This model was developed by performing fine-tuning based on DistilBERT, with the goal of identifying Named Entity Recognition (NER) tags for each token present in a sentence.

The model was trained on a dataset of English-language tweets, optimizing it for understanding short, informal content typical of the Twitter platform. Through this fine-tuning, the model is able to identify named entities such as people, places, organizations, dates, and other types of structured information within unstructured text.

Downloads last month
2
Safetensors
Model size
65.2M params
Tensor type
F32
·
Inference Providers NEW
This model is not currently available via any of the supported third-party Inference Providers, and HF Inference API was unable to determine this model's library.

Model tree for Emma-Cap/Transformer

Finetuned
(233)
this model