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 pytorch_model.bin from RogerB/kin-sentiC: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/RogerB/kin-sentiC/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RogerB/kin-sentiC/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RogerB/kin-sentiC/resolve/main/pytorch_model.bin
2.24 GB
- Xet hash:
- a298015378f519ea662b5a9e0c0f8be21d01df1991b1594eaddbb781a0922762
- Size of remote file:
- 2.24 GB
- SHA256:
- 91033ff2200cba2e9268e5a86a46a04c8e76f6f794d0a8344d3f5b3e12e037e7
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