Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use RogerB/kin-sentiC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RogerB/kin-sentiC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RogerB/kin-sentiC")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RogerB/kin-sentiC") model = AutoModelForSequenceClassification.from_pretrained("RogerB/kin-sentiC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from RogerB/kin-sentiC: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/RogerB/kin-sentiC/resolve/main/tokenizer.json
- Command line
-
hf download hf://RogerB/kin-sentiC/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/RogerB/kin-sentiC/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 4b375bdc25367c120c1c1882c5909ae3cbaf74354f709df7b97d3cd819fba395
- Size of remote file:
- 17.1 MB
- SHA256:
- 694c30f9e8975b58c473e5fd67f0b5b4d35daeab1e9694c3b8929df2a1e9195b
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