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您好，因为计算机资源不够，无法进行模型训练，请问能给一份训练好的模型数据吗？###

大佬～能求一份比赛数据集吗？原来的数据集已经在官网下架了，[email protected] 谢谢大佬

用多张Tesla T4（16G）的机器来跑，报显存不足，虽然有多张卡，但只用到其中一张的资源
把预训练的batch调小后能跑通，但后面模型训练还是会报显存不足，后来用simple跑下来了。
现在就想怎么能充分利用多张卡的性能，请教大佬，假如要支持多卡跑的话，需要改哪些地方？谢谢~

`2022-05-07 21:34:05.589168: I tensorflow/core/common_runtime/bfc_allocator.cc:824] Stats:
Limit: 14854298010
InUse: 14655244288
MaxInUse: 14662447616
NumAllocs: 8390
MaxAllocSize: 216728064

2022-05-07 21:34:05.589326: W tensorflow/core/common_runtime/bfc_allocator.cc:319] ****************************************************************************************************
2022-05-07 21:34:05.589344: W tensorflow/core/framework/op_kernel.cc:1502] OP_REQUIRES failed at cast_op.cc:109 : Resource exhausted: OOM when allocating tensor with shape[24,43,64,1024] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc

Using Gradient Accumulation with 3

Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1356, in _do_call
return fn(*args)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1341, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1429, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: 2 root error(s) found.
(0) Resource exhausted: OOM when allocating tensor with shape[43,21128,1024] and type half on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node gradients/generator_predictions/MatMul_grad/MatMul_1}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

 [[cond_1/LogicalAnd_1/_25229]]

Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

(1) Resource exhausted: OOM when allocating tensor with shape[43,21128,1024] and type half on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node gradients/generator_predictions/MatMul_grad/MatMul_1}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

0 successful operations.
0 derived errors ignored.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "run_pretraining.py", line 425, in
main()
File "run_pretraining.py", line 421, in main
args.model_name, args.data_dir, **hparams))
File "run_pretraining.py", line 384, in train_or_eval
max_steps=config.num_train_steps)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 367, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1158, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1192, in _train_model_default
saving_listeners)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1484, in _train_with_estimator_spec
_, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py", line 754, in run
run_metadata=run_metadata)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py", line 1252, in run
run_metadata=run_metadata)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py", line 1353, in run
raise six.reraise(*original_exc_info)
File "/usr/local/lib/python3.6/dist-packages/six.py", line 693, in reraise
raise value
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py", line 1338, in run
return self._sess.run(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py", line 1411, in run
run_metadata=run_metadata)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py", line 1169, in run
return self._sess.run(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 950, in run
run_metadata_ptr)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1173, in _run
feed_dict_tensor, options, run_metadata)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1350, in _do_run
run_metadata)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1370, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: 2 root error(s) found.
(0) Resource exhausted: OOM when allocating tensor with shape[43,21128,1024] and type half on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node gradients/generator_predictions/MatMul_grad/MatMul_1 (defined at /code/electra-pretrain/model/optimization.py:66) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

 [[cond_1/LogicalAnd_1/_25229]]

Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

(1) Resource exhausted: OOM when allocating tensor with shape[43,21128,1024] and type half on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node gradients/generator_predictions/MatMul_grad/MatMul_1 (defined at /code/electra-pretrain/model/optimization.py:66) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

0 successful operations.
0 derived errors ignored.
`

您好~请问一下我再运行您代码的时候
在code/assemble.py里
assert len(data[0)==len(weights)出现了AssertionError的错误，请问您了解解决方案吗，或者具体是哪里出的问题呢

大佬可以发一份.conll的完整数据吗，官网的数据看起来是乱码，邮箱是 [email protected]

大佬你好啊~我不太理解github主页的实验中，实验8：biaffine+bert+electra 为什么两个预训练模型，他们分别的作用是啥？

是把两个预训练模型融合成一个预训练模型的意思么？

另外electra pretain 是指用自己的数据重新预训练了一下吗？
感谢