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
|
Download README.md from ChunLok/CAM_sentiment_model: direct link, hf CLI and curl.
- Browser
- Download file 692 Bytes
-
https://huggingface.co/ChunLok/CAM_sentiment_model/resolve/main/README.md
- Command line
-
hf download hf://ChunLok/CAM_sentiment_model/README.md
-
curl -L -o README.md https://huggingface.co/ChunLok/CAM_sentiment_model/resolve/main/README.md
692 Bytes
metadata
license: apache-2.0
base_model: yiyanghkust/finbert-pretrain
inference: false
model-index:
- name: CAM_sentiment_model
results: []
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