Akirami
/

distillbert-uncased-ag-news

@@ -1,9 +1,26 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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@@ -17,73 +34,39 @@ tags: []
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ## How to Get Started with the Model
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@@ -92,7 +75,7 @@ Use the code below to get started with the model.
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 ## Environmental Impact
@@ -144,56 +117,7 @@ Use the code below to get started with the model.
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 ---
 library_name: transformers
+datasets:
+- fancyzhx/ag_news
+metrics:
+- accuracy
+model-index:
+- name: distillbert-uncased-ag-news
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: ag_news
+      type: ag_news
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9265
 ---
+# Akirami/distillbert-uncased-ag-news
 <!-- Provide a quick summary of what the model is/does. -->
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Developed by:** [Akirami](https://huggingface.co/Akirami)
+- **Model type:** DistillBert
+- **License:** MIT
+- **Finetuned from model [optional]:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased)
+### Model Sources
 <!-- Provide the basic links for the model. -->
+- **Repository:** [Akirami/distillbert-uncased-ag-news](https://huggingface.co/Akirami/distillbert-uncased-ag-news)
 ## How to Get Started with the Model
 Use the code below to get started with the model.
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+tokenizer = AutoTokenizer.from_pretrained("Akirami/distillbert-uncased-ag-news")
+model = AutoModelForSequenceClassification.from_pretrained("Akirami/distillbert-uncased-ag-news")
+```
 ## Training Details
 ### Training Data
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+[AG News Dataset](https://huggingface.co/datasets/fancyzhx/ag_news)
 ### Training Procedure
+The model has been trained through Knowledge Distillation, where the teacher model is [nateraw/bert-base-uncased-ag-news](https://huggingface.co/nateraw/bert-base-uncased-ag-news) and the student model is [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased)
 #### Preprocessing [optional]
 #### Training Hyperparameters
+- **Training regime:** Trained in fp16 format
 #### Speeds, Sizes, Times [optional]
 #### Testing Data
+The test portion of AG News data is used for testing
 #### Metrics
+Classification Report:
+| Class | Precision | Recall | F1-Score | Support |
+|-------|-----------|--------|----------|---------|
+| 0     | 0.95      | 0.92   | 0.94     | 1900    |
+| 1     | 0.98      | 0.98   | 0.98     | 1900    |
+| 2     | 0.90      | 0.88   | 0.89     | 1900    |
+| 3     | 0.88      | 0.92   | 0.90     | 1900    |
+| **Accuracy**  |       |        | **0.93** | **7600** |
+| **Macro Avg** | **0.93** | **0.93** | **0.93** | **7600** |
+| **Weighted Avg** | **0.93** | **0.93** | **0.93** | **7600** |
+Balanced Accuracy Score: 0.926578947368421
+Accuracy Score: 0.9265789473684211
 ## Environmental Impact
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+- **Hardware Type:** [T4 GPU]
+- **Hours used:** [25 Minutes]
+- **Cloud Provider:** [Google Colab]
+- **Carbon Emitted:** [0.01]