Updated README.md

README.md

@@ -16,7 +16,26 @@ metrics:
 - accuracy
 model-index:
 - name: bert-tiny-ontonotes
-  results: []
+  results:
+    - task:
+        type: token-classification
+      metrics:
+        - type: accuracy
+          value: 0.9476
+          name: accuracy
+        - type: precision
+          value: 0.6817
+          name: precision
+        - type: accuracy
+          value: 0.7193
+          name: recall
+        - type: accuracy
+          value: 0.7
+          name: F1
+datasets:
+  - tner/ontonotes5
+library_name: transformers
+pipeline_tag: token-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -24,7 +43,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-tiny-ontonotes
 
-This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the tner/ontonotes5 dataset.
+This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the [tner/ontonotes5](https://huggingface.co/datasets/tner/ontonotes5) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.1917
 - Recall: 0.7193
@@ -32,20 +51,82 @@ It achieves the following results on the evaluation set:
 - F1: 0.7000
 - Accuracy: 0.9476
 
-## Model description
+## How to use the Model
+
+### Using pipeline
+
+```python
+from transformers import pipeline
+import torch
+
+# Initiate the pipeline
+device = 0 if torch.cuda.is_available() else 'cpu'
+ner = pipeline("token-classification", "arnabdhar/bert-tiny-ontonotes", device=device)
+
+# use the pipeline
+input_text = "My name is Clara and I live in Berkeley, California."
+results = ner(input_text)
+```
 
-More information needed
 
 ## Intended uses & limitations
 
-More information needed
+This model is fine-tuned for **Named Entity Recognition** task and you can use the model as it is or can use this model as a base model for further fine tuning on your custom dataset.
+
+The following entities were fine-tuned on:
+CARDINAL, DATE, PERSON, NORP, GPE, LAW, PERCENT, ORDINAL, MONEY, WORK_OF_ART, FAC, TIME, QUANTITY, PRODUCT, LANGUAGE, ORG, LOC, EVENT
+
 
 ## Training and evaluation data
 
-More information needed
+The dataset has 3 partitions, `train`, `validation` and `test`, all the 3 partitions were combined and then a 80:20 train-test split was made for finet uning process. The following `ID2LABEL` mapping was used.
+
+```json
+{
+    "0": "O",
+    "1": "B-CARDINAL",
+    "2": "B-DATE",
+    "3": "I-DATE",
+    "4": "B-PERSON",
+    "5": "I-PERSON",
+    "6": "B-NORP",
+    "7": "B-GPE",
+    "8": "I-GPE",
+    "9": "B-LAW",
+    "10": "I-LAW",
+    "11": "B-ORG",
+    "12": "I-ORG",
+    "13": "B-PERCENT",
+    "14": "I-PERCENT",
+    "15": "B-ORDINAL",
+    "16": "B-MONEY",
+    "17": "I-MONEY",
+    "18": "B-WORK_OF_ART",
+    "19": "I-WORK_OF_ART",
+    "20": "B-FAC",
+    "21": "B-TIME",
+    "22": "I-CARDINAL",
+    "23": "B-LOC",
+    "24": "B-QUANTITY",
+    "25": "I-QUANTITY",
+    "26": "I-NORP",
+    "27": "I-LOC",
+    "28": "B-PRODUCT",
+    "29": "I-TIME",
+    "30": "B-EVENT",
+    "31": "I-EVENT",
+    "32": "I-FAC",
+    "33": "B-LANGUAGE",
+    "34": "I-PRODUCT",
+    "35": "I-ORDINAL",
+    "36": "I-LANGUAGE"
+  }
+```
 
 ## Training procedure
 
+The model was finetuned on Google Colab with a __NVIDIA T4__ GPU with 15GB of VRAM. It took around 5 minutes to fine tune and evaluate the model with 6000 steps of total training steps. For more details, you can look into the [Weights & Biases](https://wandb.ai/2wb2ndur/NER-ontonotes/runs/d93imv8j/overview?workspace=user-2wb2ndur) log history.
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training: