cardiffnlp/xlm-t

Repository for XLM-T, a framework for evaluating multilingual language models on Twitter data

xlm-t's Issues

AttributeError: module 'google.protobuf.descriptor' has no attribute '_internal_create_key'

Hi
Im using transformers version '4.26.1' on databricks. The code below return the following error

Code:

model_path = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)

Error:

AttributeError: module 'google.protobuf.descriptor' has no attribute '_internal_create_key'
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<command-798773577171854> in <module>
     15 
     16 model_path = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
---> 17 sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
     18 
     19 df_group_notna['sentiment'] = df_group_notna['Feedback'].apply(lambda x: sentiment_task(x)[0]['label'])

/databricks/python/lib/python3.7/site-packages/transformers/pipelines/__init__.py in pipeline(task, model, config, tokenizer, feature_extractor, framework, revision, use_fast, use_auth_token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)
    827 
    828             tokenizer = AutoTokenizer.from_pretrained(
--> 829                 tokenizer_identifier, use_fast=use_fast, _from_pipeline=task, **hub_kwargs, **tokenizer_kwargs
    830             )
    831 
/databricks/python/lib/python3.7/site-packages/transformers/models/auto/tokenization_auto.py in from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs)
    674             tokenizer_class_py, tokenizer_class_fast = TOKENIZER_MAPPING[type(config)]
    675             if tokenizer_class_fast and (use_fast or tokenizer_class_py is None):
--> 676                 return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
    677             else:
    678                 if tokenizer_class_py is not None:

/databricks/python/lib/python3.7/site-packages/transformers/tokenization_utils_base.py in from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs)
   1811             local_files_only=local_files_only,
   1812             _commit_hash=commit_hash,
-> 1813             **kwargs,
   1814         )
   1815 

/databricks/python/lib/python3.7/site-packages/transformers/tokenization_utils_base.py in _from_pretrained(cls, resolved_vocab_files, pretrained_model_name_or_path, init_configuration, use_auth_token, cache_dir, local_files_only, _commit_hash, *init_inputs, **kwargs)
   1957         # Instantiate tokenizer.
   1958         try:
-> 1959             tokenizer = cls(*init_inputs, **init_kwargs)
   1960         except OSError:
   1961             raise OSError(

/databricks/python/lib/python3.7/site-packages/transformers/models/xlm_roberta/tokenization_xlm_roberta_fast.py in __init__(self, vocab_file, tokenizer_file, bos_token, eos_token, sep_token, cls_token, unk_token, pad_token, mask_token, **kwargs)
    163             pad_token=pad_token,
    164             mask_token=mask_token,
--> 165             **kwargs,
    166         )
    167 

/databricks/python/lib/python3.7/site-packages/transformers/tokenization_utils_fast.py in __init__(self, *args, **kwargs)
    112         elif slow_tokenizer is not None:
    113             # We need to convert a slow tokenizer to build the backend
--> 114             fast_tokenizer = convert_slow_tokenizer(slow_tokenizer)
    115         elif self.slow_tokenizer_class is not None:
    116             # We need to create and convert a slow tokenizer to build the backend

/databricks/python/lib/python3.7/site-packages/transformers/convert_slow_tokenizer.py in convert_slow_tokenizer(transformer_tokenizer)
   1160     converter_class = SLOW_TO_FAST_CONVERTERS[tokenizer_class_name]
   1161 
-> 1162     return converter_class(transformer_tokenizer).converted()

/databricks/python/lib/python3.7/site-packages/transformers/convert_slow_tokenizer.py in __init__(self, *args)
    436         super().__init__(*args)
    437 
--> 438         from .utils import sentencepiece_model_pb2 as model_pb2
    439 
    440         m = model_pb2.ModelProto()

/databricks/python/lib/python3.7/site-packages/transformers/utils/sentencepiece_model_pb2.py in <module>
     32     syntax="proto2",
     33     serialized_options=b"H\003",
---> 34     create_key=_descriptor._internal_create_key,
     35     serialized_pb=(
     36         b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\xa1\n\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01'

AttributeError: module 'google.protobuf.descriptor' has no attribute '_internal_create_key'

Run on Colab CRASH

I follow the step to run the model using a dataset with 5k tweets. However, when doing the 'output = model(**encoded_input)' the colab pro+ always crashes. Is there any solution to this issue? Thanks a lot!!!!

ValueError: You have to specify either input_ids or inputs_embeds

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?

Issues with finetuning colab notebook

The fine-tuning colab notebook doesn't fully work.

The first cell in the Fine-tuning section errors out. I'm guessing it's probably due to specific versions of packages being required (which are not enforced in the first cell).

Also, as a very minor additional thing, this link is broken: This notebook was modified from https://huggingface.co/transformers/custom_datasets.html.

Distilled or smaller pretrained models?

Hi

xlm-t works really well on my dataset however to make it effective performance-wise I was wondering if you guys are planning to release smaller pretrained or distilled models?

Best
Anup

Better preprocessing

Hello,

I was wondering if the preprocess function could be enhanced as right now, it strips punctuations before and after usernames/URLs. Or was it done on purpose? I couldn't find a justification of this in your paper.

Right now, the preprocess function below would convert:

I love you @louisia!!!!

to

I love you @user

# Preprocess text (username and link placeholders)
def preprocess(text):
    new_text = []
    for t in text.split(" "):
        t = '@user' if t.startswith('@') and len(t) > 1 else t
        t = 'http' if t.startswith('http') else t
        new_text.append(t)
    return " ".join(new_text)

It seems to me that punctuations could help the model predict the sentiment of a tweet a little better if it was available to it. Another example: some users on twitter, start their tweets with a dot like this:

.@rudy is really bad. What a shame.

They do that to avoid the reply system while still quoting a username. With the actual pre-processing function, "@rudy" doesn't get replaced because there is a dot right before the @.

Is there any particular reason why the preprocessing function was done this way or we could try to make it more flexible in our end by keeping the punctuations next to usernames or URLs?

Thank you!

Will the XLM-Tw Mono models be released?

I think the Monolingual models will also make a great contribution!

Use models with zero-shot classification

Hello,

First, thanks for these great models! I was wondering if I could use these models for zero-shots classification, especially for emotion detection (Ekman). While doing so, I encountered this error regarding the config of the models and the missing entailment parameter which seems mandatory for zero-shot classification:

Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to -1. Define a descriptive label2id mapping in the model config to ensure correct outputs.

Basic code to reproduce:

model_path = "cardiffnlp/twitter-xlm-roberta-base"
zero_shot = pipeline("zero-shot-classification", model=model_path, tokenizer=model_path,)
zero_shot("You are an awful man,", candidate_labels=["anger", "disgust", "fear", "joy", "sadness", "surprise"], multi_label=True,)

It seems to lead indeed to weird outputs compared to the XLM-Roberta XNLI model that can be tested here: https://huggingface.co/zero-shot/.

Is there something missing in head_config.json?

Thanks!

Output labels

How could I get all three possible labels with probabilities in output?

readme dead link

The work on this is excellent. I've been using for fine-tuning sentiment / topic models with great success!

One very nitpicky thing I noticed in the repo is that the README.md contains a link to https://huggingface.co/docs/transformers/model_doc/xlmroberta which is now broken. I believe that this should be https://huggingface.co/docs/transformers/model_doc/xlm-roberta.

Potential (very minor) bug in fine-tuning script

I believe this line may be a bug, as it overrides the global variable set earlier in the file:

xlm-t/src/adapter_finetuning.py

Line 63 in aa9b15e

language = 'spanish'