# Error ValueError: too many values to unpack (expected 2) in model training **URL:** \ **Category:** 🤗Transformers\ **Created:** [November 9, 2024, 12:22am UTC](https://discuss.huggingface.co/t/error-valueerror-too-many-values-to-unpack-expected-2-in-model-training/116453 "2024-11-09T00:22:55Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![felipenandz](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/felipenandz/32/34240_2.png) [@felipenandz](https://discuss.huggingface.co/u/felipenandz)\ **Post date:** [November 9, 2024, 12:22am UTC](https://discuss.huggingface.co/t/error-valueerror-too-many-values-to-unpack-expected-2-in-model-training/116453/1 "2024-11-09T00:22:55Z")
Hello, I’m getting a persistente error while trying to train my model LayoutLM3. Let me describe I already encoded my dataset with this method here def process(list): ``` for data in list: labels = data["labels"] bboxes = data["bboxes"] tokens = data["tokens"] image = Image.open(datasetRootPath + data["file_name"]).convert("RGB") encoding = processor( image, text=tokens, boxes=bboxes, word_labels=labels, padding="max_length", truncation=True, return_tensors="pt" ) yield encoding ``` train\_dataset = list(process(dataset)) ```auto [{'input_ids': tensor([[ 0, 316, 17442, 2064, 337, 3938, 5553, 6560, 4006, 6623, 1696, 3842, 6304, 1168, 1322, 11903, 14502, 274, 3721, 3808, 717, 8748, 8041, 22556, 673, 274, 29092, 5945, 1723, 6, 654, 501, 910, 321, 2831, 35033, 2663, 5479, 541, 5339, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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'pixel_values': tensor([[[[1.0000, 1.0000, 1.0000, ..., 1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000, ..., 1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000, ..., 1.0000, 1.0000, 1.0000], ..., [0.9373, 0.9373, 0.9373, ..., 0.9529, 0.9529, 0.9608], [0.9137, 0.9137, 0.9137, ..., 0.9373, 0.9373, 0.9451], [0.8980, 0.8980, 0.8980, ..., 0.9216, 0.9294, 0.9294]], [[1.0000, 1.0000, 1.0000, ..., 1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000, ..., 1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000, ..., 1.0000, 1.0000, 1.0000], ..., [0.9373, 0.9373, 0.9373, ..., 0.9529, 0.9529, 0.9608], [0.9137, 0.9137, 0.9137, ..., 0.9373, 0.9373, 0.9451], [0.8980, 0.8980, 0.8980, ..., 0.9216, 0.9294, 0.9294]], [[1.0000, 1.0000, 1.0000, ..., 1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000, ..., 1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000, ..., 1.0000, 1.0000, 1.0000], ..., [0.9373, 0.9373, 0.9373, ..., 0.9529, 0.9529, 0.9608], [0.9137, 0.9137, 0.9137, ..., 0.9373, 0.9373, 0.9451], [0.8980, 0.8980, 0.8980, ..., 0.9216, 0.9294, 0.9294]]]])}] ``` but when I’m passing the train\_dataset to the trainer I’m facing the following error ```auto forward(self, input_ids, bbox, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, pixel_values, output_attentions, output_hidden_states, return_dict) 885 if input_ids is not None: 886 input_shape = input_ids.size() --> 887 batch_size, seq_length = input_shape 888 device = input_ids.device 889 elif inputs_embeds is not None: ValueError: too many values to unpack (expected 2) ``` Any clue about it ? ---
**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\ **Post date:** [November 9, 2024, 1:46am UTC](https://discuss.huggingface.co/t/error-valueerror-too-many-values-to-unpack-expected-2-in-model-training/116453/2 "2024-11-09T01:46:45Z")
Maybe this? > [@Batch\_size, seq\_length = input\_shape ValueError: too many values to unpack (expected 2) Transformer Sentence Similarity Classification](https://discuss.huggingface.co/t/batch-size-seq-length-input-shape-valueerror-too-many-values-to-unpack-expected-2-transformer-sentence-similarity-classification/76245): > > I encountering a problem when doing Sentence Similarity Classification Task Using Transformer. It happens when I was about to train my model, I load and process my dataset in this way, and this is what the training dataset element shape looks like: but when I train the model, it says: ValueError: Caught ValueError in replica 0 on device 0. Original Traceback (most recent call last): File “/home/fanshaoqi/anaconda3/envs/tf2/lib/python3.10/site-packages/torch/nn/parallel/parallel\_ap…