This model uses tokenizer that is from distilbert family. So the model may predict wrong entities for same word (different sub word). Use 'aggregation_strategy' to "max" when using transformer's pipeline. for example 'ashwin ::" ash" => Person win => Location

Out-of-Scope Use

May not work well for some long sentences.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForTokenClassification, AutoTokenizer
from transformers import pipeline

model = AutoModelForTokenClassification.from_pretrained("rv2307/electra-small-ner")
tokenizer = AutoTokenizer.from_pretrained("rv2307/electra-small-ner")

nlp = pipeline("ner",
              model=model,
              tokenizer=tokenizer,device="cpu",
              aggregation_strategy = "max")

Training Details

Training Procedure

This model is trained for 6 epoch in 3e-4 lr.

 [39168/39168 41:18, Epoch 6/6]
Step	Training Loss	Validation Loss	Precision	Recall	F1	Accuracy
10000	0.086300	0.088625	0.863476	0.876271	0.869827	0.972581
20000	0.059800	0.079611	0.894612	0.884521	0.889538	0.976563
30000	0.050400	0.074552	0.895812	0.902591	0.899188	0.978380

Evaluation

Validation loss is 0.07 for this model