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": 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

@@ -0,0 +1,721 @@
+---
+base_model: intfloat/multilingual-e5-small
+datasets: []
+language: []
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+- cosine_accuracy_threshold
+- cosine_f1
+- cosine_f1_threshold
+- cosine_precision
+- cosine_recall
+- cosine_ap
+- dot_accuracy
+- dot_accuracy_threshold
+- dot_f1
+- dot_f1_threshold
+- dot_precision
+- dot_recall
+- dot_ap
+- manhattan_accuracy
+- manhattan_accuracy_threshold
+- manhattan_f1
+- manhattan_f1_threshold
+- manhattan_precision
+- manhattan_recall
+- manhattan_ap
+- euclidean_accuracy
+- euclidean_accuracy_threshold
+- euclidean_f1
+- euclidean_f1_threshold
+- euclidean_precision
+- euclidean_recall
+- euclidean_ap
+- max_accuracy
+- max_accuracy_threshold
+- max_f1
+- max_f1_threshold
+- max_precision
+- max_recall
+- max_ap
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1273
+- loss:OnlineContrastiveLoss
+widget:
+- source_sentence: Where can I buy organic vegetables?
+  sentences:
+  - Primary export product of Saudi Arabia
+  - Share info about Amazon
+  - Where can I buy organic fruits?
+- source_sentence: How to open a bank account in the UK?
+  sentences:
+  - Steps to open a bank account in the United Kingdom
+  - How many weeks in a month?
+  - What is the process for turning in an expense report?
+- source_sentence: What is the population of Tokyo?
+  sentences:
+  - What is the smallest planet in the solar system?
+  - Author of the play 'Hamlet'
+  - What is the population of Osaka?
+- source_sentence: How to visit the Great Wall of China?
+  sentences:
+  - Where can I buy a new laptop?
+  - How do I close a bank account?
+  - Guide to visiting the Great Wall of China
+- source_sentence: Who is the President of the United States?
+  sentences:
+  - What is the velocity of sound?
+  - Who is the current US President?
+  - History of the Byzantine Empire
+model-index:
+- name: SentenceTransformer based on intfloat/multilingual-e5-small
+  results:
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: pair class dev
+      type: pair-class-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.6206896551724138
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.9036016464233398
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.7192575406032483
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.9036016464233398
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.5827067669172933
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.9393939393939394
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.6366493234478966
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.6206896551724138
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 0.9036016464233398
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.7192575406032483
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 0.9036016464233398
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.5827067669172933
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.9393939393939394
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.6366493234478966
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.6175548589341693
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 6.501791000366211
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.7232142857142857
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 7.142887115478516
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.5724381625441696
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.9818181818181818
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.64137074777591
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.6206896551724138
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 0.43908166885375977
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.7192575406032483
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 0.43908166885375977
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.5827067669172933
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.9393939393939394
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.6366493234478966
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.6206896551724138
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 6.501791000366211
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.7232142857142857
+      name: Max F1
+    - type: max_f1_threshold
+      value: 7.142887115478516
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.5827067669172933
+      name: Max Precision
+    - type: max_recall
+      value: 0.9818181818181818
+      name: Max Recall
+    - type: max_ap
+      value: 0.64137074777591
+      name: Max Ap
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: pair class test
+      type: pair-class-test
+    metrics:
+    - type: cosine_accuracy
+      value: 0.8934169278996865
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.7770164012908936
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.9034090909090907
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.7750071287155151
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.8502673796791443
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.9636363636363636
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.9467412947017336
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.8934169278996865
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 0.7770164012908936
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.9034090909090907
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 0.7750071287155151
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.8502673796791443
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.9636363636363636
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.9467412947017336
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.890282131661442
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 9.908584594726562
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.9002849002849003
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 10.437429428100586
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.8494623655913979
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.9575757575757575
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.9451852140210413
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.8934169278996865
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 0.6678076386451721
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.9034090909090907
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 0.6708062887191772
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.8502673796791443
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.9636363636363636
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.9467412947017336
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.8934169278996865
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 9.908584594726562
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.9034090909090907
+      name: Max F1
+    - type: max_f1_threshold
+      value: 10.437429428100586
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.8502673796791443
+      name: Max Precision
+    - type: max_recall
+      value: 0.9636363636363636
+      name: Max Recall
+    - type: max_ap
+      value: 0.9467412947017336
+      name: Max Ap
+---
+
+# SentenceTransformer based on intfloat/multilingual-e5-small
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). 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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision fd1525a9fd15316a2d503bf26ab031a61d056e98 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **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: 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("srikarvar/fine_tuned_model_4")
+# Run inference
+sentences = [
+    'Who is the President of the United States?',
+    'Who is the current US President?',
+    'What is the velocity of sound?',
+]
+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
+
+#### Binary Classification
+* Dataset: `pair-class-dev`
+* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
+
+| Metric                       | Value      |
+|:-----------------------------|:-----------|
+| cosine_accuracy              | 0.6207     |
+| cosine_accuracy_threshold    | 0.9036     |
+| cosine_f1                    | 0.7193     |
+| cosine_f1_threshold          | 0.9036     |
+| cosine_precision             | 0.5827     |
+| cosine_recall                | 0.9394     |
+| cosine_ap                    | 0.6366     |
+| dot_accuracy                 | 0.6207     |
+| dot_accuracy_threshold       | 0.9036     |
+| dot_f1                       | 0.7193     |
+| dot_f1_threshold             | 0.9036     |
+| dot_precision                | 0.5827     |
+| dot_recall                   | 0.9394     |
+| dot_ap                       | 0.6366     |
+| manhattan_accuracy           | 0.6176     |
+| manhattan_accuracy_threshold | 6.5018     |
+| manhattan_f1                 | 0.7232     |
+| manhattan_f1_threshold       | 7.1429     |
+| manhattan_precision          | 0.5724     |
+| manhattan_recall             | 0.9818     |
+| manhattan_ap                 | 0.6414     |
+| euclidean_accuracy           | 0.6207     |
+| euclidean_accuracy_threshold | 0.4391     |
+| euclidean_f1                 | 0.7193     |
+| euclidean_f1_threshold       | 0.4391     |
+| euclidean_precision          | 0.5827     |
+| euclidean_recall             | 0.9394     |
+| euclidean_ap                 | 0.6366     |
+| max_accuracy                 | 0.6207     |
+| max_accuracy_threshold       | 6.5018     |
+| max_f1                       | 0.7232     |
+| max_f1_threshold             | 7.1429     |
+| max_precision                | 0.5827     |
+| max_recall                   | 0.9818     |
+| **max_ap**                   | **0.6414** |
+
+#### Binary Classification
+* Dataset: `pair-class-test`
+* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
+
+| Metric                       | Value      |
+|:-----------------------------|:-----------|
+| cosine_accuracy              | 0.8934     |
+| cosine_accuracy_threshold    | 0.777      |
+| cosine_f1                    | 0.9034     |
+| cosine_f1_threshold          | 0.775      |
+| cosine_precision             | 0.8503     |
+| cosine_recall                | 0.9636     |
+| cosine_ap                    | 0.9467     |
+| dot_accuracy                 | 0.8934     |
+| dot_accuracy_threshold       | 0.777      |
+| dot_f1                       | 0.9034     |
+| dot_f1_threshold             | 0.775      |
+| dot_precision                | 0.8503     |
+| dot_recall                   | 0.9636     |
+| dot_ap                       | 0.9467     |
+| manhattan_accuracy           | 0.8903     |
+| manhattan_accuracy_threshold | 9.9086     |
+| manhattan_f1                 | 0.9003     |
+| manhattan_f1_threshold       | 10.4374    |
+| manhattan_precision          | 0.8495     |
+| manhattan_recall             | 0.9576     |
+| manhattan_ap                 | 0.9452     |
+| euclidean_accuracy           | 0.8934     |
+| euclidean_accuracy_threshold | 0.6678     |
+| euclidean_f1                 | 0.9034     |
+| euclidean_f1_threshold       | 0.6708     |
+| euclidean_precision          | 0.8503     |
+| euclidean_recall             | 0.9636     |
+| euclidean_ap                 | 0.9467     |
+| max_accuracy                 | 0.8934     |
+| max_accuracy_threshold       | 9.9086     |
+| max_f1                       | 0.9034     |
+| max_f1_threshold             | 10.4374    |
+| max_precision                | 0.8503     |
+| max_recall                   | 0.9636     |
+| **max_ap**                   | **0.9467** |
+
+<!--
+## 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
+
+#### Unnamed Dataset
+
+
+* Size: 1,273 training samples
+* Columns: <code>sentence1</code>, <code>label</code>, and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | label                                           | sentence2                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | int                                             | string                                                                            |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 10.93 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>0: ~48.90%</li><li>1: ~51.10%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.29 tokens</li><li>max: 22 tokens</li></ul> |
+* Samples:
+  | sentence1                                                                     | label          | sentence2                                                             |
+  |:------------------------------------------------------------------------------|:---------------|:----------------------------------------------------------------------|
+  | <code>What are the main ingredients in a traditional pizza Margherita?</code> | <code>1</code> | <code>What ingredients are used in a classic pizza Margherita?</code> |
+  | <code>Release date of the iPhone 14</code>                                    | <code>0</code> | <code>Release date of the iPhone 13</code>                            |
+  | <code>Who won the first Nobel Prize in Literature?</code>                     | <code>0</code> | <code>Who won the first Nobel Prize in Peace?</code>                  |
+* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 319 evaluation samples
+* Columns: <code>sentence1</code>, <code>label</code>, and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | label                                           | sentence2                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | int                                             | string                                                                            |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 11.12 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>0: ~48.28%</li><li>1: ~51.72%</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.52 tokens</li><li>max: 21 tokens</li></ul> |
+* Samples:
+  | sentence1                                                      | label          | sentence2                                                    |
+  |:---------------------------------------------------------------|:---------------|:-------------------------------------------------------------|
+  | <code>How many bones are in the human body?</code>             | <code>1</code> | <code>Total bones in an adult human</code>                   |
+  | <code>What is the price of an iPhone 12?</code>                | <code>0</code> | <code>What is the price of an iPhone 11?</code>              |
+  | <code>What are the different types of renewable energy?</code> | <code>1</code> | <code>What are the various forms of renewable energy?</code> |
+* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `gradient_accumulation_steps`: 2
+- `num_train_epochs`: 4
+- `warmup_ratio`: 0.1
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 2
+- `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`: 4
+- `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`: True
+- `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_fused
+- `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       | pair-class-dev_max_ap | pair-class-test_max_ap |
+|:---------:|:------:|:-------------:|:----------:|:---------------------:|:----------------------:|
+| 0         | 0      | -             | -          | 0.6414                | -                      |
+| 0.5       | 10     | 1.9407        | -          | -                     | -                      |
+| 1.0       | 20     | 0.9729        | 0.6810     | -                     | -                      |
+| 1.475     | 30     | 0.4822        | -          | -                     | -                      |
+| 1.975     | 40     | 0.4062        | -          | -                     | -                      |
+| 2.025     | 41     | -             | 0.5953     | -                     | -                      |
+| 2.45      | 50     | 0.2894        | -          | -                     | -                      |
+| 2.95      | 60     | 0.1977        | -          | -                     | -                      |
+| 3.0       | 61     | -             | 0.5318     | -                     | -                      |
+| 3.425     | 70     | 0.1999        | -          | -                     | -                      |
+| **3.925** | **80** | **0.1491**    | **0.5159** | **-**                 | **0.9467**             |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.1.2+cu121
+- Accelerate: 0.32.1
+- Datasets: 2.19.1
+- 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",
+}
+```
+
+<!--
+## 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

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+{
+  "_name_or_path": "intfloat/multilingual-e5-small",
+  "architectures": [
+    "BertModel"
+  ],
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+  "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",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.1.2+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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modules.json

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

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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special_tokens_map.json

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+    "rstrip": false,
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tokenizer.json

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tokenizer_config.json

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+  "sp_model_kwargs": {},
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+  "unk_token": "<unk>"
+}