Update README.md

README.md

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----
-tags:
-- setfit
-- absa
-- sentence-transformers
-- text-classification
-- generated_from_setfit_trainer
-widget: []
-metrics:
-- accuracy
-pipeline_tag: text-classification
-library_name: setfit
-inference: false
----
-
-# SetFit Aspect Model
-
-This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). 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:** [Unknown](https://huggingface.co/unknown) -->
-- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
-- **spaCy Model:** en_core_web_trf
-- **SetFitABSA Aspect Model:** [SmartIU2/setfit-imdb-absa-action-v1.0-aspect](https://huggingface.co/SmartIU2/setfit-imdb-absa-action-v1.0-aspect)
-- **SetFitABSA Polarity Model:** [SmartIU2/setfit-imdb-absa-action-v1.0-polarity](https://huggingface.co/SmartIU2/setfit-imdb-absa-action-v1.0-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)
-
-## 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(
-    "SmartIU2/setfit-imdb-absa-action-v1.0-aspect",
-    "SmartIU2/setfit-imdb-absa-action-v1.0-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
-
-### Framework Versions
-- Python: 3.10.6
-- SetFit: 1.1.2
-- Sentence Transformers: 4.1.0
-- spaCy: 3.7.5
-- Transformers: 4.52.4
-- PyTorch: 2.7.0+cu128
-- 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.*
+---
+tags:
+- setfit
+- absa
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget: []
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: false
+language:
+- en
+base_model:
+- sentence-transformers/all-distilroberta-v1
+---
+
+## Usage
+
+This model was created with a [Setfit Fork](https://github.com/smartIU2/setfit) using a custom aspect extractor.
+
+It is intended to be used in conjunction with [IMDb_ABSA](https://github.com/smartIU2/imdb_absa) only.
+
+
+Default setfit model card below:
+
+
+# SetFit Aspect Model
+
+A [SetFit](https://github.com/huggingface/setfit) model can be used for Aspect Based Sentiment Analysis (ABSA). 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:** sentence-transformers/all-distilroberta-v1
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **spaCy Model:** en_core_web_trf
+- **SetFitABSA Aspect Model:** [SmartIU2/setfit-imdb-absa-action-v1.0-aspect](https://huggingface.co/SmartIU2/setfit-imdb-absa-action-v1.0-aspect)
+- **SetFitABSA Polarity Model:** [SmartIU2/setfit-imdb-absa-action-v1.0-polarity](https://huggingface.co/SmartIU2/setfit-imdb-absa-action-v1.0-polarity)
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 2 classes
+- **Language:** English
+
+### Original Setfit 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)
+
+<!--
+### 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
+
+### Framework Versions
+- Python: 3.10.6
+- SetFit: 1.1.2
+- Sentence Transformers: 4.1.0
+- spaCy: 3.7.5
+- Transformers: 4.52.4
+- PyTorch: 2.7.0+cu128
+- 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.*
 -->