
### Description
A Multi-label text classification model trained on a customer feedback data using DistilBert.
Possible labels are:
- Delivery (delivery status, time of arrival, etc.)
- Return (return confirmation, return label requests, etc.)
- Product (quality, complaint, etc.)
- Monetary (pending transactions, refund, etc.)

### Usage
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
  
  tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_mlc_v7_distil")
  
  model = AutoModelForSequenceClassification.from_pretrained("CouchCat/ma_mlc_v7_distil") 
```
{"language": "en", "license": "mit", "tags": ["multi-label"], "widget": [{"text": "I would like to return these pants and shoes"}]}
transformers pytorch distilbert text-classification multi-label en license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #multi-label #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 


### Description
A Multi-label text classification model trained on a customer feedback data using DistilBert.
Possible labels are:
- Delivery (delivery status, time of arrival, etc.)
- Return (return confirmation, return label requests, etc.)
- Product (quality, complaint, etc.)
- Monetary (pending transactions, refund, etc.)

### Usage

### Description
A Multi-label text classification model trained on a customer feedback data using DistilBert.
Possible labels are:
- Delivery (delivery status, time of arrival, etc.)
- Return (return confirmation, return label requests, etc.)
- Product (quality, complaint, etc.)
- Monetary (pending transactions, refund, etc.) ### Usage
TAGS
#transformers #pytorch #distilbert #text-classification #multi-label #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Description
A Multi-label text classification model trained on a customer feedback data using DistilBert.
Possible labels are:
- Delivery (delivery status, time of arrival, etc.)
- Return (return confirmation, return label requests, etc.)
- Product (quality, complaint, etc.)
- Monetary (pending transactions, refund, etc.) ### Usage
passage: TAGS
#transformers #pytorch #distilbert #text-classification #multi-label #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Description
A Multi-label text classification model trained on a customer feedback data using DistilBert.
Possible labels are:
- Delivery (delivery status, time of arrival, etc.)
- Return (return confirmation, return label requests, etc.)
- Product (quality, complaint, etc.)
- Monetary (pending transactions, refund, etc.)### Usage