Duplicate from joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect

Co-authored-by: Joshua Sundance Bailey <joshuasundance@users.noreply.huggingface.co>

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1_Pooling/config.json

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README.md

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+---
+library_name: setfit
+tags:
+- setfit
+- absa
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: camera:It has no camera but, I can always buy and install one easy.
+- text: Acer:Acer was no help and Garmin could not determine the problem(after spending
+    about 2 hours with me), so I returned it and purchased a Toshiba R700 that seems
+    even nicer and I was able to load all of my software with no problem.
+- text: memory:I've been impressed with the battery life and the performance for such
+    a small amount of memory.
+- text: speed:Yes, a Mac is much more money than the average laptop out there, but
+    there is no comparison in style, speed and just cool factor.
+- text: fiance:I got it back and my built-in webcam and built-in mic were shorting
+    out anytime I touched the lid, (mind you this was my means of communication with
+    my fiance who was deployed) but I suffered thru it and would constandly have to
+    reset the computer to be able to use my cam and mic anytime they went out.
+pipeline_tag: text-classification
+inference: false
+base_model: sentence-transformers/all-MiniLM-L6-v2
+model-index:
+- name: SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: tomaarsen/setfit-absa-semeval-laptops
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.8239700374531835
+      name: Accuracy
+---
+
+# 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:** [joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect](https://huggingface.co/joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect)
+- **SetFitABSA Polarity Model:** [joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity](https://huggingface.co/joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity)
+- **Maximum Sequence Length:** 256 tokens
+- **Number of Classes:** 2 classes
+<!-- - **Training Dataset:** [tomaarsen/setfit-absa-semeval-laptops](https://huggingface.co/datasets/tomaarsen/setfit-absa-semeval-laptops) -->
+<!-- - **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>'cord:I charge it at night and skip taking the cord with me because of the good battery life.'</li><li>'battery life:I charge it at night and skip taking the cord with me because of the good battery life.'</li><li>'service center:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'</li></ul> |
+| no aspect | <ul><li>'night:I charge it at night and skip taking the cord with me because of the good battery life.'</li><li>'skip:I charge it at night and skip taking the cord with me because of the good battery life.'</li><li>'exchange:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'</li></ul>              |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8240   |
+
+## 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(
+    "joshuasundance/setfit-absa-all-MiniLM-L6-v2-laptops-aspect",
+    "joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity",
+    spacy_model="en_core_web_sm",
+)
+# Run inference
+preds = model("This laptop meets every expectation and Windows 7 is great!")
+```
+
+<!--
+### 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   | 21.1510 | 42  |
+
+| Label     | Training Sample Count |
+|:----------|:----------------------|
+| no aspect | 119                   |
+| aspect    | 126                   |
+
+### Training Hyperparameters
+- batch_size: (128, 128)
+- 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
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch      | Step    | Training Loss | Validation Loss |
+|:----------:|:-------:|:-------------:|:---------------:|
+| 0.0042     | 1       | 0.3776        | -               |
+| 0.2110     | 50      | 0.2644        | 0.2622          |
+| 0.4219     | 100     | 0.2248        | 0.2437          |
+| **0.6329** | **150** | **0.0059**    | **0.2238**      |
+| 0.8439     | 200     | 0.0017        | 0.2326          |
+| 1.0549     | 250     | 0.0012        | 0.2382          |
+| 1.2658     | 300     | 0.0008        | 0.2455          |
+| 1.4768     | 350     | 0.0006        | 0.2328          |
+| 1.6878     | 400     | 0.0005        | 0.243           |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.11.7
+- SetFit: 1.0.3
+- Sentence Transformers: 2.3.0
+- spaCy: 3.7.2
+- Transformers: 4.37.2
+- PyTorch: 2.1.2+cu118
+- Datasets: 2.16.1
+- Tokenizers: 0.15.1
+
+## 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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+{
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+  "architectures": [
+    "BertModel"
+  ],
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+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.6.1",
+    "pytorch": "1.8.1"
+  }
+}

config_setfit.json

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

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modules.json

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+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
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sentence_bert_config.json

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+  "do_lower_case": false
+}

special_tokens_map.json

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tokenizer_config.json

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