aai520-group6
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distilbert-finetuned-uncased-squad_v2

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 ---
-language: en
-license: mit
-model-index:
-- name: distilbert-finetuned-uncased
-  results:
-  - task:
-      type: question-answering
-    dataset:
-      name: SQuAD v2
-      type: squad_v2
-    metrics:
-    - type: Exact
-      value: 23.347090036216628
-    - type: F1
-      value: 26.869992349988973
-    - type: Total
-      value: 11873
-    - type: Hasans Exact
-      value: 38.630229419703106
-    - type: Hasans F1
-      value: 45.686136837283904
-    - type: Hasans Total
-      value: 5928
-    - type: Noans Exact
-      value: 8.107653490328007
-    - type: Noans F1
-      value: 8.107653490328007
-    - type: Noans Total
-      value: 5945
-    - type: Best Exact
-      value: 50.11370336056599
-    - type: Best Exact Thresh
-      value: 0.0
-    - type: Best F1
-      value: 50.11370336056599
-    - type: Best F1 Thresh
-      value: 0.0
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
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-- **Language(s) (NLP):** en
-- **License:** mit
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
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-## Uses
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-### Direct Use
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
-## Model Examination [optional]
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-## Environmental Impact
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-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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-## Technical Specifications [optional]
-### Model Architecture and Objective
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-#### Hardware
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-## Model Card Contact
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 ---
+{}
 ---
+    ---
+    language: en
+    license: mit
+    ---
+    # distilbert-finetuned-uncased Model
+    This model is fine-tuned on SQuAD v2 for question answering tasks.
+    ## Training Procedure
+    - Number of Epochs: 4
+    - Learning Rate: 2e-05
+    - Batch Size: 128 (per device)
+    - Evaluation Strategy: Every 100 steps
+    - Save Strategy: Every 100 steps
+    - FP16 Training: Yes
+    ## Evaluation Results
+    - Exact Match: 23.347090036216628
+    - F1 Score: 26.869992349988973
+    - Total: 11873
+    - Has Answer Exact: 38.630229419703106
+    - Has Answer F1: 45.686136837283904
+    - Has Answer Total: 5928
+    - No Answer Exact: 8.107653490328007
+    - No Answer F1: 8.107653490328007
+    - No Answer Total: 5945
+    - Best Exact: 50.11370336056599
+    - Best Exact Threshold: 0.0
+    - Best F1: 50.11370336056599
+    - Best F1 Threshold: 0.0