---
license: apache-2.0
datasets:
- conll2003
- ai4privacy/pii-masking-200k
language:
- en
metrics:
- accuracy
- f1
library_name: transformers
pipeline_tag: token-classification
---

## Model Details

### Model Description

This model is electra-small finetuned for NER prediction task. The model currently predicts three entities which are given below.
1. Location
2. Person
3. Organization


- **Developed by:**    
விபின் (Vipin)
- **Model type:** Google's electra small discriminator
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model [optional]:** Google's electra small discriminator

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://huggingface.co/google/electra-small-discriminator

## Uses

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