👑 Easy-to-use and powerful NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis etc.
>>> [InferBackend] Engine Created ...
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Traceback (most recent call last):
File "deploy/python/infer_gpu.py", line 93, in <module>
main()
File "deploy/python/infer_gpu.py", line 69, in main
outputs = predictor.predict(texts)
File "/home/yhao/PaddleNLP-develop/model_zoo/uie/deploy/python/uie_predictor.py", line 402, in predict
results = self._multi_stage_predict(input_data)
File "/home/yhao/PaddleNLP-develop/model_zoo/uie/deploy/python/uie_predictor.py", line 352, in _multi_stage_predict
result_list = self._single_stage_predict(examples)
File "/home/yhao/PaddleNLP-develop/model_zoo/uie/deploy/python/uie_predictor.py", line 176, in _single_stage_predict
start_prob, end_prob = self._infer(input_dict)
File "/home/yhao/PaddleNLP-develop/model_zoo/uie/deploy/python/uie_predictor.py", line 399, in _infer
return self.inference_backend.infer(data)
File "/home/yhao/PaddleNLP-develop/model_zoo/uie/deploy/python/uie_predictor.py", line 73, in infer
result = self.predictor.run(None, input_dict)
File "/home/yhao/anaconda3/envs/paddleNlp_recompute/lib/python3.8/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 200, in run
return self._sess.run(output_names, input_feed, run_options)
onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid Feed Input Name:pos_ids
软件环境
重复问题
错误描述
稳定复现步骤 & 代码
1、数据格式转换:python python doccano.py --doccano_file ./data/doccano_ext.json --splits 0.8 0.2 0 2、微调:python finetune.py --device cpu --logging_steps 10 --save_steps 100 --eval_steps 100 --seed 42 --model_name_or_path uie-base --output_dir ./checkpoint/model_best_cpu --train_path data/train.txt --dev_path data/dev.txt --max_seq_length 512 --per_device_eval_batch_size 16 --per_device_train_batch_size 16 --num_train_epochs 20 --learning_rate 1e-5 --label_names 'start_positions' 'end_positions' --do_train --do_eval --do_export --export_model_dir ./checkpoint/model_best_cpu --overwrite_output_dir --disable_tqdm True --metric_for_best_model eval_f1 --load_best_model_at_end True --save_total_limit 1 3、使用模型推理部署:python deploy/python/infer_gpu.py --model_path_prefix ${finetuned_model}/model --use_fp16 --device_id 0