Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use sumittyagi25/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sumittyagi25/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sumittyagi25/finetuning-sentiment-model-3000-samples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sumittyagi25/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("sumittyagi25/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sumittyagi25/finetuning-sentiment-model-3000-samples: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/sumittyagi25/finetuning-sentiment-model-3000-samples/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sumittyagi25/finetuning-sentiment-model-3000-samples/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sumittyagi25/finetuning-sentiment-model-3000-samples/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 9f58861d3121881dfca0da875a7c47113de8cdb33cae494b458f1e293f6656cb
- Size of remote file:
- 268 MB
- SHA256:
- 3d8ec54c77eaad496fb74f9eb1c849ec99d9cc93dee00255f56d2d4918e373e6
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