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License: MIT License
Repo for "MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction" [ACL'2023]
License: MIT License
Hi, I'm having a bit of a problem trying to reproduce your code. The module optim_orders has not been found, it would be very helpful if you could provide instructions on getting the missing files.Thanks again for making your code public.
作者你好,我在运行bash scripts/run_unified.sh这条命令的时候代码就没反应了请问是什么情况,也没有报错,也没有运行gpu资源
您好,请问这里的def prefix_allowed_tokens_fn(self, task, data_name, source_ids, batch_id,input_ids):这一步是什么意思,是加入到T5的每一层中吗,还是只是embeding部分
作者您好,如果换成中文数据集训练的话,该如何调整网络架构
Hello author, I have read your paper, your experimental results are amazing, and I look forward to your published code.
I really want to test the performance of the model without having to fine-tune it for a specific task.
I tried to follow your code, something like this:
tokenizer = T5Tokenizer.from_pretrained(model_path)
tfm_model = MyT5ForConditionalGeneration.from_pretrained(model_path)
model = T5FineTuner(config, tfm_model, tokenizer)
text = "I will be back, I love the sushi badly!"
input_tokenized = tokenizer(text, return_tensors="pt")
summary_ids = model.model.generate(input_tokenized['input_ids'])
output = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(output)
# Output: [I will be back [e] love sushi [I love badly sushi
But I'm not 100% sure about the config file and I'm getting weird results.
If you could provide an example, it would be fantastic!
I would like to perform fine-tuning training on a custom dataset. Could you please let me know which parameters and files I need to modify? What does the 'const.py' file represent? Do I need to build it based on my custom dataset?
请问,怎么用自己训练好的模型进行一句话的预测
训练的时候卡在这一步,看不到训练进程
(mvp) root@autodl-container-a34a11a952-3cb61709:~/autodl-tmp/absa/multi-view-prompting# bash scripts/run_unified.sh
This IS expected if you are initializing MyT5ForConditionalGeneration from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
This IS NOT expected if you are initializing MyT5ForConditionalGeneration from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of MyT5ForConditionalGeneration were not initialized from the model checkpoint at t5-base and are newly initialized: ['encoder.embed_tokens.weight', 'lm_head.weight', 'decoder.embed_tokens.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
/root/miniconda3/envs/mvp/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:67: UserWarning: Starting from v1.9.0, tensorboardX
has been removed as a dependency of the pytorch_lightning
package, due to potential conflicts with other packages in the ML ecosystem. For this reason, logger=True
will use CSVLogger
as the default logger, unless the tensorboard
or tensorboardX
packages are found. Please pip install lightning[extra]
or one of them to enable TensorBoard support by default
warning_cache.warn(
You are using a CUDA device ('NVIDIA GeForce RTX 3090') that has Tensor Cores. To properly utilize them, you should set torch.set_float32_matmul_precision('medium' | 'high')
which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
| Name | Type | Params
222 M Trainable params
0 Non-trainable params
222 M Total params
891.614 Total estimated model params size (MB)
我尝试更换,发现准确率极低,损失值非常大
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