Instructions to use CodeChamp95/bert_sentiment_financial_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodeChamp95/bert_sentiment_financial_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CodeChamp95/bert_sentiment_financial_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CodeChamp95/bert_sentiment_financial_model") model = AutoModelForSequenceClassification.from_pretrained("CodeChamp95/bert_sentiment_financial_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e454b488805ba547f493912f4676e595d9fd1eda768d5aaaab888bbb94cc8d8
- Size of remote file:
- 438 MB
- SHA256:
- 59be7544e2bd417926596976fa2fdcbee091738d361a90aa233ee4fff1e81659
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