Instructions to use ChunLok/CAM_sentiment_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChunLok/CAM_sentiment_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ChunLok/CAM_sentiment_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ChunLok/CAM_sentiment_model") model = AutoModelForSequenceClassification.from_pretrained("ChunLok/CAM_sentiment_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ChunLok/CAM_sentiment_model: direct link, hf CLI and curl.
- Browser
- Download file 692 Bytes
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https://huggingface.co/ChunLok/CAM_sentiment_model/resolve/main/README.md
- Command line
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hf download hf://ChunLok/CAM_sentiment_model/README.md
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curl -L -o README.md https://huggingface.co/ChunLok/CAM_sentiment_model/resolve/main/README.md
692 Bytes
| license: apache-2.0 | |
| base_model: yiyanghkust/finbert-pretrain | |
| inference: false | |
| model-index: | |
| - name: CAM_sentiment_model | |
| results: [ | |
| ] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # CAM_sentiment_model | |
| More information needed | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ### Training hyperparameters | |
| More information needed | |
| ### Training results | |
| More information needed | |
| ### Framework versions | |
| - Transformers 4.36.2 | |
| - Pytorch 2.1.2+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.0 |