Add SetFit ABSA model

1_Pooling/config.json

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+{
+  "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

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+---
+tags:
+- setfit
+- absa
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: notch:Can't complain about the display it's fantastic! You can tell the graphics
+    is top-notch.
+- text: lightweight:Wish that it is more lightweight. Its frustrating how bad the
+    design is.
+- text: RAM:Memory is adequate with plenty of RAM for doing a lot of tasks smoothly.
+- text: lie:The keyboard does not have backlight like what is advertised. Complete
+    lie
+- text: mousepad:Unfortunately, the trackpad doesnt perform well. I had issues with
+    the mousepad from the start.
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: false
+base_model: sentence-transformers/all-MiniLM-L6-v2
+---
+
+# SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **spaCy Model:** en_core_web_sm
+- **SetFitABSA Aspect Model:** [najwaa/absa-laptops-aspect-p2](https://huggingface.co/najwaa/absa-laptops-aspect-p2)
+- **SetFitABSA Polarity Model:** [setfit-absa-polarity](https://huggingface.co/setfit-absa-polarity)
+- **Maximum Sequence Length:** 256 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>'light weight:this computer is so light weight and easy to carry.'</li><li>'carry:this computer is so light weight and easy to carry.'</li><li>'lightweight:very lightweight.'</li></ul>                                                                                         |
+| no aspect | <ul><li>'computer:this computer is so light weight and easy to carry.'</li><li>"premium:The build quality feels premium but it's surprisingly heavy for daily commuting."</li><li>"commuting:The build quality feels premium but it's surprisingly heavy for daily commuting."</li></ul> |
+
+## 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(
+    "najwaa/absa-laptops-aspect-p2",
+    "setfit-absa-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   | 2   | 18.3046 | 52  |
+
+| Label     | Training Sample Count |
+|:----------|:----------------------|
+| no aspect | 341                   |
+| aspect    | 263                   |
+
+### Training Hyperparameters
+- batch_size: (32, 32)
+- num_epochs: (5, 5)
+- 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: 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0002 | 1    | 0.2288        | -               |
+| 0.0017 | 10   | -             | 0.3073          |
+| 0.0034 | 20   | -             | 0.3065          |
+| 0.0052 | 30   | -             | 0.3052          |
+| 0.0069 | 40   | -             | 0.3034          |
+| 0.0086 | 50   | 0.3393        | 0.3011          |
+| 0.0103 | 60   | -             | 0.2984          |
+| 0.0120 | 70   | -             | 0.2954          |
+| 0.0138 | 80   | -             | 0.2923          |
+| 0.0155 | 90   | -             | 0.2888          |
+| 0.0172 | 100  | 0.3016        | 0.2855          |
+| 0.0189 | 110  | -             | 0.2821          |
+| 0.0206 | 120  | -             | 0.2786          |
+| 0.0224 | 130  | -             | 0.2754          |
+| 0.0241 | 140  | -             | 0.2722          |
+| 0.0258 | 150  | 0.2875        | 0.2684          |
+| 0.0275 | 160  | -             | 0.2653          |
+| 0.0292 | 170  | -             | 0.2629          |
+| 0.0310 | 180  | -             | 0.2611          |
+| 0.0327 | 190  | -             | 0.2597          |
+| 0.0344 | 200  | 0.272         | 0.2588          |
+| 0.0361 | 210  | -             | 0.2585          |
+| 0.0378 | 220  | -             | 0.2584          |
+| 0.0396 | 230  | -             | 0.2584          |
+| 0.0413 | 240  | -             | 0.2584          |
+| 0.0430 | 250  | 0.2652        | 0.2585          |
+| 0.0447 | 260  | -             | 0.2581          |
+| 0.0464 | 270  | -             | 0.2580          |
+| 0.0482 | 280  | -             | 0.2580          |
+| 0.0499 | 290  | -             | 0.2580          |
+| 0.0516 | 300  | 0.2635        | 0.2578          |
+| 0.0533 | 310  | -             | 0.2578          |
+| 0.0550 | 320  | -             | 0.2575          |
+| 0.0567 | 330  | -             | 0.2572          |
+| 0.0585 | 340  | -             | 0.2573          |
+| 0.0602 | 350  | 0.2608        | 0.2572          |
+| 0.0619 | 360  | -             | 0.2576          |
+| 0.0636 | 370  | -             | 0.2576          |
+| 0.0653 | 380  | -             | 0.2579          |
+
+### Framework Versions
+- Python: 3.11.12
+- SetFit: 1.1.2
+- Sentence Transformers: 4.1.0
+- spaCy: 3.7.5
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Datasets: 3.6.0
+- Tokenizers: 0.21.1
+
+## 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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+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "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": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b4821e1c536f800d3bd35247973da0cf7b2562063090f832c0447c1416e7b17f
+size 90864192

model_head.pkl

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+oid sha256:0d2ef6be1cdfa64b2fe87a577fe53ead57d10c561148757fe7b593082243ef9b
+size 3919

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "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": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}