Instructions to use Pclanglais/transcript-text-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pclanglais/transcript-text-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Pclanglais/transcript-text-analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Pclanglais/transcript-text-analysis") model = AutoModelForSequenceClassification.from_pretrained("Pclanglais/transcript-text-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Pclanglais/transcript-text-analysis: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/Pclanglais/transcript-text-analysis/resolve/main/tokenizer.json
- Command line
-
hf download hf://Pclanglais/transcript-text-analysis/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Pclanglais/transcript-text-analysis/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 0cbe4b6be7901e8f6cb733179b7bb84f7cb7e02ee26a351c6018b173cf21c778
- Size of remote file:
- 16.3 MB
- SHA256:
- f42839ec04c3025630c31f069df13d750b5a3c969345d768ca8e0da9119600c7
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