
# distilbert-base-uncased-sentiment-sst2
This model will be able to identify positivity or negativity present in the sentence
## Dataset:
The Stanford Sentiment Treebank from GLUE

## Results:
```
***** eval metrics *****
  epoch                   =        3.0
  eval_accuracy           =     0.9094
  eval_loss               =     0.3514
  eval_runtime            = 0:00:03.60
  eval_samples            =        872
  eval_samples_per_second =    242.129
  eval_steps_per_second   =     30.266
```
{"language": "en", "license": "apache-2.0", "datasets": ["sst2"]}
transformers pytorch tf jax distilbert text-classification en dataset:sst2 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #jax #distilbert #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# distilbert-base-uncased-sentiment-sst2
This model will be able to identify positivity or negativity present in the sentence
## Dataset:
The Stanford Sentiment Treebank from GLUE

## Results:

# distilbert-base-uncased-sentiment-sst2
This model will be able to identify positivity or negativity present in the sentence ## Dataset:
The Stanford Sentiment Treebank from GLUE ## Results:
TAGS
#transformers #pytorch #tf #jax #distilbert #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # distilbert-base-uncased-sentiment-sst2
This model will be able to identify positivity or negativity present in the sentence ## Dataset:
The Stanford Sentiment Treebank from GLUE ## Results:
passage: TAGS
#transformers #pytorch #tf #jax #distilbert #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# distilbert-base-uncased-sentiment-sst2
This model will be able to identify positivity or negativity present in the sentence## Dataset:
The Stanford Sentiment Treebank from GLUE## Results: