| | --- |
| | 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:1972 |
| | - loss:OnlineContrastiveLoss |
| | widget: |
| | - source_sentence: Who invented the World Wide Web? |
| | sentences: |
| | - What universities does Chart Industries recruit new grads from? What majors are |
| | they looking for? |
| | - Who invented the internet? |
| | - What are the benefits of a balanced diet? |
| | - source_sentence: Who was the second President of the United States? |
| | sentences: |
| | - Second leader of the USA |
| | - How many people live in Germany? |
| | - How do I get over someone I loved now that we broke up last year and I still miss |
| | her? |
| | - source_sentence: How did you first come across porn? |
| | sentences: |
| | - How were you first introduced to porn? |
| | - Date of signing the Declaration of Independence |
| | - Do we need the IPC section 375? |
| | - source_sentence: How to invest in cryptocurrency? |
| | sentences: |
| | - What is the cheapest toothpaste? |
| | - What are the environmental advantages of recycling? |
| | - How to trade cryptocurrency? |
| | - source_sentence: What is the speed of a racing drone? |
| | sentences: |
| | - Who was the first person to swim across the Atlantic? |
| | - People say "don't try to please others." Does being nice to others mean pleasing |
| | them? |
| | - What is the speed of a racing car? |
| | 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: 0.8772727272727273 |
| | name: Cosine Accuracy |
| | - type: cosine_accuracy_threshold |
| | value: 0.8647407293319702 |
| | name: Cosine Accuracy Threshold |
| | - type: cosine_f1 |
| | value: 0.8682926829268292 |
| | name: Cosine F1 |
| | - type: cosine_f1_threshold |
| | value: 0.8647407293319702 |
| | name: Cosine F1 Threshold |
| | - type: cosine_precision |
| | value: 0.8725490196078431 |
| | name: Cosine Precision |
| | - type: cosine_recall |
| | value: 0.8640776699029126 |
| | name: Cosine Recall |
| | - type: cosine_ap |
| | value: 0.9227827652550092 |
| | name: Cosine Ap |
| | - type: dot_accuracy |
| | value: 0.8772727272727273 |
| | name: Dot Accuracy |
| | - type: dot_accuracy_threshold |
| | value: 0.8647407293319702 |
| | name: Dot Accuracy Threshold |
| | - type: dot_f1 |
| | value: 0.8682926829268292 |
| | name: Dot F1 |
| | - type: dot_f1_threshold |
| | value: 0.8647407293319702 |
| | name: Dot F1 Threshold |
| | - type: dot_precision |
| | value: 0.8725490196078431 |
| | name: Dot Precision |
| | - type: dot_recall |
| | value: 0.8640776699029126 |
| | name: Dot Recall |
| | - type: dot_ap |
| | value: 0.9227827652550092 |
| | name: Dot Ap |
| | - type: manhattan_accuracy |
| | value: 0.8772727272727273 |
| | name: Manhattan Accuracy |
| | - type: manhattan_accuracy_threshold |
| | value: 8.025869369506836 |
| | name: Manhattan Accuracy Threshold |
| | - type: manhattan_f1 |
| | value: 0.8703703703703703 |
| | name: Manhattan F1 |
| | - type: manhattan_f1_threshold |
| | value: 9.006706237792969 |
| | name: Manhattan F1 Threshold |
| | - type: manhattan_precision |
| | value: 0.831858407079646 |
| | name: Manhattan Precision |
| | - type: manhattan_recall |
| | value: 0.912621359223301 |
| | name: Manhattan Recall |
| | - type: manhattan_ap |
| | value: 0.9221498446893291 |
| | name: Manhattan Ap |
| | - type: euclidean_accuracy |
| | value: 0.8772727272727273 |
| | name: Euclidean Accuracy |
| | - type: euclidean_accuracy_threshold |
| | value: 0.5201112031936646 |
| | name: Euclidean Accuracy Threshold |
| | - type: euclidean_f1 |
| | value: 0.8682926829268292 |
| | name: Euclidean F1 |
| | - type: euclidean_f1_threshold |
| | value: 0.5201112031936646 |
| | name: Euclidean F1 Threshold |
| | - type: euclidean_precision |
| | value: 0.8725490196078431 |
| | name: Euclidean Precision |
| | - type: euclidean_recall |
| | value: 0.8640776699029126 |
| | name: Euclidean Recall |
| | - type: euclidean_ap |
| | value: 0.9227827652550092 |
| | name: Euclidean Ap |
| | - type: max_accuracy |
| | value: 0.8772727272727273 |
| | name: Max Accuracy |
| | - type: max_accuracy_threshold |
| | value: 8.025869369506836 |
| | name: Max Accuracy Threshold |
| | - type: max_f1 |
| | value: 0.8703703703703703 |
| | name: Max F1 |
| | - type: max_f1_threshold |
| | value: 9.006706237792969 |
| | name: Max F1 Threshold |
| | - type: max_precision |
| | value: 0.8725490196078431 |
| | name: Max Precision |
| | - type: max_recall |
| | value: 0.912621359223301 |
| | name: Max Recall |
| | - type: max_ap |
| | value: 0.9227827652550092 |
| | name: Max Ap |
| | - task: |
| | type: binary-classification |
| | name: Binary Classification |
| | dataset: |
| | name: pair class test |
| | type: pair-class-test |
| | metrics: |
| | - type: cosine_accuracy |
| | value: 0.8772727272727273 |
| | name: Cosine Accuracy |
| | - type: cosine_accuracy_threshold |
| | value: 0.8647407293319702 |
| | name: Cosine Accuracy Threshold |
| | - type: cosine_f1 |
| | value: 0.8682926829268292 |
| | name: Cosine F1 |
| | - type: cosine_f1_threshold |
| | value: 0.8647407293319702 |
| | name: Cosine F1 Threshold |
| | - type: cosine_precision |
| | value: 0.8725490196078431 |
| | name: Cosine Precision |
| | - type: cosine_recall |
| | value: 0.8640776699029126 |
| | name: Cosine Recall |
| | - type: cosine_ap |
| | value: 0.9227827652550092 |
| | name: Cosine Ap |
| | - type: dot_accuracy |
| | value: 0.8772727272727273 |
| | name: Dot Accuracy |
| | - type: dot_accuracy_threshold |
| | value: 0.8647407293319702 |
| | name: Dot Accuracy Threshold |
| | - type: dot_f1 |
| | value: 0.8682926829268292 |
| | name: Dot F1 |
| | - type: dot_f1_threshold |
| | value: 0.8647407293319702 |
| | name: Dot F1 Threshold |
| | - type: dot_precision |
| | value: 0.8725490196078431 |
| | name: Dot Precision |
| | - type: dot_recall |
| | value: 0.8640776699029126 |
| | name: Dot Recall |
| | - type: dot_ap |
| | value: 0.9227827652550092 |
| | name: Dot Ap |
| | - type: manhattan_accuracy |
| | value: 0.8772727272727273 |
| | name: Manhattan Accuracy |
| | - type: manhattan_accuracy_threshold |
| | value: 8.025869369506836 |
| | name: Manhattan Accuracy Threshold |
| | - type: manhattan_f1 |
| | value: 0.8703703703703703 |
| | name: Manhattan F1 |
| | - type: manhattan_f1_threshold |
| | value: 9.006706237792969 |
| | name: Manhattan F1 Threshold |
| | - type: manhattan_precision |
| | value: 0.831858407079646 |
| | name: Manhattan Precision |
| | - type: manhattan_recall |
| | value: 0.912621359223301 |
| | name: Manhattan Recall |
| | - type: manhattan_ap |
| | value: 0.9221498446893291 |
| | name: Manhattan Ap |
| | - type: euclidean_accuracy |
| | value: 0.8772727272727273 |
| | name: Euclidean Accuracy |
| | - type: euclidean_accuracy_threshold |
| | value: 0.5201112031936646 |
| | name: Euclidean Accuracy Threshold |
| | - type: euclidean_f1 |
| | value: 0.8682926829268292 |
| | name: Euclidean F1 |
| | - type: euclidean_f1_threshold |
| | value: 0.5201112031936646 |
| | name: Euclidean F1 Threshold |
| | - type: euclidean_precision |
| | value: 0.8725490196078431 |
| | name: Euclidean Precision |
| | - type: euclidean_recall |
| | value: 0.8640776699029126 |
| | name: Euclidean Recall |
| | - type: euclidean_ap |
| | value: 0.9227827652550092 |
| | name: Euclidean Ap |
| | - type: max_accuracy |
| | value: 0.8772727272727273 |
| | name: Max Accuracy |
| | - type: max_accuracy_threshold |
| | value: 8.025869369506836 |
| | name: Max Accuracy Threshold |
| | - type: max_f1 |
| | value: 0.8703703703703703 |
| | name: Max F1 |
| | - type: max_f1_threshold |
| | value: 9.006706237792969 |
| | name: Max F1 Threshold |
| | - type: max_precision |
| | value: 0.8725490196078431 |
| | name: Max Precision |
| | - type: max_recall |
| | value: 0.912621359223301 |
| | name: Max Recall |
| | - type: max_ap |
| | value: 0.9227827652550092 |
| | 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_6") |
| | # Run inference |
| | sentences = [ |
| | 'What is the speed of a racing drone?', |
| | 'What is the speed of a racing car?', |
| | 'Who was the first person to swim across the Atlantic?', |
| | ] |
| | 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 | 0.8773 | |
| | | cosine_accuracy_threshold | 0.8647 | |
| | | cosine_f1 | 0.8683 | |
| | | cosine_f1_threshold | 0.8647 | |
| | | cosine_precision | 0.8725 | |
| | | cosine_recall | 0.8641 | |
| | | cosine_ap | 0.9228 | |
| | | dot_accuracy | 0.8773 | |
| | | dot_accuracy_threshold | 0.8647 | |
| | | dot_f1 | 0.8683 | |
| | | dot_f1_threshold | 0.8647 | |
| | | dot_precision | 0.8725 | |
| | | dot_recall | 0.8641 | |
| | | dot_ap | 0.9228 | |
| | | manhattan_accuracy | 0.8773 | |
| | | manhattan_accuracy_threshold | 8.0259 | |
| | | manhattan_f1 | 0.8704 | |
| | | manhattan_f1_threshold | 9.0067 | |
| | | manhattan_precision | 0.8319 | |
| | | manhattan_recall | 0.9126 | |
| | | manhattan_ap | 0.9221 | |
| | | euclidean_accuracy | 0.8773 | |
| | | euclidean_accuracy_threshold | 0.5201 | |
| | | euclidean_f1 | 0.8683 | |
| | | euclidean_f1_threshold | 0.5201 | |
| | | euclidean_precision | 0.8725 | |
| | | euclidean_recall | 0.8641 | |
| | | euclidean_ap | 0.9228 | |
| | | max_accuracy | 0.8773 | |
| | | max_accuracy_threshold | 8.0259 | |
| | | max_f1 | 0.8704 | |
| | | max_f1_threshold | 9.0067 | |
| | | max_precision | 0.8725 | |
| | | max_recall | 0.9126 | |
| | | **max_ap** | **0.9228** | |
| | |
| | #### 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 | 0.8773 | |
| | | cosine_accuracy_threshold | 0.8647 | |
| | | cosine_f1 | 0.8683 | |
| | | cosine_f1_threshold | 0.8647 | |
| | | cosine_precision | 0.8725 | |
| | | cosine_recall | 0.8641 | |
| | | cosine_ap | 0.9228 | |
| | | dot_accuracy | 0.8773 | |
| | | dot_accuracy_threshold | 0.8647 | |
| | | dot_f1 | 0.8683 | |
| | | dot_f1_threshold | 0.8647 | |
| | | dot_precision | 0.8725 | |
| | | dot_recall | 0.8641 | |
| | | dot_ap | 0.9228 | |
| | | manhattan_accuracy | 0.8773 | |
| | | manhattan_accuracy_threshold | 8.0259 | |
| | | manhattan_f1 | 0.8704 | |
| | | manhattan_f1_threshold | 9.0067 | |
| | | manhattan_precision | 0.8319 | |
| | | manhattan_recall | 0.9126 | |
| | | manhattan_ap | 0.9221 | |
| | | euclidean_accuracy | 0.8773 | |
| | | euclidean_accuracy_threshold | 0.5201 | |
| | | euclidean_f1 | 0.8683 | |
| | | euclidean_f1_threshold | 0.5201 | |
| | | euclidean_precision | 0.8725 | |
| | | euclidean_recall | 0.8641 | |
| | | euclidean_ap | 0.9228 | |
| | | max_accuracy | 0.8773 | |
| | | max_accuracy_threshold | 8.0259 | |
| | | max_f1 | 0.8704 | |
| | | max_f1_threshold | 9.0067 | |
| | | max_precision | 0.8725 | |
| | | max_recall | 0.9126 | |
| | | **max_ap** | **0.9228** | |
| |
|
| | <!-- |
| | ## 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: 1,972 training samples |
| | * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | sentence1 | sentence2 | label | |
| | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| |
| | | type | string | string | int | |
| | | details | <ul><li>min: 6 tokens</li><li>mean: 12.22 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.89 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>0: ~51.60%</li><li>1: ~48.40%</li></ul> | |
| | * Samples: |
| | | sentence1 | sentence2 | label | |
| | |:--------------------------------------------------------------|:----------------------------------------------------------------|:---------------| |
| | | <code>What is the distance between the Earth and Mars?</code> | <code>What is the distance between the Earth and Saturn?</code> | <code>0</code> | |
| | | <code>Tell me a joke</code> | <code>Make me laugh with a joke</code> | <code>1</code> | |
| | | <code>How can I make money online with free of cost?</code> | <code>How do I to make money online?</code> | <code>1</code> | |
| | * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) |
| |
|
| | ### Evaluation Dataset |
| |
|
| | #### Unnamed Dataset |
| |
|
| |
|
| | * Size: 220 evaluation samples |
| | * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | sentence1 | sentence2 | label | |
| | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------| |
| | | type | string | string | int | |
| | | details | <ul><li>min: 6 tokens</li><li>mean: 12.44 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.4 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>0: ~53.18%</li><li>1: ~46.82%</li></ul> | |
| | * Samples: |
| | | sentence1 | sentence2 | label | |
| | |:------------------------------------------------------|:-------------------------------------------------------|:---------------| |
| | | <code>Who discovered the structure of DNA?</code> | <code>Scientist who identified the double helix</code> | <code>1</code> | |
| | | <code>How to create a website from scratch?</code> | <code>How to create a blog from scratch?</code> | <code>0</code> | |
| | | <code>What is the population of New York City?</code> | <code>What is the population of Chicago?</code> | <code>0</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 |
| | - `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`: 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`: 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 | - | - | 0.6615 | - | |
| | | 0.3226 | 10 | 1.7113 | - | - | - | |
| | | 0.6452 | 20 | 0.9588 | - | - | - | |
| | | 0.9677 | 30 | 0.9243 | - | - | - | |
| | | 1.0 | 31 | - | 0.8485 | 0.8985 | - | |
| | | 1.2903 | 40 | 0.689 | - | - | - | |
| | | 1.6129 | 50 | 0.4289 | - | - | - | |
| | | 1.9355 | 60 | 0.4655 | - | - | - | |
| | | 2.0 | 62 | - | 0.8143 | 0.9203 | - | |
| | | 2.2581 | 70 | 0.4183 | - | - | - | |
| | | 2.5806 | 80 | 0.3038 | - | - | - | |
| | | 2.9032 | 90 | 0.2979 | - | - | - | |
| | | **3.0** | **93** | **-** | **0.8121** | **0.9228** | **0.9228** | |
| |
|
| | * 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", |
| | } |
| | ``` |
| |
|
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