rv2307
/

electra-small-ner

@@ -11,197 +11,67 @@ metrics:
 library_name: transformers
 pipeline_tag: token-classification
 ---
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 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:** 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 fine tuning is like sub - word token prediciton. So the model may predict wrong entities for same word (different sub word). Use 'aggregation_strategy' to "max" when using transformer's pipeline.
 ### 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
+model = AutoModelForTokenClassification.from_pretrained("rv2307/electra-small-ner")
+tokenizer = AutoTokenizer.from_pretrained("rv2307/electra-small-ner")
+nlp = pipeline("ner", model=model, tokenizer=tokenizer,device=device,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