Text Classification
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
roberta
autogenerated-modelcard
text-embeddings-inference
Instructions to use FacebookAI/roberta-large-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FacebookAI/roberta-large-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FacebookAI/roberta-large-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-large-mnli") model = AutoModelForSequenceClassification.from_pretrained("FacebookAI/roberta-large-mnli", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from FacebookAI/roberta-large-mnli: direct link, hf CLI and curl.
- Browser
- Download file 1.43 GB
-
https://huggingface.co/FacebookAI/roberta-large-mnli/resolve/main/tf_model.h5
- Command line
-
hf download hf://FacebookAI/roberta-large-mnli/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/FacebookAI/roberta-large-mnli/resolve/main/tf_model.h5
1.43 GB
- Xet hash:
- 216d1dfdad873074227fabea97959173c6087de51fa93534707794f9f0bd61f1
- Size of remote file:
- 1.43 GB
- SHA256:
- cf9b30ac6668afcdbb9ae8e77ab12c65a67edfb26cb4bd7b626acd372c7ae851
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.