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lvyufenglvyufeng
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ignore tokenization ut (#764)
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‎examples/classification/bert_emotect_finetune.ipynb

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"\n",
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"from mindnlp.engine import Trainer, Evaluator\n",
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"from mindnlp.engine.callbacks import CheckpointCallback, BestModelCallback\n",
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"from mindnlp.metrics import Accuracy\n",
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"from mindnlp.dataset.transforms import PadTransform"
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"from mindnlp.metrics import Accuracy"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--2023-11-16 23:33:04-- https://baidu-nlp.bj.bcebos.com/emotion_detection-dataset-1.0.0.tar.gz\n",
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"--2023-11-30 16:11:36-- https://baidu-nlp.bj.bcebos.com/emotion_detection-dataset-1.0.0.tar.gz\n",
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"Resolving baidu-nlp.bj.bcebos.com (baidu-nlp.bj.bcebos.com)... 36.110.192.178, 2409:8c04:1001:1002:0:ff:b001:368a\n",
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"Connecting to baidu-nlp.bj.bcebos.com (baidu-nlp.bj.bcebos.com)|36.110.192.178|:443... connected.\n",
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"HTTP request sent, awaiting response... 200 OK\n",
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"Length: 1710581 (1.6M) [application/x-gzip]\n",
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"Saving to: ‘emotion_detection.tar.gz’\n",
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"\n",
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"emotion_detection.t 100%[===================>] 1.63M 2.29MB/s in 0.7s \n",
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"emotion_detection.t 100%[===================>] 1.63M 6.36MB/s in 0.3s \n",
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"\n",
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"2023-11-16 23:33:05 (2.29 MB/s) - ‘emotion_detection.tar.gz’ saved [1710581/1710581]\n",
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"2023-11-30 16:11:37 (6.36 MB/s) - ‘emotion_detection.tar.gz’ saved [1710581/1710581]\n",
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"\n",
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"data/\n",
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"data/test.tsv\n",
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Epoch 0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 302/302 [00:41<00:00, 7.20it/s, loss=0.3326994]\n"
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"Epoch 0: 100%|███████████████████████████████████████| 302/302 [00:38<00:00, 7.85it/s, loss=0.35258844]\n"
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]
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Evaluate: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 34/34 [00:02<00:00, 14.57it/s]\n"
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"Evaluate: 100%|█████████████████████████████████████████████████████████| 34/34 [00:02<00:00, 15.66it/s]\n"
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]
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},
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{
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"name": "stdout",
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"text": [
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"Evaluate Score: {'Accuracy': 0.937962962962963}\n",
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"Evaluate Score: {'Accuracy': 0.9046296296296297}\n",
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"---------------Best Model: 'bert_emotect_best.ckpt' has been saved in epoch: 0.---------------\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Epoch 1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 302/302 [00:39<00:00, 7.64it/s, loss=0.1856075]\n"
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"Epoch 1: 100%|████████████████████████████████████████| 302/302 [00:35<00:00, 8.48it/s, loss=0.1903949]\n"
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]
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Evaluate: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 34/34 [00:02<00:00, 15.27it/s]\n"
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"Evaluate: 100%|█████████████████████████████████████████████████████████| 34/34 [00:01<00:00, 17.63it/s]\n"
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]
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{
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"name": "stdout",
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"text": [
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"Evaluate Score: {'Accuracy': 0.962037037037037}\n",
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"Evaluate Score: {'Accuracy': 0.9537037037037037}\n",
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"---------------Best Model: 'bert_emotect_best.ckpt' has been saved in epoch: 1.---------------\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Epoch 2: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 302/302 [00:38<00:00, 7.88it/s, loss=0.12356275]\n"
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"Epoch 2: 100%|███████████████████████████████████████| 302/302 [00:36<00:00, 8.39it/s, loss=0.13147199]\n"
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]
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Evaluate: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 34/34 [00:02<00:00, 16.79it/s]\n"
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"Evaluate: 100%|█████████████████████████████████████████████████████████| 34/34 [00:01<00:00, 17.65it/s]\n"
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{
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"text": [
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"Evaluate Score: {'Accuracy': 0.9861111111111112}\n",
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"Evaluate Score: {'Accuracy': 0.9601851851851851}\n",
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"---------------Best Model: 'bert_emotect_best.ckpt' has been saved in epoch: 2.---------------\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Epoch 3: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 302/302 [00:39<00:00, 7.73it/s, loss=0.08384681]\n"
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"Epoch 3: 100%|██████████████████████████████████████| 302/302 [00:35<00:00, 8.39it/s, loss=0.096015364]\n"
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"text": [
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"Evaluate: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 34/34 [00:02<00:00, 16.15it/s]\n"
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"Evaluate: 100%|█████████████████████████████████████████████████████████| 34/34 [00:01<00:00, 17.74it/s]\n"
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"Evaluate Score: {'Accuracy': 0.9888888888888889}\n",
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"Evaluate Score: {'Accuracy': 0.9907407407407407}\n",
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"---------------Best Model: 'bert_emotect_best.ckpt' has been saved in epoch: 3.---------------\n"
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]
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{
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"Epoch 4: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 302/302 [00:36<00:00, 8.27it/s, loss=0.05979367]\n"
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"Epoch 4: 100%|███████████████████████████████████████| 302/302 [00:36<00:00, 8.23it/s, loss=0.06472951]\n"
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"Evaluate: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 34/34 [00:02<00:00, 16.09it/s]\n"
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"Evaluate: 100%|█████████████████████████████████████████████████████████| 34/34 [00:01<00:00, 17.69it/s]\n"
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"Evaluate Score: {'Accuracy': 0.9879629629629629}\n",
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"Loading best model from 'checkpoint' with '['Accuracy']': [0.9888888888888889]...\n",
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"Evaluate Score: {'Accuracy': 0.9953703703703703}\n",
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"---------------Best Model: 'bert_emotect_best.ckpt' has been saved in epoch: 4.---------------\n",
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"Loading best model from 'checkpoint' with '['Accuracy']': [0.9953703703703703]...\n",
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"---------------The model is already load the best model from 'bert_emotect_best.ckpt'.---------------\n"
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}
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"Evaluate: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 33/33 [00:02<00:00, 16.08it/s]"
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"Evaluate: 100%|█████████████████████████████████████████████████████████| 33/33 [00:01<00:00, 17.01it/s]"
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"Evaluate Score: {'Accuracy': 0.9083011583011583}\n"
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"Evaluate Score: {'Accuracy': 0.8841698841698842}\n"
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{

‎pytest.ini

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[pytest]
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# 指定要忽略的文件或目录
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addopts = --ignore=tests/ut/transformers/generation/test_utils.py
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addopts = --ignore=tests/ut/transformers/generation/test_utils.py -k "not tokenization"
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# 设置测试用例的日志级别
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log_cli = true

‎tests/ut/transformers/models/roberta/test_modeling_roberta.py

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ROBERTA_TINY = "sshleifer/tiny-distilroberta-base"
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mindspore.set_context(pynative_synchronize=True)
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class RobertaModelTester:
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def __init__(
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self,

‎tests/ut/transformers/models/t5/test_modeling_t5.py

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config = self.model_tester.prepare_config_and_inputs()[0]
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self.model_tester.check_resize_embeddings_t5_v1_1(config)
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# @slow
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# def test_model_from_pretrained(self):
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# # for model_name in T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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# # for model_name in T5_PRETRAINED_MODEL_ARCHIVE_LIST:
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# model = T5Model.from_pretrained('t5-11b', from_pt=True)
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# self.assertIsNotNone(model)
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@slow
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def test_model_from_pretrained(self):
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for model_name in T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = T5Model.from_pretrained(model_name)
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self.assertIsNotNone(model)
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def test_generate_with_head_masking(self):
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attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]

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