Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use singhshiva/robert_mnli_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use singhshiva/robert_mnli_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="singhshiva/robert_mnli_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("singhshiva/robert_mnli_3") model = AutoModelForSequenceClassification.from_pretrained("singhshiva/robert_mnli_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c8ed0506a2ee515893dcfbde81ac8b85d5bcb0581d6fbbf1da0b71b53622183
- Size of remote file:
- 4.92 kB
- SHA256:
- e555c02916345e32c50c31b8eb9b718296b4d72156e3f8549dabb32b81125d9a
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