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 +1,761 @@
+---
+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:1030
+- loss:ContrastiveLoss
+widget:
+- source_sentence: First climber to reach the summit of Everest
+  sentences:
+  - How to create a podcast?
+  - How to cook sushi rice?
+  - Who was the first person to climb Mount Everest?
+- source_sentence: What methods are used to measure a nation's GDP?
+  sentences:
+  - How is the GDP of a country measured?
+  - How do I sign out of my email account?
+  - How does digital marketing differ from traditional marketing?
+- source_sentence: Steps to sign up for a new account
+  sentences:
+  - How to grow tomatoes in a garden?
+  - What is the process for creating a new account?
+  - What is the GDP of India?
+- source_sentence: Name of the tallest building in New York
+  sentences:
+  - What are the symptoms of anxiety?
+  - What is the tallest building in New York?
+  - Who was the first female Prime Minister of the UK?
+- source_sentence: How do you make a paper boat?
+  sentences:
+  - What are the benefits of using solar energy?
+  - Where can I buy a new phone?
+  - How do you make a paper airplane?
+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: 0.9478260869565217
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.6633322238922119
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.9558823529411764
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.6633322238922119
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.9154929577464789
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 1.0
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.9777355464218691
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.9478260869565217
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 0.6633322238922119
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.9558823529411764
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 0.6633322238922119
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.9154929577464789
+      name: Dot Precision
+    - type: dot_recall
+      value: 1.0
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.9777355464218691
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.9391304347826087
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 9.603110313415527
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.9489051094890512
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 12.660685539245605
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.9027777777777778
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 1.0
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.975614621691024
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.9478260869565217
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 0.8205450773239136
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.9558823529411764
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 0.8205450773239136
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.9154929577464789
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 1.0
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.9777355464218691
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.9478260869565217
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 9.603110313415527
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.9558823529411764
+      name: Max F1
+    - type: max_f1_threshold
+      value: 12.660685539245605
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.9154929577464789
+      name: Max Precision
+    - type: max_recall
+      value: 1.0
+      name: Max Recall
+    - type: max_ap
+      value: 0.9777355464218691
+      name: Max Ap
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: pair class test
+      type: pair-class-test
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9478260869565217
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.7873066663742065
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.9558823529411764
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.6542514562606812
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.9154929577464789
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 1.0
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.9776721343444097
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.9478260869565217
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 0.7873067259788513
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.9558823529411764
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 0.6542515158653259
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.9154929577464789
+      name: Dot Precision
+    - type: dot_recall
+      value: 1.0
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.9776721343444097
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.9478260869565217
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 11.123205184936523
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.9558823529411764
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 12.862250328063965
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.9154929577464789
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 1.0
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.9774497925836063
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.9478260869565217
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 0.652188777923584
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.9558823529411764
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 0.8315430879592896
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.9154929577464789
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 1.0
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.9776721343444097
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.9478260869565217
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 11.123205184936523
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.9558823529411764
+      name: Max F1
+    - type: max_f1_threshold
+      value: 12.862250328063965
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.9154929577464789
+      name: Max Precision
+    - type: max_recall
+      value: 1.0
+      name: Max Recall
+    - type: max_ap
+      value: 0.9776721343444097
+      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_2")
+# Run inference
+sentences = [
+    'How do you make a paper boat?',
+    'How do you make a paper airplane?',
+    'What are the benefits of using solar energy?',
+]
+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              | 0.9478     |
+| cosine_accuracy_threshold    | 0.6633     |
+| cosine_f1                    | 0.9559     |
+| cosine_f1_threshold          | 0.6633     |
+| cosine_precision             | 0.9155     |
+| cosine_recall                | 1.0        |
+| cosine_ap                    | 0.9777     |
+| dot_accuracy                 | 0.9478     |
+| dot_accuracy_threshold       | 0.6633     |
+| dot_f1                       | 0.9559     |
+| dot_f1_threshold             | 0.6633     |
+| dot_precision                | 0.9155     |
+| dot_recall                   | 1.0        |
+| dot_ap                       | 0.9777     |
+| manhattan_accuracy           | 0.9391     |
+| manhattan_accuracy_threshold | 9.6031     |
+| manhattan_f1                 | 0.9489     |
+| manhattan_f1_threshold       | 12.6607    |
+| manhattan_precision          | 0.9028     |
+| manhattan_recall             | 1.0        |
+| manhattan_ap                 | 0.9756     |
+| euclidean_accuracy           | 0.9478     |
+| euclidean_accuracy_threshold | 0.8205     |
+| euclidean_f1                 | 0.9559     |
+| euclidean_f1_threshold       | 0.8205     |
+| euclidean_precision          | 0.9155     |
+| euclidean_recall             | 1.0        |
+| euclidean_ap                 | 0.9777     |
+| max_accuracy                 | 0.9478     |
+| max_accuracy_threshold       | 9.6031     |
+| max_f1                       | 0.9559     |
+| max_f1_threshold             | 12.6607    |
+| max_precision                | 0.9155     |
+| max_recall                   | 1.0        |
+| **max_ap**                   | **0.9777** |
+
+#### 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              | 0.9478     |
+| cosine_accuracy_threshold    | 0.7873     |
+| cosine_f1                    | 0.9559     |
+| cosine_f1_threshold          | 0.6543     |
+| cosine_precision             | 0.9155     |
+| cosine_recall                | 1.0        |
+| cosine_ap                    | 0.9777     |
+| dot_accuracy                 | 0.9478     |
+| dot_accuracy_threshold       | 0.7873     |
+| dot_f1                       | 0.9559     |
+| dot_f1_threshold             | 0.6543     |
+| dot_precision                | 0.9155     |
+| dot_recall                   | 1.0        |
+| dot_ap                       | 0.9777     |
+| manhattan_accuracy           | 0.9478     |
+| manhattan_accuracy_threshold | 11.1232    |
+| manhattan_f1                 | 0.9559     |
+| manhattan_f1_threshold       | 12.8623    |
+| manhattan_precision          | 0.9155     |
+| manhattan_recall             | 1.0        |
+| manhattan_ap                 | 0.9774     |
+| euclidean_accuracy           | 0.9478     |
+| euclidean_accuracy_threshold | 0.6522     |
+| euclidean_f1                 | 0.9559     |
+| euclidean_f1_threshold       | 0.8315     |
+| euclidean_precision          | 0.9155     |
+| euclidean_recall             | 1.0        |
+| euclidean_ap                 | 0.9777     |
+| max_accuracy                 | 0.9478     |
+| max_accuracy_threshold       | 11.1232    |
+| max_f1                       | 0.9559     |
+| max_f1_threshold             | 12.8623    |
+| max_precision                | 0.9155     |
+| max_recall                   | 1.0        |
+| **max_ap**                   | **0.9777** |
+
+<!--
+## 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: 1,030 training samples
+* Columns: <code>label</code>, <code>sentence2</code>, and <code>sentence1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | label                                           | sentence2                                                                         | sentence1                                                                        |
+  |:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | int                                             | string                                                                            | string                                                                           |
+  | details | <ul><li>0: ~49.60%</li><li>1: ~50.40%</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.27 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.9 tokens</li><li>max: 22 tokens</li></ul> |
+* Samples:
+  | label          | sentence2                                             | sentence1                                                              |
+  |:---------------|:------------------------------------------------------|:-----------------------------------------------------------------------|
+  | <code>1</code> | <code>Speed of sound in air</code>                    | <code>What is the speed of sound?</code>                               |
+  | <code>1</code> | <code>World's most popular tourist destination</code> | <code>What is the most visited tourist attraction in the world?</code> |
+  | <code>1</code> | <code>How do I write a resume?</code>                 | <code>How do I create a resume?</code>                                 |
+* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
+      "margin": 0.6,
+      "size_average": true
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 115 evaluation samples
+* Columns: <code>label</code>, <code>sentence2</code>, and <code>sentence1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | label                                           | sentence2                                                                         | sentence1                                                                         |
+  |:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | int                                             | string                                                                            | string                                                                            |
+  | details | <ul><li>0: ~43.48%</li><li>1: ~56.52%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.04 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.81 tokens</li><li>max: 20 tokens</li></ul> |
+* Samples:
+  | label          | sentence2                                                     | sentence1                                          |
+  |:---------------|:--------------------------------------------------------------|:---------------------------------------------------|
+  | <code>0</code> | <code>What methods are used to measure a nation's GDP?</code> | <code>How is the GDP of a country measured?</code> |
+  | <code>0</code> | <code>What is the currency of Japan?</code>                   | <code>What is the currency of China?</code>        |
+  | <code>1</code> | <code>Steps to cultivate tomatoes at home</code>              | <code>How to grow tomatoes in a garden?</code>     |
+* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
+      "margin": 0.6,
+      "size_average": true
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `gradient_accumulation_steps`: 2
+- `weight_decay`: 0.01
+- `num_train_epochs`: 8
+- `lr_scheduler_type`: reduce_lr_on_plateau
+- `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`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 8
+- `max_steps`: -1
+- `lr_scheduler_type`: reduce_lr_on_plateau
+- `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       | -             | -          | 0.7625                | -                      |
+| 0.6061     | 10      | 0.0417        | -          | -                     | -                      |
+| 0.9697     | 16      | -             | 0.0119     | 0.9695                | -                      |
+| 1.2121     | 20      | 0.0189        | -          | -                     | -                      |
+| 1.8182     | 30      | 0.0148        | -          | -                     | -                      |
+| 2.0        | 33      | -             | 0.0102     | 0.9741                | -                      |
+| 2.4242     | 40      | 0.0114        | -          | -                     | -                      |
+| 2.9697     | 49      | -             | 0.0098     | 0.9752                | -                      |
+| 3.0303     | 50      | 0.009         | -          | -                     | -                      |
+| 3.6364     | 60      | 0.008         | -          | -                     | -                      |
+| 4.0        | 66      | -             | 0.0095     | 0.9778                | -                      |
+| 4.2424     | 70      | 0.0065        | -          | -                     | -                      |
+| 4.8485     | 80      | 0.0056        | -          | -                     | -                      |
+| 4.9697     | 82      | -             | 0.0092     | 0.9749                | -                      |
+| 5.4545     | 90      | 0.0056        | -          | -                     | -                      |
+| 6.0        | 99      | -             | 0.0088     | 0.9766                | -                      |
+| 6.0606     | 100     | 0.0045        | -          | -                     | -                      |
+| 6.6667     | 110     | 0.0044        | -          | -                     | -                      |
+| **6.9697** | **115** | **-**         | **0.0087** | **0.9777**            | **-**                  |
+| 7.2727     | 120     | 0.0038        | -          | -                     | -                      |
+| 7.7576     | 128     | -             | 0.0090     | 0.9777                | 0.9777                 |
+
+* 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",
+}
+```
+
+#### ContrastiveLoss
+```bibtex
+@inproceedings{hadsell2006dimensionality,
+    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
+    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, 
+    title={Dimensionality Reduction by Learning an Invariant Mapping}, 
+    year={2006},
+    volume={2},
+    number={},
+    pages={1735-1742},
+    doi={10.1109/CVPR.2006.100}
+}
+```
+
+<!--
+## 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"
+  ],
+  "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",
+  "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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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "type": "sentence_transformers.models.Pooling"
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+  {
+    "idx": 2,
+    "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,
+    "single_word": false
+  },
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+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "content": "<unk>",
+    "lstrip": false,
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+    "rstrip": false,
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+}

tokenizer.json

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+size 17083053

tokenizer_config.json

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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}