# Data2Vec NLP Base

This model was converted from `fairseq`.  
The original weights can be found in https://dl.fbaipublicfiles.com/fairseq/data2vec/nlp_base.pt

Example usage:
```python
from transformers import RobertaTokenizer, Data2VecForSequenceClassification, Data2VecConfig
import torch

tokenizer = RobertaTokenizer.from_pretrained("roberta-large")
config = Data2VecConfig.from_pretrained("edugp/data2vec-nlp-base")
model = Data2VecForSequenceClassification.from_pretrained("edugp/data2vec-nlp-base", config=config)
# Fine-tune this model

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)

prediction_logits = outputs.logits
```

{"license": "apache-2.0", "model-index": [{"name": "data2vec-nlp-base", "results": []}]}
transformers pytorch data2vec fill-mask license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #data2vec #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

# Data2Vec NLP Base

This model was converted from 'fairseq'.  
The original weights can be found in URL

Example usage:


# Data2Vec NLP Base

This model was converted from 'fairseq'.  
The original weights can be found in URL

Example usage:
TAGS
#transformers #pytorch #data2vec #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Data2Vec NLP Base

This model was converted from 'fairseq'.  
The original weights can be found in URL

Example usage:
passage: TAGS
#transformers #pytorch #data2vec #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# Data2Vec NLP Base

This model was converted from 'fairseq'.  
The original weights can be found in URL

Example usage: