Text Classification
Transformers
Safetensors
English
distilbert
document-classification
document-ai
pii-detection
redaction
text-embeddings-inference
Instructions to use FahrenheitResearch/FR-Docs-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FahrenheitResearch/FR-Docs-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FahrenheitResearch/FR-Docs-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FahrenheitResearch/FR-Docs-v1") model = AutoModelForSequenceClassification.from_pretrained("FahrenheitResearch/FR-Docs-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44d7f2994c6c1ab0a0933c02b80de7686331cdc4b3ee689f00a2ae839db7d839
- Size of remote file:
- 5.33 kB
- SHA256:
- ac5c43f4be9967ce521155112fba766b54001701cd26c7f8a7aa129e167e96bb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.