Instructions to use FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis") model = AutoModelForSequenceClassification.from_pretrained("FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis: direct link, hf CLI and curl.
- Browser
- Download file 867 MB
-
https://huggingface.co/FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis/resolve/main/optimizer.pt
- Command line
-
hf download hf://FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis/resolve/main/optimizer.pt
867 MB
- Xet hash:
- 0588a681a166c4a23853764b9ab46b7451260ef6ffab8633a9910e5589f94846
- Size of remote file:
- 867 MB
- SHA256:
- 036fbd814b094596b624efbeb4d605cfb5d2d92e7f4a21b582d692c2b20ce63f
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