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 pytorch_model.bin from FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- f73590badab1ba6824bbc083cc5edebab63ce0f1b637eecf835f02271c17e955
- Size of remote file:
- 433 MB
- SHA256:
- b94422547fbdb5e7503f6aba57b0204f26596ebb9c21607c909dabbd546567ef
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.