Updated README.md

README.md

@@ -13,7 +13,16 @@ metrics:
 - matthews_correlation
 model-index:
 - name: tiny-imdb
-  results: []
+  results:
+    - task:
+        type: text-classification
+      metrics:
+        - type: accuracy
+          value: 0.8944
+          name: accuracy
+        - type: accuracy
+          value: 0.7888
+          name: matthews_correlation 
 datasets:
 - imdb
 library_name: transformers
@@ -23,7 +32,7 @@ pipeline_tag: text-classification
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# tiny-imdb
+# bert-tiny-imdb
 
 This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the imdb dataset.
 It achieves the following results on the evaluation set:
@@ -33,17 +42,59 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This is the smallest version of BERT model suggested by Google in this [GitHub Repo](https://github.com/google-research/bert), this model contains 2 transformer layers and an a hidden layer output length of 128, ie __(L=2, H=128)__. There are a total 4.39 million paramteres in the model.
 
 ## Intended uses & limitations
 
-More information needed
+This model should be used for text classification tasks specifically on movie reviews or other such text data. Also you can use this model for other downstream tasks like:
 
-## Training and evaluation data
+- Sentiment Analysis
+- Named Entity Recognition or Token Classification
 
-More information needed
+This model should not be used for any tasks other than the above mentioned or any language other than English.
+
+### How to use the Model
+
+__Pytorch Model__
+
+```python
+from transformers import pipeline
+
+# load pipeline
+tiny_bert = pipeline("text-classification", "arnabdhar/tinybert-imdb")
+
+# perform inference
+results = pipeline(input_text, truncation=True, max_length=128)
+```
+
+__ONNX Model__
+
+```python
+from transformers import AutoTokenizer, pipeline
+from optimum.onnxruntime import ORTModelForSequenceClassification
+
+# load tokenizer & model
+model_name = "arnabdhar/tinybert-imdb"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+onnx_model = ORTModelForSequenceClassification.from_pretrained(model_name)
+
+# build pipeline
+tiny_bert_onnx = pipeline(
+  task = "text-classification",
+  tokenizer = tokenizer,
+  model = onnx_model
+)
+
+# perform inference
+results = tiny_bert_onnx(input_text, truncation=True, max_length=128)
+```
+
+## Training
+
+The model was finetuned on Google Colab using the NVIDIA V100 GPU and was trained for 9 epochs, it took around 12 minutes to finish finetuning.
+
+This model has been trained on the [imdb](https://huggingface.co/datasets/imdb) dataset which has 25,000 data text data for each training set and testing set, but I have combined both the partitions and then split the dataset in 80:20 ratio and used it for finetuning. This approach gave me a larger dataset to finetune the model.
 
-## Training procedure
 
 ### Training hyperparameters
 

all_results.json

@@ -1,9 +0,0 @@
-{
-    "epoch": 9.0,
-    "eval_accuracy": 0.8944,
-    "eval_loss": 0.27750933170318604,
-    "eval_matthews_correlation": 0.788794543433118,
-    "eval_runtime": 12.4798,
-    "eval_samples_per_second": 801.293,
-    "eval_steps_per_second": 2.564
-}

evaluate_results.json

@@ -1,9 +0,0 @@
-{
-    "epoch": 9.0,
-    "eval_accuracy": 0.8944,
-    "eval_loss": 0.27750933170318604,
-    "eval_matthews_correlation": 0.788794543433118,
-    "eval_runtime": 12.4798,
-    "eval_samples_per_second": 801.293,
-    "eval_steps_per_second": 2.564
-}