Update README.md

README.md

@@ -1 +1,97 @@
-hello
+---
+language: "english"
+tags:
+- race
+- multiple choice
+license: "any valid license identifier"
+datasets:
+- RACE
+metrics:
+- accuracy
+---
+
+# Roberta Large Fine Tuned on RACE
+
+## Model description
+
+This model is a fine-tuned model of Roberta-large applied on RACE
+
+#### How to use
+
+```pythonimport datasets
+from transformers import RobertaTokenizer, RobertaForMultipleChoice
+tokenizer = RobertaTokenizer.from_pretrained('LIAMF-USP/roberta-large-finetuned-race')
+model = RobertaForMultipleChoice.from_pretrained("LIAMF-USP/roberta-large-finetuned-race")
+dataset = datasets.load_dataset(
+    "race",
+    "all",
+    split=["train", "validation", "test"],
+)training_examples = dataset[0]
+evaluation_examples = dataset[1]
+test_examples = dataset[2]example=training_examples[0] example_id = example["example_id"]question = example["question"]
+context = example["article"]
+options = example["options"]
+label_example = example["answer"]
+label_map = {label: i for i, label in enumerate(["A", "B", "C", "D"])}
+choices_inputs = []
+for ending_idx, (_, ending) in enumerate(zip(context, options)):
+    if question.find("_") != -1:
+        # fill in the banks questions
+        question_option = question.replace("_", ending)
+    else:
+        question_option = question + " " + ending
+    inputs = tokenizer(
+        context,
+        question_option,
+        add_special_tokens=True,
+        max_length=MAX_SEQ_LENGTH,
+        padding="max_length",
+        truncation=True,
+        return_overflowing_tokens=False,
+    )    
+label = label_map[label_example]
+input_ids = [x["input_ids"] for x in choices_inputs]
+attention_mask = (
+    [x["attention_mask"] for x in choices_inputs]
+     # as the senteces follow the same structure, just one of them is
+     # necessary to check
+    if "attention_mask" in choices_inputs[0]
+    else None
+)
+example_encoded = {
+    "example_id": example_id,
+    "input_ids": input_ids,
+    "attention_mask": attention_mask,
+    "label": label,
+}
+output = model(**example_encoded)
+```
+
+
+## Training data
+
+The initial model was [roberta large model](https://huggingface.co/roberta-large) which was then fine-tuned on [RACE dataset](https://www.cs.cmu.edu/~glai1/data/race/)
+
+## Training procedure
+
+It was necessary to preprocess the data with a method that is exemplified for a single instance in the _How to use_ section. The used hyperparameters were the following:
+
+| Hyperparameter | Value |
+|:----:|:----:|
+| adam_beta1                  | 0.9      |
+| adam_beta2                  | 0.98     |
+| adam_epsilon                | 1.000e-8 |
+| eval_batch_size             | 32       |
+| train_batch_size            | 1        |
+| fp16                        | True     |
+| gradient_accumulation_steps | 16       |
+| learning_rate               | 0.00001  |
+| warmup_steps                | 1000     |
+| max_length                  | 512      |
+
+## Eval results:
+| Dataset Acc | Eval | All Test |High School Test |Middle School Test |
+|:----:|:----:|:----:|:----:|:----:|
+|      | 95.2 | 84.9|83.5|88.0|
+
+**The model was trained with a Tesla V100-PCIE-16GB**