Text Classification
Transformers
PyTorch
TensorFlow
JAX
English
bert
financial-sentiment-analysis
sentiment-analysis
Instructions to use Ziffirpetek/Text-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ziffirpetek/Text-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ziffirpetek/Text-Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ziffirpetek/Text-Classification") model = AutoModelForSequenceClassification.from_pretrained("Ziffirpetek/Text-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Ziffirpetek/Text-Classification: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Ziffirpetek/Text-Classification/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://Ziffirpetek/Text-Classification@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Ziffirpetek/Text-Classification/resolve/refs%2Fpr%2F1/flax_model.msgpack
438 MB
- Xet hash:
- 9d1a014c44a024a62b1970f0c10f6ee787a4cf6d11c35f5cecdebf0669c06ec6
- Size of remote file:
- 438 MB
- SHA256:
- 04c231ff252c4b5ed3e277120b1cc961b97be14d81825c51c44ba66b4ee8033e
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