wehub-resource-sync
99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import paddle
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import paddle.nn.functional as F
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from paddlenlp.utils.log import logger
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@paddle.no_grad()
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def evaluate(model, criterion, metric, data_loader):
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"""
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Given a dataset, it evaluates model and computes the metric.
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Args:
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model(obj:`paddle.nn.Layer`): A model to classify texts.
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criterion(obj:`paddle.nn.Layer`): It can compute the loss.
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metric(obj:`paddle.metric.Metric`): The evaluation metric.
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data_loader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches.
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"""
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model.eval()
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metric.reset()
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losses = []
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for batch in data_loader:
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labels = batch.pop("labels")
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logits = model(**batch)
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loss = criterion(logits, labels)
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probs = F.sigmoid(logits)
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losses.append(loss.numpy())
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metric.update(probs, labels)
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micro_f1_score, macro_f1_score = metric.accumulate()
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logger.info(
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"eval loss: %.5f, micro f1 score: %.5f, macro f1 score: %.5f"
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% (np.mean(losses), micro_f1_score, macro_f1_score)
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)
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model.train()
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metric.reset()
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return micro_f1_score, macro_f1_score
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def preprocess_function(examples, tokenizer, max_seq_length, label_nums, is_test=False):
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"""
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Builds model inputs from a sequence for sequence classification tasks
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by concatenating and adding special tokens.
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Args:
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examples(obj:`list[str]`): List of input data, containing text and label if it have label.
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tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer`
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which contains most of the methods. Users should refer to the superclass for more information regarding methods.
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max_seq_length(obj:`int`): The maximum total input sequence length after tokenization.
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Sequences longer than this will be truncated, sequences shorter will be padded.
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label_nums(obj:`int`): The number of the labels.
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Returns:
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result(obj:`dict`): The preprocessed data including input_ids, token_type_ids, labels.
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"""
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result = tokenizer(text=examples["sentence"], max_seq_len=max_seq_length)
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# One-Hot label
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if not is_test:
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result["labels"] = [float(1) if i in examples["label"] else float(0) for i in range(label_nums)]
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return result
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def read_local_dataset(path, label_list=None, is_test=False):
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"""
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Read dataset
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"""
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with open(path, "r", encoding="utf-8") as f:
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for line in f:
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if is_test:
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items = line.strip().split("\t")
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sentence = "".join(items)
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yield {"sentence": sentence}
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else:
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items = line.strip().split("\t")
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if len(items) == 0:
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continue
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elif len(items) == 1:
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sentence = items[0]
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labels = []
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else:
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sentence = "".join(items[:-1])
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label = items[-1]
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labels = [label_list[l] for l in label.split(",")]
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yield {"sentence": sentence, "label": labels}
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