Datasets:

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DanielPFlorian

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Transformers-Github-Issues

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[ { "id": 1314768611, "node_id": "MDU6TGFiZWwxMzE0NzY4NjEx", "url": "https://api.github.com/repos/huggingface/transformers/labels/wontfix", "name": "wontfix", "color": "ffffff", "default": true, "description": null } ]

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[ "We can't really help without seeing the code you are running. Some of the models do not support FP16 for instance, and we have no idea which model you are using.", "Oh, sorry. It's the example script ```examples/language_modeling/run_language_modeling.py``` but with modified data loader.\r\n\r\nfull code below:\r\n```\r\nimport logging\r\nimport math\r\nimport os\r\nimport glob\r\nimport datasets\r\nfrom dataclasses import dataclass, field\r\nfrom typing import Optional\r\n\r\nfrom datasets import list_datasets, load_dataset\r\n\r\nfrom transformers import (\r\n CONFIG_MAPPING,\r\n MODEL_WITH_LM_HEAD_MAPPING,\r\n AutoConfig,\r\n AutoModelWithLMHead,\r\n AutoTokenizer,\r\n DataCollatorForLanguageModeling,\r\n DataCollatorForPermutationLanguageModeling,\r\n HfArgumentParser,\r\n LineByLineTextDataset,\r\n PreTrainedTokenizer,\r\n TextDataset,\r\n Trainer,\r\n TrainingArguments,\r\n set_seed,\r\n)\r\n\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n\r\nMODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys())\r\nMODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)\r\n\r\n\r\n@dataclass\r\nclass ModelArguments:\r\n \"\"\"\r\n Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.\r\n \"\"\"\r\n\r\n model_name_or_path: Optional[str] = field(\r\n default=None,\r\n metadata={\r\n \"help\": \"The model checkpoint for weights initialization. Leave None if you want to train a model from scratch.\"\r\n },\r\n )\r\n model_type: Optional[str] = field(\r\n default=None,\r\n metadata={\"help\": \"If training from scratch, pass a model type from the list: \" + \", \".join(MODEL_TYPES)},\r\n )\r\n config_name: Optional[str] = field(\r\n default=None, metadata={\"help\": \"Pretrained config name or path if not the same as model_name\"}\r\n )\r\n tokenizer_name: Optional[str] = field(\r\n default=None, metadata={\"help\": \"Pretrained tokenizer name or path if not the same as model_name\"}\r\n )\r\n cache_dir: Optional[str] = field(\r\n default=None, metadata={\"help\": \"Where do you want to store the pretrained models downloaded from s3\"}\r\n )\r\n\r\n\r\n@dataclass\r\nclass DataTrainingArguments:\r\n \"\"\"\r\n Arguments pertaining to what data we are going to input our model for training and eval.\r\n \"\"\"\r\n\r\n train_data_file: Optional[str] = field(\r\n default=None, metadata={\"help\": \"The input training data file (a text file).\"}\r\n )\r\n eval_data_file: Optional[str] = field(\r\n default=None,\r\n metadata={\"help\": \"An optional input evaluation data file to evaluate the perplexity on (a text file).\"},\r\n )\r\n line_by_line: bool = field(\r\n default=False,\r\n metadata={\"help\": \"Whether distinct lines of text in the dataset are to be handled as distinct sequences.\"},\r\n )\r\n\r\n mlm: bool = field(\r\n default=False, metadata={\"help\": \"Train with masked-language modeling loss instead of language modeling.\"}\r\n )\r\n mlm_probability: float = field(\r\n default=0.15, metadata={\"help\": \"Ratio of tokens to mask for masked language modeling loss\"}\r\n )\r\n plm_probability: float = field(\r\n default=1 / 6,\r\n metadata={\r\n \"help\": \"Ratio of length of a span of masked tokens to surrounding context length for permutation language modeling.\"\r\n },\r\n )\r\n max_span_length: int = field(\r\n default=5, metadata={\"help\": \"Maximum length of a span of masked tokens for permutation language modeling.\"}\r\n )\r\n\r\n block_size: int = field(\r\n default=-1,\r\n metadata={\r\n \"help\": \"Optional input sequence length after tokenization.\"\r\n \"The training dataset will be truncated in block of this size for training.\"\r\n \"Default to the model max input length for single sentence inputs (take into account special tokens).\"\r\n },\r\n )\r\n overwrite_cache: bool = field(\r\n default=False, metadata={\"help\": \"Overwrite the cached training and evaluation sets\"}\r\n )\r\n arrow: bool = field(\r\n default=True,\r\n metadata={\r\n \"help\": \"Use Arrow-based HF NLP for optimization.\"\r\n },\r\n )\r\n\r\n\r\ndef get_dataset(\r\n args: DataTrainingArguments,\r\n tokenizer: PreTrainedTokenizer,\r\n evaluate: bool = False,\r\n cache_dir: Optional[str] = \"./cache\",\r\n):\r\n tokenizer.pad_token = \"<|endoftext|>\"\r\n tokenizer._pad_token = \"<|endoftext|>\"\r\n #tokenizer.pad_token_id = 50256\r\n file_path = args.eval_data_file if evaluate else args.train_data_file\r\n if True:\r\n dataset = datasets.load_from_disk(file_path)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n return dataset\r\n \r\n if False:\r\n dataset = load_dataset(\"text\", data_files=[file_path], split='train')\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n dataset.save_to_disk(file_path+'.arrow')\r\n return dataset\r\n \r\n if args.line_by_line:\r\n return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\n else:\r\n return TextDataset(\r\n tokenizer=tokenizer,\r\n file_path=file_path,\r\n block_size=args.block_size,\r\n overwrite_cache=args.overwrite_cache,\r\n cache_dir=cache_dir,\r\n )\r\n \"\"\"\r\n dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n return dataset\r\n \"\"\"\r\n\r\ndef main():\r\n # See all possible arguments in src/transformers/training_args.py\r\n # or by passing the --help flag to this script.\r\n # We now keep distinct sets of args, for a cleaner separation of concerns.\r\n\r\n parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))\r\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\r\n\r\n if data_args.eval_data_file is None and training_args.do_eval:\r\n raise ValueError(\r\n \"Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file \"\r\n \"or remove the --do_eval argument.\"\r\n )\r\n\r\n if (\r\n os.path.exists(training_args.output_dir)\r\n and os.listdir(training_args.output_dir)\r\n and training_args.do_train\r\n and not training_args.overwrite_output_dir\r\n ):\r\n raise ValueError(\r\n f\"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome.\"\r\n )\r\n\r\n # Setup logging\r\n logging.basicConfig(\r\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\r\n datefmt=\"%m/%d/%Y %H:%M:%S\",\r\n level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,\r\n )\r\n logger.warning(\r\n \"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s\",\r\n training_args.local_rank,\r\n training_args.device,\r\n training_args.n_gpu,\r\n bool(training_args.local_rank != -1),\r\n training_args.fp16,\r\n )\r\n logger.info(\"Training/evaluation parameters %s\", training_args)\r\n\r\n # Set seed\r\n set_seed(training_args.seed)\r\n\r\n # Load pretrained model and tokenizer\r\n #\r\n # Distributed training:\r\n # The .from_pretrained methods guarantee that only one local process can concurrently\r\n # download model & vocab.\r\n\r\n if model_args.config_name:\r\n config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)\r\n elif model_args.model_name_or_path:\r\n config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)\r\n else:\r\n config = CONFIG_MAPPING[model_args.model_type]()\r\n logger.warning(\"You are instantiating a new config instance from scratch.\")\r\n\r\n if model_args.tokenizer_name:\r\n tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n elif model_args.model_name_or_path:\r\n tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)\r\n else:\r\n raise ValueError(\r\n \"You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another script, save it,\"\r\n \"and load it from here, using --tokenizer_name\"\r\n )\r\n\r\n tokenizer.pad_token = \"<|endoftext|>\"\r\n tokenizer._pad_token = \"<|endoftext|>\"\r\n\r\n if model_args.model_name_or_path:\r\n model = AutoModelWithLMHead.from_pretrained(\r\n model_args.model_name_or_path,\r\n from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\r\n config=config,\r\n cache_dir=model_args.cache_dir,\r\n )\r\n else:\r\n logger.info(\"Training new model from scratch\")\r\n model = AutoModelWithLMHead.from_config(config)\r\n\r\n model.resize_token_embeddings(len(tokenizer))\r\n\r\n if config.model_type in [\"bert\", \"roberta\", \"distilbert\", \"camembert\"] and not data_args.mlm:\r\n raise ValueError(\r\n \"BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the\"\r\n \"--mlm flag (masked language modeling).\"\r\n )\r\n\r\n if data_args.block_size <= 0:\r\n data_args.block_size = tokenizer.max_len\r\n # Our input block size will be the max possible for the model\r\n else:\r\n data_args.block_size = min(data_args.block_size, tokenizer.max_len)\r\n\r\n # Get datasets\r\n\r\n train_dataset = (\r\n get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n )\r\n eval_dataset = (\r\n get_dataset(data_args, tokenizer=tokenizer, evaluate=True, cache_dir=model_args.cache_dir)\r\n if training_args.do_eval\r\n else None\r\n )\r\n if config.model_type == \"xlnet\":\r\n data_collator = DataCollatorForPermutationLanguageModeling(\r\n tokenizer=tokenizer,\r\n plm_probability=data_args.plm_probability,\r\n max_span_length=data_args.max_span_length,\r\n )\r\n else:\r\n data_collator = DataCollatorForLanguageModeling(\r\n tokenizer=tokenizer, mlm=data_args.mlm, mlm_probability=data_args.mlm_probability\r\n )\r\n\r\n # Initialize our Trainer\r\n trainer = Trainer(\r\n model=model,\r\n args=training_args,\r\n data_collator=data_collator,\r\n train_dataset=train_dataset,\r\n eval_dataset=eval_dataset,\r\n prediction_loss_only=True,\r\n )\r\n\r\n # Training\r\n if training_args.do_train:\r\n model_path = (\r\n model_args.model_name_or_path\r\n if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path)\r\n else None\r\n )\r\n trainer.train(model_path=model_path)\r\n trainer.save_model()\r\n # For convenience, we also re-save the tokenizer to the same directory,\r\n # so that you can share your model easily on huggingface.co/models =)\r\n if trainer.is_world_master():\r\n tokenizer.save_pretrained(training_args.output_dir)\r\n\r\n # Evaluation\r\n results = {}\r\n if training_args.do_eval:\r\n logger.info(\"*** Evaluate ***\")\r\n\r\n eval_output = trainer.evaluate()\r\n\r\n perplexity = math.exp(eval_output[\"eval_loss\"])\r\n result = {\"perplexity\": perplexity}\r\n\r\n output_eval_file = os.path.join(training_args.output_dir, \"eval_results_lm.txt\")\r\n if trainer.is_world_master():\r\n with open(output_eval_file, \"w\") as writer:\r\n logger.info(\"***** Eval results *****\")\r\n for key in sorted(result.keys()):\r\n logger.info(\" %s = %s\", key, str(result[key]))\r\n writer.write(\"%s = %s\\n\" % (key, str(result[key])))\r\n\r\n results.update(result)\r\n\r\n return results\r\n\r\n\r\ndef _mp_fn(index):\r\n # For xla_spawn (TPUs)\r\n main()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```", "Thanks, and which type of model do you use (e.g., what's the full command you launch)?", "```\r\npython3.7 examples/language-modeling/run_language_modeling.py --output_dir=kogpt1 --model_type=gpt2 --do_train --train_data_file=/home/ksjae/kogpt-2/data/NEWS_ARROW --overwrite_output_dir --per_device_train_batch_size=12 --per_device_eval_batch_size=8 --save_steps 10000 --num_train_epochs=1 --block_size 2048 --eval_steps 25000 --logging_steps=1000 --tokenizer_name kotok --model_name_or_path gpt2-medium --fp16\r\n```", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

[ { "id": 1314768611, "node_id": "MDU6TGFiZWwxMzE0NzY4NjEx", "url": "https://api.github.com/repos/huggingface/transformers/labels/wontfix", "name": "wontfix", "color": "ffffff", "default": true, "description": null } ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Hi,\r\ncould you assist in adding an example in tutorial for this, I followed the tutorial sample for summarization, Is the way I made my inputs (\"Question: Question sequence \") correct? thanks \r\nhere is the tutorial sample: \r\n```\r\n>>> input_ids = tokenizer(\"summarize: studies have shown that owning a dog is good for you \", return_tensors=\"pt\").input_ids # Batch size 1\r\n>>> outputs = model.generate(input_ids)\r\n```", "solved when I load the config from a pretrained model, maybe this helps to add the extra info needed as the default setting :) " ]

[ { "id": 1843244711, "node_id": "MDU6TGFiZWwxODQzMjQ0NzEx", "url": "https://api.github.com/repos/huggingface/transformers/labels/New%20model", "name": "New model", "color": "fbca04", "default": false, "description": "" } ]

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[ "wrongly posted. I delete it." ]

[ { "id": 1314768611, "node_id": "MDU6TGFiZWwxMzE0NzY4NjEx", "url": "https://api.github.com/repos/huggingface/transformers/labels/wontfix", "name": "wontfix", "color": "ffffff", "default": true, "description": null } ]

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[ "Hi @TheoRenLi, this question should go on https://discuss.huggingface.co\r\n\r\nWe keep the issues of the repo for bug and features request (with clear descriptions).", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "the same issue, is there any workaround?", "same issue, I think there is a bug in PreTrainedTokenizer class\r\n```\r\n def get_input_ids(text):\r\n print(text)\r\n if isinstance(text, str):\r\n tokens = self.tokenize(text, **kwargs)\r\n return self.convert_tokens_to_ids(tokens)\r\n elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):\r\n if is_split_into_words:\r\n tokens = list(\r\n itertools.chain(*(self.tokenize(t, is_split_into_words=True, **kwargs) for t in text))\r\n )\r\n return self.convert_tokens_to_ids(tokens)\r\n else:\r\n return self.convert_tokens_to_ids(text)\r\n```\r\nin `if is_split_into_words` case (where the input is pretokenized words), the tokenizer should directly return ids.", "Hello! I think all of the confusion here may be because you're expecting `is_split_into_words` to understand that the text was already pre-tokenized. This is not the case, it means that the string was split into words (not tokens), i.e., split on spaces.\r\n\r\n@HenryPaik1, in your example, your list of words is the following:\r\n```py\r\n['the', 'latest', 'investigation', 'was', 'authorized', 'after', 'the', 'supreme', 'court', 'in', '2007', 'found', 'dc', '##c', 'and', 'its', 'founder', ',', 'jim', 'fl', '##avi', '##n', ',', 'guilty', 'of', 'selling', 'dc', '##c', \"'\", 's', '(', 'euro', ')', '106', 'million', '(', 'then', '$', '130', 'million', ')', 'stake', 'in', 'f', '##y', '##ffe', '##s', 'after', 'fl', '##avi', '##n', '-', '-', 'also', 'a', 'f', '##y', '##ffe', '##s', 'director', 'at', 'the', 'time', '-', '-', 'received', 'inside', 'information', 'about', 'bad', 'f', '##y', '##ffe', '##s', 'news', 'in', 'the', 'pipeline', '.']\r\n```\r\nSome of these strings are tokens, but not words. Running the encoding method on it once again means that you're re-tokenizing some of these tokens.\r\n\r\nYou can see it is the case, as the following token:\r\n```py\r\n [..., '##c', ...]\r\n```\r\nbecame:\r\n```py\r\n[..., '#', '#', 'c', ...]\r\n```\r\n\r\nI think in your case you're looking for the method `convert_tokens_to_ids`: your sequence is already tokenized, you only need the IDs. If you're looking to use `encode_plus` because you need padding/trunc/conversion to tensors, etc., then you can simply use it without specifying that the sequence is separated into words. Please be aware that the following code only works on python tokenizers, i.e., slow tokenizers.\r\n\r\n```py\r\nfrom transformers import BertTokenizer\r\n\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")\r\n\r\nsent = \"the latest investigation was authorized after the supreme court in 2007 found dcc and its founder , jim flavin , guilty of selling dcc 's ( euro ) 106 million ( then $ 130 million ) stake in fyffes after flavin -- also a fyffes director at the time -- received inside information about bad fyffes news in the pipeline .\"\r\n\r\nencoded_dict = tokenizer.encode_plus(\r\n sent, # Sentence to encode.\r\n add_special_tokens = False, # Add '[CLS]' and '[SEP]'\r\n max_length = 314, # Pad & truncate all sentences.\r\n padding = 'max_length',\r\n return_attention_mask = True, # Construct attn. masks.\r\n return_tensors = 'pt',\r\n truncation=False,\r\n is_split_into_words=False)\r\ninput_ids = encoded_dict['input_ids']\r\ntokenized = tokenizer.convert_ids_to_tokens([i.item() for i in input_ids.squeeze() if i > 1])\r\nprint(len(tokenized))\r\n#80 \r\n\r\n###### tokenizing pretokenized tokens as list\r\nencoded_dict = tokenizer.encode_plus(\r\n tokenized, # Sentence to encode.\r\n add_special_tokens = False, # Add '[CLS]' and '[SEP]'\r\n max_length = 314, # Pad & truncate all sentences.\r\n padding = 'max_length',\r\n return_attention_mask = True, # Construct attn. masks.\r\n return_tensors = 'pt',\r\n truncation=False,\r\n )\r\n\r\ninput_ids = encoded_dict['input_ids']\r\ntokenized = tokenizer.convert_ids_to_tokens([i.item() for i in input_ids.squeeze() if i > 1])\r\nprint(len(tokenized))\r\n# 80\r\n```", "@LysandreJik Thanks for your explanation. Yes, I want to use `encode_plus` for padding/trunc. It looks I thought the argument, `is_split_into_words`, the other way around. `is_split_into_words=True` seems for the \"not tokenized sentence.\" \r\nAnd if I understand correctly, you mean the part below is executed by python:\r\n```\r\ndef get_input_ids(text):\r\n if isinstance(text, str):\r\n tokens = self.tokenize(text, **kwargs)\r\n return self.convert_tokens_to_ids(tokens)\r\n elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):\r\n if is_split_into_words:\r\n ####### this part ########\r\n tokens = list(\r\n itertools.chain(*(self.tokenize(t, is_split_into_words=True, **kwargs) for t in text))\r\n )\r\n ####### this part ########\r\n return self.convert_tokens_to_ids(tokens)\r\n else:\r\n return self.convert_tokens_to_ids(text)\r\n```", "The part you've highlighted is performing tokenization on each individual word (not token!). You can see here that if it was already tokenized, then applying a second tokenization would be incorrect.", "@LysandreJik Understood, Thanks. I close the issue.", "I think the tokenizer should support a new kwarg such as:\r\n` is_already_tokens=False/True`" ]

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[ "Can you give all the information you can about your environment and pip list and I’m pinging @lhoestq.\r\n\r\nIf you can manage to reproduce the error in a google colab or shareable environment that would be the top for debugging.", "@VictorSanh got a similar issue once. Did you install transformers using `pip install -e .` ?", "> Can you give all the information you can about your environment and pip list and I’m pinging @lhoestq.\r\n> \r\n> If you can manage to reproduce the error in a google colab or shareable environment that would be the top for debugging.\r\n\r\n@thomwolf Please have a look at the [colab](https://colab.research.google.com/drive/1BlQF0-JYBVNsZXuIQsyVSGRQzsQzp0pl?usp=sharing). It is also reproducing the same error as before.", "> @VictorSanh got a similar issue once. Did you install transformers using `pip install -e .` ?\r\n\r\n@lhoestq Yes, I have installed it from source using:\r\n```\r\ngit clone https://github.com/huggingface/transformers.git\r\ncd transformers\r\npip install -e .\r\n```\r\n\r\nI also tried installing as suggested here in [examples](https://github.com/huggingface/transformers/tree/master/examples) as:\r\n\r\n```\r\ngit clone https://github.com/huggingface/transformers\r\ncd transformers\r\npip install .\r\npip install -r ./examples/requirements.txt\r\n```", "I am trying to train roberta model from scratch using run_mlm.py file. But, facing the same issue.\r\n\r\n> Didn't find file ./model_output/tokenizer.json. We won't load it.\r\n> Didn't find file ./model_output/added_tokens.json. We won't load it.\r\n> Didn't find file ./model_output/special_tokens_map.json. We won't load it.\r\n> Didn't find file ./model_output/tokenizer_config.json. We won't load it.\r\n> loading file ./model_output/vocab.json\r\n> loading file ./model_output/merges.txt\r\n> loading file None\r\n> loading file None\r\n> loading file None\r\n> loading file None\r\n> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Defaultto no truncation.\r\n> Traceback (most recent call last):\r\n> File \"transformers/examples/language-modeling/run_mlm.py\", line 310, in <module>\r\n> main()\r\n> File \"transformers/examples/language-modeling/run_mlm.py\", line 259, in main\r\n> load_from_cache_file=not data_args.overwrite_cache,\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 300, in map\r\n> for k, dataset in self.items()\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 300, in <dictcomp>\r\n> for k, dataset in self.items()\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1256, in map\r\n> update_data=update_data,\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 156, in wrapper\r\n> out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/datasets/fingerprint.py\", line 158, in wrapper\r\n> self._fingerprint, transform, kwargs_for_fingerprint\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/datasets/fingerprint.py\", line 105, in update_fingerprint\r\n> hasher.update(transform_args[key])\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/datasets/fingerprint.py\", line 57, in update\r\n> self.m.update(self.hash(value).encode(\"utf-8\"))\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/datasets/fingerprint.py\", line 53, in hash\r\n> return cls.hash_default(value)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/datasets/fingerprint.py\", line 46, in hash_default\r\n> return cls.hash_bytes(dumps(value))\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/datasets/utils/py_utils.py\", line 367, in dumps\r\n> dump(obj, file)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/datasets/utils/py_utils.py\", line 339, in dump\r\n> Pickler(file, recurse=True).dump(obj)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/dill/_dill.py\", line 446, in dump\r\n> StockPickler.dump(self, obj)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 409, in dump\r\n> self.save(obj)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 476, in save\r\n> f(self, obj) # Call unbound method with explicit self\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/dill/_dill.py\", line 1438, in save_function\r\n> obj.__dict__, fkwdefaults), obj=obj)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 610, in save_reduce\r\n> save(args)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 476, in save\r\n> f(self, obj) # Call unbound method with explicit self\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 751, in save_tuple\r\n> save(element)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 476, in save\r\n> f(self, obj) # Call unbound method with explicit self\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 736, in save_tuple\r\n> save(element)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 476, in save\r\n> f(self, obj) # Call unbound method with explicit self\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/dill/_dill.py\", line 1170, in save_cell\r\n> pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 610, in save_reduce\r\n> save(args)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 476, in save\r\n> f(self, obj) # Call unbound method with explicit self\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 736, in save_tuple\r\n> save(element)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 521, in save\r\n> self.save_reduce(obj=obj, *rv)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 605, in save_reduce\r\n> save(cls)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 476, in save\r\n> f(self, obj) # Call unbound method with explicit self\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/dill/_dill.py\", line 1365, in save_type\r\n> obj.__bases__, _dict), obj=obj)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 610, in save_reduce\r\n> save(args)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 476, in save\r\n> f(self, obj) # Call unbound method with explicit self\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 751, in save_tuple\r\n> save(element)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 476, in save\r\n> f(self, obj) # Call unbound method with explicit self\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/dill/_dill.py\", line 933, in save_module_dict\r\n> StockPickler.save_dict(pickler, obj)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 821, in save_dict\r\n> self._batch_setitems(obj.items())\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 847, in _batch_setitems\r\n> save(v)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 476, in save\r\n> f(self, obj) # Call unbound method with explicit self\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/site-packages/dill/_dill.py\", line 933, in save_module_dict\r\n> StockPickler.save_dict(pickler, obj)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 821, in save_dict\r\n> self._batch_setitems(obj.items())\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 847, in _batch_setitems\r\n> save(v)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 507, in save\r\n> self.save_global(obj, rv)\r\n> File \"/anaconda/envs/azureml_py36/lib/python3.6/pickle.py\", line 927, in save_global\r\n> (obj, module_name, name))\r\n> _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union", "I have the same problem, how to fix it?", "\r\n\r\n\r\n> > @VictorSanh got a similar issue once. Did you install transformers using `pip install -e .` ?\r\n> \r\n> @lhoestq Yes, I have installed it from source using:\r\n> \r\n> ```\r\n> git clone https://github.com/huggingface/transformers.git\r\n> cd transformers\r\n> pip install -e .\r\n> ```\r\n> \r\n> I also tried installing as suggested here in [examples](https://github.com/huggingface/transformers/tree/master/examples) as:\r\n> \r\n> ```\r\n> git clone https://github.com/huggingface/transformers\r\n> cd transformers\r\n> pip install .\r\n> pip install -r ./examples/requirements.txt\r\n> ```\r\n\r\nYes @naturecreator, I had the same error last week. I managed to circumvent that by removing the editable mode when pip installing (from `pip install -e .` to a standard `pip install .`).\r\nIt worked for me both for python 3.6 and 3.7.", "After the recent commit made to the [script](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py), it is running as expected without any errors.", "Hey, I forked it and followed the solution given by @VictorSanh, but I am still getting this error. I am loading a custom dataset (text file) not a predefined one, and for Roberta-Base. Also using the --line_by_line parameter.Any ideas why this may be happening?", "Tried by removing --line_by_line parameter. It works, but it is not taking line by line input anymore since we removed the parameter. I processed the file as a JSON for now. Is there a fix using the --line_by_line?", "This an error that none of us on the team managed to fully reproduce, so if you could give us your full environment, that would be super helpful.", "I would love to help. I am a bit new to this, do let me know if any more specifics are required. The versions of the required lib/lang are - \r\nPython - 3.6.7\r\ntransformers - 3.4.0\r\npickle - 4.0\r\n\r\nThe command I ran was -\r\npython3 run_mlm.py \\\r\n--model_name_or_path roberta-base \\ \r\n--train_file train.txt \\\r\n--validation_file test.txt \\ \r\n--do_train \\\r\n--do_eval \\\r\n--output_dir results/ \\ \r\n--line_by_line \\\r\n", "Ahah! Can reproduce! This will make investigation easier.", "For future reference, here is how I create an env reproducing the bug, and the command that shows it (self-contained to the repo):\r\n```\r\npyenv install 3.6.7\r\npyenv virtualenv 3.6.7 picklebug\r\npyenv activate picklebug\r\npip install --upgrade pip\r\npip install transformers[torch]\r\npip install datasets\r\ncd git/transformers # Adapt to your local path to the cloned repo\r\npip install -e .\r\npython examples/language-modeling/run_mlm.py \\\r\n--model_name_or_path roberta-base \\\r\n--train_file ./tests/fixtures/sample_text.txt \\\r\n--validation_file ./tests/fixtures/sample_text.txt \\\r\n--do_train \\\r\n--do_eval \\\r\n--output_dir /tmp/test=clm \\\r\n--line_by_line\r\n```", "The bug disappears for me with python 3.7.9 so if you can upgrade your python version, you should be good to go.", "Further reduced, the bug appears in all python versions <= 3.6.12 but disappears in python 3.7.0.", "Thanks, this was really helpful !!!" ]

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[ "General questions should be asked on the [forum](https://discuss.huggingface.co/) as we keep the issues for bugs.\r\n`run_clm` doesn't use the approach of taking the different lines of a dataset (that's in `run_mlm` as it's usually done for masked language modeling) so you will have to tweak the example script to do this.\r\n\r\nThere is no way to pretokenzie, but the result of the tokenization will be cached, so it will only be run once on a given machine.", "Thank you for the help! I won't make that mistake again my apologies. Editing the clm file now. New to open source software and excited to dig in and help please bear with me while I learn the ropes!", "No worries, I'm just telling you for next time :-)\r\nGood luck with your scripting, closing this issue for now." ]

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[ "Hi there, as mentioned in the README of the examples (in bold), you need to install transformers [from source](https://huggingface.co/transformers/installation.html#installing-from-source) to use that script.", "I did thank, you for the rapid reply! Comfortable programmer, files not so much so again thank you for bearing with me. Installed via:\r\n\r\n!pip install git+https://github.com/huggingface/transformers.git", "Yep thats the issue sorry for being a moron and this can be closed. Perhaps someone of multicellular intelligence could explain why my !pip git+ solution is insufficient?\r\n\r\nThank you @sgugger", "Mmmm, maybe you had the repo cached somewhere and it didn't update to the latest version? Glad your issue is fixed :-) ", "Maybe because you already had `transformers` installed in your environment, in which case you would have to supply the `-U` option to update to the specific version you're targeting ", "I have a issue of fine-tuning the T5 model(t5-base). \r\n\r\n!python /content/transformers/examples/language-modeling/run_clm.py\r\n--model_name_or_path t5-base\r\n--train_file /content/train.txt\r\n--do_train\r\n--output_dir /tmp/test-clm\r\n\r\nThis code is not working. How can I fine-tune the t5-base model?" ]

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[ "Indeed this seems a bit strange.\r\n\r\nPining @n1t0 and @Narsil here (actually this should probably rather be an issue in the https://github.com/huggingface/tokenizers repo)", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "@n1t0 Any update on this?", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "> Looks great! Can we maybe add one test with the small `BlenderbotModel` to `/home/patrick/hugging_face/transformers/tests/test_pipelines_conversational.py` ?\r\n\r\nThank you @patrickvonplaten , I added an integration test using BlenderBot 90M.", "Merging, unrelated failure." ]

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[ "This will also be fixed by #8073 ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Hi @LysandreJik , thanks for the great review! \r\nI will fix and test the code as suggested.\r\n\r\nHowever, I have a very newbie question about style and document fixing. I admit that I can access only Colab (I have only windows PC) so I am really not sure how to do `make fixup` and `make docs` (in my understanding, should run in linux environment) . Could you please make some suggestions on this issue ?", "Hmmm I think you could do the following in your colab environment:\r\n\r\n```py\r\n# Clone the repo or your fork, I assume this is your working directory\r\n!git clone https://github.com/huggingface/transformers\r\n!cd transformers\r\n\r\n# Install all the dev dependencies (you've probably done that already)\r\n!pip install -e .[dev]\r\n\r\n# Then you should be able to run `make fixup`\r\n!make fixup\r\n\r\n# Same for the docs!\r\n!make docs\r\n```\r\nLet me know if that works!", "@LysandreJik , thanks again and I could run the two make commands.\r\n\r\n1) `make docs` , produced the error message which I have no clue, so I still need suggestion on this issue, sorry:\r\n```\r\ncd docs && make html SPHINXOPTS=\"-W\"\r\nmake[1]: Entering directory '/content/transformers/docs'\r\nRunning Sphinx v1.8.5\r\n\r\nExtension error:\r\nCould not import extension recommonmark (exception: No module named recommonmark)\r\nMakefile:19: recipe for target 'html' failed\r\nmake[1]: *** [html] Error 2\r\nmake[1]: Leaving directory '/content/transformers/docs'\r\nMakefile:68: recipe for target 'docs' failed\r\nmake: *** [docs] Error 2\r\n```\r\n\r\n\r\n2) `make fixup` , required `make fix-copies` and then `make fixup` suggest me to add more tests on `test_modeling_tf_dpr.py` which I will investigate this issue and come back :D", "Hey @ratthachat - great work! I helped you a bit with the docs and did some cleaning. I think we can merge the PR soon. It would be great if you could take a look at the comments above (a lot of them should already be resolved now) and also it would be awesome if you could add one integration test to both TF and PT (we forgot to do this originally for PyTorch).\r\n\r\nAn integration tests should be a slow test, where you can just statically type some input_ids vector and run it through one of the PyTorch pretrained models and test against its expected output. You should use the same input / expected output array then for Tensorflow, similar to how it's done here:\r\n\r\nhttps://github.com/huggingface/transformers/blob/4185b115d4b3fd408265ffd91581698325652c47/tests/test_modeling_roberta.py#L423\r\n\r\nLet me know if you have any questions!", "Thanks very much for your great help, Patrick @patrickvonplaten !! I will get back to you guys as soon as possible.", "Hi guys, with the great helps of Patrick, most comments of @LysandreJik were already dealt with. So I further addressed the rest as replied above. BTW, I did only very minimal and necessary changes, but many tests are now failed again , sorry.. I have no idea about this :( .\r\n\r\n@patrickvonplaten I added one slow model integration test to `test_modeling_tf_dpr.py` . However, at the moment I still could not find a way to run original DPR repo to produce original output yet. So at the moment, the integration test is just a chek that TF and Pytorch `DPRQuestionEncoder` models produce the same output (with acceptable margin of error) -- i.e. the `tf.constant` expected slice comes from Pytorch's model.\r\n\r\n(test can be played around here : https://colab.research.google.com/drive/1czS_m9zy5k-iSJbzA_DP1k1xAAC_sdkf?usp=sharing )\r\n\r\nI will come back to add more model integration tests if I succeed to run the [original DPR](https://github.com/facebookresearch/DPR/blob/master/generate_dense_embeddings.py).", "> Hi Patrick, I have a question. At the moment, we do not have native TF weights, so removing this is OK ?\r\n\r\nI uploaded them a minute ago ;-) ", "Thanks so much everyone. Very happy :D 👯 \r\nSee you guys again soon on TFRag (WIP)", "@patrickvonplaten I think you didn't upload all of the weights on the model hub. I'm uploading the remaining weights now:\r\n\r\n- `facebook/dpr-ctx_encoder-single-nq-base`\r\n- `facebook/dpr-ctx_encoder-multiset-base`\r\n- `facebook/dpr-question_encoder-multiset-base`\r\n- `facebook/dpr-reader-single-nq-base`\r\n- `facebook/dpr-reader-multiset-base`\r\n", "(Current slow CI is failing because it's trying to load some of them)", "They're all uploaded." ]

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[ "##", "P#86", "user blocked" ]

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[ "😁", "#876 " ]

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[ "This will be fixed when #8073 is merged.", "Is there any help in terms of what version to pin to in order to avoid this? This is currently a huge blocker on my end.", "On the sentencepiece side I don’t know (you can open an issue on their side to ask) but on the `transformers` side we are actively working on removing the hard dependency on sentencepiece and we estimate we should have a new release removing this dependency around the end of next week.\r\n\r\nCc @n1t0 and @Narsil whose work on `tokenizers` is essential to unlock this.", "Great! Thanks for the info!", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Wow, so cool! Thanks for your contribution." ]

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[ "I got the same error while loading BERT tokeniser and model from torch hub", "Hello! Do you mind pasting the result of `pip list` done in your environment? Thank you!", "It’s an Anaconda virtual environment.\nPython 3.6.11\n\n$ pip list\nPackage Version Location\n--------------------------------- ------------------- ----------------------------------------------------------\nabsl-py 0.11.0\naiohttp 3.7.2\nappdirs 1.4.4\nargon2-cffi 20.1.0\nastor 0.8.1\nastunparse 1.6.3\nasync-generator 1.10\nasync-timeout 3.0.1\nattrs 20.2.0\nAutomat 20.2.0\nawscli 1.18.169\nBabel 2.8.0\nbackcall 0.2.0\nbackports.functools-lru-cache 1.6.1\nbcrypt 3.2.0\nbeautifulsoup4 4.9.3\nbertopic 0.2.3\nblack 20.8b1\nbleach 3.2.1\nblinker 1.4\nbokeh 2.2.3\nboto 2.49.0\nboto3 1.16.9\nbotocore 1.19.9\nbrotlipy 0.7.0\nbz2file 0.98\ncachetools 4.1.1\ncertifi 2020.6.20\ncffi 1.14.3\nchainer 7.7.0\nchardet 3.0.4\nclick 7.1.2\ncloudpickle 1.2.2\ncolorama 0.4.3\nconstantly 15.1.0\ncryptography 3.2.1\ncssselect 1.1.0\ncycler 0.10.0\ncymem 1.31.2\nCython 0.29.21\ndataclasses 0.7\ndecorator 4.4.2\ndeepdist 0.1\ndefusedxml 0.6.0\ndill 0.3.2\ndiskcache 4.0.0\ndocutils 0.15.2\nentrypoints 0.3\nfeynman 2.0.0\nfilelock 3.0.12\nfindspark 1.3.0\nFlask 1.1.2\nflatbuffers 1.12\nfuncy 1.15\nfuture 0.18.2\ngast 0.3.3\ngensim 3.8.3\ngoogle-auth 1.23.0\ngoogle-auth-oauthlib 0.4.2\ngoogle-pasta 0.2.0\ngoogleapis-common-protos 1.52.0\ngrpcio 1.33.2\nh5py 2.10.0\nhdbscan 0.8.26\nhtml5lib 1.1\nhyperlink 20.0.1\nhypothesis 5.41.0\nidna 2.10\nidna-ssl 1.1.0\nimportlib-metadata 2.0.0\nincremental 17.5.0\niniconfig 1.1.1\nipykernel 5.3.4\nipython 7.12.0\nipython-genutils 0.2.0\nipywidgets 7.5.1\nitemadapter 0.1.1\nitemloaders 1.0.3\nitsdangerous 1.1.0\njedi 0.17.2\nJinja2 2.11.2\njmespath 0.10.0\njoblib 0.17.0\njson5 0.9.5\njsonschema 3.2.0\njupyter-client 6.1.7\njupyter-console 6.2.0\njupyter-contrib-core 0.3.3\njupyter-core 4.6.3\njupyter-nbextensions-configurator 0.4.1\njupyterlab 2.2.9\njupyterlab-pygments 0.1.2\njupyterlab-server 1.2.0\nKeras-Applications 1.0.8\nKeras-Preprocessing 1.1.2\nkiwisolver 1.3.0\nllvmlite 0.34.0\nlxml 4.6.1\nMarkdown 3.3.3\nMarkupSafe 1.1.1\nmatplotlib 3.3.2\nmistune 0.8.4\nmnist 0.2.2\nmore-itertools 8.6.0\nmpmath 1.1.0\nMulticoreTSNE 0.1\nmultidict 4.7.5\nmurmurhash 0.26.4\nmypy-extensions 0.4.3\nnbclient 0.5.1\nnbconvert 6.0.7\nnbformat 5.0.8\nnest-asyncio 1.4.1\nnltk 3.4.4\nnotebook 6.1.4\nnumba 0.51.2\nnumexpr 2.7.1\nnumpy 1.19.2\noauthlib 3.0.1\nolefile 0.46\nopt-einsum 3.3.0\npackaging 20.4\npandas 1.1.4\npandocfilters 1.4.2\nparameterized 0.7.4\nparsel 1.6.0\nparso 0.7.1\npathspec 0.8.0\npatsy 0.5.1\npetastorm 0.7.6 /home/ubuntu/petastorm\npexpect 4.8.0\npickleshare 0.7.5\nPillow 8.0.1\npip 20.2.4\nplac 1.0.0\npluggy 0.13.1\npreshed 0.46.4\nprometheus-client 0.8.0\npromise 2.3\nprompt-toolkit 3.0.8\nProtego 0.1.16\nprotobuf 3.13.0\npsutil 5.7.3\nptyprocess 0.6.0\npy 1.9.0\npy4j 0.10.9\npyarrow 2.0.0\npyasn1 0.4.8\npyasn1-modules 0.2.7\npycparser 2.20\nPyDispatcher 2.0.5\npydot 1.4.1\nPygments 2.7.2\nPyHamcrest 2.0.2\nPyJWT 1.7.1\npyLDAvis 2.1.2\npyOpenSSL 19.1.0\npyparsing 2.4.7\nPyQt5 5.12.3\nPyQt5-sip 4.19.18\nPyQtChart 5.12\nPyQtWebEngine 5.12.1\npyrsistent 0.17.3\nPySocks 1.7.1\npyspark 3.0.1\npytest 6.1.2\npython-dateutil 2.8.1\npytz 2020.1\nPyWavelets 1.1.1\nPyYAML 5.3.1\npyzmq 19.0.2\nqtconsole 4.7.7\nQtPy 1.9.0\nqueuelib 1.5.0\nregex 2020.10.28\nrequests 2.24.0\nrequests-oauthlib 1.3.0\nrsa 4.4.1\ns3transfer 0.3.3\nsacremoses 0.0.43\nscapy 2.4.4\nscikit-learn 0.23.2\nscipy 1.5.2\nScrapy 2.4.0\nseaborn 0.11.0\nsemver 2.8.1\nSend2Trash 1.5.0\nsense2vec 0.6.0\nsentence-transformers 0.3.6\nsentencepiece 0.1.91\nservice-identity 18.1.0\nsetuptools 49.6.0.post20201009\nsix 1.15.0\nsklearn 0.0\nsmart-open 1.6.0\nsortedcontainers 2.2.2\nsoupsieve 2.0.1\nspacy 0.101.0\nsputnik 0.9.3\nstatsmodels 0.12.1\nsympy 1.6.2\ntensorboard 2.3.0\ntensorboard-plugin-wit 1.7.0\ntensorflow 2.2.0\ntensorflow-datasets 1.2.0\ntensorflow-estimator 2.2.0\ntensorflow-metadata 0.14.0\ntensorflow-probability 0.6.0\ntensorflowonspark 1.4.1\ntermcolor 1.1.0\nterminado 0.9.1\ntestpath 0.4.4\ntfp-nightly 0.5.0.dev20190522\nthinc 5.0.8\nthreadpoolctl 2.1.0\ntimeout-decorator 0.4.1\ntokenizers 0.9.2\ntoml 0.10.1\ntorch 1.7.0\ntorchaudio 0.7.0a0+ac17b64\ntorchvision 0.8.1\ntornado 6.1\ntqdm 4.51.0\ntraitlets 4.3.3\ntransformers 3.1.0 /home/ubuntu/anaconda2/envs/ai/lib/python3.6/site-packages\nTwisted 20.3.0\ntwython 3.8.2\ntyped-ast 1.4.1\ntyping-extensions 3.7.4.3\numap-learn 0.4.6\nurllib3 1.25.11\nw3lib 1.22.0\nwcwidth 0.2.5\nwebencodings 0.5.1\nWerkzeug 1.0.1\nwheel 0.35.1\nwidgetsnbextension 3.5.1\nwordcloud 1.8.0\nwrapt 1.12.1\nyarl 1.6.2\nzipp 3.4.0\nzope.interface 5.1.2\n> On Nov 2, 2020, at 7:33 AM, Lysandre Debut <[email protected]> wrote:\n> \n> \n> Hello! Do you mind pasting the result of pip list done in your environment? Thank you!\n> \n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub <https://github.com/huggingface/transformers/issues/8196#issuecomment-720544945>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAXWFW2Z4DMWNXRLVPMHSD3SN3GLNANCNFSM4TFJGMGQ>.\n> \n\n", "It seems you have a conflict between your `transformers` version, as `transformers-cli env` returns v3.4.0, while your `pip list` returns v3.1.0?", "Mea culpa! I sent you the pip list from my Mac.\nHere’s the Ubuntu 20.04 LTS results\n\n$ conda list transformers\n# packages in environment at /home/ubuntu/anaconda2/envs/ai:\n#\n# Name Version Build Channel\nsentence-transformers 0.3.6 pypi_0 pypi\ntransformers 3.4.0 dev_0 <develop>\n(ai) ubuntu@ip-10-0-1-82:~/transformers$ \n\n\n$ pip list\nPackage Version Location\n--------------------------------- ------------------- ---------------------------------------------------------------------------------------\nabsl-py 0.11.0\naiohttp 3.7.2\nappdirs 1.4.4\nargon2-cffi 20.1.0\nastor 0.8.1\nastunparse 1.6.3\nasync-generator 1.10\nasync-timeout 3.0.1\nattrs 20.2.0\nAutomat 20.2.0\nawscli 1.18.169\nBabel 2.8.0\nbackcall 0.2.0\nbackports.functools-lru-cache 1.6.1\nbcrypt 3.2.0\nbeautifulsoup4 4.9.3\nbertopic 0.2.3\nblack 20.8b1\nbleach 3.2.1\nblinker 1.4\nbokeh 2.2.3\nboto 2.49.0\nboto3 1.16.9\nbotocore 1.19.9\nbrotlipy 0.7.0\nbz2file 0.98\ncachetools 4.1.1\ncertifi 2020.6.20\ncffi 1.14.3\nchainer 7.7.0\nchardet 3.0.4\nclick 7.1.2\ncloudpickle 1.2.2\ncolorama 0.4.3\nconstantly 15.1.0\ncryptography 3.2.1\ncssselect 1.1.0\ncycler 0.10.0\ncymem 1.31.2\nCython 0.29.21\ndataclasses 0.7\ndecorator 4.4.2\ndeepdist 0.1\ndefusedxml 0.6.0\ndill 0.3.2\ndiskcache 4.0.0\ndocutils 0.15.2\nentrypoints 0.3\nfeynman 2.0.0\nfilelock 3.0.12\nfindspark 1.3.0\nFlask 1.1.2\nflatbuffers 1.12\nfuncy 1.15\nfuture 0.18.2\ngast 0.3.3\ngensim 3.8.3\ngoogle-auth 1.23.0\ngoogle-auth-oauthlib 0.4.2\ngoogle-pasta 0.2.0\ngoogleapis-common-protos 1.52.0\ngrpcio 1.33.2\nh5py 2.10.0\nhdbscan 0.8.26\nhtml5lib 1.1\nhyperlink 20.0.1\nhypothesis 5.41.0\nidna 2.10\nidna-ssl 1.1.0\nimportlib-metadata 2.0.0\nincremental 17.5.0\niniconfig 1.1.1\nipykernel 5.3.4\nipython 7.12.0\nipython-genutils 0.2.0\nipywidgets 7.5.1\nitemadapter 0.1.1\nitemloaders 1.0.3\nitsdangerous 1.1.0\njedi 0.17.2\nJinja2 2.11.2\njmespath 0.10.0\njoblib 0.17.0\njson5 0.9.5\njsonschema 3.2.0\njupyter-client 6.1.7\njupyter-console 6.2.0\njupyter-contrib-core 0.3.3\njupyter-core 4.6.3\njupyter-nbextensions-configurator 0.4.1\njupyterlab 2.2.9\njupyterlab-pygments 0.1.2\njupyterlab-server 1.2.0\nKeras-Applications 1.0.8\nKeras-Preprocessing 1.1.2\nkiwisolver 1.3.0\nllvmlite 0.34.0\nlxml 4.6.1\nMarkdown 3.3.3\nMarkupSafe 1.1.1\nmatplotlib 3.3.2\nmistune 0.8.4\nmnist 0.2.2\nmore-itertools 8.6.0\nmpmath 1.1.0\nMulticoreTSNE 0.1\nmultidict 4.7.5\nmurmurhash 0.26.4\nmypy-extensions 0.4.3\nnbclient 0.5.1\nnbconvert 6.0.7\nnbformat 5.0.8\nnest-asyncio 1.4.1\nnltk 3.4.4\nnotebook 6.1.4\nnumba 0.51.2\nnumexpr 2.7.1\nnumpy 1.19.2\noauthlib 3.0.1\nolefile 0.46\nopt-einsum 3.3.0\npackaging 20.4\npandas 1.1.4\npandocfilters 1.4.2\nparameterized 0.7.4\nparsel 1.6.0\nparso 0.7.1\npathspec 0.8.0\npatsy 0.5.1\npetastorm 0.7.6 /home/ubuntu/petastorm\npexpect 4.8.0\npickleshare 0.7.5\nPillow 8.0.1\npip 20.2.4\nplac 1.0.0\npluggy 0.13.1\npreshed 0.46.4\nprometheus-client 0.8.0\npromise 2.3\nprompt-toolkit 3.0.8\nProtego 0.1.16\nprotobuf 3.13.0\npsutil 5.7.3\nptyprocess 0.6.0\npy 1.9.0\npy4j 0.10.9\npyarrow 2.0.0\npyasn1 0.4.8\npyasn1-modules 0.2.7\npycparser 2.20\nPyDispatcher 2.0.5\npydot 1.4.1\nPygments 2.7.2\nPyHamcrest 2.0.2\nPyJWT 1.7.1\npyLDAvis 2.1.2\npyOpenSSL 19.1.0\npyparsing 2.4.7\nPyQt5 5.12.3\nPyQt5-sip 4.19.18\nPyQtChart 5.12\nPyQtWebEngine 5.12.1\npyrsistent 0.17.3\nPySocks 1.7.1\npyspark 3.0.1\npytest 6.1.2\npython-dateutil 2.8.1\npytz 2020.1\nPyWavelets 1.1.1\nPyYAML 5.3.1\npyzmq 19.0.2\nqtconsole 4.7.7\nQtPy 1.9.0\nqueuelib 1.5.0\nregex 2020.10.28\nrequests 2.24.0\nrequests-oauthlib 1.3.0\nrsa 4.4.1\ns3transfer 0.3.3\nsacremoses 0.0.43\nscapy 2.4.4\nscikit-learn 0.23.2\nscipy 1.5.2\nScrapy 2.4.0\nseaborn 0.11.0\nsemver 2.8.1\nSend2Trash 1.5.0\nsense2vec 0.6.0\nsentence-transformers 0.3.6\nsentencepiece 0.1.91\nservice-identity 18.1.0\nsetuptools 49.6.0.post20201009\nsix 1.15.0\nsklearn 0.0\nsmart-open 1.6.0\nsortedcontainers 2.2.2\nsoupsieve 2.0.1\nspacy 0.101.0\nsputnik 0.9.3\nstatsmodels 0.12.1\nsympy 1.6.2\ntensorboard 2.3.0\ntensorboard-plugin-wit 1.7.0\ntensorflow 2.2.0\ntensorflow-datasets 1.2.0\ntensorflow-estimator 2.2.0\ntensorflow-metadata 0.14.0\ntensorflow-probability 0.6.0\ntensorflowonspark 1.4.1\ntermcolor 1.1.0\nterminado 0.9.1\ntestpath 0.4.4\ntfp-nightly 0.5.0.dev20190522\nthinc 5.0.8\nthreadpoolctl 2.1.0\ntimeout-decorator 0.4.1\ntokenizers 0.9.2\ntoml 0.10.1\ntorch 1.7.0\ntorchaudio 0.7.0a0+ac17b64\ntorchvision 0.8.1\ntornado 6.1\ntqdm 4.51.0\ntraitlets 4.3.3\ntransformers 3.4.0 /home/ubuntu/anaconda2/envs/ai/lib/python3.6/site-packages/transformers-3.4.0-py3.6.egg\nTwisted 20.3.0\ntwython 3.8.2\ntyped-ast 1.4.1\ntyping-extensions 3.7.4.3\numap-learn 0.4.6\nurllib3 1.25.11\nw3lib 1.22.0\nwcwidth 0.2.5\nwebencodings 0.5.1\nWerkzeug 1.0.1\nwheel 0.35.1\nwidgetsnbextension 3.5.1\nwordcloud 1.8.0\nwrapt 1.12.1\nyarl 1.6.2\nzipp 3.4.0\nzope.interface 5.1.2\n> On Nov 2, 2020, at 9:15 AM, Lysandre Debut <[email protected]> wrote:\n> \n> \n> It seems you have a conflict between your transformers version, as transformers-cli env returns v3.4.0, while your pip list returns v3.1.0?\n> \n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub <https://github.com/huggingface/transformers/issues/8196#issuecomment-720607259>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAXWFWYIWSYOAK3B7CD7PRTSN3SM7ANCNFSM4TFJGMGQ>.\n> \n\n", "After looking a bit into it, it seems there was the initialization of the XLMProphetNetTokenizer missing when the `sentencepiece` dependency was not detected. #8245 should solve it, thank you for raising an issue!", "Great! Thank you.\n\nbtw - There are many missing packages when I try to run ‘pytest' for tests and examples.\nE.g. - datasets, timeout-decorator, faiss, parameterized, etc.\nIt would be nice if there was a requirements.txt file. (Just a suggestion).\n;-)\n\n> On Nov 2, 2020, at 10:58 AM, Lysandre Debut <[email protected]> wrote:\n> \n> \n> After looking a bit into it, it seems there was the initialization of the XLMProphetNetTokenizer missing when the sentencepiece dependency was not detected. #8245 <https://github.com/huggingface/transformers/pull/8245> should solve it, thank you for raising an issue!\n> \n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub <https://github.com/huggingface/transformers/issues/8196#issuecomment-720662390>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAXWFWYOLUGKB3SN3IT3CH3SN36LVANCNFSM4TFJGMGQ>.\n> \n\n", "For the tests, you should be able to get it working with `pip install transformers[testing]` or `pip install . [testing]` if you have cloned the repository.\r\n\r\nFor the examples, there is a `requirements.txt` file in the `examples/` directory:\r\n\r\n```shell-script\r\ncd examples\r\npip install -r requirements.txt\r\n```", "Just merged #8245, installing from source should remove the error mentioned previously. Thanks again for letting us know!" ]

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[ "No, `model_max_length` is not defined in the `tokenizer.json` for these models so truncation is off, or fails at inference in the model.", "Well we also load the `transformers` specific configuration with `from_pretrained`, not only the `tokenizers` file (because We have additional attributes in `transformers`). It should be in this configuration file. I’ll take a look.", "Okay, I'll wait @thomwolf for your advice on this then.", "Stale", "Too old" ]

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[ "I'm okay with this, but users will still need to copy paste or rewrite `Seq2SeqTrainingArguments` and pass those as `args` instead of default `TrainingArguments`, since we assume `args` is `Seq2SeqTrainingArguments` in multiple methods \r\n\r\n" ]

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[ "Thanks for your PR! For the first point, I think we should fix the docs, not add an evaluation. It doesn't make any sense to evaluate at step 1 (one could call `trainer.evaluate()` before training if they really wanted to).\r\nFor the second point, good catch, this is certainly useful!", "OK, both the description and the behavior is now logging only (no eval).", "Perfect, thanks!" ]

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[ "There is no plan for that as both those APIs will be deprecated soon. Users should directly use the [Datasets](https://github.com/huggingface/datasets) library which works for both PyTorch and TF. There are examples of how to replicate `TextDataset` and `LineByLineTextDataset` using that library in the new [`run_clm`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_clm.py) and [`run_mlm`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py) scripts. To convert the datasets to the TF format, just use their `set_format` method (see the [doc here](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.set_format)). ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Good point.\r\n\r\nMaybe what we could do is:\r\n- initializing the configuration with `from_pretrained` initializes the `name_or_path` attribute of the config as you mention, and\r\n- using the configuration in a *model* `from_pretrained` method override the `name_or_path` attribute with the one of the model so that it's in priority linked to the weights path.\r\n\r\nAnother option would be to have two attributes in the configuration:\r\n- `configuration_name_or_path`\r\n- `weights_name_or_path`\r\n\r\nrespectively populated by the config `from_pretrained` and the model `from_pretrained`. Maybe with a property linking to one in priority.\r\n\r\nbut I'm wondering if it's worth so many attributes... 🤔", "I would go for the first version you proposed: having `name_or_path` for the configuration initialized if used alongside `from_pretrained`, which gets overridden by the model `from_pretrained`.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Don't manage to get this test passing even with the new design of #7562 -> the problem to me is that the shape of `attentions` in Longformer depends on the input tensor => so not sure we'll find a good solution here", "If it can't pass the test defined in the common tests, then the best would be to override the test in the `LongformerModelTester` and do a test to ensure that the correct behavior still works, even if not adhering to the common tests.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "You can easily ignore that value though.\r\n\r\nThe problem is that you won't have it at each eval during the training loop if we don't include it. There could be something smarter there, but it would take time for something that is just purely cosmetic.", "I added a small PR for improved documentation about this: #8273", "Closing this since PR was merged." ]

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[ "Hey @harung1993, \r\n\r\nsorry I'm having trouble understanding your question here. Also this seems like a question that should rather be posted in https://discuss.huggingface.co/ . We are trying to use github issues only for bug reports. Thanks!", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Hi! Could you provide the code you're using, as well as all the environment information?", "@LysandreJik should we update the issue template for this last option `Questions & Help`?\r\n\r\nI feel like our first question to everybody is always `Could you provide the code you're using, as well as all the environment information`", "You're right, we should!", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Hello! Indeed, do you want to open a PR to fix this? Thanks!", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Sure before the change\r\n\r\n```python\r\nfrom transformers import pipeline\r\n\r\npipe = pipeline(task='fill-mask', model='bert-base-uncased')\r\npipe(\"I am a real [MASK]\", targets=[\"superhero\", \"legend\"])\r\n# [{'sequence': '[CLS] i am a real superhero [SEP]',\r\n# 'score': 1.21390044682812e-07,\r\n# 'token': 16251,\r\n# 'token_str': 'superhero'},\r\n# {'sequence': '[CLS] i am a real legend [SEP]',\r\n# 'score': 4.292454747201191e-08,\r\n# 'token': 5722,\r\n# 'token_str': 'legend'}]\r\n\r\npipe(\"I am a real [MASK]\", otherarg=True)\r\nValueError Traceback (most recent call last)\r\n<ipython-input-13-4784fa412984> in <module>\r\n----> 1 pipe(\"I am a real [MASK]\", otherarg=True)\r\n\r\n~/.pyenv/versions/3.8.5/lib/python3.8/site-packages/transformers/pipelines.py in __call__(self, targets, *args, **kwargs)\r\n 1201 - **token** (:obj:`str`) -- The predicted token (to replace the masked one).\r\n 1202 \"\"\"\r\n-> 1203 inputs = self._parse_and_tokenize(*args, **kwargs)\r\n 1204 outputs = self._forward(inputs, return_tensors=True)\r\n 1205 \r\n\r\n~/.pyenv/versions/3.8.5/lib/python3.8/site-packages/transformers/pipelines.py in _parse_and_tokenize(self, padding, add_special_tokens, *args, **kwargs)\r\n 625 \"\"\"\r\n 626 # Parse arguments\r\n--> 627 inputs = self._args_parser(*args, **kwargs)\r\n 628 inputs = self.tokenizer(\r\n 629 inputs,\r\n\r\n~/.pyenv/versions/3.8.5/lib/python3.8/site-packages/transformers/pipelines.py in __call__(self, *args, **kwargs)\r\n 179 def __call__(self, *args, **kwargs):\r\n 180 if len(kwargs) > 0 and len(args) > 0:\r\n--> 181 raise ValueError(\"Pipeline cannot handle mixed args and kwargs\")\r\n 182 \r\n 183 if len(kwargs) > 0:\r\n\r\nValueError: Pipeline cannot handle mixed args and kwargs\r\n\r\n```\r\n\r\nAnd afterwards:\r\n\r\n```python\r\nfrom transformers import pipeline\r\n\r\npipe = pipeline(task='fill-mask', model='bert-base-uncased')\r\npipe(\"I am a real [MASK]\", otherarg=True)\r\n# [{'sequence': '[CLS] i am a real. [SEP]',\r\n# 'score': 0.94329434633255,\r\n# 'token': 1012,\r\n# 'token_str': '.'},\r\n# {'sequence': '[CLS] i am a real ; [SEP]',\r\n# 'score': 0.02879592962563038,\r\n# 'token': 1025,\r\n# 'token_str': ';'},\r\n# {'sequence': '[CLS] i am a real! [SEP]',\r\n# 'score': 0.022438935935497284,\r\n # 'token': 999,\r\n# 'token_str': '!'},\r\n# {'sequence': '[CLS] i am a real? [SEP]',\r\n# 'score': 0.00518036400899291,\r\n# 'token': 1029,\r\n# 'token_str': '?'},\r\n# {'sequence': '[CLS] i am a real... [SEP]',\r\n# 'score': 3.598905823309906e-05,\r\n# 'token': 2133,\r\n# 'token_str': '...'}]\r\n```", "Should I merge ?", "I could start that.", "(no need to do it in this PR, it can wait :)" ]

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[ "The `do_predict` argument (like `do-train` and `do-eval` is not used by `Trainer`), just by the training scripts provided as examples.\r\n\r\nGetting predictions on a test set is done with `trainer.predict(test_dataset)`.", "Should corresponding documentation be added?", "Sure, do you want to take a stab at it?", "@sgugger I can do a PR if you want. But...\r\n\r\n... for me it smells like a design flaw when this is only for CLI usage and has no meaning for the \"normal use\".\r\nShould we consider just removing it?\r\n\r\nHow would a documentation look like? _\"This field is just a workaround for CLI value storage for the example code and has no meaning for normal usage.\"_?", "It's not `TrainerArguments` but `TrainingArguments`, so I don't see the problem with some of those arguments being only for CLI usage. Besides, removing them would break existing code so it would do more harm than good IMO.\r\n\r\nFor the documentation itself, it's not just a workaround. Something along the line of\r\n```\r\nThis argument is not directly used by :class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. See the `example scripts <https://github.com/huggingface/transformers/tree/master/examples>`___ for more details.\r\n```\r\nwould sound better.", "> ```\r\n> This argument is not directly used by :class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. See the `example scripts <https://github.com/huggingface/transformers/tree/master/examples>`___ for more details.\r\n> ```\r\n> \r\n> would sound better.\r\n\r\nPR has been created: #8270", "closing this since PR was merged" ]

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[ "cc @LysandreJik @mfuntowicz ", "Offline approval from @mfuntowicz!" ]

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[ "Hi! Could you provide an example of the usage that you would like to see with the `transformers` library, so that we may see what can be done?", "Is this the same as #8125 ?", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "It was working for me when using --pipeline sentiment-analysis", "Hi @user06039 did you find a solution for this? Cause I am also facing the same issue." ]

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[ "Hi Dana! I don't think this is a bug. You are not providing `BertForPreTraining` the `nsp_labels` it also requires for training, so it does not compute the loss (and then the rest fails). You should use `DataCollatorForNextSentencePrediction` to have the batches get those labels too (and it might requires using `TextDatasetForNextSentencePrediction` with it) or write your own `data_collator` that will add those labels.", "Hi Sylvain! Thanks for the quick response! My understanding of the process for pre-training BERT is that it is self-supervised and creates it's own labels. For example, for \"next sentence prediction\", it looks at the input sentences and uses the \"next sentence\" as the label for that task. That's how it worked when I used the TensorFlow model to train my BERT from scratch. Does the HuggingFace trainer not do that? I will look at \"extDatasetForNextSentencePrediction\" to see if that has some answers. I just thought that HuggingFace framework would be easier to fine-tune than using Google TensorFlow code.", "The trainer just does the training loop, it is independent from the tasks. Transformers provides tools to get the data together (which I mentioned) and ready for the Trainer on all the most common NLP tasks, BERT-pretraining objective included.", "Hi Sylvain,\r\nYour suggestion did the trick! I used ‘TextDatasetForNextSentencePrediction’ to build my dataset and ‘DataCollatorForNextSentencePrediction’ for my collator. It’s training now and the validation loss is getting lower, so everything looks fine. As you remember, fine-tuning the baseline pre-trained model with new task-specific data was part of your workflow for ULMFIT, so I can imagine this use-case will come up a lot. If you would like me to clean up my test example notebook, I’d be glad to let you post it in your examples section. It took me days to get this far, so I’d like to save the next person some time if possible. \r\n\r\nThank you! Dana" ]

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[ "Hello! Could you open a post on the [forum](https://discuss.huggingface.co) instead? We try to keep issues for bugs only.", "Hi, sure." ]

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[ "Hello, could you provide more information on what this is? The template is empty, and I'm not sure what this brings to the library.", "Don't spend time on this @LysandreJik this is just HacktoberFest spam" ]

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[ "Hello! Thanks for offering to contribute the TF implementation of the DPR model! Something that may help you is to open a PR very early on, even if you have a lot of questions. This way we can help provide pointers, and we can guide you in the right direction. \r\n\r\nAnother aspect that may be of tremendous help, would be to follow the checklist when adding a new model. It is available [here](https://github.com/huggingface/transformers/tree/master/templates/adding_a_new_model). If you open a PR, we recommend to put this checklist in the description so that everybody can follow better.\r\n\r\nLet me know if I can help further.", "@LysandreJik Thanks for your suggestion and the checklist which is just what I want!\r\nI will try to follow the checklist as much as possible and then PR. (UPDATED : already open a PR with checklist)\r\nPlease let me know if I should close this issue.", "This is great, only the tests are left! No need to close the issue here, we can close this issue once the PR is merged.", "Thanks for your kind words @LysandreJik !\r\nAt first, I have no idea how to test. Now I know I have to translate `test_modeling_dpr.py` and see an example on the recent `test_modeling_tf_bart.py` . \r\n", "@LysandreJik :D\r\nAfter several hours of testing and debugging, my current model is alreay passed 27 tests :D \r\nThe test run is in Colab here : (in the last cell)\r\nhttps://colab.research.google.com/drive/1czS_m9zy5k-iSJbzA_DP1k1xAAC_sdkf?usp=sharing\r\n\r\nMy [current repo](https://github.com/ratthachat/transformers) already contained `test_modeling_tf_dpr.py` \r\nCould you please suggest me the next step (make a repo update with latest Transformers ?)", "The next steps would be for us to review what you've contributed until now! We'll take a look as soon as possible.", "Thanks Lysandre! I actually have aimed for TFRag . Meanwhile, I will make a new branch and use TFDPR on translating TFRag .", "Close the issue as TFDPR is already merged. Very happy. Thanks a lot everybody!!" ]

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[ "Thanks for sharing! You should add more metadata to your model card if possible: https://huggingface.co/docs#what-metadata-can-i-add-to-my-model-card" ]

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[ "Thanks @TFUsers for this important PR.\r\n\r\nAs far as I know the activation names are also directly inside the the `config.json` files in the model hub. @sgugger @LysandreJik Do we plan to update all of them?", "Very good point @jplu. We can't change those names in the config hosted online because it wouldn't be backward-compatible, so we need to still accept the old names (without documenting the behavior). So we should leave the old `swish` in the dictionaries `ACT2FN`.", "It looks like everything passes (ignoring \"src/transformers/activations.py:52:5: F811 redefinition of unused 'silu' from line 40\").", "> I guess the cleanest approach in that regard would be to remove the definition of ACT2FN in these files, and instead import the centralized ACT2FN from the activations files\r\n\r\nLine 30 of `modeling_bert.py` is\r\n`from .activations import ACT2FN`\r\n\r\nAre the main concerns resolved?", "You're right, I was checking in the wrong file. Could you fix the code quality issue related to the redefinition of `silu`? You can follow what's done with the `gelu` method, by renaming the `silu` method to `_silu_python` and doing an if/else statement according to the torch version.\r\n\r\nAlso that version check (same with the `gelu`) doesn't seem robust at all. Could we use the `packaging` util to do something better? Something like:\r\n\r\n```py\r\nfrom packaging import version\r\n\r\nif version.parse(torch.__version__) < version.parse(\"1.4\"):\r\n ...\r\n```", "Thanks!" ]

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[ "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread." ]

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[ "Thanks!", "OK, everything seems to be working well. Let me know if you have any comments/suggestions/recommendations before replicating this to the rest of the jobs.\r\n\r\nSee: https://github.com/huggingface/transformers/runs/1329578690?check_suite_focus=true \r\n\r\nI will wait for https://github.com/huggingface/transformers/pull/8007 to be merged before spreading the love to the rest of the jobs, so that they won't need to deal with a lot of conflicts.", "I also proposed this a `pytest` feature: https://github.com/pytest-dev/pytest/issues/7972 - probably others would benefit from it.\r\n" ]

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[ "Oh, it was already fixed in #8159" ]

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[ "Hey @j-min, I don't think we will change this behavior because a) huge backward breaking change b) I think it's important to understand that generate **has** to start with a BOS/decoder_start_token_id (see Encoder-Decoder blog post: https://huggingface.co/blog/encoder-decoder)\r\n\r\nAlso you could add `skip_special_tokens=True` to the decode method to not return this token" ]

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[ "cc @julien-c ", "We've seen similar issues in the past @danyaljj. \r\n\r\nWe are going to release a new upload system for models in the coming week, can this wait till then? If it can't, and if you can upload your model to another bucket, we can copy it over manually. Let us know.", "Hey, @julien-c 👋 Sounds fair, I think I can wait until your new system is rolled out. " ]

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[ "> Looks good, thanks! Just make sure to run `make style` to have our scripts automatically fix the files you changed.\r\n\r\nOh. So should I send a new patch with the changes after running `make style`?", "I'm not following. Your last commit had the styling changes so all is good.", "> I'm not following. Your last commit had the styling changes so all is good.\r\n\r\nOh. But if I run `make style` I see tons of changes (193 files changed, 754 insertions(+), 2969 deletions(-)). For example, this file:\r\n\r\n```diff\r\ndiff --git a/examples/adversarial/utils_hans.py b/examples/adversarial/utils_hans.py\r\nindex bf0623ff..17d4a8c4 100644\r\n--- a/examples/adversarial/utils_hans.py\r\n+++ b/examples/adversarial/utils_hans.py\r\n@@ -112,10 +112,7 @@ if is_torch_available():\r\n cached_features_file = os.path.join(\r\n data_dir,\r\n \"cached_{}_{}_{}_{}\".format(\r\n- \"dev\" if evaluate else \"train\",\r\n- tokenizer.__class__.__name__,\r\n- str(max_seq_length),\r\n- task,\r\n+ \"dev\" if evaluate else \"train\", tokenizer.__class__.__name__, str(max_seq_length), task,\r\n ),\r\n )\r\n label_list = processor.get_labels()\r\n@@ -281,10 +278,7 @@ class HansProcessor(DataProcessor):\r\n \r\n \r\n def hans_convert_examples_to_features(\r\n- examples: List[InputExample],\r\n- label_list: List[str],\r\n- max_length: int,\r\n- tokenizer: PreTrainedTokenizer,\r\n+ examples: List[InputExample], label_list: List[str], max_length: int, tokenizer: PreTrainedTokenizer,\r\n ):\r\n \"\"\"\r\n Loads a data file into a list of ``InputFeatures``\r\n```", "Are you sure you have proper versions of black/isort/flake8 ? Run `pip install -e .[dev]` in the repo to make sure you have them.\r\n", "Oh, yeah, it was that. Silly mistake. :hand: Sorry for the noise!", "Np!" ]

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[ "Yeah I think you have a good point here! Was discussing this with @ibeltagy and I think we should add a `_tie_encoder_decoder_word_embeddings(...)` that does exactly what you suggested. We should probably run this method when initializing an Encoder-Decoder model and if word embeddings are of the same size. We can provide a `tie_encoder_decoder_word_embeds` config params that defaults to True. \r\n\r\n@alexyalunin do you want to try to make a PR for this ? :-)", "> Yeah I think you have a good point here! Was discussing this with @ibeltagy and I think we should add a `_tie_encoder_decoder_word_embeddings(...)` that does exactly what you suggested. We should probably run this method when initializing an Encoder-Decoder model and if word embeddings are of the same size. We can provide a `tie_encoder_decoder_word_embeds` config params that defaults to True.\r\n> \r\n> @alexyalunin do you want to try to make a PR for this ? :-)\r\n\r\nOk, let me try this. I will put you in the reviewers. ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ { "id": 1314768611, "node_id": "MDU6TGFiZWwxMzE0NzY4NjEx", "url": "https://api.github.com/repos/huggingface/transformers/labels/wontfix", "name": "wontfix", "color": "ffffff", "default": true, "description": null } ]

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[ "I'm having a similar problem since upgrading to 4.0 where certain unicode characters are being \"eaten\" even though I set 'use_fast' = False.\r\n\r\n## Environment info\r\n\r\n- `transformers` version: 4.0.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.6\r\n- PyTorch version (GPU?): 1.7.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: No\r\n- Using distributed or parallel set-up in script?: No\r\n \r\n### Who can help\r\n@mfuntowicz\r\n\r\n## Information\r\n\r\nBertTokenizer\r\n\r\nThe problem comes from certain unicode character being not turned correctly into UNK characters by the tokenizers when they did in earlier versions.\r\n\r\n## To reproduce\r\n\r\n```\r\nimport transformers\r\nimport torch\r\nfrom transformers import BertTokenizer\r\n\r\nprint(torch.__version__)\r\nprint(transformers.__version__)\r\n\r\n# THERE IS A UNICODE character between the , and '' (specifically \\U+200D\\U+200D\\U+200D\\U+200D)\r\nsentence = \": ءُپَاعَر سِيْا , ‍‍‍‍ '' Upal\"\r\n\r\ntokenizer = BertTokenizer.from_pretrained(\r\n \"bert-base-cased\",\r\n do_lower_case=False,\r\n use_fast=False\r\n )\r\n\r\nprint(tokenizer.tokenize(sentence))\r\n\r\n# output 4.0.0\r\n\r\n[':', '[UNK]', '[UNK]', ',', \"'\", \"'\", 'Up', '##al']\r\n\r\n# output from 3.0.1\r\n\r\n[':', '[UNK]', '[UNK]', ',', '[UNK]', \"'\", \"'\", 'Up', '##al'\r\n```", "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread." ]