Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "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: thenlper/gte-small
+datasets:
+- sentence-transformers/all-nli
+language:
+- en
+library_name: sentence-transformers
+license: apache-2.0
+metrics:
+- cosine_accuracy
+- dot_accuracy
+- manhattan_accuracy
+- euclidean_accuracy
+- max_accuracy
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:100000
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: A man is jumping unto his filthy bed.
+  sentences:
+  - A young male is looking at a newspaper while 2 females walks past him.
+  - The bed is dirty.
+  - The man is on the moon.
+- source_sentence: A carefully balanced male stands on one foot near a clean ocean
+    beach area.
+  sentences:
+  - A man is ouside near the beach.
+  - Three policemen patrol the streets on bikes
+  - A man is sitting on his couch.
+- source_sentence: The man is wearing a blue shirt.
+  sentences:
+  - Near the trashcan the man stood and smoked
+  - A man in a blue shirt leans on a wall beside a road with a blue van and red car
+    with water in the background.
+  - A man in a black shirt is playing a guitar.
+- source_sentence: The girls are outdoors.
+  sentences:
+  - Two girls riding on an amusement part ride.
+  - a guy laughs while doing laundry
+  - Three girls are standing together in a room, one is listening, one is writing
+    on a wall and the third is talking to them.
+- source_sentence: A construction worker peeking out of a manhole while his coworker
+    sits on the sidewalk smiling.
+  sentences:
+  - A worker is looking out of a manhole.
+  - A man is giving a presentation.
+  - The workers are both inside the manhole.
+model-index:
+- name: gte small finetuned on NLI
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all nli dev
+      type: all-nli-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9260328068043743
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.07396719319562577
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.925273390036452
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.9260328068043743
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.9260328068043743
+      name: Max Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all nli test
+      type: all-nli-test
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9347858980178544
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.06521410198214556
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.9331215009835073
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.9347858980178544
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.9347858980178544
+      name: Max Accuracy
+---
+
+# gte small finetuned on NLI
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 384-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:** [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) <!-- at revision 50c7dd33df1027ef560fd504d95e277948c3c886 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
+- **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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, '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("SMARTICT/gte-small-finetune-test")
+# Run inference
+sentences = [
+    'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
+    'A worker is looking out of a manhole.',
+    'The workers are both inside the manhole.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# 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    | 0.926     |
+| dot_accuracy       | 0.074     |
+| manhattan_accuracy | 0.9253    |
+| euclidean_accuracy | 0.926     |
+| **max_accuracy**   | **0.926** |
+
+#### Triplet
+* Dataset: `all-nli-test`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| cosine_accuracy    | 0.9348     |
+| dot_accuracy       | 0.0652     |
+| manhattan_accuracy | 0.9331     |
+| euclidean_accuracy | 0.9348     |
+| **max_accuracy**   | **0.9348** |
+
+<!--
+## 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
+
+#### sentence-transformers/all-nli
+
+* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
+* Size: 100,000 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: 7 tokens</li><li>mean: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> |
+* Samples:
+  | anchor                                                                     | positive                                         | negative                                                   |
+  |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
+  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>A person is at a diner, ordering an omelette.</code> |
+  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>The kids are frowning</code>                         |
+  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</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
+
+#### sentence-transformers/all-nli
+
+* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
+* Size: 6,584 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: 6 tokens</li><li>mean: 17.95 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.78 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.35 tokens</li><li>max: 29 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                         | positive                                                    | negative                                                |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
+  | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>Two woman are holding packages.</code>                | <code>The men are fighting outside a deli.</code>       |
+  | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code>        |
+  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>A man selling donuts to a customer.</code>            | <code>A woman drinks her coffee in a small cafe.</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`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `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`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_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`: 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`: 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`: 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
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch | Step | Training Loss | loss   | all-nli-dev_max_accuracy | all-nli-test_max_accuracy |
+|:-----:|:----:|:-------------:|:------:|:------------------------:|:-------------------------:|
+| 0     | 0    | -             | -      | 0.9160                   | -                         |
+| 0.016 | 100  | 1.4107        | 0.6660 | 0.9069                   | -                         |
+| 0.032 | 200  | 0.7368        | 0.6155 | 0.8950                   | -                         |
+| 0.048 | 300  | 1.0729        | 0.5522 | 0.9054                   | -                         |
+| 0.064 | 400  | 0.719         | 0.5647 | 0.8957                   | -                         |
+| 0.08  | 500  | 0.7273        | 0.6278 | 0.8829                   | -                         |
+| 0.096 | 600  | 0.9222        | 0.5652 | 0.8975                   | -                         |
+| 0.112 | 700  | 0.8402        | 0.5837 | 0.8947                   | -                         |
+| 0.128 | 800  | 0.9511        | 0.6110 | 0.8864                   | -                         |
+| 0.144 | 900  | 1.0713        | 0.5923 | 0.8852                   | -                         |
+| 0.16  | 1000 | 0.9495        | 0.5216 | 0.8888                   | -                         |
+| 0.176 | 1100 | 1.0079        | 0.6263 | 0.8777                   | -                         |
+| 0.192 | 1200 | 0.9195        | 0.5970 | 0.8777                   | -                         |
+| 0.208 | 1300 | 0.8018        | 0.6342 | 0.8765                   | -                         |
+| 0.224 | 1400 | 0.7124        | 0.6462 | 0.8764                   | -                         |
+| 0.24  | 1500 | 0.709         | 0.5232 | 0.8964                   | -                         |
+| 0.256 | 1600 | 0.6055        | 0.6109 | 0.8838                   | -                         |
+| 0.272 | 1700 | 0.7887        | 0.6620 | 0.8768                   | -                         |
+| 0.288 | 1800 | 0.789         | 0.5957 | 0.8829                   | -                         |
+| 0.304 | 1900 | 0.6711        | 0.5377 | 0.8946                   | -                         |
+| 0.32  | 2000 | 0.6086        | 0.5596 | 0.8932                   | -                         |
+| 0.336 | 2100 | 0.5067        | 0.5676 | 0.8861                   | -                         |
+| 0.352 | 2200 | 0.5387        | 0.5704 | 0.8900                   | -                         |
+| 0.368 | 2300 | 0.6574        | 0.5308 | 0.8890                   | -                         |
+| 0.384 | 2400 | 0.6232        | 0.5051 | 0.8928                   | -                         |
+| 0.4   | 2500 | 0.6045        | 0.5179 | 0.9023                   | -                         |
+| 0.416 | 2600 | 0.4795        | 0.4766 | 0.8960                   | -                         |
+| 0.432 | 2700 | 0.7372        | 0.5463 | 0.8979                   | -                         |
+| 0.448 | 2800 | 0.7593        | 0.5337 | 0.8878                   | -                         |
+| 0.464 | 2900 | 0.7384        | 0.5203 | 0.8923                   | -                         |
+| 0.48  | 3000 | 0.6336        | 0.5099 | 0.8897                   | -                         |
+| 0.496 | 3100 | 0.6634        | 0.4803 | 0.8954                   | -                         |
+| 0.512 | 3200 | 0.5443        | 0.4524 | 0.9048                   | -                         |
+| 0.528 | 3300 | 0.5292        | 0.4232 | 0.9104                   | -                         |
+| 0.544 | 3400 | 0.4633        | 0.4414 | 0.9093                   | -                         |
+| 0.56  | 3500 | 0.4442        | 0.4393 | 0.9087                   | -                         |
+| 0.576 | 3600 | 0.4443        | 0.4178 | 0.9128                   | -                         |
+| 0.592 | 3700 | 0.4736        | 0.4123 | 0.9134                   | -                         |
+| 0.608 | 3800 | 0.4077        | 0.4025 | 0.9174                   | -                         |
+| 0.624 | 3900 | 0.4069        | 0.4032 | 0.9156                   | -                         |
+| 0.64  | 4000 | 0.6939        | 0.4353 | 0.9146                   | -                         |
+| 0.656 | 4100 | 0.865         | 0.4154 | 0.9172                   | -                         |
+| 0.672 | 4200 | 0.8518        | 0.3925 | 0.9172                   | -                         |
+| 0.688 | 4300 | 0.5989        | 0.3864 | 0.9190                   | -                         |
+| 0.704 | 4400 | 0.5399        | 0.3679 | 0.9197                   | -                         |
+| 0.72  | 4500 | 0.497         | 0.3766 | 0.9221                   | -                         |
+| 0.736 | 4600 | 0.585         | 0.3708 | 0.9228                   | -                         |
+| 0.752 | 4700 | 0.6454        | 0.3608 | 0.9203                   | -                         |
+| 0.768 | 4800 | 0.5414        | 0.3593 | 0.9213                   | -                         |
+| 0.784 | 4900 | 0.4648        | 0.3634 | 0.9210                   | -                         |
+| 0.8   | 5000 | 0.5781        | 0.3782 | 0.9216                   | -                         |
+| 0.816 | 5100 | 0.4401        | 0.3662 | 0.9227                   | -                         |
+| 0.832 | 5200 | 0.5241        | 0.3595 | 0.9215                   | -                         |
+| 0.848 | 5300 | 0.459         | 0.3618 | 0.9215                   | -                         |
+| 0.864 | 5400 | 0.5529        | 0.3693 | 0.9216                   | -                         |
+| 0.88  | 5500 | 0.5202        | 0.3573 | 0.9218                   | -                         |
+| 0.896 | 5600 | 0.4703        | 0.3529 | 0.9231                   | -                         |
+| 0.912 | 5700 | 0.5658        | 0.3513 | 0.9245                   | -                         |
+| 0.928 | 5800 | 0.5016        | 0.3491 | 0.9236                   | -                         |
+| 0.944 | 5900 | 0.6306        | 0.3492 | 0.9257                   | -                         |
+| 0.96  | 6000 | 0.6721        | 0.3507 | 0.9266                   | -                         |
+| 0.976 | 6100 | 0.586         | 0.3509 | 0.9257                   | -                         |
+| 0.992 | 6200 | 0.0014        | 0.3511 | 0.9260                   | -                         |
+| 1.0   | 6250 | -             | -      | -                        | 0.9348                    |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.4
+- PyTorch: 2.4.0+cu121
+- Accelerate: 0.32.1
+- Datasets: 2.21.0
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```
+
+#### 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 +1,25 @@
+{
+  "_name_or_path": "thenlper/gte-small",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "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": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.42.4",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2ad3c54e6a07390da90f6d75d0c5fd0c59884b04ea713b1d14cd70267703346c
+size 133462128

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "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 +1,64 @@
+{
+  "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
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}