Push model using huggingface_hub.

1_Pooling/config.json

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+{
+  "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
+}

README.md

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+---
+base_model: sentence-transformers/all-mpnet-base-v2
+library_name: setfit
+metrics:
+- f1
+pipeline_tag: text-classification
+tags:
+- setfit
+- absa
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: The ambience is very calm and quiet:The ambience is very calm and quiet.
+- text: For great chinese food nearby, you have Wu:For great chinese food nearby,
+    you have Wu Liang Ye and Grand Sichuan just a block away.
+- text: The menu choices are similar but the taste:The menu choices are similar but
+    the taste lacked more flavor than it looked.
+- text: The food was authentic.:The food was authentic.
+- text: prompt to jump behind the bar and fix drinks, they:The staff is very kind
+    and well trained, they're fast, they are always prompt to jump behind the bar
+    and fix drinks, they know details of every item in the menu and make excelent
+    recomendations.
+inference: false
+model-index:
+- name: SetFit Polarity Model with sentence-transformers/all-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: f1
+      value: 0.8170404156194555
+      name: F1
+---
+
+# SetFit Polarity Model with sentence-transformers/all-mpnet-base-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification. In particular, this model is in charge of classifying aspect polarities.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+This model was trained within the context of a larger system for ABSA, which looks like so:
+
+1. Use a spaCy model to select possible aspect span candidates.
+2. Use a SetFit model to filter these possible aspect span candidates.
+3. **Use this SetFit model to classify the filtered aspect span candidates.**
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
+- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
+- **spaCy Model:** en_core_web_trf
+- **SetFitABSA Aspect Model:** [setfit-absa-aspect](https://huggingface.co/setfit-absa-aspect)
+- **SetFitABSA Polarity Model:** [MattiaTintori/Final_polarity_Colab](https://huggingface.co/MattiaTintori/Final_polarity_Colab)
+- **Maximum Sequence Length:** 384 tokens
+- **Number of Classes:** 3 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1     | <ul><li>'waiter) We got no cheese offered for the pasta,:(food was delivered by a busboy, not waiter) We got no cheese offered for the pasta, our water and wine glasses remained EMPTY our entire meal, when we would have easily spent another $20 on wine.'</li><li>'by a busboy, not waiter) We got no cheese:(food was delivered by a busboy, not waiter) We got no cheese offered for the pasta, our water and wine glasses remained EMPTY our entire meal, when we would have easily spent another $20 on wine.'</li><li>'for the pasta, our water and wine glasses remained EMPTY our entire meal:(food was delivered by a busboy, not waiter) We got no cheese offered for the pasta, our water and wine glasses remained EMPTY our entire meal, when we would have easily spent another $20 on wine.'</li></ul> |
+| 2     | <ul><li>'(food was delivered by a busboy:(food was delivered by a busboy, not waiter) We got no cheese offered for the pasta, our water and wine glasses remained EMPTY our entire meal, when we would have easily spent another $20 on wine.'</li><li>'glasses remained EMPTY our entire meal, when we would have:(food was delivered by a busboy, not waiter) We got no cheese offered for the pasta, our water and wine glasses remained EMPTY our entire meal, when we would have easily spent another $20 on wine.'</li><li>'spent another $20 on wine.:(food was delivered by a busboy, not waiter) We got no cheese offered for the pasta, our water and wine glasses remained EMPTY our entire meal, when we would have easily spent another $20 on wine.'</li></ul>                                              |
+| 0     | <ul><li>'few cocktails and enjoy our surroundings and each other.:20 minutes for our reservation but it gave us time to have a few cocktails and enjoy our surroundings and each other.'</li><li>'Barbecued codfish was gorgeously moist - as:Barbecued codfish was gorgeously moist - as if poached - yet the fabulous texture was let down by curiously bland seasoning - a spice rub might have overwhelmed, however herb mix or other sauce would have done much to enhance.'</li><li>'Even though its good seafood, the prices are too:Even though its good seafood, the prices are too high.'</li></ul>                                                                                                                                                                                                             |
+
+## Evaluation
+
+### Metrics
+| Label   | F1     |
+|:--------|:-------|
+| **all** | 0.8170 |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import AbsaModel
+
+# Download from the 🤗 Hub
+model = AbsaModel.from_pretrained(
+    "setfit-absa-aspect",
+    "MattiaTintori/Final_polarity_Colab",
+)
+# Run inference
+preds = model("The food was great, but the venue is just way too busy.")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## 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 Set Metrics
+| Training set | Min | Median  | Max |
+|:-------------|:----|:--------|:----|
+| Word count   | 1   | 25.0463 | 79  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 1148                  |
+| 1     | 607                   |
+| 2     | 489                   |
+
+### Training Hyperparameters
+- batch_size: (64, 4)
+- num_epochs: (5, 32)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 10
+- body_learning_rate: (5e-05, 5e-05)
+- head_learning_rate: 0.04
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: True
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch      | Step    | Training Loss | Validation Loss |
+|:----------:|:-------:|:-------------:|:---------------:|
+| 0.0014     | 1       | 0.3084        | -               |
+| 0.0285     | 20      | 0.2735        | 0.2591          |
+| 0.0570     | 40      | 0.2228        | 0.2351          |
+| 0.0855     | 60      | 0.2071        | 0.1993          |
+| 0.1140     | 80      | 0.1522        | 0.1696          |
+| 0.1425     | 100     | 0.1441        | 0.1671          |
+| 0.1709     | 120     | 0.1632        | 0.161           |
+| 0.1994     | 140     | 0.0966        | 0.1575          |
+| 0.2279     | 160     | 0.1737        | 0.1504          |
+| 0.2564     | 180     | 0.1092        | 0.1671          |
+| 0.2849     | 200     | 0.1314        | 0.1459          |
+| 0.3134     | 220     | 0.0972        | 0.1483          |
+| 0.3419     | 240     | 0.1014        | 0.1537          |
+| 0.3704     | 260     | 0.0506        | 0.1514          |
+| **0.3989** | **280** | **0.0817**    | **0.143**       |
+| 0.4274     | 300     | 0.0592        | 0.1526          |
+| 0.4558     | 320     | 0.0311        | 0.1562          |
+| 0.4843     | 340     | 0.038         | 0.1546          |
+| 0.5128     | 360     | 0.0852        | 0.1497          |
+| 0.5413     | 380     | 0.0359        | 0.144           |
+| 0.5698     | 400     | 0.0449        | 0.1639          |
+| 0.5983     | 420     | 0.0314        | 0.1517          |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 3.0.1
+- spaCy: 3.7.6
+- Transformers: 4.39.0
+- PyTorch: 2.4.0+cu121
+- Datasets: 2.21.0
+- Tokenizers: 0.15.2
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## 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": "checkpoints/step_280",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.0",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.39.0",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+{
+  "labels": null,
+  "span_context": 5,
+  "normalize_embeddings": true,
+  "spacy_model": "en_core_web_trf"
+}

model.safetensors

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model_head.pkl

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

sentence_bert_config.json

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+{
+  "max_seq_length": 384,
+  "do_lower_case": false
+}

special_tokens_map.json

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+    "lstrip": false,
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+      "content": "<s>",
+      "lstrip": false,
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+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_lower_case": true,
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+  "max_length": 128,
+  "model_max_length": 384,
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+  "padding_side": "right",
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+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}