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Commit 715c3621 authored by umlauf's avatar umlauf
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parents 935a2b71 5ce419a1
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......@@ -20,7 +20,19 @@ metric=evaluate.load("accuracy")
torch.cuda.empty_cache()
def evaluate_model(model, name,test_dataset, learning_rate, batch_size, imdb=False):
def evaluate_model(model, name,test_dataset, batch_size, imdb=False):
"""Evaluation for model. Iterates over test set and computes accuracy, f1, precision
and recall based on outputs of models and true labels.
Params:
model:mode -> model trained in train loop
name:str -> name of the model (for input format)
test_dataset: list of dictionaries -> test dataset
batch_size: int -> batch size for test dataset
imdb: bool -> whether or not imdb dataset is used
Returns: Accuracy, F1, Precision, Recall
"""
torch.cuda.empty_cache()
print("eval swp")
metric=evaluate.combine(["accuracy", "f1", "precision", "recall"])
......@@ -55,29 +67,28 @@ def evaluate_model(model, name,test_dataset, learning_rate, batch_size, imdb=Fal
'end_position': batch[3],
'labels': batch[4]}
outputs=model(**inputs)
print("len of outputs: ", len(outputs))
print("outputs 1: ", outputs[1].size())
print("labels size: ", batch[3].size())
prediction=torch.argmax(outputs[1], dim=-1)
print("prediciton sizes: ", prediction)
outputs=model(**inputs) #get logits
prediction=torch.argmax(outputs[1], dim=-1) #get predictions
if name[0] =="b":
if imdb==False:
metric.add_batch(predictions=prediction, references=batch[5])
else:
print("batch 3: ", batch[3])
metric.add_batch(predictions=prediction, references=batch[3])
if name[0] =="r":
metric.add_batch(predictions=prediction, references=batch[4])
res=metric.compute()
#print(f"learning rate {learning_rate}: ", res)
res=metric.compute()
return res
def compute_metrics(eval_pred):
print("eval salami")
""""Compute metrics function to apply predictions and references to
accuracy, f1, precision and recall, also used in salami train loop.
Params:
eval_pred: tuple ->(logits, labels)
Returns: Accuracy, F1, Precision, Recall"""
metric=evaluate.combine(["accuracy", "f1", "precision", "recall"])
logits, labels=eval_pred
predictions=np.argmax(logits, axis=-1)
......
......@@ -144,8 +144,8 @@ def train(model, name, imdb, seed,mixup,lambda_value, mixepoch, tmix, mixlayer,
torch.save(model, "saved_models/bert_baseline.pth")
#evaluate trained model
evaluation_test = evaluation.evaluate_model(model, name, test_dataset, learning_rate, test_batch_size, imdb)
evaluation_train = evaluation.evaluate_model(model, name, train_dataset, learning_rate, test_batch_size, imdb)
evaluation_test = evaluation.evaluate_model(model, name, test_dataset, test_batch_size, imdb)
evaluation_train = evaluation.evaluate_model(model, name, train_dataset, test_batch_size, imdb)
print("TEST: ", evaluation_test)
print("TRAIN: ", evaluation_train)
......
import argparse
import preprocess
import train
import evaluation
import models
import Code.preprocess
import Code.train
import Code.evaluation
import Code.models
import json
import copy
from transformers import BertTokenizer, RobertaTokenizer, BertModel, RobertaModel, RobertaPreTrainedModel, RobertaConfig, BertConfig, BertPreTrainedModel, PreTrainedModel, AutoConfig, AutoModel, AutoTokenizer
......
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