Original BC scores: AI: 0.983885645866394, HUMAN: 0.01611432246863842 Calibration BC scores: AI: 0.5142857142857142, HUMAN: 0.48571428571428577 Input Text: sOperation Title was an unsuccessful 1942 Allied attack on the German battleship Tirpitz during World War II. The Allies considered Tirpitz to be a major threat to their shipping and after several Royal Air Force heavy bomber raids failed to inflict any damage it was decided to use Royal Navy midget submarines instead. /s correcting text..: 0%| | 0/2 [00:00, ] Received outputs: ["Operation Title was an unsuccessful 1942 Allied attack on the German battleship Tirpitz during World War II. The Allies considered Tirpitz to be a major threat to their shipping and after several Royal Air Force heavy bomber raids failed to inflict any damage it was decided to use Royal Navy midget submarines instead."] /usr/lib/python3/dist-packages/requests/__init__.py:87: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (4.0.0) doesn't match a supported version! warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported " 2024-05-15 18:41:05.953508: 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. 2024-05-15 18:41:11.449382: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT [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! 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} ***** 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} [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/lib/python3/dist-packages/requests/__init__.py:87: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (4.0.0) doesn't match a supported version! warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported " Collecting en_core_web_sm==2.3.1 Using cached en_core_web_sm-2.3.1-py3-none-any.whl Requirement already satisfied: spacy<2.4.0,>=2.3.0 in /usr/local/lib/python3.9/dist-packages (from en_core_web_sm==2.3.1) (2.3.9) Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (3.0.9) Requirement already satisfied: blis<0.8.0,>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.7.11) Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (4.66.2) Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.7) Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/lib/python3/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.25.1) Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.1.3) Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (52.0.0) Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.0.8) Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.10) Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.10.1) Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.26.4) Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.2) Requirement already satisfied: thinc<7.5.0,>=7.4.1 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (7.4.6) ✔ Download and installation successful You can now load the model via spacy.load('en_core_web_sm') /usr/local/lib/python3.9/dist-packages/gradio/utils.py:953: UserWarning: Expected 1 arguments for function , received 2. warnings.warn( /usr/local/lib/python3.9/dist-packages/gradio/utils.py:961: UserWarning: Expected maximum 1 arguments for function , received 2. warnings.warn( IMPORTANT: You are using gradio version 4.28.3, however version 4.29.0 is available, please upgrade. -------- Running on local URL: http://0.0.0.0:80 Running on public URL: https://1f9431205fb743687b.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%| | 0/2 [00:00 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} ***** 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} [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/lib/python3/dist-packages/requests/__init__.py:87: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (4.0.0) doesn't match a supported version! warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported " Collecting en_core_web_sm==2.3.1 Using cached en_core_web_sm-2.3.1-py3-none-any.whl Requirement already satisfied: spacy<2.4.0,>=2.3.0 in /usr/local/lib/python3.9/dist-packages (from en_core_web_sm==2.3.1) (2.3.9) Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.26.4) Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (3.0.9) Requirement already satisfied: thinc<7.5.0,>=7.4.1 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (7.4.6) Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.2) Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.1.3) Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/lib/python3/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.25.1) Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.10.1) Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.7) Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (4.66.2) Requirement already satisfied: blis<0.8.0,>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.7.11) Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (52.0.0) Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.10) Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.0.8) ✔ Download and installation successful You can now load the model via spacy.load('en_core_web_sm') /usr/local/lib/python3.9/dist-packages/gradio/utils.py:953: UserWarning: Expected 1 arguments for function , received 2. warnings.warn( /usr/local/lib/python3.9/dist-packages/gradio/utils.py:961: UserWarning: Expected maximum 1 arguments for function , received 2. warnings.warn( WARNING: Invalid HTTP request received. huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /usr/local/lib/python3.9/dist-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML warnings.warn("Can't initialize NVML") /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) /usr/lib/python3/dist-packages/requests/__init__.py:87: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (4.0.0) doesn't match a supported version! warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported " 2024-05-15 22:08:54.473739: 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. 2024-05-15 22:09:00.121158: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT [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! 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} ***** 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} [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/lib/python3/dist-packages/requests/__init__.py:87: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (4.0.0) doesn't match a supported version! warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported " Collecting en_core_web_sm==2.3.1 Using cached en_core_web_sm-2.3.1-py3-none-any.whl Requirement already satisfied: spacy<2.4.0,>=2.3.0 in /usr/local/lib/python3.9/dist-packages (from en_core_web_sm==2.3.1) (2.3.9) Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.1.3) Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.10) Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.2) Requirement already satisfied: blis<0.8.0,>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.7.11) Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (52.0.0) Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.26.4) Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/lib/python3/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.25.1) Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (4.66.2) Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.10.1) Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (3.0.9) Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.0.8) Requirement already satisfied: thinc<7.5.0,>=7.4.1 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (7.4.6) Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.7) ✔ Download and installation successful You can now load the model via spacy.load('en_core_web_sm') /usr/local/lib/python3.9/dist-packages/gradio/utils.py:953: UserWarning: Expected 1 arguments for function , received 2. warnings.warn( /usr/local/lib/python3.9/dist-packages/gradio/utils.py:961: UserWarning: Expected maximum 1 arguments for function , received 2. warnings.warn( huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /usr/local/lib/python3.9/dist-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML warnings.warn("Can't initialize NVML") /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) WARNING: Invalid HTTP request received. WARNING: Invalid HTTP request received. WARNING: Invalid HTTP request received. WARNING: Invalid HTTP request received. WARNING: Invalid HTTP request received. WARNING: Invalid HTTP request received. WARNING: Invalid HTTP request received. 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