
### Description

A Named Entity Recognition model trained on a customer feedback data using DistilBert.
Possible labels are in BIO-notation. Performance of the PERS tag could be better because of low data samples:

- PROD: for certain products
- BRND: for brands
- PERS: people names

The following tags are simply in place to help better categorize the previous tags

- MATR: relating to materials, e.g. cloth, leather, seam, etc.
- TIME: time related entities
- MISC: any other entity that might skew the results

### Usage

```
from transformers import AutoTokenizer, AutoModelForTokenClassification

  tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_ner_v7_distil")

  model = AutoModelForTokenClassification.from_pretrained("CouchCat/ma_ner_v7_distil")
```

{"language": "en", "license": "mit", "tags": ["ner"], "widget": [{"text": "These shoes I recently bought from Tommy Hilfiger fit quite well. The shirt, however, has got a hole"}]}
transformers pytorch distilbert token-classification ner en license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #token-classification #ner #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 


### Description

A Named Entity Recognition model trained on a customer feedback data using DistilBert.
Possible labels are in BIO-notation. Performance of the PERS tag could be better because of low data samples:

- PROD: for certain products
- BRND: for brands
- PERS: people names

The following tags are simply in place to help better categorize the previous tags

- MATR: relating to materials, e.g. cloth, leather, seam, etc.
- TIME: time related entities
- MISC: any other entity that might skew the results

### Usage



### Description

A Named Entity Recognition model trained on a customer feedback data using DistilBert.
Possible labels are in BIO-notation. Performance of the PERS tag could be better because of low data samples:

- PROD: for certain products
- BRND: for brands
- PERS: people names

The following tags are simply in place to help better categorize the previous tags

- MATR: relating to materials, e.g. cloth, leather, seam, etc.
- TIME: time related entities
- MISC: any other entity that might skew the results ### Usage
TAGS
#transformers #pytorch #distilbert #token-classification #ner #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Description

A Named Entity Recognition model trained on a customer feedback data using DistilBert.
Possible labels are in BIO-notation. Performance of the PERS tag could be better because of low data samples:

- PROD: for certain products
- BRND: for brands
- PERS: people names

The following tags are simply in place to help better categorize the previous tags

- MATR: relating to materials, e.g. cloth, leather, seam, etc.
- TIME: time related entities
- MISC: any other entity that might skew the results ### Usage
passage: TAGS
#transformers #pytorch #distilbert #token-classification #ner #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Description

A Named Entity Recognition model trained on a customer feedback data using DistilBert.
Possible labels are in BIO-notation. Performance of the PERS tag could be better because of low data samples:

- PROD: for certain products
- BRND: for brands
- PERS: people names

The following tags are simply in place to help better categorize the previous tags

- MATR: relating to materials, e.g. cloth, leather, seam, etc.
- TIME: time related entities
- MISC: any other entity that might skew the results### Usage