Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Ved2001/sentiment-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ved2001/sentiment-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ved2001/sentiment-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ved2001/sentiment-model") model = AutoModelForSequenceClassification.from_pretrained("Ved2001/sentiment-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Ved2001/sentiment-model: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/Ved2001/sentiment-model/resolve/main/training_args.bin
- Command line
-
hf download hf://Ved2001/sentiment-model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ved2001/sentiment-model/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- 08ecfe34d0e6623a18abe143bd56d402788378807c53f559e3f0e112408be5e5
- Size of remote file:
- 5.2 kB
- SHA256:
- a0a353cd8e0b88db6dd53afe17ec37b83dad58c1891b98991b2e99a52f24d077
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