Instructions to use CouchCat/ma_sa_v7_distil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CouchCat/ma_sa_v7_distil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CouchCat/ma_sa_v7_distil")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_sa_v7_distil") model = AutoModelForSequenceClassification.from_pretrained("CouchCat/ma_sa_v7_distil") - Notebooks
- Google Colab
- Kaggle
Add readme
Browse files
README.md
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---
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language: en
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license: mit
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tags:
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- sentiment-analysis
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widget:
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- text: "I am very disappointed with the quality"
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---
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### Description
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A Sentiment Analysis model trained on customer feedback data using DistilBert.
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Possible sentiments are:
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* negative
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* neutral
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* positive
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### Usage
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```
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_sa_v7_distil")
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model = AutoModelForSequenceClassification.from_pretrained("CouchCat/ma_sa_v7_distil")
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```
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