
### Description
A Sentiment Analysis model trained on customer feedback data using DistilBert.
Possible sentiments are:
* negative
* neutral
* positive

### Usage

```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
  
  tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_sa_v7_distil")
  
  model = AutoModelForSequenceClassification.from_pretrained("CouchCat/ma_sa_v7_distil")
```
{"language": "en", "license": "mit", "tags": ["sentiment-analysis"], "widget": [{"text": "I am disappointed in the terrible quality of my dress"}]}
transformers pytorch distilbert text-classification sentiment-analysis en license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #sentiment-analysis #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 


### Description
A Sentiment Analysis model trained on customer feedback data using DistilBert.
Possible sentiments are:
* negative
* neutral
* positive

### Usage


### Description
A Sentiment Analysis model trained on customer feedback data using DistilBert.
Possible sentiments are:
* negative
* neutral
* positive ### Usage
TAGS
#transformers #pytorch #distilbert #text-classification #sentiment-analysis #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Description
A Sentiment Analysis model trained on customer feedback data using DistilBert.
Possible sentiments are:
* negative
* neutral
* positive ### Usage
passage: TAGS
#transformers #pytorch #distilbert #text-classification #sentiment-analysis #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Description
A Sentiment Analysis model trained on customer feedback data using DistilBert.
Possible sentiments are:
* negative
* neutral
* positive### Usage