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final/1_Pooling/config.json

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+{
+  "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
+}

final/README.md

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+---
+language:
+- en
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dataset_size:100K<n<1M
+- loss:CachedMultipleNegativesRankingLoss
+base_model: Unbabel/xlm-roberta-comet-small
+metrics:
+- cosine_accuracy
+- dot_accuracy
+- manhattan_accuracy
+- euclidean_accuracy
+- max_accuracy
+widget:
+- source_sentence: There's a dock
+  sentences:
+  - There is a door.
+  - the animal is running
+  - The woman is singing.
+- source_sentence: The boy scowls
+  sentences:
+  - A boy is blowing bubbles.
+  - He is playing a song.
+  - They are driving cars.
+- source_sentence: A bird flying.
+  sentences:
+  - A butterfly flys freely.
+  - A dog carries a bone.
+  - Two dogs are playing.
+- source_sentence: A woman sings.
+  sentences:
+  - The woman is singing.
+  - The man is in a city.
+  - there is a man in a pool.
+- source_sentence: a baby smiling
+  sentences:
+  - A baby is unhappy.
+  - The dog has big ears.
+  - They are driving cars.
+pipeline_tag: sentence-similarity
+model-index:
+- name: SentenceTransformer based on Unbabel/xlm-roberta-comet-small
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all nli dev
+      type: all-nli-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.849
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.163
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.837
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.841
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.849
+      name: Max Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all nli test
+      type: all-nli-test
+    metrics:
+    - type: cosine_accuracy
+      value: 0.839
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.15
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.827
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.827
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.839
+      name: Max Accuracy
+---
+
+# SentenceTransformer based on Unbabel/xlm-roberta-comet-small
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Unbabel/xlm-roberta-comet-small](https://huggingface.co/Unbabel/xlm-roberta-comet-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:** [Unbabel/xlm-roberta-comet-small](https://huggingface.co/Unbabel/xlm-roberta-comet-small) <!-- at revision df568a015df5cefbf2f449314b61ce9afb0cb593 -->
+- **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:** 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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (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})
+)
+```
+
+## 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 baby smiling',
+    'A baby is unhappy.',
+    'The dog has big ears.',
+]
+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    | 0.849     |
+| dot_accuracy       | 0.163     |
+| manhattan_accuracy | 0.837     |
+| euclidean_accuracy | 0.841     |
+| **max_accuracy**   | **0.849** |
+
+#### 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    | 0.839     |
+| dot_accuracy       | 0.15      |
+| manhattan_accuracy | 0.827     |
+| euclidean_accuracy | 0.827     |
+| **max_accuracy**   | **0.839** |
+
+<!--
+## 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: 10.9 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.62 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.76 tokens</li><li>max: 55 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>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) 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: 1,000 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: 20.31 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.71 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.39 tokens</li><li>max: 32 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>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) 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
+- `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    | -             | -      | 0.541                    | -                         |
+| 0.016 | 100  | 3.5308        | 3.1817 | 0.558                    | -                         |
+| 0.032 | 200  | 3.2784        | 3.0406 | 0.597                    | -                         |
+| 0.048 | 300  | 3.113         | 2.7572 | 0.635                    | -                         |
+| 0.064 | 400  | 2.8296        | 2.4646 | 0.68                     | -                         |
+| 0.08  | 500  | 2.631         | 2.3583 | 0.676                    | -                         |
+| 0.096 | 600  | 2.3247        | 2.1394 | 0.706                    | -                         |
+| 0.112 | 700  | 2.2211        | 2.0201 | 0.711                    | -                         |
+| 0.128 | 800  | 2.1263        | 1.9560 | 0.757                    | -                         |
+| 0.144 | 900  | 2.2105        | 1.9074 | 0.748                    | -                         |
+| 0.16  | 1000 | 2.0637        | 1.9289 | 0.728                    | -                         |
+| 0.176 | 1100 | 2.1772        | 1.8796 | 0.741                    | -                         |
+| 0.192 | 1200 | 2.1518        | 1.8346 | 0.761                    | -                         |
+| 0.208 | 1300 | 1.728         | 1.8213 | 0.765                    | -                         |
+| 0.224 | 1400 | 1.8101        | 1.6321 | 0.772                    | -                         |
+| 0.24  | 1500 | 1.7516        | 1.5669 | 0.793                    | -                         |
+| 0.256 | 1600 | 1.4988        | 1.5538 | 0.8                      | -                         |
+| 0.272 | 1700 | 1.6695        | 1.5462 | 0.803                    | -                         |
+| 0.288 | 1800 | 1.5971        | 1.5499 | 0.783                    | -                         |
+| 0.304 | 1900 | 1.5614        | 1.5047 | 0.788                    | -                         |
+| 0.32  | 2000 | 1.522         | 1.4957 | 0.794                    | -                         |
+| 0.336 | 2100 | 1.3624        | 1.4153 | 0.814                    | -                         |
+| 0.352 | 2200 | 1.4773        | 1.4169 | 0.809                    | -                         |
+| 0.368 | 2300 | 1.6066        | 1.3697 | 0.813                    | -                         |
+| 0.384 | 2400 | 1.5106        | 1.3203 | 0.819                    | -                         |
+| 0.4   | 2500 | 1.4783        | 1.3417 | 0.817                    | -                         |
+| 0.416 | 2600 | 1.3696        | 1.2650 | 0.824                    | -                         |
+| 0.432 | 2700 | 1.5115        | 1.2779 | 0.829                    | -                         |
+| 0.448 | 2800 | 1.4834        | 1.2668 | 0.834                    | -                         |
+| 0.464 | 2900 | 1.4823        | 1.2621 | 0.836                    | -                         |
+| 0.48  | 3000 | 1.4163        | 1.2465 | 0.837                    | -                         |
+| 0.496 | 3100 | 1.4232        | 1.2475 | 0.837                    | -                         |
+| 0.512 | 3200 | 1.2193        | 1.1975 | 0.838                    | -                         |
+| 0.528 | 3300 | 1.2569        | 1.1816 | 0.838                    | -                         |
+| 0.544 | 3400 | 1.2988        | 1.1936 | 0.839                    | -                         |
+| 0.56  | 3500 | 1.5068        | 1.2213 | 0.835                    | -                         |
+| 0.576 | 3600 | 1.3022        | 1.1799 | 0.842                    | -                         |
+| 0.592 | 3700 | 1.3823        | 1.1910 | 0.831                    | -                         |
+| 0.608 | 3800 | 1.4224        | 1.1786 | 0.834                    | -                         |
+| 0.624 | 3900 | 1.3765        | 1.1541 | 0.843                    | -                         |
+| 0.64  | 4000 | 1.4987        | 1.1365 | 0.844                    | -                         |
+| 0.656 | 4100 | 1.7525        | 1.1394 | 0.843                    | -                         |
+| 0.672 | 4200 | 1.6013        | 1.1178 | 0.841                    | -                         |
+| 0.688 | 4300 | 1.3326        | 1.0959 | 0.846                    | -                         |
+| 0.704 | 4400 | 1.355         | 1.0757 | 0.848                    | -                         |
+| 0.72  | 4500 | 1.2834        | 1.0681 | 0.846                    | -                         |
+| 0.736 | 4600 | 1.2939        | 1.0696 | 0.85                     | -                         |
+| 0.752 | 4700 | 1.4069        | 1.0645 | 0.848                    | -                         |
+| 0.768 | 4800 | 1.4503        | 1.0609 | 0.849                    | -                         |
+| 0.784 | 4900 | 1.2833        | 1.0587 | 0.847                    | -                         |
+| 0.8   | 5000 | 1.3321        | 1.0563 | 0.849                    | -                         |
+| 0.816 | 5100 | 1.3006        | 1.0539 | 0.847                    | -                         |
+| 0.832 | 5200 | 1.4332        | 1.0527 | 0.847                    | -                         |
+| 0.848 | 5300 | 1.3101        | 1.0505 | 0.848                    | -                         |
+| 0.864 | 5400 | 1.3658        | 1.0523 | 0.849                    | -                         |
+| 0.88  | 5500 | 1.353         | 1.0520 | 0.849                    | -                         |
+| 0.896 | 5600 | 1.2429        | 1.0521 | 0.848                    | -                         |
+| 0.912 | 5700 | 1.3512        | 1.0505 | 0.848                    | -                         |
+| 0.928 | 5800 | 1.2995        | 1.0501 | 0.848                    | -                         |
+| 0.944 | 5900 | 1.3514        | 1.0491 | 0.849                    | -                         |
+| 0.96  | 6000 | 1.3976        | 1.0490 | 0.848                    | -                         |
+| 0.976 | 6100 | 1.2112        | 1.0487 | 0.848                    | -                         |
+| 0.992 | 6200 | 0.0033        | 1.0492 | 0.849                    | -                         |
+| 1.0   | 6250 | -             | -      | -                        | 0.839                     |
+
+
+### Framework Versions
+- Python: 3.9.10
+- Sentence Transformers: 3.0.0
+- Transformers: 4.41.2
+- PyTorch: 2.3.0+cu121
+- Accelerate: 0.26.1
+- Datasets: 2.16.1
+- 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",
+}
+```
+
+#### CachedMultipleNegativesRankingLoss
+```bibtex
+@misc{gao2021scaling,
+    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, 
+    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
+    year={2021},
+    eprint={2101.06983},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## 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.*
+-->

final/config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "Unbabel/xlm-roberta-comet-small",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
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