nner_as_parsing's Issues
Error regarding instantiating 'fastNLP.embeddings.static_embedding.StaticEmbedding'
raise ValueError(f"Cannot recognize {model_dir_or_name}.")
ValueError: Error instantiating 'fastNLP.embeddings.static_embedding.StaticEmbedding' : Cannot recognize /home/narsupalli-pg/BTP2/model4/nner_as_parsing/data/bio_nlp_vec/PubMed-shuffle-win-30.txt.
如何获取/data/bio_nlp_vec/PubMed-shuffle-win-30.txt文件
您好,目前我正在genia数据集上复现您的NNER模型,但是在复现过程中遇到了一些问题,我想问一下配置文件中涉及到的/data/bio_nlp_vec/PubMed-shuffle-win-30.txt文件该如何获得,目前我只能在网上找到PubMed-shuffle-win-30.bin和PubMed-shuffle-win-2.bin文件,但是好像不能用在该模型中,不知道您是否可以提供一下PubMed-shuffle-win-30.txt词向量文件呢?
what is ideal size of GPU, Batch_size for training ? I'm getting a CUDA out of memory error for genia dataset.
RuntimeError: CUDA out of memory. Tried to allocate 14.00 MiB (GPU 0; 44.40 GiB total capacity; 26.45 GiB already allocated; 10.81 MiB free; 27.44 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
I'm using the similar config file as ACE dataset. Please tell me what extra tweaks maybe needed.
What should be the choice of hyperparameters (for config file) for another dataset with 86 types of entities ? When I use the same config file as genia I get cuda out of memory error (using a 45gb GPU)
My dataset has about 86 types of labels and it is quite small and has only 11k lines and their Span tags. Can you tell how to choose hyperparameters used in config file ?? @LouChao98
why is this error coming ? Also is the function for multilabel_categorical_crossentropy correct ?
File "model4/nner_as_parsing/src/modules/loss/sjl_multilabel.py", line 8, in multilabel_categorical_crossentropy
loss = loss_fn(y_true, y_pred)
File "anaconda3/envs/model4/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "anaconda3/envs/model4/lib/python3.9/site-packages/torch/nn/modules/loss.py", line 720, in forward
return F.binary_cross_entropy_with_logits(input, target,
File "anaconda3/envs/model4/lib/python3.9/site-packages/torch/nn/functional.py", line 3160, in binary_cross_entropy_with_logits
raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
ValueError: Target size (torch.Size([13, 10, 51, 6])) must be the same as input size (torch.Size([13, 10, 1, 8]))
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