Update README.md

README.md

@@ -10,489 +10,5 @@ tags:
 - dataset_size:100K<n<1M
 - loss:MultipleNegativesRankingLoss
 base_model: microsoft/mpnet-base
-metrics:
-- cosine_accuracy
-- dot_accuracy
-- manhattan_accuracy
-- euclidean_accuracy
-- max_accuracy
-widget:
-- source_sentence: A woman sings.
-  sentences:
-  - The woman is singing.
-  - A woman sits outside.
-  - Two men are sleeping.
-- source_sentence: The boy scowls
-  sentences:
-  - An insecure boy.
-  - A person is climbing.
-  - Two women are sleeping.
-- source_sentence: There's a dock
-  sentences:
-  - There is people outside.
-  - two women are outside
-  - a boy sleeps on the couch
-- source_sentence: A bird flying.
-  sentences:
-  - an eagle flies
-  - The girl is outdoors.
-  - Two men are sleeping.
-- source_sentence: a baby smiling
-  sentences:
-  - The boy is smiling
-  - The girl is standing.
-  - Two men are in a kayak.
-pipeline_tag: sentence-similarity
-model-index:
-- name: MPNet base trained on AllNLI triplets
-  results:
-  - task:
-      type: triplet
-      name: Triplet
-    dataset:
-      name: all nli dev
-      type: all-nli-dev
-    metrics:
-    - type: cosine_accuracy
-      value: 0.85
-      name: Cosine Accuracy
-    - type: dot_accuracy
-      value: 0.155
-      name: Dot Accuracy
-    - type: manhattan_accuracy
-      value: 0.848
-      name: Manhattan Accuracy
-    - type: euclidean_accuracy
-      value: 0.846
-      name: Euclidean Accuracy
-    - type: max_accuracy
-      value: 0.85
-      name: Max Accuracy
-  - task:
-      type: triplet
-      name: Triplet
-    dataset:
-      name: all nli test
-      type: all-nli-test
-    metrics:
-    - type: cosine_accuracy
-      value: 0.946
-      name: Cosine Accuracy
-    - type: dot_accuracy
-      value: 0.05
-      name: Dot Accuracy
-    - type: manhattan_accuracy
-      value: 0.942
-      name: Manhattan Accuracy
-    - type: euclidean_accuracy
-      value: 0.942
-      name: Euclidean Accuracy
-    - type: max_accuracy
-      value: 0.946
-      name: Max Accuracy
 ---
 
-# MPNet base trained on AllNLI triplets
-
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
-
-## Model Details
-
-### Model Description
-- **Model Type:** Sentence Transformer
-- **Base model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
-- **Maximum Sequence Length:** 512 tokens
-- **Output Dimensionality:** 768 tokens
-- **Similarity Function:** Cosine Similarity
-- **Training Dataset:**
-    - [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
-- **Language:** en
-- **License:** apache-2.0
-
-### 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: MPNetModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-)
-```
-
-## 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("ayoubkirouane/Mpnet-base-ALL-NLI")
-# Run inference
-sentences = [
-    'a baby smiling',
-    'The boy is smiling',
-    'The girl is standing.',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 768]
-
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities.shape)
-# [3, 3]
-```
-
-<!--
-### Direct Usage (Transformers)
-
-<details><summary>Click to see the direct usage in Transformers</summary>
-
-</details>
--->
-
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-</details>
--->
-
-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-
-## 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    | 0.85     |
-| dot_accuracy       | 0.155    |
-| manhattan_accuracy | 0.848    |
-| euclidean_accuracy | 0.846    |
-| **max_accuracy**   | **0.85** |
-
-#### 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    | 0.946     |
-| dot_accuracy       | 0.05      |
-| manhattan_accuracy | 0.942     |
-| euclidean_accuracy | 0.942     |
-| **max_accuracy**   | **0.946** |
-
-<!--
-## 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: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> |
-* Samples:
-  | anchor                                                                     | positive                                         | negative                                                   |
-  |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
-  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>A person is at a diner, ordering an omelette.</code> |
-  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>The kids are frowning</code>                         |
-  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code>             |
-* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
-  ```json
-  {
-      "scale": 20.0,
-      "similarity_fct": "cos_sim"
-  }
-  ```
-
-### Evaluation Dataset
-
-#### 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: 17.95 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.78 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.35 tokens</li><li>max: 29 tokens</li></ul> |
-* Samples:
-  | anchor                                                                                                                                                                         | positive                                                    | negative                                                |
-  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
-  | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>Two woman are holding packages.</code>                | <code>The men are fighting outside a deli.</code>       |
-  | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code>        |
-  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>A man selling donuts to a customer.</code>            | <code>A woman drinks her coffee in a small cafe.</code> |
-* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
-  ```json
-  {
-      "scale": 20.0,
-      "similarity_fct": "cos_sim"
-  }
-  ```
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `eval_strategy`: steps
-- `per_device_train_batch_size`: 16
-- `per_device_eval_batch_size`: 16
-- `num_train_epochs`: 1
-- `warmup_ratio`: 0.1
-- `fp16`: 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`: False
-- `fp16`: True
-- `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    | -             | -      | 0.636                    | -                         |
-| 0.032 | 100  | 2.6736        | 0.8660 | 0.881                    | -                         |
-| 0.064 | 200  | 1.0541        | 0.9318 | 0.866                    | -                         |
-| 0.096 | 300  | 1.1691        | 1.0155 | 0.876                    | -                         |
-| 0.128 | 400  | 1.2233        | 1.3754 | 0.85                     | -                         |
-| 0.032 | 100  | 1.5484        | 0.9666 | -                        | -                         |
-| 0.064 | 200  | 0.5988        | 0.8912 | -                        | -                         |
-| 0.096 | 300  | 0.4046        | 1.0413 | -                        | -                         |
-| 0.128 | 400  | 0.2979        | 1.2470 | -                        | -                         |
-| 0.16  | 500  | 1.1653        | 1.2219 | -                        | -                         |
-| 0.192 | 600  | 1.1348        | 1.1751 | -                        | -                         |
-| 0.224 | 700  | 1.2606        | 1.2407 | -                        | -                         |
-| 0.256 | 800  | 1.083         | 1.1729 | -                        | -                         |
-| 0.288 | 900  | 1.0435        | 1.1577 | -                        | -                         |
-| 0.32  | 1000 | 0.9209        | 1.0593 | -                        | -                         |
-| 0.352 | 1100 | 1.0499        | 1.0049 | -                        | -                         |
-| 0.384 | 1200 | 1.194         | 1.1318 | -                        | -                         |
-| 0.416 | 1300 | 1.2979        | 1.0062 | -                        | -                         |
-| 0.448 | 1400 | 1.2356        | 1.0485 | -                        | -                         |
-| 0.48  | 1500 | 1.0414        | 0.8570 | -                        | -                         |
-| 0.512 | 1600 | 0.8688        | 0.8401 | -                        | -                         |
-| 0.544 | 1700 | 0.8349        | 0.7505 | -                        | -                         |
-| 0.576 | 1800 | 0.8965        | 0.7833 | -                        | -                         |
-| 0.608 | 1900 | 0.9347        | 0.7959 | -                        | -                         |
-| 0.64  | 2000 | 1.0194        | 0.6819 | -                        | -                         |
-| 0.672 | 2100 | 0.928         | 0.6060 | -                        | -                         |
-| 0.704 | 2200 | 0.9087        | 0.5785 | -                        | -                         |
-| 0.736 | 2300 | 0.8015        | 0.5598 | -                        | -                         |
-| 0.768 | 2400 | 0.7945        | 0.5644 | -                        | -                         |
-| 0.8   | 2500 | 0.8071        | 0.5606 | -                        | -                         |
-| 0.832 | 2600 | 0.7321        | 0.5724 | -                        | -                         |
-| 0.864 | 2700 | 0.7732        | 0.5478 | -                        | -                         |
-| 0.896 | 2800 | 0.8436        | 0.5054 | -                        | -                         |
-| 0.928 | 2900 | 0.9542        | 0.4962 | -                        | -                         |
-| 0.96  | 3000 | 0.6193        | 0.5048 | -                        | -                         |
-| 0.992 | 3100 | 0.0198        | 0.5503 | -                        | -                         |
-| 1.0   | 3125 | -             | -      | -                        | 0.946                     |
-
-
-### Framework Versions
-- Python: 3.10.13
-- Sentence Transformers: 3.0.0
-- Transformers: 4.41.1
-- PyTorch: 2.1.2
-- Accelerate: 0.30.1
-- 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",
-}
-```
-
-#### MultipleNegativesRankingLoss
-```bibtex
-@misc{henderson2017efficient,
-    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
-    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
-    year={2017},
-    eprint={1705.00652},
-    archivePrefix={arXiv},
-    primaryClass={cs.CL}
-}
-```
-
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
-
-<!--
-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
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-## Model Card Contact
-
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