
* IMDBSentimentDistilBertModel:

- I have used IMDB movie review dataset to create custom model by using DistilBertForSequenceClassification.

from transformers import DistilBertForSequenceClassification, Trainer, TrainingArguments

model = DistilBertForSequenceClassification.from_pretrained('./imdbsentdistilbertmodel')



{"language": ["en"], "license": "apache-2.0", "tags": ["text Classification"], "widget": [{"text": "I like you. </s></s> I love you."}]}
transformers pytorch distilbert text-classification text Classification en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #text Classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


* IMDBSentimentDistilBertModel:

- I have used IMDB movie review dataset to create custom model by using DistilBertForSequenceClassification.

from transformers import DistilBertForSequenceClassification, Trainer, TrainingArguments

model = DistilBertForSequenceClassification.from_pretrained('./imdbsentdistilbertmodel')



TAGS
#transformers #pytorch #distilbert #text-classification #text Classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #distilbert #text-classification #text Classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
