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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
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