from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

tokenizer = AutoTokenizer.from_pretrained("MMG/xlm-roberta-base-sa-spanish")

model = AutoModelForSequenceClassification.from_pretrained("MMG/xlm-roberta-base-sa-spanish")

pipe = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

pipe(["Este bar es horrible", "Este bar es buenísimo"]))

{"language": ["es"], "pipeline_tag": "text-classification"}
transformers pytorch safetensors xlm-roberta text-classification es autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #es #autotrain_compatible #endpoints_compatible #region-us 

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

tokenizer = AutoTokenizer.from_pretrained("MMG/xlm-roberta-base-sa-spanish")

model = AutoModelForSequenceClassification.from_pretrained("MMG/xlm-roberta-base-sa-spanish")

pipe = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

pipe(["Este bar es horrible", "Este bar es buenísimo"]))

TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #es #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #es #autotrain_compatible #endpoints_compatible #region-us 
