Instructions to use mkbashar/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mkbashar/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mkbashar/finetuning-sentiment-model-3000-samples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mkbashar/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("mkbashar/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mkbashar/finetuning-sentiment-model-3000-samples: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/mkbashar/finetuning-sentiment-model-3000-samples/resolve/main/training_args.bin
- Command line
-
hf download hf://mkbashar/finetuning-sentiment-model-3000-samples/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mkbashar/finetuning-sentiment-model-3000-samples/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- bb3279e58ff5e273c0e5c9f02a064e006282126d90bdfb222baa7b56bf623542
- Size of remote file:
- 4.6 kB
- SHA256:
- 7eef8564006f63c883430ae3158cb28a486873d2de3730c76859ed91bf996b31
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