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1_Pooling/config.json +10 -0
README.md +546 -0
config.json +61 -0
config_sentence_transformers.json +10 -0
model.safetensors +3 -0
modules.json +20 -0
optimizer.pt +3 -0
rng_state.pth +3 -0
scheduler.pt +3 -0
sentence_bert_config.json +4 -0
special_tokens_map.json +107 -0
tokenizer.json +0 -0
tokenizer_config.json +939 -0
trainer_state.json +1338 -0
training_args.bin +3 -0

1_Pooling/config.json ADDED Viewed

	@@ -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 ADDED Viewed

	@@ -0,0 +1,546 @@

+---
+language:
+- en
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:557850
+- loss:MultipleNegativesRankingLoss
+base_model: google-t5/t5-base
+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.
+datasets:
+- sentence-transformers/all-nli
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+# SentenceTransformer based on google-t5/t5-base
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) 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:** [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) <!-- at revision a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1 -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
+- **Language:** en
+<!-- - **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': 256, 'do_lower_case': False}) with Transformer model: T5EncoderModel
+  (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 = [
+    '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, 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
+#### all-nli
+* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
+* Size: 557,850 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: 6 tokens</li><li>mean: 9.96 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.79 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.02 tokens</li><li>max: 57 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
+#### all-nli
+* Dataset: [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: 5 tokens</li><li>mean: 19.41 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.69 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.35 tokens</li><li>max: 30 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`: 64
+- `per_device_eval_batch_size`: 64
+- `learning_rate`: 1e-05
+- `warmup_ratio`: 0.1
+- `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`: 1e-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`: 3
+- `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`: 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
+<details><summary>Click to expand</summary>
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0011 | 10   | -             | 1.8733          |
+| 0.0023 | 20   | -             | 1.8726          |
+| 0.0034 | 30   | -             | 1.8714          |
+| 0.0046 | 40   | -             | 1.8697          |
+| 0.0057 | 50   | -             | 1.8675          |
+| 0.0069 | 60   | -             | 1.8649          |
+| 0.0080 | 70   | -             | 1.8619          |
+| 0.0092 | 80   | -             | 1.8584          |
+| 0.0103 | 90   | -             | 1.8544          |
+| 0.0115 | 100  | 3.1046        | 1.8499          |
+| 0.0126 | 110  | -             | 1.8451          |
+| 0.0138 | 120  | -             | 1.8399          |
+| 0.0149 | 130  | -             | 1.8343          |
+| 0.0161 | 140  | -             | 1.8283          |
+| 0.0172 | 150  | -             | 1.8223          |
+| 0.0184 | 160  | -             | 1.8159          |
+| 0.0195 | 170  | -             | 1.8091          |
+| 0.0206 | 180  | -             | 1.8016          |
+| 0.0218 | 190  | -             | 1.7938          |
+| 0.0229 | 200  | 3.0303        | 1.7858          |
+| 0.0241 | 210  | -             | 1.7775          |
+| 0.0252 | 220  | -             | 1.7693          |
+| 0.0264 | 230  | -             | 1.7605          |
+| 0.0275 | 240  | -             | 1.7514          |
+| 0.0287 | 250  | -             | 1.7417          |
+| 0.0298 | 260  | -             | 1.7320          |
+| 0.0310 | 270  | -             | 1.7227          |
+| 0.0321 | 280  | -             | 1.7134          |
+| 0.0333 | 290  | -             | 1.7040          |
+| 0.0344 | 300  | 2.9459        | 1.6941          |
+| 0.0356 | 310  | -             | 1.6833          |
+| 0.0367 | 320  | -             | 1.6725          |
+| 0.0379 | 330  | -             | 1.6614          |
+| 0.0390 | 340  | -             | 1.6510          |
+| 0.0402 | 350  | -             | 1.6402          |
+| 0.0413 | 360  | -             | 1.6296          |
+| 0.0424 | 370  | -             | 1.6187          |
+| 0.0436 | 380  | -             | 1.6073          |
+| 0.0447 | 390  | -             | 1.5962          |
+| 0.0459 | 400  | 2.7813        | 1.5848          |
+| 0.0470 | 410  | -             | 1.5735          |
+| 0.0482 | 420  | -             | 1.5620          |
+| 0.0493 | 430  | -             | 1.5495          |
+| 0.0505 | 440  | -             | 1.5375          |
+| 0.0516 | 450  | -             | 1.5256          |
+| 0.0528 | 460  | -             | 1.5133          |
+| 0.0539 | 470  | -             | 1.5012          |
+| 0.0551 | 480  | -             | 1.4892          |
+| 0.0562 | 490  | -             | 1.4769          |
+| 0.0574 | 500  | 2.6308        | 1.4640          |
+| 0.0585 | 510  | -             | 1.4513          |
+| 0.0597 | 520  | -             | 1.4391          |
+| 0.0608 | 530  | -             | 1.4262          |
+| 0.0619 | 540  | -             | 1.4130          |
+| 0.0631 | 550  | -             | 1.3998          |
+| 0.0642 | 560  | -             | 1.3874          |
+| 0.0654 | 570  | -             | 1.3752          |
+| 0.0665 | 580  | -             | 1.3620          |
+| 0.0677 | 590  | -             | 1.3485          |
+| 0.0688 | 600  | 2.4452        | 1.3350          |
+| 0.0700 | 610  | -             | 1.3213          |
+| 0.0711 | 620  | -             | 1.3088          |
+| 0.0723 | 630  | -             | 1.2965          |
+| 0.0734 | 640  | -             | 1.2839          |
+| 0.0746 | 650  | -             | 1.2713          |
+| 0.0757 | 660  | -             | 1.2592          |
+| 0.0769 | 670  | -             | 1.2466          |
+| 0.0780 | 680  | -             | 1.2332          |
+| 0.0792 | 690  | -             | 1.2203          |
+| 0.0803 | 700  | 2.2626        | 1.2077          |
+| 0.0815 | 710  | -             | 1.1959          |
+| 0.0826 | 720  | -             | 1.1841          |
+| 0.0837 | 730  | -             | 1.1725          |
+| 0.0849 | 740  | -             | 1.1619          |
+| 0.0860 | 750  | -             | 1.1516          |
+| 0.0872 | 760  | -             | 1.1416          |
+| 0.0883 | 770  | -             | 1.1320          |
+| 0.0895 | 780  | -             | 1.1227          |
+| 0.0906 | 790  | -             | 1.1138          |
+| 0.0918 | 800  | 2.0044        | 1.1053          |
+| 0.0929 | 810  | -             | 1.0965          |
+| 0.0941 | 820  | -             | 1.0879          |
+| 0.0952 | 830  | -             | 1.0796          |
+| 0.0964 | 840  | -             | 1.0718          |
+| 0.0975 | 850  | -             | 1.0644          |
+| 0.0987 | 860  | -             | 1.0564          |
+| 0.0998 | 870  | -             | 1.0490          |
+| 0.1010 | 880  | -             | 1.0417          |
+| 0.1021 | 890  | -             | 1.0354          |
+| 0.1032 | 900  | 1.8763        | 1.0296          |
+| 0.1044 | 910  | -             | 1.0239          |
+| 0.1055 | 920  | -             | 1.0180          |
+| 0.1067 | 930  | -             | 1.0123          |
+| 0.1078 | 940  | -             | 1.0065          |
+| 0.1090 | 950  | -             | 1.0008          |
+| 0.1101 | 960  | -             | 0.9950          |
+| 0.1113 | 970  | -             | 0.9894          |
+| 0.1124 | 980  | -             | 0.9840          |
+| 0.1136 | 990  | -             | 0.9793          |
+| 0.1147 | 1000 | 1.7287        | 0.9752          |
+| 0.1159 | 1010 | -             | 0.9706          |
+| 0.1170 | 1020 | -             | 0.9659          |
+| 0.1182 | 1030 | -             | 0.9615          |
+| 0.1193 | 1040 | -             | 0.9572          |
+| 0.1205 | 1050 | -             | 0.9531          |
+| 0.1216 | 1060 | -             | 0.9494          |
+| 0.1227 | 1070 | -             | 0.9456          |
+| 0.1239 | 1080 | -             | 0.9415          |
+| 0.1250 | 1090 | -             | 0.9377          |
+| 0.1262 | 1100 | 1.6312        | 0.9339          |
+| 0.1273 | 1110 | -             | 0.9303          |
+| 0.1285 | 1120 | -             | 0.9267          |
+| 0.1296 | 1130 | -             | 0.9232          |
+| 0.1308 | 1140 | -             | 0.9197          |
+| 0.1319 | 1150 | -             | 0.9162          |
+| 0.1331 | 1160 | -             | 0.9128          |
+| 0.1342 | 1170 | -             | 0.9097          |
+| 0.1354 | 1180 | -             | 0.9069          |
+| 0.1365 | 1190 | -             | 0.9040          |
+| 0.1377 | 1200 | 1.5316        | 0.9010          |
+| 0.1388 | 1210 | -             | 0.8979          |
+| 0.1400 | 1220 | -             | 0.8947          |
+| 0.1411 | 1230 | -             | 0.8915          |
+| 0.1423 | 1240 | -             | 0.8888          |
+| 0.1434 | 1250 | -             | 0.8861          |
+| 0.1445 | 1260 | -             | 0.8833          |
+| 0.1457 | 1270 | -             | 0.8806          |
+| 0.1468 | 1280 | -             | 0.8779          |
+| 0.1480 | 1290 | -             | 0.8748          |
+| 0.1491 | 1300 | 1.4961        | 0.8718          |
+| 0.1503 | 1310 | -             | 0.8690          |
+| 0.1514 | 1320 | -             | 0.8664          |
+| 0.1526 | 1330 | -             | 0.8635          |
+| 0.1537 | 1340 | -             | 0.8603          |
+| 0.1549 | 1350 | -             | 0.8574          |
+| 0.1560 | 1360 | -             | 0.8545          |
+| 0.1572 | 1370 | -             | 0.8521          |
+| 0.1583 | 1380 | -             | 0.8497          |
+| 0.1595 | 1390 | -             | 0.8474          |
+| 0.1606 | 1400 | 1.451         | 0.8453          |
+| 0.1618 | 1410 | -             | 0.8429          |
+| 0.1629 | 1420 | -             | 0.8404          |
+| 0.1640 | 1430 | -             | 0.8380          |
+| 0.1652 | 1440 | -             | 0.8357          |
+| 0.1663 | 1450 | -             | 0.8336          |
+| 0.1675 | 1460 | -             | 0.8312          |
+| 0.1686 | 1470 | -             | 0.8289          |
+| 0.1698 | 1480 | -             | 0.8262          |
+| 0.1709 | 1490 | -             | 0.8236          |
+| 0.1721 | 1500 | 1.4177        | 0.8213          |
+</details>
+### Framework Versions
+- Python: 3.12.8
+- Sentence Transformers: 3.4.1
+- Transformers: 4.49.0
+- PyTorch: 2.2.0+cu121
+- Accelerate: 1.4.0
+- Datasets: 3.3.2
+- 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.*
+-->

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