Text Classification
Transformers
Safetensors
mistral
Generated from Trainer
text-embeddings-inference
Instructions to use contemmcm/bf09ed6dcff722bf2b60a7c4160452d5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use contemmcm/bf09ed6dcff722bf2b60a7c4160452d5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="contemmcm/bf09ed6dcff722bf2b60a7c4160452d5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("contemmcm/bf09ed6dcff722bf2b60a7c4160452d5") model = AutoModelForSequenceClassification.from_pretrained("contemmcm/bf09ed6dcff722bf2b60a7c4160452d5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70de076fa18896073beef6149fbfc8ac2a287bc510c1a6f422ca4b8538b7a952
- Size of remote file:
- 587 kB
- SHA256:
- 37f00374dea48658ee8f5d0f21895b9bc55cb0103939607c8185bfd1c6ca1f89
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