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/usr/local/lib/python3.9/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
warnings.warn(
/usr/local/lib/python3.9/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
warnings.warn(
2024-05-16 12:11:24.934695: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data] Package punkt is already up-to-date!
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data] Package stopwords is already up-to-date!
/usr/local/lib/python3.9/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
warnings.warn(
The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
Some weights of the model checkpoint at textattack/roberta-base-CoLA were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']
- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
Framework not specified. Using pt to export the model.
Some weights of the model checkpoint at textattack/roberta-base-CoLA were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']
- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Using the export variant default. Available variants are:
- default: The default ONNX variant.
***** Exporting submodel 1/1: RobertaForSequenceClassification *****
Using framework PyTorch: 2.3.0+cu121
Overriding 1 configuration item(s)
- use_cache -> False
Framework not specified. Using pt to export the model.
Using the export variant default. Available variants are:
- default: The default ONNX variant.
Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.
Non-default generation parameters: {'max_length': 512, 'min_length': 8, 'num_beams': 2, 'no_repeat_ngram_size': 4}
/usr/local/lib/python3.9/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
warnings.warn(
***** Exporting submodel 1/3: T5Stack *****
Using framework PyTorch: 2.3.0+cu121
Overriding 1 configuration item(s)
- use_cache -> False
***** Exporting submodel 2/3: T5ForConditionalGeneration *****
Using framework PyTorch: 2.3.0+cu121
Overriding 1 configuration item(s)
- use_cache -> True
/usr/local/lib/python3.9/dist-packages/transformers/modeling_utils.py:1017: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if causal_mask.shape[1] < attention_mask.shape[1]:
***** Exporting submodel 3/3: T5ForConditionalGeneration *****
Using framework PyTorch: 2.3.0+cu121
Overriding 1 configuration item(s)
- use_cache -> True
/usr/local/lib/python3.9/dist-packages/transformers/models/t5/modeling_t5.py:503: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
elif past_key_value.shape[2] != key_value_states.shape[1]:
In-place op on output of tensor.shape. See https://pytorch.org/docs/master/onnx.html#avoid-inplace-operations-when-using-tensor-shape-in-tracing-mode
In-place op on output of tensor.shape. See https://pytorch.org/docs/master/onnx.html#avoid-inplace-operations-when-using-tensor-shape-in-tracing-mode
Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.
Non-default generation parameters: {'max_length': 512, 'min_length': 8, 'num_beams': 2, 'no_repeat_ngram_size': 4}
/usr/local/lib/python3.9/dist-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML
warnings.warn("Can't initialize NVML")
[nltk_data] Downloading package cmudict to /root/nltk_data...
[nltk_data] Package cmudict is already up-to-date!
[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data] Package punkt is already up-to-date!
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data] Package stopwords is already up-to-date!
[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data] Package wordnet is already up-to-date!
/usr/local/lib/python3.9/dist-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML
warnings.warn("Can't initialize NVML")
Collecting en-core-web-sm==3.7.1
Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl (12.8 MB)
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[38;5;2m✔ Download and installation successful[0m
You can now load the package via spacy.load('en_core_web_sm')
/usr/local/lib/python3.9/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
warnings.warn(
/usr/local/lib/python3.9/dist-packages/gradio/utils.py:924: UserWarning: Expected 1 arguments for function <function depth_analysis at 0x7f402afeff70>, received 2.
warnings.warn(
/usr/local/lib/python3.9/dist-packages/gradio/utils.py:932: UserWarning: Expected maximum 1 arguments for function <function depth_analysis at 0x7f402afeff70>, received 2.
warnings.warn(
IMPORTANT: You are using gradio version 4.26.0, however version 4.29.0 is available, please upgrade.
--------
Running on local URL: http://0.0.0.0:80
Running on public URL: https://3122a891c774a52363.gradio.live
This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)
correcting text..: 0%| | 0/1 [00:00<?, ?it/s]
correcting text..: 100%|██████████| 1/1 [00:00<00:00, 13.00it/s]
/usr/local/lib/python3.9/dist-packages/optimum/bettertransformer/models/encoder_models.py:301: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)
hidden_states = torch._nested_tensor_from_mask(hidden_states, ~attention_mask)
Traceback (most recent call last):
File "/usr/local/lib/python3.9/dist-packages/gradio/queueing.py", line 527, in process_events
response = await route_utils.call_process_api(
File "/usr/local/lib/python3.9/dist-packages/gradio/route_utils.py", line 261, in call_process_api
output = await app.get_blocks().process_api(
File "/usr/local/lib/python3.9/dist-packages/gradio/blocks.py", line 1786, in process_api
result = await self.call_function(
File "/usr/local/lib/python3.9/dist-packages/gradio/blocks.py", line 1338, in call_function
prediction = await anyio.to_thread.run_sync(
File "/usr/local/lib/python3.9/dist-packages/anyio/to_thread.py", line 56, in run_sync
return await get_async_backend().run_sync_in_worker_thread(
File "/usr/local/lib/python3.9/dist-packages/anyio/_backends/_asyncio.py", line 2144, in run_sync_in_worker_thread
return await future
File "/usr/local/lib/python3.9/dist-packages/anyio/_backends/_asyncio.py", line 851, in run
result = context.run(func, *args)
File "/usr/local/lib/python3.9/dist-packages/gradio/utils.py", line 759, in wrapper
response = f(*args, **kwargs)
File "/home/aliasgarov/copyright_checker/app.py", line 35, in ai_generated_test
return predict_bc_scores(input), predict_mc_scores(input)
File "/home/aliasgarov/copyright_checker/predictors.py", line 390, in predict_mc_scores
for key in models
NameError: name 'models' is not defined
Traceback (most recent call last):
File "/usr/local/lib/python3.9/dist-packages/gradio/queueing.py", line 527, in process_events
response = await route_utils.call_process_api(
File "/usr/local/lib/python3.9/dist-packages/gradio/route_utils.py", line 261, in call_process_api
output = await app.get_blocks().process_api(
File "/usr/local/lib/python3.9/dist-packages/gradio/blocks.py", line 1786, in process_api
result = await self.call_function(
File "/usr/local/lib/python3.9/dist-packages/gradio/blocks.py", line 1338, in call_function
prediction = await anyio.to_thread.run_sync(
File "/usr/local/lib/python3.9/dist-packages/anyio/to_thread.py", line 56, in run_sync
return await get_async_backend().run_sync_in_worker_thread(
File "/usr/local/lib/python3.9/dist-packages/anyio/_backends/_asyncio.py", line 2144, in run_sync_in_worker_thread
return await future
File "/usr/local/lib/python3.9/dist-packages/anyio/_backends/_asyncio.py", line 851, in run
result = context.run(func, *args)
File "/usr/local/lib/python3.9/dist-packages/gradio/utils.py", line 759, in wrapper
response = f(*args, **kwargs)
File "/home/aliasgarov/copyright_checker/app.py", line 35, in ai_generated_test
return predict_bc_scores(input), predict_mc_scores(input)
File "/home/aliasgarov/copyright_checker/predictors.py", line 390, in predict_mc_scores
for key in models
NameError: name 'models' is not defined
|