SaraPiscitelli
/

roberta-base-qa-v1

@@ -10,202 +10,86 @@ metrics:
 library_name: transformers
 pipeline_tag: question-answering
 ---
-# Model Card for Model ID
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-## Model Details
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 #### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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 ## Evaluation
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-### Info to format
 Evaluation Dataset:
 Dataset({
     features: ['id', 'title', 'context', 'question', 'answers'],
@@ -216,21 +100,22 @@ Max Tokens Length:
 Evaluation Metrics:
 {'exact': 66.00660066006601, 'f1': 78.28040573606134, 'total': 909, 'HasAns_exact': 66.00660066006601, 'HasAns_f1': 78.28040573606134, 'HasAns_total': 909, 'best_exact': 66.00660066006601, 'best_exact_thresh': 0.0, 'best_f1': 78.28040573606134, 'best_f1_thresh': 0.0}
-Train Dataset({
-    features: ['id', 'title', 'context', 'question', 'answers'],
-    num_rows: 8207
-})
-Eval dataset:
 Dataset({
     features: ['id', 'title', 'context', 'question', 'answers'],
-    num_rows: 637
 })
-Model: Roberta-base for question answering
-Dataset:
-squad = load_dataset("squad")
-squad['train'] = squad['train'].select(range(30000))
-squad['test'] = squad['validation']
-squad['validation'] = squad['validation'].select(range(2000))

 library_name: transformers
 pipeline_tag: question-answering
 ---
+# Model card for SaraPiscitelli/roberta-base-qa-v1
+This model is a **finetuned** model starting from the base transformer model  [roberta-base](https://huggingface.co/roberta-base).
+This model is finetuned on **extractive question answering** task using [squad dataset](https://huggingface.co/datasets/squad).
+You can access the training code [here](https://github.com/sarapiscitelli/nlp-tasks/blob/main/scripts/train/question_answering.py) and the evaluation code [here](https://github.com/sarapiscitelli/nlp-tasks/blob/main/scripts/evaluation/question_answering.py).
 ### Model Description
+- **Developed by:**  Sara Piscitelli
+- **Model type:** Transformer Encoder - RobertaBaseForQuestionAnswering (124.056.578 params)
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** [roberta-base](https://huggingface.co/roberta-base)
+### Model Sources
+- **training code:** [here](https://github.com/sarapiscitelli/nlp-tasks/blob/main/scripts/train/question_answering.py)
+- **evaluation code:** [here](https://github.com/sarapiscitelli/nlp-tasks/blob/main/scripts/evaluation/question_answering.py).
 ## Training Details
 ### Training Data
+Train Dataset({
+    features: ['id', 'title', 'context', 'question', 'answers'],
+    num_rows: 8207
+})
+Eval dataset:
+Dataset({
+    features: ['id', 'title', 'context', 'question', 'answers'],
+    num_rows: 637
+})
+Dataset:
+squad = load_dataset("squad")
+squad['train'] = squad['train'].select(range(30000))
+squad['test'] = squad['validation']
+squad['validation'] = squad['validation'].select(range(2000))
+### Training Procedure
+#### Preprocessing
+max-tokens-length = 512
 #### Training Hyperparameters
+- **Training regime:** fp32
+- **base_model_name_or_path:** roberta-base
+- **max_tokens_length:** 512
+- **weighted_loss** true
+- **training_arguments:** TrainingArguments(
+    output_dir=results_dir,
+    num_train_epochs=5,
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    gradient_accumulation_steps=1,
+    learning_rate=0.0001,
+    lr_scheduler_type="linear",
+    optim="adamw_torch",
+    eval_accumulation_steps=1,
+    evaluation_strategy="steps",
+    eval_steps=0.01,
+    save_strategy="steps",
+    save_steps=0.01,
+    logging_strategy="steps",
+    logging_steps=1,
+    report_to="tensorboard",
+    do_train=True,
+    do_eval=True,
+    max_grad_norm=0.3,
+    warmup_ratio=0.03,
+    group_by_length=True,
+    dataloader_drop_last=False,
+    fp16=False,
+    bf16=False
+)
 ## Evaluation
 Evaluation Dataset:
 Dataset({
     features: ['id', 'title', 'context', 'question', 'answers'],
 Evaluation Metrics:
 {'exact': 66.00660066006601, 'f1': 78.28040573606134, 'total': 909, 'HasAns_exact': 66.00660066006601, 'HasAns_f1': 78.28040573606134, 'HasAns_total': 909, 'best_exact': 66.00660066006601, 'best_exact_thresh': 0.0, 'best_f1': 78.28040573606134, 'best_f1_thresh': 0.0}
+### Testing Data, Factors & Metrics
+#### Testing Data
+squad = load_dataset("squad")
+squad['test'] = squad['validation']
 Dataset({
     features: ['id', 'title', 'context', 'question', 'answers'],
+    num_rows: 10570
 })
+#### Metrics
+metric_eval = evaluate.load("squad_v2")
+### Results
+{'exact': 66.00660066006601, 'f1': 78.28040573606134, 'total': 909, 'HasAns_exact': 66.00660066006601, 'HasAns_f1': 78.28040573606134, 'HasAns_total': 909, 'best_exact': 66.00660066006601, 'best_exact_thresh': 0.0, 'best_f1': 78.28040573606134, 'best_f1_thresh': 0.0}