Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Carick/roberta-base-wordnet_dataset_three-fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Carick/roberta-base-wordnet_dataset_three-fine-tuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Carick/roberta-base-wordnet_dataset_three-fine-tuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Carick/roberta-base-wordnet_dataset_three-fine-tuned") model = AutoModelForSequenceClassification.from_pretrained("Carick/roberta-base-wordnet_dataset_three-fine-tuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Carick/roberta-base-wordnet_dataset_three-fine-tuned: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Carick/roberta-base-wordnet_dataset_three-fine-tuned/resolve/main/training_args.bin
- Command line
-
hf download hf://Carick/roberta-base-wordnet_dataset_three-fine-tuned/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Carick/roberta-base-wordnet_dataset_three-fine-tuned/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- b826df015a2a3b8afd13773a91dd21b06c43c4871479341e6b841dc2793b7cd2
- Size of remote file:
- 5.37 kB
- SHA256:
- dcaba03d8f47bd8b5dcb3ee563e1762aaf91de087affeee658dc9f5c8726e94d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.