - Kontrast işaretlenemedi.
+- source_sentence: Bir 1997 Henry J. Kaiser Aile Vakfı anket yönetilen bakım planlarında
+    Amerikalılar temelde kendi bakımı ile memnun olduğunu bulundu.
+  sentences:
+  - Kaplanları takip ederken çok sessiz olmalısın.
+  - Henry Kaiser vakfı insanların sağlık hizmetlerinden hoşlandığını gösteriyor.
+  - Henry Kaiser Vakfı insanların sağlık hizmetlerinden nefret ettiğini gösteriyor.
+- source_sentence: Eminim yapmışlardır.
+  sentences:
+  - Eminim öyle yapmışlardır.
+  - Batı Teksas'ta 100 10 dereceydi.
+  - Eminim yapmamışlardır.
+- source_sentence: Ve gerçekten, baba haklıydı, oğlu zaten her şeyi tecrübe etmişti,
+    her şeyi denedi ve daha az ilgileniyordu.
+  sentences:
+  - Oğlu her şeye olan ilgisini kaybediyordu.
+  - Pek bir şey yapmadım.
+  - Baba oğlunun tecrübe için hala çok şey olduğunu biliyordu.
+datasets:
+- emrecan/all-nli-tr
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+model-index:
+- name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all nli dev
+      type: all-nli-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.943809986114502
+      name: Cosine Accuracy
+---
+
+# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) on the [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr) 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:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision ca1791e0bcc104f6db161f27de1340241b13c5a4 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr)
+- **Language:** tr
+<!-- - **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: NewModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("x1saint/gte-multi-triplet-v2")
+# Run inference
+sentences = [
+    'Ve gerçekten, baba haklıydı, oğlu zaten her şeyi tecrübe etmişti, her şeyi denedi ve daha az ilgileniyordu.',
+    'Oğlu her şeye olan ilgisini kaybediyordu.',
+    'Baba oğlunun tecrübe için hala çok şey olduğunu biliyordu.',
+]
+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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Triplet
+
+* Dataset: `all-nli-dev`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| **cosine_accuracy** | **0.9438** |
+
+<!--
+## 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
+
+#### all-nli-tr
+
+* Dataset: [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr) at [daeabfb](https://huggingface.co/datasets/emrecan/all-nli-tr/tree/daeabfbc01f82757ab998bd23ce0ddfceaa5e24d)
+* Size: 482,091 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                          | negative                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 28.16 tokens</li><li>max: 151 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.14 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.33 tokens</li><li>max: 55 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                                                                    | positive                                                                                 | negative                                                                   |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|
+  | <code>Mevsim boyunca ve sanırım senin seviyendeyken onları bir sonraki seviyeye düşürürsün. Eğer ebeveyn takımını çağırmaya karar verirlerse Braves üçlü A'dan birini çağırmaya karar verirlerse çifte bir adam onun yerine geçmeye gider ve bekar bir adam gelir.</code> | <code>Eğer insanlar hatırlarsa, bir sonraki seviyeye düşersin.</code>                    | <code>Hiçbir şeyi hatırlamazlar.</code>                                    |
+  | <code>Numaramızdan biri talimatlarınızı birazdan yerine getirecektir.</code>                                                                                                                                                                                              | <code>Ekibimin bir üyesi emirlerinizi büyük bir hassasiyetle yerine getirecektir.</code> | <code>Şu anda boş kimsek yok, bu yüzden sen de harekete geçmelisin.</code> |
+  | <code>Bunu nereden biliyorsun? Bütün bunlar yine onların bilgileri.</code>                                                                                                                                                                                                | <code>Bu bilgi onlara ait.</code>                                                        | <code>Hiçbir bilgileri yok.</code>                                         |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### all-nli-tr
+
+* Dataset: [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr) at [daeabfb](https://huggingface.co/datasets/emrecan/all-nli-tr/tree/daeabfbc01f82757ab998bd23ce0ddfceaa5e24d)
+* Size: 6,567 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                          | negative                                                                         |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | string                                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 26.66 tokens</li><li>max: 121 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.98 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.4 tokens</li><li>max: 37 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                               | positive                                                                                               | negative                                                                    |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------|
+  | <code>Bilemiyorum. Onunla ilgili karışık duygularım var. Bazen ondan hoşlanıyorum ama aynı zamanda birisinin onu dövmesini görmeyi seviyorum.</code> | <code>Çoğunlukla ondan hoşlanıyorum, ama yine de birinin onu dövdüğünü görmekten zevk alıyorum.</code> | <code>O benim favorim ve kimsenin onu yendiğini görmek istemiyorum.</code>  |
+  | <code>Sen ve arkadaşların burada hoş karşılanmaz, Severn söyledi.</code>                                                                             | <code>Severn orada insanların hoş karşılanmadığını söyledi.</code>                                     | <code>Severn orada insanların her zaman hoş karşılanacağını söyledi.</code> |
+  | <code>Gecenin en aşağısı ne olduğundan emin değilim.</code>                                                                                          | <code>Dün gece ne kadar soğuk oldu bilmiyorum.</code>                                                  | <code>Dün gece hava 37 dereceydi.</code>                                    |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `gradient_accumulation_steps`: 4
+- `num_train_epochs`: 10
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `dataloader_num_workers`: 4
+
+#### 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`: 4
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-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`: 10
+- `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`: 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`: 4
+- `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`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | Validation Loss | all-nli-dev_cosine_accuracy |
+|:------:|:-----:|:-------------:|:---------------:|:---------------------------:|
+| 0.2655 | 500   | 3.0729        | 0.4237          | 0.9229                      |
+| 0.5310 | 1000  | 2.2154        | 0.3830          | 0.9257                      |
+| 0.7965 | 1500  | 1.9267        | 0.3517          | 0.9319                      |
+| 1.0616 | 2000  | 1.7078        | 0.3424          | 0.9354                      |
+| 1.3271 | 2500  | 1.4602        | 0.3362          | 0.9368                      |
+| 1.5926 | 3000  | 1.3925        | 0.3290          | 0.9379                      |
+| 1.8581 | 3500  | 1.3124        | 0.3116          | 0.9417                      |
+| 2.1232 | 4000  | 1.1537        | 0.3154          | 0.9382                      |
+| 2.3887 | 4500  | 1.0209        | 0.3205          | 0.9412                      |
+| 2.6542 | 5000  | 0.9897        | 0.3065          | 0.9441                      |
+| 2.9197 | 5500  | 0.9611        | 0.3025          | 0.9420                      |
+| 3.1848 | 6000  | 0.8276        | 0.3162          | 0.9446                      |
+| 3.4503 | 6500  | 0.7779        | 0.3101          | 0.9408                      |
+| 3.7158 | 7000  | 0.7738        | 0.3110          | 0.9426                      |
+| 3.9813 | 7500  | 0.7641        | 0.3056          | 0.9434                      |
+| 4.2464 | 8000  | 0.6338        | 0.3152          | 0.9429                      |
+| 4.5119 | 8500  | 0.6397        | 0.3133          | 0.9421                      |
+| 4.7774 | 9000  | 0.6207        | 0.3160          | 0.9420                      |
+| 5.0425 | 9500  | 0.6044        | 0.3156          | 0.9408                      |
+| 5.3080 | 10000 | 0.5305        | 0.3205          | 0.9449                      |
+| 5.5735 | 10500 | 0.5377        | 0.3124          | 0.9450                      |
+| 5.8390 | 11000 | 0.5311        | 0.3168          | 0.9443                      |
+| 6.1041 | 11500 | 0.5017        | 0.3250          | 0.9435                      |
+| 6.3696 | 12000 | 0.46          | 0.3213          | 0.9429                      |
+| 6.6351 | 12500 | 0.4679        | 0.3212          | 0.9443                      |
+| 6.9006 | 13000 | 0.4692        | 0.3221          | 0.9434                      |
+| 7.1657 | 13500 | 0.4285        | 0.3231          | 0.9446                      |
+| 7.4312 | 14000 | 0.4161        | 0.3265          | 0.9456                      |
+| 7.6967 | 14500 | 0.409         | 0.3240          | 0.9456                      |
+| 7.9622 | 15000 | 0.4127        | 0.3250          | 0.9444                      |
+| 8.2273 | 15500 | 0.3843        | 0.3290          | 0.9447                      |
+| 8.4928 | 16000 | 0.3755        | 0.3259          | 0.9438                      |
+| 8.7583 | 16500 | 0.3786        | 0.3328          | 0.9438                      |
+| 9.0234 | 17000 | 0.3702        | 0.3284          | 0.9453                      |
+| 9.2889 | 17500 | 0.3525        | 0.3326          | 0.9444                      |
+| 9.5544 | 18000 | 0.3589        | 0.3320          | 0.9443                      |
+| 9.8199 | 18500 | 0.3483        | 0.3314          | 0.9438                      |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.48.3
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.3.0
+- 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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 +1,50 @@
+{
+  "_name_or_path": "Alibaba-NLP/gte-multilingual-base",
+  "architectures": [
+    "NewModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig",
+    "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
+    "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
+    "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
+    "AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
+    "AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
+    "AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
+  },
+  "classifier_dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
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config_sentence_transformers.json

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modules.json

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sentence_bert_config.json

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