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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-large-mnli") model = AutoModelForSequenceClassification.from_pretrained("FacebookAI/roberta-large-mnli") - Inference
- Notebooks
- Google Colab
- Kaggle
Adds the tokenizer configuration file
#8
by lysandre HF Staff - opened
The tokenizer configuration file is missing/incorrect and therefore leading to unforeseen errors after the migration of the canonical models.
Refer to the following issue for more information: transformers#29050
The current failing code is the following:
from transformers import AutoTokenizer
>>> previous_tokenizer = AutoTokenizer.from_pretrained("roberta-large-mnli")
>>> current_tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-large-mnli")
>>> print(previous_tokenizer.model_max_length, current_tokenizer.model_max_length)
1000000000000000019884624838656, 512
This is the result after the fix:
from transformers import AutoTokenizer
>>> previous_tokenizer = AutoTokenizer.from_pretrained("roberta-large-mnli")
>>> current_tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-large-mnli")
>>> print(previous_tokenizer.model_max_length, current_tokenizer.model_max_length)
512, 512
lysandre changed pull request status to open
lysandre changed pull request status to merged