Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Jedida/tweet_sentiments_analysis_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jedida/tweet_sentiments_analysis_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jedida/tweet_sentiments_analysis_bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jedida/tweet_sentiments_analysis_bert") model = AutoModelForSequenceClassification.from_pretrained("Jedida/tweet_sentiments_analysis_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Jedida/tweet_sentiments_analysis_bert: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/Jedida/tweet_sentiments_analysis_bert/resolve/main/training_args.bin
- Command line
-
hf download hf://Jedida/tweet_sentiments_analysis_bert/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jedida/tweet_sentiments_analysis_bert/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 1a6bfba2ef7a786b1e2bd7c5e3bcd198e92c3e36be757845426c80ab7bfdcbcf
- Size of remote file:
- 4.03 kB
- SHA256:
- 32e29ecbce809c230ea2f8cd3f838f6a783e8c17772a12755944fd8db76c4c3b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.