Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use anth0nyhak1m/PFPC_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anth0nyhak1m/PFPC_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anth0nyhak1m/PFPC_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anth0nyhak1m/PFPC_model") model = AutoModelForSequenceClassification.from_pretrained("anth0nyhak1m/PFPC_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47f0f1df0bf5c587836bacd93fe18006fc4729e7368bf6a5482f2fed990d3297
- Size of remote file:
- 268 MB
- SHA256:
- 56cab6eeed6ea5f6c66a4b71e625cf2186f1264b3d1ef9602603b22a1dd4a204
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