Why does LanguageModelFeaturizers only download the model having tf_model.h5 but not pytorch_model.bin

Rasa Open Source version

2.2.5

Rasa SDK version

No response

Rasa X version

No response

Python version

3.7

What operating system are you using?

Other

What happened?

I was train chatbot for spanish language and i have found some pretrained finetuned models on huggingface which i wanted to test for featurizers. But trying several such models what i found was that rasa only downloads weights if there is tf_model.h5 present on that huggingface/models/… directories. But if there is only pytorch_model.bin present it throws error that no model is found on the huggingface/models/given_name. And in error it says it doesn’t found tf_model.h5 or pytorch_model.bin.

Command / Request

nest_asyncio.apply()
 print("event loop ready")

start_time=time.time()
 domain= '/content/drive/MyDrive/spanish/small/domain_small.yml'
 config='/content/drive/MyDrive/spanish/small/config/config_bert-base-spanish-wwm-cased-xnli.yml'
 training_files='/content/drive/MyDrive/spanish/small/data/small_spanish.yml'
 rasa.train(domain,config,training_files)
 print(f"Time Taken to train the model : 

 {time.time()-start_time}")

h3. Relevant log output

404 Client Error: Not Found for url: [https://huggingface.co/Recognai/bert-base-spanish-wwm-cased-xnli/resolve/main/tf_model.h5](https://huggingface.co/Recognai/bert-base-spanish-wwm-cased-xnli/resolve/main/tf_model.h5)

 ---------------------------------------------------------------------------

 HTTPError Traceback (most recent call last)

 /usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)

 1247 use_auth_token=use_auth_token,

 -> 1248 user_agent=user_agent,

 1249 )

 20 frames

 /usr/local/lib/python3.7/dist-packages/transformers/file_utils.py in cached_path(url_or_filename, cache_dir, force_download, proxies, resume_download, user_agent, extract_compressed_file, force_extract, use_auth_token, local_files_only)

 1336 use_auth_token=use_auth_token,

 -> 1337 local_files_only=local_files_only,

 1338 )

 /usr/local/lib/python3.7/dist-packages/transformers/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, use_auth_token, local_files_only)

 1499 r = requests.head(url, headers=headers, allow_redirects=False, proxies=proxies, timeout=etag_timeout)

 -> 1500 r.raise_for_status()

 1501 etag = r.headers.get("X-Linked-Etag") or r.headers.get("ETag")

 /usr/local/lib/python3.7/dist-packages/requests/models.py in raise_for_status(self)

 942 if http_error_msg:

 --> 943 raise HTTPError(http_error_msg, response=self)

 944 

 HTTPError: 404 Client Error: Not Found for url: [https://huggingface.co/Recognai/bert-base-spanish-wwm-cased-xnli/resolve/main/tf_model.h5](https://huggingface.co/Recognai/bert-base-spanish-wwm-cased-xnli/resolve/main/tf_model.h5)

 During handling of the above exception, another exception occurred:

 OSError Traceback (most recent call last)

 <ipython-input-13-ae58ff173a41>

 in 

 <module>

 6 config='/content/drive/MyDrive/spanish/small/config/config_bert-base-spanish-wwm-cased-xnli.yml'

 7 training_files='/content/drive/MyDrive/spanish/small/data/small_spanish.yml'

 ----> 8 rasa.train(domain,config,training_files)

 9 print(f"Time Taken to train the model : {time.time()-start_time} 

")

/usr/local/lib/python3.7/dist-packages/rasa/train.py in train(domain, config, training_files, output, dry_run, force_training, fixed_model_name, persist_nlu_training_data, core_additional_arguments, nlu_additional_arguments, loop, model_to_finetune, finetuning_epoch_fraction)
 107 finetuning_epoch_fraction=finetuning_epoch_fraction,
 108 ),
 --> 109 loop,
 110 )
 111 

/usr/local/lib/python3.7/dist-packages/rasa/utils/common.py in run_in_loop(f, loop)
 306 loop = asyncio.new_event_loop()
 307 asyncio.set_event_loop(loop)
 --> 308 result = loop.run_until_complete(f)
 309 
 310 # Let's also finish all running tasks:

/usr/local/lib/python3.7/dist-packages/nest_asyncio.py in run_until_complete(self, future)
 68 raise RuntimeError(
 69 'Event loop stopped before Future completed.')
 ---> 70 return f.result()
 71 
 72 def _run_once(self):

/usr/lib/python3.7/asyncio/futures.py in result(self)
 179 self.__log_traceback = False
 180 if self._exception is not None:
 --> 181 raise self._exception
 182 return self._result
 183 

/usr/lib/python3.7/asyncio/tasks.py in __step(****failed resolving arguments****)
 247 # We use the `send` method directly, because coroutines
 248 # don't have `__iter__` and `__next__` methods.
 --> 249 result = coro.send(None)
 250 else:
 251 result = coro.throw(exc)

/usr/local/lib/python3.7/dist-packages/rasa/train.py in train_async(domain, config, training_files, output, dry_run, force_training, fixed_model_name, persist_nlu_training_data, core_additional_arguments, nlu_additional_arguments, model_to_finetune, finetuning_epoch_fraction)
 172 nlu_additional_arguments=nlu_additional_arguments,
 173 model_to_finetune=model_to_finetune,
 --> 174 finetuning_epoch_fraction=finetuning_epoch_fraction,
 175 )
 176 

/usr/local/lib/python3.7/dist-packages/rasa/train.py in _train_async_internal(file_importer, train_path, output_path, dry_run, force_training, fixed_model_name, persist_nlu_training_data, core_additional_arguments, nlu_additional_arguments, model_to_finetune, finetuning_epoch_fraction)
 303 additional_arguments=nlu_additional_arguments,
 304 model_to_finetune=model_to_finetune,
 --> 305 finetuning_epoch_fraction=finetuning_epoch_fraction,
 306 )
 307 return TrainingResult(model=trained_model)

/usr/local/lib/python3.7/dist-packages/rasa/train.py in _train_nlu_with_validated_data(file_importer, output, train_path, fixed_model_name, persist_nlu_training_data, additional_arguments, model_to_finetune, finetuning_epoch_fraction)
 816 persist_nlu_training_data=persist_nlu_training_data,
 817 model_to_finetune=model_to_finetune,
 --> 818 **additional_arguments,
 819 )
 820 rasa.shared.utils.cli.print_color(

/usr/local/lib/python3.7/dist-packages/rasa/nlu/train.py in train(nlu_config, data, path, fixed_model_name, storage, component_builder, training_data_endpoint, persist_nlu_training_data, model_to_finetune, **kwargs)
 96 # trained in another subprocess
 97 trainer = Trainer(
 ---> 98 nlu_config, component_builder, model_to_finetune=model_to_finetune
 99 )
 100 persistor = create_persistor(storage)

/usr/local/lib/python3.7/dist-packages/rasa/nlu/model.py in __init__(self, cfg, component_builder, skip_validation, model_to_finetune)
 161 self.pipeline = model_to_finetune.pipeline
 162 else:
 --> 163 self.pipeline = self._build_pipeline(cfg, component_builder)
 164 
 165 def _build_pipeline(

/usr/local/lib/python3.7/dist-packages/rasa/nlu/model.py in _build_pipeline(self, cfg, component_builder)
 172 for index, pipeline_component in enumerate(cfg.pipeline):
 173 component_cfg = cfg.for_component(index)
 --> 174 component = component_builder.create_component(component_cfg, cfg)
 175 components.validate_component_keys(component, pipeline_component)
 176 pipeline.append(component)

/usr/local/lib/python3.7/dist-packages/rasa/nlu/components.py in create_component(self, component_config, cfg)
 850 )
 851 if component is None:
 --> 852 component = registry.create_component_by_config(component_config, cfg)
 853 self.__add_to_cache(component, cache_key)
 854 return component

/usr/local/lib/python3.7/dist-packages/rasa/nlu/registry.py in create_component_by_config(component_config, config)
 191 component_name = component_config.get("class", component_config<span class="error">["name"]</span>)
 192 component_class = get_component_class(component_name)
 --> 193 return component_class.create(component_config, config)

/usr/local/lib/python3.7/dist-packages/rasa/nlu/featurizers/dense_featurizer/lm_featurizer.py in create(cls, component_config, config)
 103 else:
 104 hf_transformers_loaded = "HFTransformersNLP" in config.component_names
 --> 105 return cls(component_config, hf_transformers_loaded=hf_transformers_loaded)
 106 
 107 @classmethod

/usr/local/lib/python3.7/dist-packages/rasa/nlu/featurizers/dense_featurizer/lm_featurizer.py in __init__(self, component_config, skip_model_load, hf_transformers_loaded)
 86 return
 87 self._load_model_metadata()
 ---> 88 self._load_model_instance(skip_model_load)
 89 
 90 @classmethod

/usr/local/lib/python3.7/dist-packages/rasa/nlu/featurizers/dense_featurizer/lm_featurizer.py in _load_model_instance(self, skip_model_load)
 197 )
 198 self.model = model_class_dict<span class="error">[self.model_name]</span>.from_pretrained(
 --> 199 self.model_weights, cache_dir=self.cache_dir
 200 )
 201 

/usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
 1255 f"- or '

 {pretrained_model_name_or_path} 

' is the correct path to a directory containing a file named one of 

 {TF2_WEIGHTS_NAME} 

, 

 {WEIGHTS_NAME} 

.\n\n"
 1256 )
 -> 1257 raise EnvironmentError(msg)
 1258 if resolved_archive_file == archive_file:
 1259 logger.info(f"loading weights file 

 {archive_file} 

")

OSError: Can't load weights for 'Recognai/bert-base-spanish-wwm-cased-xnli'. Make sure that:

*   'Recognai/bert-base-spanish-wwm-cased-xnli' is a correct model identifier listed on 'https://huggingface.co/models'

*   or 'Recognai/bert-base-spanish-wwm-cased-xnli' is the correct path to a directory containing a file named one of tf_model.h5, pytorch_model.bin.
     </module>
     </ipython-input-13-ae58ff173a41>

[lhr0909]

Exalate commented:

lhr0909 commented:

@JEM-Mosig Actually I have tested by adding this from_pt flag and also including the pytorch dependency and it is working fine. I have a working example repository that contains this change - https://github.com/lhr0909/rasa-v2-nlu-bert-chinese/commit/2a431adde81095f705858925b02ecf07a932fda4

For PyTorch dependency, I only had to manually include a version of transformers with torch extras - https://github.com/lhr0909/rasa-v2-nlu-bert-chinese/blob/main/pyproject.toml#L10

[sync-by-unito[bot]]

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