add small_model_training package including training config training_p…

…arameters.json and text_classification.py

src/small_model_training/text_classification.py

@@ -0,0 +1,84 @@
+import numpy as np
+from transformers import AutoTokenizer
+from transformers import DataCollatorWithPadding
+from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
+from datasets import load_dataset
+import json
+import evaluate
+
+def get_influential_subset(dataset):
+    # get parameters from dict
+    data = get_training_parameters()
+    small_model = data['small_model']
+    batch_size = data['batch_size']
+
+    tokenized_imdb = dataset.map(preprocess_function, batched=True)
+
+    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+    id2label = {0: "NEGATIVE", 1: "POSITIVE"}
+    label2id = {"NEGATIVE": 0, "POSITIVE": 1}
+
+    model = AutoModelForSequenceClassification.from_pretrained(
+        "distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
+    )
+
+    training_args = TrainingArguments(
+        output_dir="my_awesome_model",
+        learning_rate=2e-5,
+        per_device_train_batch_size=16,
+        per_device_eval_batch_size=16,
+        num_train_epochs=2,
+        weight_decay=0.01,
+        evaluation_strategy="epoch",
+        save_strategy="epoch",
+        load_best_model_at_end=True,
+        push_to_hub=False,
+    )
+
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_imdb["train"],
+        eval_dataset=tokenized_imdb["test"],
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics,
+    )
+
+    trainer.train()
+
+
+    # TO-DO: calculate influential dataset
+    inf_subset = dataset
+
+    # TO-DO: check for pre-processing
+    return inf_subset
+
+def get_training_parameters():
+
+    # open config file
+    f = open('training_parameters.json')
+
+    # return json object as dict
+    data = json.load(f)
+
+    # close file
+    f.close()
+    return data
+
+def preprocess_function(examples):
+    return tokenizer(examples["text"], truncation=True)
+
+def compute_metrics(eval_pred):
+    predictions, labels = eval_pred
+    predictions = np.argmax(predictions, axis=1)
+    return accuracy.compute(predictions=predictions, references=labels)
+
+accuracy = evaluate.load("accuracy")
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+
+# example dataset for debugging
+imdb = load_dataset("imdb")
+get_influential_subset(imdb)
+

src/small_model_training/training_parameters.json

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+{
+  "small_model": "bert-base-uncased",
+  "batch_size": 128
+}