Add SetFit ABSA model

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
+}

README.md

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+---
+library_name: setfit
+tags:
+- setfit
+- absa
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: bar:After really enjoying ourselves at the bar we sat down at a table and
+    had dinner.
+- text: interior decor:this little place has a cute interior decor and affordable
+    city prices.
+- text: cuisine:The cuisine from what I've gathered is authentic Taiwanese, though
+    its very different from what I've been accustomed to in Taipei.
+- text: dining:Go here for a romantic dinner but not for an all out wow dining experience.
+- text: Taipei:The cuisine from what I've gathered is authentic Taiwanese, though
+    its very different from what I've been accustomed to in Taipei.
+pipeline_tag: text-classification
+inference: false
+co2_eq_emissions:
+  emissions: 8.62132655272333
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
+  ram_total_size: 31.777088165283203
+  hours_used: 0.111
+  hardware_used: 1 x NVIDIA GeForce RTX 3090
+base_model: sentence-transformers/paraphrase-mpnet-base-v2
+model-index:
+- name: SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.8779507785032646
+      name: Accuracy
+---
+
+# SetFit Aspect Model with sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of filtering aspect span candidates.
+
+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 this SetFit model to filter these possible aspect span candidates.**
+3. Use a SetFit model to classify the filtered aspect span candidates.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **spaCy Model:** en_core_web_lg
+- **SetFitABSA Aspect Model:** [tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect](https://huggingface.co/tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect)
+- **SetFitABSA Polarity Model:** [tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity](https://huggingface.co/tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity)
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 2 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+|:----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| aspect    | <ul><li>'staff:But the staff was so horrible to us.'</li><li>"food:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"food:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."</li></ul>                                                                                                                              |
+| no aspect | <ul><li>"factor:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"deficiencies:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"Teodora:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8780   |
+
+## 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(
+    "tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect",
+    "tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity",
+)
+# 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   | 4   | 17.9296 | 37  |
+
+| Label     | Training Sample Count |
+|:----------|:----------------------|
+| no aspect | 71                    |
+| aspect    | 128                   |
+
+### Training Hyperparameters
+- batch_size: (16, 2)
+- num_epochs: (1, 16)
+- max_steps: -1
+- sampling_strategy: oversampling
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0007 | 1    | 0.3388        | -               |
+| 0.0370 | 50   | 0.2649        | -               |
+| 0.0740 | 100  | 0.1562        | -               |
+| 0.1109 | 150  | 0.1072        | -               |
+| 0.1479 | 200  | 0.0021        | -               |
+| 0.1849 | 250  | 0.0007        | -               |
+| 0.2219 | 300  | 0.0008        | -               |
+| 0.2589 | 350  | 0.0003        | -               |
+| 0.2959 | 400  | 0.0002        | -               |
+| 0.3328 | 450  | 0.0003        | -               |
+| 0.3698 | 500  | 0.0002        | -               |
+| 0.4068 | 550  | 0.0001        | -               |
+| 0.4438 | 600  | 0.0001        | -               |
+| 0.4808 | 650  | 0.0001        | -               |
+| 0.5178 | 700  | 0.0001        | -               |
+| 0.5547 | 750  | 0.0001        | -               |
+| 0.5917 | 800  | 0.0001        | -               |
+| 0.6287 | 850  | 0.0002        | -               |
+| 0.6657 | 900  | 0.0001        | -               |
+| 0.7027 | 950  | 0.0001        | -               |
+| 0.7396 | 1000 | 0.0001        | -               |
+| 0.7766 | 1050 | 0.0001        | -               |
+| 0.8136 | 1100 | 0.0001        | -               |
+| 0.8506 | 1150 | 0.0001        | -               |
+| 0.8876 | 1200 | 0.0001        | -               |
+| 0.9246 | 1250 | 0.0001        | -               |
+| 0.9615 | 1300 | 0.0001        | -               |
+| 0.9985 | 1350 | 0.0           | -               |
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Carbon Emitted**: 0.009 kg of CO2
+- **Hours Used**: 0.111 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
+- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
+- **RAM Size**: 31.78 GB
+
+### Framework Versions
+- Python: 3.9.16
+- SetFit: 1.0.0.dev0
+- Sentence Transformers: 2.2.2
+- spaCy: 3.7.2
+- Transformers: 4.29.0
+- PyTorch: 1.13.1+cu117
+- Datasets: 2.15.0
+- Tokenizers: 0.13.3
+
+## 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": "C:\\Users\\tom/.cache\\torch\\sentence_transformers\\sentence-transformers_paraphrase-mpnet-base-v2\\",
+  "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": "4.29.0",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

config_setfit.json

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+{
+  "normalize_embeddings": false,
+  "spacy_model": "en_core_web_lg",
+  "span_context": 0,
+  "labels": [
+    "no aspect",
+    "aspect"
+  ]
+}

model_head.pkl

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+size 6991

modules.json

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+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
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+    "name": "1",
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+    "type": "sentence_transformers.models.Pooling"
+  }
+]

pytorch_model.bin

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sentence_bert_config.json

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

special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": {
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+    "content": "<s>",
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+    "normalized": true,
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+    "single_word": false
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+  "do_lower_case": true,
+  "eos_token": {
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+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
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+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": true,
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+  "never_split": null,
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+    "rstrip": false,
+    "single_word": false
+  },
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}