ValueError: You have to specify either input_ids or inputs_embeds

[longshared]

ubuntu16.04 adapter-transformers==1.1.1 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 465.19.01 Driver Version: 465.19.01 CUDA Version: 11.3 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 NVIDIA GeForce ... Off | 00000000:81:00.0 Off | N/A | | 41% 26C P8 20W / 250W | 0MiB / 11019MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+

when I run adapter_fintuning.py I got this error:

root@ubuntu:/home/project/xlm-t-main# python src/adapter_finetuning.py Some weights of the model checkpoint at cardiffnlp/twitter-xlm-roberta-base were not used when initializing XLMRobertaModelWithHeads: ['lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.decoder.bias']

• This IS expected if you are initializing XLMRobertaModelWithHeads 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 XLMRobertaModelWithHeads from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). Some weights of XLMRobertaModelWithHeads were not initialized from the model checkpoint at cardiffnlp/twitter-xlm-roberta-base and are newly initialized: ['roberta.pooler.dense.weight', 'roberta.pooler.dense.bias'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. 0%| | 0/200 [00:00<?, ?it/s]Traceback (most recent call last): File "src/adapter_finetuning.py", line 157, in trainer.train() File "/root/anaconda3/envs/train_cpu/lib/python3.7/site-packages/transformers/trainer.py", line 787, in train tr_loss += self.training_step(model, inputs) File "/root/anaconda3/envs/train_cpu/lib/python3.7/site-packages/transformers/trainer.py", line 1138, in training_step loss = self.compute_loss(model, inputs) File "/root/anaconda3/envs/train_cpu/lib/python3.7/site-packages/transformers/trainer.py", line 1162, in compute_loss outputs = model(inputs) File "/root/anaconda3/envs/train_cpu/lib/python3.7/site-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(*input, *kwargs) File "/root/anaconda3/envs/train_cpu/lib/python3.7/site-packages/transformers/modeling_roberta.py", line 805, in forward return_dict=return_dict, File "/root/anaconda3/envs/train_cpu/lib/python3.7/site-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(input, kwargs) File "/root/anaconda3/envs/train_cpu/lib/python3.7/site-packages/transformers/modeling_roberta.py", line 685, in forward raise ValueError("You have to specify either input_ids or inputs_embeds") ValueError: You have to specify either input_ids or inputs_embeds 0%| | 0/200 [00:00<?, ?it/s] (train_cpu) root@ubuntu:/home/project/xlm-t-main# python src/adapter_finetuning.py Some weights of the model checkpoint at cardiffnlp/twitter-xlm-roberta-base were not used when initializing XLMRobertaModelWithHeads: ['lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.decoder.bias']
• This IS expected if you are initializing XLMRobertaModelWithHeads 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 XLMRobertaModelWithHeads from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). Some weights of XLMRobertaModelWithHeads were not initialized from the model checkpoint at cardiffnlp/twitter-xlm-roberta-base and are newly initialized: ['roberta.pooler.dense.weight', 'roberta.pooler.dense.bias'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. 0%| | 0/200 [00:00<?, ?it/s]Traceback (most recent call last): File "src/adapter_finetuning.py", line 157, in trainer.train() File "/root/anaconda3/envs/train_cpu/lib/python3.7/site-packages/transformers/trainer.py", line 787, in train tr_loss += self.training_step(model, inputs) File "/root/anaconda3/envs/train_cpu/lib/python3.7/site-packages/transformers/trainer.py", line 1138, in training_step loss = self.compute_loss(model, inputs) File "/root/anaconda3/envs/train_cpu/lib/python3.7/site-packages/transformers/trainer.py", line 1162, in compute_loss outputs = model(inputs) File "/root/anaconda3/envs/train_cpu/lib/python3.7/site-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(*input, *kwargs) File "/root/anaconda3/envs/train_cpu/lib/python3.7/site-packages/transformers/modeling_roberta.py", line 805, in forward return_dict=return_dict, File "/root/anaconda3/envs/train_cpu/lib/python3.7/site-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(input, kwargs) File "/root/anaconda3/envs/train_cpu/lib/python3.7/site-packages/transformers/modeling_roberta.py", line 685, in forward raise ValueError("You have to specify either input_ids or inputs_embeds") ValueError: You have to specify either input_ids or inputs_embeds 0%| | 0/200 [00:00<?, ?it/s]

anybody can help?