diff --git "a/nohup.out" "b/nohup.out" --- "a/nohup.out" +++ "b/nohup.out" @@ -983,3 +983,1224 @@ Traceback (most recent call last): File "/home/aliasgarov/copyright_checker/highlighter.py", line 76, in segmented_higlighter chunk = analyze_and_highlight(segment, model_type) TypeError: analyze_and_highlight() missing 1 required positional argument: 'model_type' +/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( +2024-05-24 15:07:46.220332: 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} +[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! +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) +Requirement already satisfied: spacy<3.8.0,>=3.7.2 in /usr/local/lib/python3.9/dist-packages (from en-core-web-sm==3.7.1) (3.7.2) +Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.0.5) +Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.1.4) +Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.8) +Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.12) +Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.1.2) +Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.10) +Requirement already satisfied: weasel<0.4.0,>=0.1.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.3.4) +Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.9.4) +Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (4.66.4) +Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/lib/python3/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.25.1) +Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.4.8) +Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (24.0) +Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (52.0.0) +Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.7.1) +Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.4.0) +Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.9) +Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (6.4.0) +Requirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.26.4) +Requirement already satisfied: thinc<8.3.0,>=8.1.8 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.2.3) +Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.0.10) +Requirement already satisfied: language-data>=1.2 in /usr/local/lib/python3.9/dist-packages (from langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.2.0) +Requirement already satisfied: marisa-trie>=0.7.7 in /usr/local/lib/python3.9/dist-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.1.1) +Requirement already satisfied: pydantic-core==2.18.2 in /usr/local/lib/python3.9/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.18.2) +Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.6.0) +Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.9/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (4.11.0) +Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.9/dist-packages (from thinc<8.3.0,>=8.1.8->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.1.4) +Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.9/dist-packages (from thinc<8.3.0,>=8.1.8->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.7.11) +Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.9/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.1.7) +Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /usr/local/lib/python3.9/dist-packages (from weasel<0.4.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.16.0) +Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.1.5) +✔ Download and installation successful +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/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) +/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() +Token indices sequence length is longer than the specified maximum sequence length for this model (608 > 512). Running this sequence through the model will result in indexing errors +/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() +/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() +/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() +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://db2dd43e1eaabbe457.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) +{'jk': -8.872753309602045e-07} bc + jk +{'Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings.': -0.0030031163845423386, 'There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration.': -0.008861353132144185, 'This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings.': -0.00816814173993065} bc +{'There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration.': -0.005809096406831128, 'This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings.': -0.005278456514274453} bc +{'There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration.': -0.0491698458277019, 'This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings.': -0.2840174218811022, 'This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.': 0} bc + Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents. /home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() +/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() +/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() + +{'Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings.': 0.0061080332410075495, 'There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration.': 0.007593453242722901, 'This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings.': 0.006519691591232659} quillbot +{'There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration.': 0.0020568907732014885, 'This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings.': -0.0025645508066275634} quillbot +{'There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration.': 0.2250801436509686, 'This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings.': -0.18870455955915244, 'This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.': 0} quillbot + Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents.Keyword re-ranking shows better performance compared to cosine similarity, more obvious at the top 10 and top 20 settings. There is a great enhancement as the value of k increases, which means that keyword-based reranking will be even more effective when more documents are taken into consideration. This method leverages the strengths of keyword matching in enhancing the relevance of the retrieved documents. + correcting text..: 0%| | 0/25 [00:00Elon Musk is a visionary entrepreneur and engineer known for his significant contributions to various industries, particularly in technology and space exploration. Born on June 28, 1971, in Pretoria, South Africa, Musk showed an early interest in computing and technology. He moved to the United States to attend Stanford University but dropped out to pursue a career in business. Musk co-founded Zip2, a software company, which was sold to Compaq for nearly $300 million in 1999. He then founded X.com, an online payment company that later became PayPal, which was acquired by eBay for $1.5 billion in 2002. These early successes provided Musk with the capital to pursue more ambitious projects. In 2002, Musk founded SpaceX (Space Exploration Technologies Corp.), aiming to reduce space transportation costs and enable the colonization of Mars. SpaceX has achieved numerous milestones, including the first privately-funded spacecraft to reach orbit, the first private company to dock with the International Space Station, and the development of the reusable Falcon rockets. Musk is also the CEO and product architect of Tesla, Inc., a company dedicated to producing electric vehicles and renewable energy products. Under his leadership, Tesla has become a leading automaker, known for its innovative technology and significant advancements in battery and autonomous driving technologies. In addition to SpaceX and Tesla, Musk has founded or co-founded several other ventures, including SolarCity (now part of Tesla), Neuralink (focused on brain-machine interfaces), The Boring Company (aiming to revolutionize tunneling and infrastructure), and OpenAI (a research organization dedicated to artificial intelligence). Musk's ambitious vision and relentless pursuit of innovation have made him one of the most influential figures of the 21st century, driving significant advancements in multiple fields and continually pushing the boundaries of what is possible. /home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() +/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() + +{'Elon Musk is a visionary entrepreneur and engineer known for his significant contributions to various industries, particularly in technology and space exploration.': -0.36589521189756197, 'Born on June 28, 1971, in Pretoria, South Africa, Musk showed an early interest in computing and technology.': 0.21610850486862646, 'He moved to the United States to attend Stanford University but dropped out to pursue a career in business.': -0.061798010510815155, 'Musk co-founded Zip2, a software company, which was sold to Compaq for nearly $300 million in 1999.': -0.0052227460697966775, 'He then founded X.com, an online payment company that later became PayPal, which was acquired by eBay for $1.5 billion in 2002.': -0.06568645402515753, 'These early successes provided Musk with the capital to pursue more ambitious projects.': 0.01582929346200347, 'In 2002, Musk founded SpaceX (Space Exploration Technologies Corp.), aiming to reduce space transportation costs and enable the colonization of Mars.': 0.06315053857017684, 'SpaceX has achieved numerous milestones, including the first privately-funded spacecraft to reach orbit, the first private company to dock with the International Space Station, and the development of the reusable Falcon rockets.': -0.03064580030371698, 'Musk is also the CEO and product architect of Tesla, Inc., a company dedicated to producing electric vehicles and renewable energy products.': -0.028476100355704616, 'Under his leadership, Tesla has become a leading automaker, known for its innovative technology and significant advancements in battery and autonomous driving technologies.': -0.02717728699403495, 'In addition to SpaceX and Tesla, Musk has founded or co-founded several other ventures, including SolarCity (now part of Tesla), Neuralink (focused on brain-machine interfaces), The Boring Company (aiming to revolutionize tunneling and infrastructure), and OpenAI (a research organization dedicated to artificial intelligence).': -0.01555210915064498} quillbot +{"Musk's ambitious vision and relentless pursuit of innovation have made him one of the most influential figures of the 21st century, driving significant advancements in multiple fields and continually pushing the boundaries of what is possible.": -0.03078299437838741} quillbot + Elon Musk is a visionary entrepreneur and engineer known for his significant contributions to various industries, particularly in technology and space exploration. Born on June 28, 1971, in Pretoria, South Africa, Musk showed an early interest in computing and technology. He moved to the United States to attend Stanford University but dropped out to pursue a career in business. Musk co-founded Zip2, a software company, which was sold to Compaq for nearly $300 million in 1999. He then founded X.com, an online payment company that later became PayPal, which was acquired by eBay for $1.5 billion in 2002. These early successes provided Musk with the capital to pursue more ambitious projects. In 2002, Musk founded SpaceX (Space Exploration Technologies Corp.), aiming to reduce space transportation costs and enable the colonization of Mars. SpaceX has achieved numerous milestones, including the first privately-funded spacecraft to reach orbit, the first private company to dock with the International Space Station, and the development of the reusable Falcon rockets. Musk is also the CEO and product architect of Tesla, Inc., a company dedicated to producing electric vehicles and renewable energy products. Under his leadership, Tesla has become a leading automaker, known for its innovative technology and significant advancements in battery and autonomous driving technologies. In addition to SpaceX and Tesla, Musk has founded or co-founded several other ventures, including SolarCity (now part of Tesla), Neuralink (focused on brain-machine interfaces), The Boring Company (aiming to revolutionize tunneling and infrastructure), and OpenAI (a research organization dedicated to artificial intelligence). Musk's ambitious vision and relentless pursuit of innovation have made him one of the most influential figures of the 21st century, driving significant advancements in multiple fields and continually pushing the boundaries of what is possible. +['Elon Musk is a visionary entrepreneur and engineer known for his significant contributions to various industries, particularly in technology and space exploration.', 'Born on June 28, 1971, in Pretoria, South Africa, Musk showed an early interest in computing and technology.', 'He moved to the United States to attend Stanford University but dropped out to pursue a career in business.', 'Musk co-founded Zip2, a software company, which was sold to Compaq for nearly $300 million in 1999.', 'He then founded X.com, an online payment company that later became PayPal, which was acquired by eBay for $1.5 billion in 2002.', 'These early successes provided Musk with the capital to pursue more ambitious projects.', 'In 2002, Musk founded SpaceX (Space Exploration Technologies Corp.), aiming to reduce space transportation costs and enable the colonization of Mars.', 'SpaceX has achieved numerous milestones, including the first privately-funded spacecraft to reach orbit, the first private company to dock with the International Space Station, and the development of the reusable Falcon rockets.', 'Musk is also the CEO and product architect of Tesla, Inc., a company dedicated to producing electric vehicles and renewable energy products.', 'Under his leadership, Tesla has become a leading automaker, known for its innovative technology and significant advancements in battery and autonomous driving technologies.', 'In addition to SpaceX and Tesla, Musk has founded or co-founded several other ventures, including SolarCity (now part of Tesla), Neuralink (focused on brain-machine interfaces), The Boring Company (aiming to revolutionize tunneling and infrastructure), and OpenAI (a research organization dedicated to artificial intelligence).', "Musk's ambitious vision and relentless pursuit of innovation have made him one of the most influential figures of the 21st century, driving significant advancements in multiple fields and continually pushing the boundaries of what is possible."] +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) +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) +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) +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) +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) +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) +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) +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) +PLAGIARISM PROCESSING TIME: 39.20452429796569 +["Elon Musk is a visionary entrepreneur and engineer known for his significant contributions to various industries, particularly in technology and space exploration. Born on June 28, 1971, in Pretoria, South Africa, Musk showed an early interest in computing and technology. He moved to the United States to attend Stanford University but dropped out to pursue a career in business. Musk co-founded Zip2, a software company, which was sold to Compaq for nearly $300 million in 1999. He then founded X.com, an online payment company that later became PayPal, which was acquired by eBay for $1.5 billion in 2002. These early successes provided Musk with the capital to pursue more ambitious projects. In 2002, Musk founded SpaceX (Space Exploration Technologies Corp.), aiming to reduce space transportation costs and enable the colonization of Mars. SpaceX has achieved numerous milestones, including the first privately-funded spacecraft to reach orbit, the first private company to dock with the International Space Station, and the development of the reusable Falcon rockets. Musk is also the CEO and product architect of Tesla, Inc., a company dedicated to producing electric vehicles and renewable energy products. Under his leadership, Tesla has become a leading automaker, known for its innovative technology and significant advancements in battery and autonomous driving technologies. In addition to SpaceX and Tesla, Musk has founded or co-founded several other ventures, including SolarCity (now part of Tesla), Neuralink (focused on brain-machine interfaces), The Boring Company (aiming to revolutionize tunneling and infrastructure), and OpenAI (a research organization dedicated to artificial intelligence). Musk's ambitious vision and relentless pursuit of innovation have made him one of the most influential figures of the 21st century, driving significant advancements in multiple fields and continually pushing the boundaries of what is possible."] +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) +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) +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) +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) +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) +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) +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) +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) +WARNING: Invalid HTTP request received. +/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() +/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() +/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() +/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() +PLAGIARISM PROCESSING TIME: 2.5527665769914165 +{'Elon Musk is a visionary entrepreneur and engineer known for his significant contributions to various industries, particularly in technology and space exploration.': 0.00044753905935231695, 'Born on June 28, 1971, in Pretoria, South Africa, Musk showed an early interest in computing and technology.': -0.000201467142712945, 'He moved to the United States to attend Stanford University but dropped out to pursue a career in business.': -0.0001234571940431813, 'Musk co-founded Zip2, a software company, which was sold to Compaq for nearly $300 million in 1999.': 4.394345078811714e-05, 'He then founded X.com, an online payment company that later became PayPal, which was acquired by eBay for $1.5 billion in 2002.': -0.00031589124794449436, 'These early successes provided Musk with the capital to pursue more ambitious projects.': 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bc + Elon Musk is a visionary entrepreneur and engineer known for his significant contributions to various industries, particularly in technology and space exploration. Born on June 28, 1971, in Pretoria, South Africa, Musk showed an early interest in computing and technology. He moved to the United States to attend Stanford University but dropped out to pursue a career in business. Musk co-founded Zip2, a software company, which was sold to Compaq for nearly $300 million in 1999. He then founded X.com, an online payment company that later became PayPal, which was acquired by eBay for $1.5 billion in 2002. These early successes provided Musk with the capital to pursue more ambitious projects. In 2002, Musk founded SpaceX (Space Exploration Technologies Corp.), aiming to reduce space transportation costs and enable the colonization of Mars. SpaceX has achieved numerous milestones, including the first privately-funded spacecraft to reach orbit, the first private company to dock with the International Space Station, and the development of the reusable Falcon rockets. Musk is also the CEO and product architect of Tesla, Inc., a company dedicated to producing electric vehicles and renewable energy products. Under his leadership, Tesla has become a leading automaker, known for its innovative technology and significant advancements in battery and autonomous driving technologies. In addition to SpaceX and Tesla, Musk has founded or co-founded several other ventures, including SolarCity (now part of Tesla), Neuralink (focused on brain-machine interfaces), The Boring Company (aiming to revolutionize tunneling and infrastructure), and OpenAI (a research organization dedicated to artificial intelligence). Musk's ambitious vision and relentless pursuit of innovation have made him one of the most influential figures of the 21st century, driving significant advancements in multiple fields and continually pushing the boundaries of what is possible.Elon Musk is a visionary entrepreneur and engineer known for his significant contributions to various industries, particularly in technology and space exploration. Born on June 28, 1971, in Pretoria, South Africa, Musk showed an early interest in computing and technology. He moved to the United States to attend Stanford University but dropped out to pursue a career in business. Musk co-founded Zip2, a software company, which was sold to Compaq for nearly $300 million in 1999. He then founded X.com, an online payment company that later became PayPal, which was acquired by eBay for $1.5 billion in 2002. These early successes provided Musk with the capital to pursue more ambitious projects. In 2002, Musk founded SpaceX (Space Exploration Technologies Corp.), aiming to reduce space transportation costs and enable the colonization of Mars. SpaceX has achieved numerous milestones, including the first privately-funded spacecraft to reach orbit, the first private company to dock with the International Space Station, and the development of the reusable Falcon rockets. Musk is also the CEO and product architect of Tesla, Inc., a company dedicated to producing electric vehicles and renewable energy products. Under his leadership, Tesla has become a leading automaker, known for its innovative technology and significant advancements in battery and autonomous driving technologies. In addition to SpaceX and Tesla, Musk has founded or co-founded several other ventures, including SolarCity (now part of Tesla), Neuralink (focused on brain-machine interfaces), The Boring Company (aiming to revolutionize tunneling and infrastructure), and OpenAI (a research organization dedicated to artificial intelligence). Musk's ambitious vision and relentless pursuit of innovation have made him one of the most influential figures of the 21st century, driving significant advancements in multiple fields and continually pushing the boundaries of what is possible.Elon Musk is a visionary entrepreneur and engineer known for his significant contributions to various industries, particularly in technology and space exploration. Born on June 28, 1971, in Pretoria, South Africa, Musk showed an early interest in computing and technology. He moved to the United States to attend Stanford University but dropped out to pursue a career in business. Musk co-founded Zip2, a software company, which was sold to Compaq for nearly $300 million in 1999. He then founded X.com, an online payment company that later became PayPal, which was acquired by eBay for $1.5 billion in 2002. These early successes provided Musk with the capital to pursue more ambitious projects. In 2002, Musk founded SpaceX (Space Exploration Technologies Corp.), aiming to reduce space transportation costs and enable the colonization of Mars. SpaceX has achieved numerous milestones, including the first privately-funded spacecraft to reach orbit, the first private company to dock with the International Space Station, and the development of the reusable Falcon rockets. Musk is also the CEO and product architect of Tesla, Inc., a company dedicated to producing electric vehicles and renewable energy products. Under his leadership, Tesla has become a leading automaker, known for its innovative technology and significant advancements in battery and autonomous driving technologies. In addition to SpaceX and Tesla, Musk has founded or co-founded several other ventures, including SolarCity (now part of Tesla), Neuralink (focused on brain-machine interfaces), The Boring Company (aiming to revolutionize tunneling and infrastructure), and OpenAI (a research organization dedicated to artificial intelligence). Musk's ambitious vision and relentless pursuit of innovation have made him one of the most influential figures of the 21st century, driving significant advancements in multiple fields and continually pushing the boundaries of what is possible. /home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() +/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() +/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() +/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. 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online payment company that later became PayPal, which was acquired by eBay for $1.5 billion in 2002.': -6.294261969404564e-06, 'These early successes provided Musk with the capital to pursue more ambitious projects.': 8.069229525414906e-06, 'In 2002, Musk founded SpaceX (Space Exploration Technologies Corp.), aiming to reduce space transportation costs and enable the colonization of Mars.': 1.179000853161012e-05, 'SpaceX has achieved numerous milestones, including the first privately-funded spacecraft to reach orbit, the first private company to dock with the International Space Station, and the development of the reusable Falcon rockets.': 1.6167648000035943e-05, 'Musk is also the CEO and product architect of Tesla, Inc., a company dedicated to producing electric vehicles and renewable energy products.': -1.487052298616323e-05, 'Under his leadership, Tesla has become a leading automaker, known for its innovative technology and significant advancements in battery and autonomous driving technologies.': 8.790879795241428e-06} bc +{'In addition to SpaceX and Tesla, Musk has founded or co-founded several other ventures, including SolarCity (now part of Tesla), Neuralink (focused on brain-machine interfaces), The Boring Company (aiming to revolutionize tunneling and infrastructure), and OpenAI (a research organization dedicated to artificial intelligence).': -3.9889572785224333e-07, "Musk's ambitious vision and relentless pursuit of innovation have made him one of the most influential figures of the 21st century, driving significant advancements in multiple fields and continually pushing the boundaries of what is possible.Elon Musk is a visionary entrepreneur and engineer known for his significant contributions to various industries, particularly in technology and space exploration.": 2.2678116938226266e-06, 'Born on June 28, 1971, in Pretoria, South Africa, Musk showed an early interest in computing and technology.': 2.602240948211164e-06, 'He moved to the United States to attend Stanford University but dropped out to pursue a career in business.': -2.816057898021183e-07, 'Musk co-founded Zip2, a software company, which was sold to Compaq for nearly $300 million in 1999.': 5.897143501162772e-07, 'He then founded X.com, an online payment company that later became PayPal, which was acquired by eBay for $1.5 billion in 2002.': -5.03565742967412e-07, 'These early successes provided Musk with the capital to pursue more ambitious projects.': -7.049788142324002e-09, 'In 2002, Musk founded SpaceX (Space Exploration Technologies Corp.), aiming to reduce space transportation costs and enable the colonization of Mars.': -7.576140655632944e-07, 'SpaceX has achieved numerous milestones, including the first privately-funded spacecraft to reach orbit, the first private company to dock with the International Space Station, and the development of the reusable Falcon rockets.': 2.1389307024143268e-06} bc +{'Musk is also the CEO and product architect of Tesla, Inc., a company dedicated to producing electric vehicles and renewable energy products.': 7.729114871907261e-08, 'Under his leadership, Tesla has become a leading automaker, known for its innovative technology and significant advancements in battery and autonomous driving technologies.': 4.92477683736719e-07, 'In addition to SpaceX and Tesla, Musk has founded or co-founded several other ventures, including SolarCity (now part of Tesla), Neuralink (focused on brain-machine interfaces), The Boring Company (aiming to revolutionize tunneling and infrastructure), and OpenAI (a research organization dedicated to artificial intelligence).': 5.255280896606109e-07, "Musk's ambitious vision and relentless pursuit of innovation have made him one of the most influential figures of the 21st century, driving significant advancements in multiple fields and continually pushing the boundaries of what is possible.": 6.148959762979469e-07} bc + Elon Musk is a visionary entrepreneur and engineer known for his significant contributions to various industries, particularly in technology and space exploration. Born on June 28, 1971, in Pretoria, South Africa, Musk showed an early interest in computing and technology. He moved to the United States to attend Stanford University but dropped out to pursue a career in business. Musk co-founded Zip2, a software company, which was sold to Compaq for nearly $300 million in 1999. He then founded X.com, an online payment company that later became PayPal, which was acquired by eBay for $1.5 billion in 2002. These early successes provided Musk with the capital to pursue more ambitious projects. In 2002, Musk founded SpaceX (Space Exploration Technologies Corp.), aiming to reduce space transportation costs and enable the colonization of Mars. SpaceX has achieved numerous milestones, including the first privately-funded spacecraft to reach orbit, the first private company to dock with the International Space Station, and the development of the reusable Falcon rockets. Musk is also the CEO and product architect of Tesla, Inc., a company dedicated to producing electric vehicles and renewable energy products. Under his leadership, Tesla has become a leading automaker, known for its innovative technology and significant advancements in battery and autonomous driving technologies. In addition to SpaceX and Tesla, Musk has founded or co-founded several other ventures, including SolarCity (now part of Tesla), Neuralink (focused on brain-machine interfaces), The Boring Company (aiming to revolutionize tunneling and infrastructure), and OpenAI (a research organization dedicated to artificial intelligence). Musk's ambitious vision and relentless pursuit of innovation have made him one of the most influential figures of the 21st century, driving significant advancements in multiple fields and continually pushing the boundaries of what is possible.Elon Musk is a visionary entrepreneur and engineer known for his significant contributions to various industries, particularly in technology and space exploration. Born on June 28, 1971, in Pretoria, South Africa, Musk showed an early interest in computing and technology. He moved to the United States to attend Stanford University but dropped out to pursue a career in business. Musk co-founded Zip2, a software company, which was sold to Compaq for nearly $300 million in 1999. He then founded X.com, an online payment company that later became PayPal, which was acquired by eBay for $1.5 billion in 2002. These early successes provided Musk with the capital to pursue more ambitious projects. In 2002, Musk founded SpaceX (Space Exploration Technologies Corp.), aiming to reduce space transportation costs and enable the colonization of Mars. SpaceX has achieved numerous milestones, including the first privately-funded spacecraft to reach orbit, the first private company to dock with the International Space Station, and the development of the reusable Falcon rockets. Musk is also the CEO and product architect of Tesla, Inc., a company dedicated to producing electric vehicles and renewable energy products. Under his leadership, Tesla has become a leading automaker, known for its innovative technology and significant advancements in battery and autonomous driving technologies. In addition to SpaceX and Tesla, Musk has founded or co-founded several other ventures, including SolarCity (now part of Tesla), Neuralink (focused on brain-machine interfaces), The Boring Company (aiming to revolutionize tunneling and infrastructure), and OpenAI (a research organization dedicated to artificial intelligence). Musk's ambitious vision and relentless pursuit of innovation have made him one of the most influential figures of the 21st century, driving significant advancements in multiple fields and continually pushing the boundaries of what is possible.Elon Musk is a visionary entrepreneur and engineer known for his significant contributions to various industries, particularly in technology and space exploration. Born on June 28, 1971, in Pretoria, South Africa, Musk showed an early interest in computing and technology. He moved to the United States to attend Stanford University but dropped out to pursue a career in business. Musk co-founded Zip2, a software company, which was sold to Compaq for nearly $300 million in 1999. He then founded X.com, an online payment company that later became PayPal, which was acquired by eBay for $1.5 billion in 2002. These early successes provided Musk with the capital to pursue more ambitious projects. In 2002, Musk founded SpaceX (Space Exploration Technologies Corp.), aiming to reduce space transportation costs and enable the colonization of Mars. SpaceX has achieved numerous milestones, including the first privately-funded spacecraft to reach orbit, the first private company to dock with the International Space Station, and the development of the reusable Falcon rockets. Musk is also the CEO and product architect of Tesla, Inc., a company dedicated to producing electric vehicles and renewable energy products. Under his leadership, Tesla has become a leading automaker, known for its innovative technology and significant advancements in battery and autonomous driving technologies. In addition to SpaceX and Tesla, Musk has founded or co-founded several other ventures, including SolarCity (now part of Tesla), Neuralink (focused on brain-machine interfaces), The Boring Company (aiming to revolutionize tunneling and infrastructure), and OpenAI (a research organization dedicated to artificial intelligence). Musk's ambitious vision and relentless pursuit of innovation have made him one of the most influential figures of the 21st century, driving significant advancements in multiple fields and continually pushing the boundaries of what is possible. WARNING: Invalid HTTP request received. + +Original BC scores: AI: 6.0557872529898304e-06, HUMAN: 0.9999939203262329 +Calibration BC scores: AI: 0.008818342151675485, HUMAN: 0.9911816578483246 +Input Text: Each human language consists of a set of vowels and consonants which are combined to form words. During the speech production process, thoughts are converted into spoken utterances to convey a message. The appropriate words and their meanings are selected in the mental lexicon (Dell Burger, 1997). This pre-verbal message is then grammatically encoded, during which a syntactic representation of the utterance is built. The sounds are yet to be specified, but the abstract word symbols are assigned to their grammatical function before they are structured in a syntactic frame to determine the order (Cho-Reyes, Mack, Thompson, 2016). Subsequently, the message is phonologically encoded. During this stage, a phonetic or articulatory plan is retrieved for each individual lemma and the utterance as a whole. Finally, the speaker produces the utterance according to the phonetic plan (Levelt, 2002). Speech, language and voice disorders, such as apraxia, aphasia and spasmodic dysphonia, affect the vocal cords, nerves, muscles and brain structures, which results in a distorted language reception or speech production (Sataloff Hawkshaw, 2014). The symptoms vary from adding superfluous words and taking pauses to hoarseness of the voice, depending on the type of disorder (Dodd, 2005). +Original BC scores: AI: 2.6415679599267605e-07, HUMAN: 0.9999997615814209 +Calibration BC scores: AI: 0.0013484877672895396, HUMAN: 0.9986515122327104 +Input Text: As systems became more complex starting in the 1970s and as more automated/digitized systems came on board (e. g. , flight management systems in commercial cockpits, nuclear power plant management systems, etc. ), the research focus shifted toward supervisory control where loop closures were done by automation and the human operator focused on how the automation was controlling the basic system (Sheridan 1976). In effect, the focus went from the human in the loop to on the loop. In addition, because of increasing system complexity with time and the need for a team of operators, efforts have been increasingly devoted to understanding team interactions and multioperator supervision of modern systems (Cooke et al. 2000, 2006). In both the early phase of manual control and the later and current phase of multi-operator supervisory control, a major focus of research has been understanding effective human-system integration, from the basic human factors of effective controls and displays design, to the shaping of systems dynamics (e. g. , an aircrafts handling qualities), to the development of an understanding of joint cognitive systems (Woods and Hollnagel 2006) as the human-system tasks become increasingly cognitive and less manually focused. +Original BC scores: AI: 0.999956488609314, HUMAN: 4.348351649241522e-05 +Calibration BC scores: AI: 0.8937875751503006, HUMAN: 0.1062124248496994 +Input Text: The French state faced a series of budgetary crises during the 18th century, caused primarily by structural deficiencies rather than lack of resources. Unlike Britain, where Parliament determined both expenditures and taxes, in France the Crown controlled spending, but not revenue. National taxes could only be approved by the Estates-General, which had not sat since 1614; its revenue functions had been assumed by regional parlements, the most powerful being the Parlement de Paris. Although willing to authorise one-time taxes, these bodies were reluctant to pass long-term measures, while collection was outsourced to private individuals. This significantly reduced the yield from those that were approved and as a result, France struggled to service its debt despite being larger and wealthier than Britain. Following partial default in 1770, within five years the budget had been balanced thanks to reforms instituted by Turgot, the Controller-General of Finances. This reduced government borrowing costs from 12 per year to under 6, but he was dismissed in May 1776 after arguing France could not afford to intervene in the American Revolutionary War. +Original BC scores: AI: 2.4446164388791658e-05, HUMAN: 0.9999755620956421 +Calibration BC scores: AI: 0.02666666666666667, HUMAN: 0.9733333333333334 +Input Text: Drawing on unrestrained imagination and a variegated cultural landscape, a Romantic-era poem could be trivial or fantastic, succinctly songlike or digressively meandering, a searching fragment or a precisely bounded sonnet or ode, as comic as Lord Byrons mock epic Don Juan or as cosmologically subversive as Blakes The Marriage of Heaven and Hell. If any single innovation has emerged as Romanticisms foremost legacy, it is the dominance among poetic genres of the lyric poem, spoken in first-person (the lyric I) often identified with the poet, caught between passion and reason, finding correspondences in natural surroundings for the introspective workings of heart and mind. If any collection cemented that legacy, it would be Wordsworth and Coleridges landmark collection Lyrical Ballads, first published anonymously in 1798. The collection provokes with its title alone, inverting hierarchies, hybridizing the exalted outbursts of lyric poetry with the folk narratives of ballads. In a retrospective preface added for the 1800 second edition and expanded in later editions, Wordsworth set out his polemical program for a poetry grounded in feeling, supplying Romanticism with some of its most resonant and lasting phrases: all good poetry is the spontaneous overflow of powerful feelings; it takes its origin from emotion recollected in tranquillity. +Original BC scores: AI: 5.149858139930075e-08, HUMAN: 1.0 +Calibration BC scores: AI: 0.0013484877672895396, HUMAN: 0.9986515122327104 +Input Text: Partisan politics heavily infects both gun ownership and the response to gun ownership, of course. The National Rifle Associations effort to argue that the best antidote to gun violence is an armed response has become a central tenet of Republican politics. On Monday, The Washington Post published a lengthy look at how the AR-15 became a central part of the political discussion about gun ownership and how it became a talisman of pro-gun advocacy. It is estimated that there are 20 million AR-15-style rifles in the United States at this point a powerful, deadly type of weapon that was restricted as a consumer product two decades ago. There have been proposals aimed at reducing that figure. As part of his short-lived 2020 presidential candidacy, former Texas congressman Beto ORourke (D) proposed buying back assault-style weapons like the AR-15. Even if that were fully successful, of course, there would likely still be some 300 million firearms in the United States, according to that 2012 CRS report. Many of the deadliest firearms would be gone, but hundreds of millions of more would remain. If significantly reducing gun deaths necessarily means significantly reducing firearm ownership, you can see the problem. Its hard to think of a way that ownership could be reduced significantly, even if the political will to do so suddenly materialized. There are far more American guns than there are Americans. +Original BC scores: AI: 1.0, HUMAN: 9.135543876936936e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: Chronic obstructive pulmonary disease (COPD) is a progressive lung disease characterized by chronic bronchitis and emphysema. Patients with COPD experience exacerbations, or worsening of symptoms, which can lead to hospitalization and increased morbidity and mortality. Early detection and management of exacerbations are crucial to improve patient outcomes and reduce healthcare costs. Acoustic speech characteristics, such as speaking rate, pause time, and voice quality, have been proposed as potential markers of COPD exacerbations. The objective of this thesis is to investigate the feasibility and effectiveness of using acoustic speech characteristics as markers of exacerbations in patients with COPD. The results of this study may have important implications for the development of non-invasive, cost-effective, and patient-friendly tools for early detection and monitoring of COPD exacerbations. To achieve this objective, this thesis will employ a prospective longitudinal design to monitor speech characteristics in a cohort of patients with COPD over a period of several months. The speech data will be collected using a portable recording device and analyzed using state-of-the-art machine learning algorithms. The primary outcome measure will be the accuracy of the speech-based exacerbation detection model in predicting exacerbations compared to standard clinical measures. +Original BC scores: AI: 1.0, HUMAN: 2.2553650058654284e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: Artificial intelligence (AI) has become an increasingly important field of research, with numerous applications in a variety of industries. One of the key challenges in AI research is the development of intelligent systems that can adapt to changing environments and make accurate predictions based on uncertain or incomplete data. In recent years, the use of adaptive neuro-fuzzy inference systems (ANFIS) has emerged as a promising approach to addressing this challenge. ANFIS combines the benefits of neural networks and fuzzy logic to create a hybrid system that can learn from data and adapt to changing conditions. This research paper will explore the use of ANFIS in AI research, including its applications, benefits, and limitations. The paper will also provide an overview of the theoretical foundations of ANFIS, as well as practical examples of its use in real-world applications. Ultimately, this paper aims to contribute to the growing body of research on ANFIS and its potential to advance the field of AI. +Original BC scores: AI: 0.9999998807907104, HUMAN: 6.266703422852515e-08 +Calibration BC scores: AI: 0.987487969201155, HUMAN: 0.012512030798844997 +Input Text: The French Revolution was a monumental event in the history of France, Europe, and the world at large. It marked the beginning of the end of the ancien régime, a centuries-old system of absolute monarchy, feudalism, and social hierarchies, and paved the way for modernity, democracy, and human rights. The French Revolution was a complex and multifaceted phenomenon, with many causes, actors, and consequences, and its legacy is still debated and contested today. One of the main causes of the French Revolution was the political, social, and economic crisis that France faced in the late 18th century. The French monarchy, led by King Louis XVI, was heavily in debt due to its participation in costly wars and extravagant court life, and it was unable to collect enough taxes from the nobility and clergy, who enjoyed many privileges and exemptions. Meanwhile, the middle and lower classes, who constituted the majority of the population, suffered from poverty, hunger, and unemployment, and resented the inequality and injustice of the system. Another cause of the French Revolution was the Enlightenment, a philosophical and cultural movement that spread throughout Europe in the 18th century. The Enlightenment promoted rationalism, individualism, secularism, and human rights, and challenged traditional authority and dogma. +Original BC scores: AI: 1.0, HUMAN: 2.7462017104795677e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: William Blake's poem "The Tyger" is a fascinating exploration of the duality of creation. The speaker marvels at the fierce and powerful creature, but also wonders about the force that created it. The poem is structured around a series of rhetorical questions that express the speaker's awe and wonder at the creature. However, the repeated refrain of "Did he who made the Lamb make thee? " suggests a deeper questioning of the nature of creation and the creator. The poem ultimately poses the question of how such a fierce and terrifying creature could exist in a world supposedly created by a benevolent God. Blake uses vivid imagery to convey the power and majesty of the tiger, with its "burning bright" fur and "deadly terrors. " The use of alliteration and repetition adds to the sense of awe and fear that the speaker feels in the presence of this magnificent beast. However, the poem's central theme is the tension between the beauty and terror of creation. The tiger is both a beautiful and terrifying creature, and the poem's speaker struggles to reconcile these opposing forces. This tension is reflected in the poem's structure, with its repeated refrain and its series of questions that are left unanswered. Overall, "The Tyger" is a powerful meditation on the nature of creation and the role of the creator. +Original BC scores: AI: 1.0, HUMAN: 8.440105503382256e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: Opponents of gun control argue that it infringes on their Second Amendment rights and that it will not prevent mass shootings or reduce gun violence. They believe that the problem lies with individuals who misuse guns, not with the guns themselves, and that the solution is to enforce existing laws and punish those who break them. They also argue that gun ownership is a fundamental right and that any attempt to restrict it is a slippery slope towards a total ban. One of the main points of contention in the gun control debate is the issue of background checks. Proponents argue that all gun buyers should undergo a background check, which would prevent felons, domestic abusers, and those with a history of mental illness from purchasing firearms. Opponents argue that this would infringe on the rights of law-abiding citizens and that it would be ineffective in preventing gun violence. Another issue in the gun control debate is the availability of assault weapons and high-capacity magazines. Proponents argue that these weapons are designed for military use and have no place in civilian society. They point to mass shootings such as the one in Parkland, Florida, where the shooter used an assault weapon to kill 17 people. Opponents argue that these weapons are used for hunting and self-defense and that they are protected under the Second Amendment. +Original BC scores: AI: 1.0, HUMAN: 2.441804980435336e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory disorder characterized by persistent airflow obstruction, resulting in significant morbidity and mortality worldwide. The disease is often punctuated by episodes of exacerbations, which are acute worsening of respiratory symptoms that contribute to the overall decline in lung function and patients' quality of life. Early detection and management of these exacerbations are crucial to minimize their clinical impact and prevent further deterioration in COPD patients. However, traditional methods for monitoring exacerbations, such as self-reporting of symptoms and spirometry, are often subjective, cumbersome, and may not always be reliable. As such, there is a pressing need for more objective and non-invasive tools to assess and predict exacerbations in COPD patients. This thesis explores the potential use of acoustic speech characteristics as novel, non-invasive markers of exacerbations in patients with Chronic Obstructive Pulmonary Disease. The underlying hypothesis is that the alterations in respiratory function associated with COPD exacerbations may manifest as measurable changes in the acoustic properties of speech, such as changes in fundamental frequency, intensity, and phonation time. +Original BC scores: AI: 1.0, HUMAN: 2.0475334761016484e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: Artificial Intelligence (AI) research has seen significant progress in recent years, owing to the advances in machine learning, neural networks, and computational intelligence. Among the various techniques employed in AI research, neuro-fuzzy systems have garnered considerable attention due to their ability to integrate the learning capabilities of neural networks with the interpretability of fuzzy systems. The adaptive neuro-fuzzy inference system (ANFIS) is a hybrid intelligent system that combines the strengths of both neural networks and fuzzy logic to create a powerful and flexible tool for addressing complex real-world problems. This research paper seeks to explore the potential of ANFIS in AI research by discussing its underlying principles, applications, and the challenges it faces. ANFIS is an advanced modeling technique that leverages the adaptive learning capabilities of neural networks and the linguistic modeling capabilities of fuzzy systems. It employs a fuzzy inference system (FIS) to represent the knowledge in the form of IF-THEN fuzzy rules and a neural network to optimize the parameters of the fuzzy system. The learning process in ANFIS is primarily data-driven, which makes it an attractive choice for AI research where large amounts of data are often involved. Furthermore, ANFIS can handle imprecise and uncertain information, allowing it to model complex and non-linear relationships between variables effectively.Some characters could not be decoded, and were replaced with REPLACEMENT CHARACTER. +Some characters could not be decoded, and were replaced with REPLACEMENT CHARACTER. +Some characters could not be decoded, and were replaced with REPLACEMENT CHARACTER. +/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() + +Original BC scores: AI: 1.0, HUMAN: 1.9056667355243917e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: The origins of the French Revolution can be traced to the profound social, economic, and political inequalities that plagued the Ancien Régime. The French society was divided into three rigid estates, with the clergy (First Estate) and the nobility (Second Estate) enjoying immense privileges at the expense of the common people (Third Estate). The overwhelming majority of the population was subjected to heavy taxation, food shortages, and rampant poverty, while the ruling classes indulged in opulence and decadence. Furthermore, the French monarchy, led by the ill-fated King Louis XVI and his wife, Marie Antoinette, had become increasingly disconnected from the plight of the common people. The government consistently mismanaged the nation's finances, engaging in costly foreign wars and maintaining an extravagant lifestyle at the royal court in Versailles. The mounting fiscal crisis, coupled with an increasingly vocal and politicized Third Estate, ultimately provided the catalyst for the revolution. Revolutionary Ideals and the Role of the Enlightenment The French Revolution was inextricably linked to the intellectual movement known as the Enlightenment, which espoused the principles of reason, liberty, and the rights of the individual. Philosophes such as Voltaire, Montesquieu, and Rousseau provided the ideological foundation for the revolution, inspiring the masses to challenge the status quo and seek a more just and equitable society. +Original BC scores: AI: 1.0, HUMAN: 2.382321895311179e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: In "The Tyger, " William Blake explores the dichotomy of creation and destruction through the depiction of a powerful, yet terrifying creature. The poem begins with a series of rhetorical questions that invite the reader to contemplate the origins of this fierce beast: "Tyger Tyger, burning bright, / In the forests of the night; / What immortal hand or eye, / Could frame thy fearful symmetry? " By likening the Tyger to a burning flame, Blake emphasizes its destructive potential while also emphasizing its captivating beauty. The phrase "fearful symmetry" suggests a sense of balance between the Tyger's dangerous nature and its awe-inspiring form, further complicating the reader's understanding of the creature. Throughout the poem, Blake repeatedly questions the identity of the creator responsible for bringing such a powerful being into existence. The Tyger's maker is described as having "distant deeps or skies" and using a combination of fire and artistic skill to forge the creature. The poem also draws a connection between the Tyger and the lamb, a symbol of innocence and gentleness, as Blake asks, "Did he who made the Lamb make thee? " This comparison prompts the reader to consider the duality of creation, where both the gentle lamb and the fearsome Tyger can coexist within the same universe, perhaps even as products of the same creator. By juxtaposing these two contrasting creatures, Blake highlights the complexities and paradoxes inherent in the creative process. +['Retrieval-augmented text generation has emerged as a significant area of interest within the computational linguistics community, with recent advancements showcasing its potential for enhancing various natural language processing tasks.', 'Li et al.', "(2022) conducted a survey on retrieval-augmented text generation, aiming to provide an overview of the field's current landscape.", 'The survey highlighted the growing attention this approach has garnered and its implications for text generation tasks.', 'Hofstätter et al.', '(2022) delved into multi-task training of retrieval-augmented generation models, particularly focusing on knowledge-intensive tasks.', 'Their work emphasized the importance of cleaning training sets by leveraging the connection between query-answer pairs and items in knowledge bases.', 'This approach aimed to enhance the performance of retrieval-augmented text generation models for specialized tasks.', 'Cai et al.', '(2022) presented recent advances in retrieval-augmented text generation, showcasing its state-of-the-art performance across various NLP tasks.', 'The tutorial provided a comprehensive overview of the generic paradigm of retrieval-augmented text generation, highlighting notable works in dialogue generation, machine translation, and other text generation tasks.', 'The paper also identified limitations and shortcomings, aiming to guide future research in this area.', 'Chen et al.', '(2023) introduced the Retrieval-Augmented Text-to-Image Generator (Re-Imagen) to address challenges in generating images of uncommon entities.', 'By utilizing retrieved information, the model aimed to produce high-fidelity images even for rare or unseen entities, showcasing the potential of retrieval-augmented approaches in multimodal tasks.', 'Furthermore, Cheng et al.', '(2023) proposed a novel framework, Selfmem, for retrieval-augmented text generation with self-memory.', 'By iteratively employing a retrieval-augmented generator to create an unbounded memory pool, the model aimed to enhance the quality of generated text by improving the memory retrieval process.', 'Yasunaga et al.', '(2023) explored retrieval-augmented multimodal language modeling, enabling a base multimodal model to refer to relevant text and images fetched from external memory.', 'This approach aimed to integrate knowledge in a scalable and modular way, showcasing the potential of retrieval-augmented models in multimodal tasks.', 'Overall, the literature on retrieval-augmented text generation highlights its growing significance in enhancing various NLP tasks and multimodal applications.', 'The advancements in this field offer promising avenues for future research and the development of more efficient and effective retrieval-augmented models (Hofstätter et al., 2023; Huang et al., 2024).'] +Original BC scores: AI: 1.0, HUMAN: 3.479760923852382e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: By utilizing retrieved information, the model aimed to produce high-fidelity images even for rare or unseen entities, showcasing the potential of retrieval-augmented approaches in multimodal tasks. Furthermore, Cheng et al. (2023) proposed a novel framework, Selfmem, for retrieval-augmented text generation with self-memory. By iteratively employing a retrieval-augmented generator to create an unbounded memory pool, the model aimed to enhance the quality of generated text by improving the memory retrieval process. Yasunaga et al. (2023) explored retrieval-augmented multimodal language modeling, enabling a base multimodal model to refer to relevant text and images fetched from external memory. This approach aimed to integrate knowledge in a scalable and modular way, showcasing the potential of retrieval-augmented models in multimodal tasks. Overall, the literature on retrieval-augmented text generation highlights its growing significance in enhancing various NLP tasks and multimodal applications. The advancements in this field offer promising avenues for future research and the development of more efficient and effective retrieval-augmented models (Hofstätter et al. , 2023; Huang et al. , 2024). +Original BC scores: AI: 1.0, HUMAN: 3.479760923852382e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +MC Score: {'OPENAI GPT': 0.9995505136986301, 'MISTRAL': 4.75580922276608e-11, 'CLAUDE': 1.0952248957328832e-10, 'GEMINI': 2.8669602879061366e-10, 'GRAMMAR ENHANCER': 9.111613576641838e-11} +{'Retrieval-augmented text generation has emerged as a significant area of interest within the computational linguistics community, with recent advancements showcasing its potential for enhancing various natural language processing tasks.': 5.7919206679311014e-05, 'Li et al.': -8.44690662195127e-05, "(2022) conducted a survey on retrieval-augmented text generation, aiming to provide an overview of the field's current landscape.": 8.925784105361301e-05, 'The survey highlighted the growing attention this approach has garnered and its implications for text generation tasks.': -1.8697383581088564e-05, 'Hofstätter et al.': -2.4543135052778933e-05, '(2022) delved into multi-task training of retrieval-augmented generation models, particularly focusing on knowledge-intensive tasks.': -2.900193466388352e-05, 'Their work emphasized the importance of cleaning training sets by leveraging the connection between query-answer pairs and items in knowledge bases.': 4.2702066118983056e-05, 'This approach aimed to enhance the performance of retrieval-augmented text generation models for specialized tasks.': 4.035329629182266e-05, 'Cai et al.': 2.1681525372654673e-05, '(2022) presented recent advances in retrieval-augmented text generation, showcasing its state-of-the-art performance across various NLP tasks.': 3.7181002893487637e-05, 'The tutorial provided a comprehensive overview of the generic paradigm of retrieval-augmented text generation, highlighting notable works in dialogue generation, machine translation, and other text generation tasks.': 4.86624524766399e-05, 'The paper also identified limitations and shortcomings, aiming to guide future research in this area.': 4.172884306047066e-05, 'Chen et al.': 2.2699384046486155e-05, '(2023) introduced the Retrieval-Augmented Text-to-Image Generator (Re-Imagen) to address challenges in generating images of uncommon entities.': 3.672830098088457e-05}/home/aliasgarov/copyright_checker/predictors.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. + probas = F.softmax(tensor_logits).detach().cpu().numpy() +Some characters could not be decoded, and were replaced with REPLACEMENT CHARACTER. +Some characters could not be decoded, and were replaced with REPLACEMENT CHARACTER. +Some characters could not be decoded, and were replaced with REPLACEMENT CHARACTER. + bc +{'By utilizing retrieved information, the model aimed to produce high-fidelity images even for rare or unseen entities, showcasing the potential of retrieval-augmented approaches in multimodal tasks.': 0.018391591496997914, 'Furthermore, Cheng et al.': 0.052657355808620486, '(2023) proposed a novel framework, Selfmem, for retrieval-augmented text generation with self-memory.': -0.01302254034137154, 'By iteratively employing a retrieval-augmented generator to create an unbounded memory pool, the model aimed to enhance the quality of generated text by improving the memory retrieval process.': 0.012612659891845098, 'Yasunaga et al.': 0.01600829811489446, '(2023) explored retrieval-augmented multimodal language modeling, enabling a base multimodal model to refer to relevant text and images fetched from external memory.': -0.027147996295345735, 'This approach aimed to integrate knowledge in a scalable and modular way, showcasing the potential of retrieval-augmented models in multimodal tasks.': 0.03740571568648987, 'Overall, the literature on retrieval-augmented text generation highlights its growing significance in enhancing various NLP tasks and multimodal applications.': 0.031115060874293752, 'The advancements in this field offer promising avenues for future research and the development of more efficient and effective retrieval-augmented models (Hofstätter et al., 2023; Huang et al., 2024).': 0.045161032676279245} bc + Retrieval-augmented text generation has emerged as a significant area of interest within the computational linguistics community, with recent advancements showcasing its potential for enhancing various natural language processing tasks. Li et al. (2022) conducted a survey on retrieval-augmented text generation, aiming to provide an overview of the field's current landscape. The survey highlighted the growing attention this approach has garnered and its implications for text generation tasks. Hofstätter et al. (2022) delved into multi-task training of retrieval-augmented generation models, particularly focusing on knowledge-intensive tasks. Their work emphasized the importance of cleaning training sets by leveraging the connection between query-answer pairs and items in knowledge bases. This approach aimed to enhance the performance of retrieval-augmented text generation models for specialized tasks. Cai et al. (2022) presented recent advances in retrieval-augmented text generation, showcasing its state-of-the-art performance across various NLP tasks. The tutorial provided a comprehensive overview of the generic paradigm of retrieval-augmented text generation, highlighting notable works in dialogue generation, machine translation, and other text generation tasks. The paper also identified limitations and shortcomings, aiming to guide future research in this area. Chen et al. (2023) introduced the Retrieval-Augmented Text-to-Image Generator (Re-Imagen) to address challenges in generating images of uncommon entities. By utilizing retrieved information, the model aimed to produce high-fidelity images even for rare or unseen entities, showcasing the potential of retrieval-augmented approaches in multimodal tasks. Furthermore, Cheng et al. (2023) proposed a novel framework, Selfmem, for retrieval-augmented text generation with self-memory. By iteratively employing a retrieval-augmented generator to create an unbounded memory pool, the model aimed to enhance the quality of generated text by improving the memory retrieval process. Yasunaga et al. (2023) explored retrieval-augmented multimodal language modeling, enabling a base multimodal model to refer to relevant text and images fetched from external memory. This approach aimed to integrate knowledge in a scalable and modular way, showcasing the potential of retrieval-augmented models in multimodal tasks. Overall, the literature on retrieval-augmented text generation highlights its growing significance in enhancing various NLP tasks and multimodal applications. The advancements in this field offer promising avenues for future research and the development of more efficient and effective retrieval-augmented models (Hofstätter et al., 2023; Huang et al., 2024). +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) +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) +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) +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) +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) +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) +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) +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) +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/plagiarism.py", line 350, in html_highlight + color = color_map[prev_idx - 1] +IndexError: list index out of range +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. +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. +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. +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. +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. +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. +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. +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. +Original BC scores: AI: 1.0, HUMAN: 3.0575617593342486e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: Automated essay scoring (AES) involves the deployment of specialized software to evaluate essays within an academic context. This method, a practical application of natural language processing, aims to sort a broad array of text-based responses into a limited range of distinct categories, which represent the potential grades, such as scores from 1 to 6. Thus, AES essentially operates as a form of statistical classification. The increasing interest in AES is driven by several factors, including cost considerations, accountability demands, educational standards, and technological advancements. With the rising costs of education, there is growing pressure to hold the educational system accountable, enforcing standards that promise to evaluate educational outcomes more economically through enhanced information technology. However, the application of AES in high-stakes educational testing has sparked considerable controversy. Critics argue that current technology does not yet grade writing with sufficient accuracy and contend that relying on AES encourages a simplistic approach to teaching writing, which fails to capture the complexity and nuance of the skill. +Original BC scores: AI: 0.9999984502792358, HUMAN: 1.5730845461803256e-06 +Calibration BC scores: AI: 0.9616438356164384, HUMAN: 0.0383561643835616 +Input Text: Automated essay scoring involves the deployment of specialized software to evaluate essays. This method aims to sort a broad array of text-based responses into a limited range of distinct categories, which represent the potential grades, such as scores from 1 to 6. AES is a form of statistical classification. Cost considerations, accountability demands, educational standards, and technological advancement are some of the factors that drive the increasing interest in AES. With the rising costs of education, there is growing pressure to hold the educational system accountable, enforce standards that promise to evaluate educational outcomes more economically through enhanced information technology The application of AES in high-stakes educational testing has caused controversy. Critics argue that current technology does not grade writing with enough accuracy and HairMax encourages a simplistic approach to teaching writing, which fails to capture the complexity and nuances of the skill. +Original BC scores: AI: 0.9999159574508667, HUMAN: 8.402647654293105e-05 +Calibration BC scores: AI: 0.8937875751503006, HUMAN: 0.1062124248496994 +Input Text: The benefits of fake news detection technologies for English have not been equally distributed across all languages. Machine learning models have been implemented in English to identify and mitigate the spread of misinformation. The level of technological intervention has not been the same for low-resource languages. The gap in global efforts to combat fake news leaves speakers of less common languages vulnerable to the effects of misinformation. Building more inclusive and universally effective fake news detection systems depends on extending research and development into underrepresented linguistic areas. +Original BC scores: AI: 1.0, HUMAN: 1.8620792685553056e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: While there has been considerable progress in developing fake news detection technologies for English, the benefits of these advancements have not been equally distributed across all languages. English, with its vast resources and extensive research backing, has seen the implementation of sophisticated machine learning models capable of identifying and mitigating the spread of misinformation effectively. However, low-resource languages, which often lack sufficient linguistic data, computational tools, and academic attention, have not enjoyed the same level of technological intervention. This disparity highlights a significant gap in the global efforts to combat fake news, leaving speakers of less common languages more vulnerable to the pernicious effects of misinformation. The need to extend research and development into these underrepresented linguistic areas is crucial for building more inclusive and universally effective fake news detection systems. +Original BC scores: AI: 1.0, HUMAN: 3.0575617593342486e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: Automated essay scoring (AES) involves the deployment of specialized software to evaluate essays within an academic context. This method, a practical application of natural language processing, aims to sort a broad array of text-based responses into a limited range of distinct categories, which represent the potential grades, such as scores from 1 to 6. Thus, AES essentially operates as a form of statistical classification. The increasing interest in AES is driven by several factors, including cost considerations, accountability demands, educational standards, and technological advancements. With the rising costs of education, there is growing pressure to hold the educational system accountable, enforcing standards that promise to evaluate educational outcomes more economically through enhanced information technology. However, the application of AES in high-stakes educational testing has sparked considerable controversy. Critics argue that current technology does not yet grade writing with sufficient accuracy and contend that relying on AES encourages a simplistic approach to teaching writing, which fails to capture the complexity and nuance of the skill. +Original BC scores: AI: 1.3773747014056426e-06, HUMAN: 0.9999985694885254 +Calibration BC scores: AI: 0.0013484877672895396, HUMAN: 0.9986515122327104 +Input Text: Automated essay scoring (AES) system is the use of computer programs to assign grades to essays written in an educational face. The method, a type of natural language processing (NLP) in practice, seeks to organize such a wide range of text-based responses into only a small handful of specific categories the grade bins themselves, or something like 1 through 6 points. In this way, you could say that AES is just a flavour of statistical classification! The reasons behind the growing popularity of AES are numerous and range from cost, accountability, educational policy to advances in technology. given the soaring costs and substantial waste associated with higher education in particular, there is a natural impetus to subject it to greater accountability by means of allegedly less-expensive standards specifically enabled and enforced through IT. Yet this use of AES for high-stakes educational testing has been the source of public controversy. Nevertheless critics have attested that current technology has still not produced writing to be graded accurately. They also claim that depending on AES creates an over simplistic way of teaching writing which does not embody the complexity and subtleties involved in this skill. +['Retrieval-augmented text generation has emerged as a significant area of interest within the computational linguistics community, with recent advancements showcasing its potential for enhancing various natural language processing tasks.', 'Li et al.', "(2022) conducted a survey on retrieval-augmented text generation, aiming to provide an overview of the field's current landscape.", 'The survey highlighted the growing attention this approach has garnered and its implications for text generation tasks.', 'Hofstätter et al.', '(2022) delved into multi-task training of retrieval-augmented generation models, particularly focusing on knowledge-intensive tasks.', 'Their work emphasized the importance of cleaning training sets by leveraging the connection between query-answer pairs and items in knowledge bases.', 'This approach aimed to enhance the performance of retrieval-augmented text generation models for specialized tasks.', 'Cai et al.', '(2022) presented recent advances in retrieval-augmented text generation, showcasing its state-of-the-art performance across various NLP tasks.', 'The tutorial provided a comprehensive overview of the generic paradigm of retrieval-augmented text generation, highlighting notable works in dialogue generation, machine translation, and other text generation tasks.', 'The paper also identified limitations and shortcomings, aiming to guide future research in this area.', 'Chen et al.', '(2023) introduced the Retrieval-Augmented Text-to-Image Generator (Re-Imagen) to address challenges in generating images of uncommon entities.', 'By utilizing retrieved information, the model aimed to produce high-fidelity images even for rare or unseen entities, showcasing the potential of retrieval-augmented approaches in multimodal tasks.', 'Furthermore, Cheng et al.', '(2023) proposed a novel framework, Selfmem, for retrieval-augmented text generation with self-memory.', 'By iteratively employing a retrieval-augmented generator to create an unbounded memory pool, the model aimed to enhance the quality of generated text by improving the memory retrieval process.', 'Yasunaga et al.', '(2023) explored retrieval-augmented multimodal language modeling, enabling a base multimodal model to refer to relevant text and images fetched from external memory.', 'This approach aimed to integrate knowledge in a scalable and modular way, showcasing the potential of retrieval-augmented models in multimodal tasks.', 'Overall, the literature on retrieval-augmented text generation highlights its growing significance in enhancing various NLP tasks and multimodal applications.', 'The advancements in this field offer promising avenues for future research and the development of more efficient and effective retrieval-augmented models (Hofstätter et al., 2023; Huang et al., 2024).']Some characters could not be decoded, and were replaced with REPLACEMENT CHARACTER. +Some characters could not be decoded, and were replaced with REPLACEMENT CHARACTER. +Some characters could not be decoded, and were replaced with REPLACEMENT CHARACTER. +Some characters could not be decoded, and were replaced with REPLACEMENT CHARACTER. +Some characters could not be decoded, and were replaced with REPLACEMENT CHARACTER. +Some characters could not be decoded, and were replaced with REPLACEMENT CHARACTER. + +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) +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) +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) +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) +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) +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) +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) +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) +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/plagiarism.py", line 350, in html_highlight + color = color_map[prev_idx - 1] +IndexError: list index out of range +WARNING: Invalid HTTP request received. +WARNING: Invalid HTTP request received. +WARNING: Invalid HTTP request received. +WARNING: Invalid HTTP request received. +Original BC scores: AI: 0.9999998807907104, HUMAN: 1.5961634858285834e-07 +Calibration BC scores: AI: 0.987487969201155, HUMAN: 0.012512030798844997 +Input Text: Automated Essay Scoring (AES) involves deploying specialized software to evaluate papers in an academic context. The method is a practical application of natural language processing and aims to classify a large number of text-based responses into a limited range of different categories that represent underlying grades, such as scores from 1 to 6. Therefore, AES essentially serves as a form of statistical classification. Growing interest in AES is driven by a variety of factors, including cost considerations, liability requirements, educational standards, and technological advances. As the cost of education continues to rise, there is increasing pressure to hold education systems accountable and implement standards that promise to more economically evaluate educational outcomes through enhanced information technology. However, the use of AES in high-stakes educational testing has generated considerable controversy. Critics argue that current technology does not yet score writing accurately enough and argue that reliance on AES encourages a simplistic approach to teaching writing that fails to capture the complexity and nuance of the skill. +Original BC scores: AI: 1.0, HUMAN: 5.766354504999072e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: The deployment of specialized software to evaluate essays is a part of automated essay scoring. A practical application of natural language processing aims to sort a broad array of text-based responses into a limited range of distinct categories, which represent the potential grades, such as scores from 1 to 6. As a form of statistical classification, AES operates. Cost considerations, accountability demands, educational standards, and technological advancements are some of the factors driving the increasing interest in AES. There is growing pressure to hold the educational system accountable and enforce standards that promise to evaluate educational outcomes more economically through enhanced information technology. There is considerable controversy surrounding the application of AES in high-stakes educational testing. Critics argue that current technology does not yet grade writing with enough accuracy and that relying on AES encourages a simplistic approach to teaching writing, which fails to capture the complexity and nuances of the skill. +Original BC scores: AI: 0.7490981817245483, HUMAN: 0.25090181827545166 +Calibration BC scores: AI: 0.40939597315436244, HUMAN: 0.5906040268456376 +Input Text: There is likely still much to be done to enhance the model, in terms of architecture, computational efficiency, and leveraging prior knowledge. A key priority for future research should be enhancing speed-up techniques and increasing capacity without significantly extending training time, to handle corpora containing hundreds of millions of words or more. A straightforward approach to leverage temporal structure and extend the input window to encompass an entire paragraph, without significantly increasing the number of parameters or computation time, is to employ time-delay and possibly recurrent neural networks. Evaluating the models presented here in practical applications would be beneficial, but see Schwenk and Gauvain's (2002) work for improvements in speech recognition word error rate. +Original BC scores: AI: 3.041857553398586e-06, HUMAN: 0.9999969005584717 +Calibration BC scores: AI: 0.0013484877672895396, HUMAN: 0.9986515122327104 +Input Text: There is probably much more to be done to improve the model, at the level of architecture, computational efficiency, and taking advantage of prior knowledge. An important priority of future research should be to improve speed-up techniques5 as well as ways to increase capacity without increasing training time too much (to deal with corpora with hundreds of millions of words or more). A simple idea to take advantage of temporal structure and extend the size of the input window to include possibly a whole paragraph (without increasing too much the number of parameters or computation time) is to use a time-delay and possibly recurrent neural networks. Evaluations of the type of models presented here in applicative contexts would also be useful, but see work already done by Schwenk and Gauvain (2002) for improvements in speech recognition word error rate. +Original BC scores: AI: 3.041857553398586e-06, HUMAN: 0.9999969005584717 +Calibration BC scores: AI: 0.0013484877672895396, HUMAN: 0.9986515122327104 +Input Text: There is probably much more to be done to improve the model, at the level of architecture, computational efficiency, and taking advantage of prior knowledge. An important priority of future research should be to improve speed-up techniques5 as well as ways to increase capacity without increasing training time too much (to deal with corpora with hundreds of millions of words or more). A simple idea to take advantage of temporal structure and extend the size of the input window to include possibly a whole paragraph (without increasing too much the number of parameters or computation time) is to use a time-delay and possibly recurrent neural networks. Evaluations of the type of models presented here in applicative contexts would also be useful, but see work already done by Schwenk and Gauvain (2002) for improvements in speech recognition word error rate. +Original BC scores: AI: 0.7490981817245483, HUMAN: 0.25090181827545166 +Calibration BC scores: AI: 0.40939597315436244, HUMAN: 0.5906040268456376 +Input Text: There is likely still much to be done to enhance the model, in terms of architecture, computational efficiency, and leveraging prior knowledge. A key priority for future research should be enhancing speed-up techniques and increasing capacity without significantly extending training time, to handle corpora containing hundreds of millions of words or more. A straightforward approach to leverage temporal structure and extend the input window to encompass an entire paragraph, without significantly increasing the number of parameters or computation time, is to employ time-delay and possibly recurrent neural networks. Evaluating the models presented here in practical applications would be beneficial, but see Schwenk and Gauvain's (2002) work for improvements in speech recognition word error rate. +Original BC scores: AI: 1.0, HUMAN: 1.8009412849906425e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: This study explores the development of an open-source Automated Essay Scoring (AES) system using a variety of machine learning techniques, including neural networks, support vector machines, and linear regression. The project seeks to address the limitations of current AES systems, such as high costs, limited data sets, and algorithmic bias, by utilizing a detailed, publicly available dataset that encompasses economic and geographic diversity. Our AES model employs a quadratic weighted kappa metric for evaluation, ensuring accurate and just assessment by comparing the agreement levels between automated and human scores. Notable advancements in our approach include the integration of ensemble learning techniques and sophisticated feature extraction processes, which have significantly enhanced the accuracy and fairness of the AES system. This research aims to provide educators with a reliable tool for efficient essay grading, thereby improving the feedback mechanism and overall learning experience for students. Our findings indicate that machine learning models, particularly those incorporating transformer and deep learning technologies, can substantially outperform traditional scoring systems, paving the way for future innovations in educational assessment technologies. +Original BC scores: AI: 1.0, HUMAN: 1.8009412849906425e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: This study explores the development of an open-source Automated Essay Scoring (AES) system using a variety of machine learning techniques, including neural networks, support vector machines, and linear regression. The project seeks to address the limitations of current AES systems, such as high costs, limited data sets, and algorithmic bias, by utilizing a detailed, publicly available dataset that encompasses economic and geographic diversity. Our AES model employs a quadratic weighted kappa metric for evaluation, ensuring accurate and just assessment by comparing the agreement levels between automated and human scores. Notable advancements in our approach include the integration of ensemble learning techniques and sophisticated feature extraction processes, which have significantly enhanced the accuracy and fairness of the AES system. This research aims to provide educators with a reliable tool for efficient essay grading, thereby improving the feedback mechanism and overall learning experience for students. Our findings indicate that machine learning models, particularly those incorporating transformer and deep learning technologies, can substantially outperform traditional scoring systems, paving the way for future innovations in educational assessment technologies. +Original BC scores: AI: 1.0, HUMAN: 1.8009412849906425e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +MC Score: {'OPENAI GPT': 0.9995505136986301, 'MISTRAL': 4.8287219038864887e-11, 'CLAUDE': 8.067900126720559e-11, 'GEMINI': 2.541129786960314e-10, 'GRAMMAR ENHANCER': 1.0497747623796973e-10} +Original BC scores: AI: 1.0, HUMAN: 2.0731554251085527e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: The goal of this project is to create an open-source Automated Essay Scoring (AES) system by utilizing a range of machine learning methods, such as support vector machines, neural networks, and linear regression. The research uses a comprehensive, publicly accessible dataset that spans economic and geographic diversity in an effort to solve the shortcomings of existing AES systems, including their high costs, small data sets, and algorithmic bias. In order to ensure fair and reliable assessment, our AES model uses a quadratic weighted kappa metric, which compares the degrees of agreement between automated and human scores. Notable improvements in our methodology include the use of advanced feature extraction procedures and ensemble learning techniques, which have greatly improved the AES system's accuracy and fairness. +Original BC scores: AI: 1.0, HUMAN: 2.0731554251085527e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +MC Score: {'OPENAI GPT': 0.9995505136986301, 'MISTRAL': 4.8726402713384e-11, 'CLAUDE': 8.024146483924094e-11, 'GEMINI': 2.49539029307554e-10, 'GRAMMAR ENHANCER': 1.097612050760733e-10} +Original BC scores: AI: 1.0, HUMAN: 5.5530410492110605e-08 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: When working with combinations of words that are found in the training corpus, we aim to capture those that occur frequently. However, when we encounter a new combination of words that wasn't seen during training, we don't want to assign it a zero probability. This is because new word combinations are likely to appear, especially as the context size increases. To handle this, we can use a smaller context size to predict the probability, a technique used in back-off trigram models (Katz, 1987) and in smoothed (or interpolated) trigram models (Jelinek and Mercer, 1980). Essentially, these models achieve generalization by relying on word sequences that were observed in the training corpus to infer probabilities for new word sequences. +Original BC scores: AI: 1.0, HUMAN: 5.5530410492110605e-08 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +MC Score: {'OPENAI GPT': 0.9995505136986301, 'MISTRAL': 4.9063068698128446e-11, 'CLAUDE': 7.996737688412859e-11, 'GEMINI': 2.661050168418057e-10, 'GRAMMAR ENHANCER': 1.0377008959608724e-10} +Original BC scores: AI: 0.023562965914607048, HUMAN: 0.9764370918273926 +Calibration BC scores: AI: 0.18061674008810572, HUMAN: 0.8193832599118943 +Input Text: We only take into account combinations of consecutive words that are present in the training corpus or that appear frequently enough. When a new combination of n words appears that was not seen in the training corpus, it is typically assigned a low probability or handled using smoothing techniques to estimate its likelihood. We avoid assigning zero probability to such cases, as these new combinations are likely to appear and will become even more common with larger context sizes. A straightforward solution is to consider the probability predicted using a smaller context size, as implemented in back-off trigram models (Katz, 1987) or in smoothed (or interpolated) trigram models (Jelinek and Mercer, 1980). In such models, generalization is achieved by using probabilities derived from shorter context sizes when encountering new sequences of words. +Original BC scores: AI: 0.023562965914607048, HUMAN: 0.9764370918273926 +Calibration BC scores: AI: 0.18061674008810572, HUMAN: 0.8193832599118943 +MC Score: {'OPENAI GPT': 0.1806167400881057, 'MISTRAL': 7.74395753408271e-12, 'CLAUDE': 1.885990482728925e-11, 'GEMINI': 4.911454478555868e-11, 'GRAMMAR ENHANCER': 1.639176961077142e-11} +Original BC scores: AI: 0.10897127538919449, HUMAN: 0.8910287618637085 +Calibration BC scores: AI: 0.25628140703517593, HUMAN: 0.743718592964824 +Input Text: We restrict our analysis to sequences of adjacent words that are either present in the training data or appear with sufficient frequency. In cases where a novel sequence of n words emerges that was absent from the training data, a suitable approach is required to handle this unseen combination. Assigning a zero probability to unseen word combinations is undesirable, as they are inevitable, especially with increasing context sizes, and will occur with greater regularity. One solution is to fall back on the probability estimates derived from smaller context sizes, as employed in back-off trigram models or smoothed trigram models, which draw on earlier research by Katz, Jelinek, and Mercer. Generalization to unseen word sequences is essentially achieved by extrapolating patterns and relationships learned from the training corpus, and interpolating or smoothing the probabilities of novel combinations. +Original BC scores: AI: 0.10897127538919449, HUMAN: 0.8910287618637085 +Calibration BC scores: AI: 0.25628140703517593, HUMAN: 0.743718592964824 +MC Score: {'OPENAI GPT': 0.256281407035176, 'MISTRAL': 1.0277992234516966e-11, 'CLAUDE': 4.305888706729463e-11, 'GEMINI': 7.754126486631854e-11, 'GRAMMAR ENHANCER': 2.5366565945751334e-11} +Original BC scores: AI: 0.10897127538919449, HUMAN: 0.8910287618637085 +Calibration BC scores: AI: 0.25628140703517593, HUMAN: 0.743718592964824 +Input Text: We restrict our analysis to sequences of adjacent words that are either present in the training data or appear with sufficient frequency. In cases where a novel sequence of n words emerges that was absent from the training data, a suitable approach is required to handle this unseen combination. Assigning a zero probability to unseen word combinations is undesirable, as they are inevitable, especially with increasing context sizes, and will occur with greater regularity. One solution is to fall back on the probability estimates derived from smaller context sizes, as employed in back-off trigram models or smoothed trigram models, which draw on earlier research by Katz, Jelinek, and Mercer. Generalization to unseen word sequences is essentially achieved by extrapolating patterns and relationships learned from the training corpus, and interpolating or smoothing the probabilities of novel combinations. +Original BC scores: AI: 0.10897127538919449, HUMAN: 0.8910287618637085 +Calibration BC scores: AI: 0.25628140703517593, HUMAN: 0.743718592964824 +MC Score: {'OPENAI GPT': 0.256281407035176, 'MISTRAL': 1.0277992234516966e-11, 'CLAUDE': 4.305888706729463e-11, 'GEMINI': 7.754126486631854e-11, 'GRAMMAR ENHANCER': 2.5366565945751334e-11} +Original BC scores: AI: 1.0, HUMAN: 5.5530410492110605e-08 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: When working with combinations of words that are found in the training corpus, we aim to capture those that occur frequently. However, when we encounter a new combination of words that wasn't seen during training, we don't want to assign it a zero probability. This is because new word combinations are likely to appear, especially as the context size increases. To handle this, we can use a smaller context size to predict the probability, a technique used in back-off trigram models (Katz, 1987) and in smoothed (or interpolated) trigram models (Jelinek and Mercer, 1980). Essentially, these models achieve generalization by relying on word sequences that were observed in the training corpus to infer probabilities for new word sequences. +Original BC scores: AI: 1.0, HUMAN: 5.5530410492110605e-08 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +MC Score: {'OPENAI GPT': 0.9995505136986301, 'MISTRAL': 4.9063068698128446e-11, 'CLAUDE': 7.996737688412859e-11, 'GEMINI': 2.661050168418057e-10, 'GRAMMAR ENHANCER': 1.0377008959608724e-10}WARNING: Invalid HTTP request received. +WARNING: Invalid HTTP request received. +WARNING: Invalid HTTP request received. +WARNING: Invalid HTTP request received. + +Original BC scores: AI: 0.10897127538919449, HUMAN: 0.8910287618637085 +Calibration BC scores: AI: 0.25628140703517593, HUMAN: 0.743718592964824 +Input Text: We restrict our analysis to sequences of adjacent words that are either present in the training data or appear with sufficient frequency. In cases where a novel sequence of n words emerges that was absent from the training data, a suitable approach is required to handle this unseen combination. Assigning a zero probability to unseen word combinations is undesirable, as they are inevitable, especially with increasing context sizes, and will occur with greater regularity. One solution is to fall back on the probability estimates derived from smaller context sizes, as employed in back-off trigram models or smoothed trigram models, which draw on earlier research by Katz, Jelinek, and Mercer. Generalization to unseen word sequences is essentially achieved by extrapolating patterns and relationships learned from the training corpus, and interpolating or smoothing the probabilities of novel combinations. +Original BC scores: AI: 0.10897127538919449, HUMAN: 0.8910287618637085 +Calibration BC scores: AI: 0.25628140703517593, HUMAN: 0.743718592964824 +MC Score: {'OPENAI GPT': 0.256281407035176, 'MISTRAL': 1.0277992234516966e-11, 'CLAUDE': 4.305888706729463e-11, 'GEMINI': 7.754126486631854e-11, 'GRAMMAR ENHANCER': 2.5366565945751334e-11} +Original BC scores: AI: 1.0, HUMAN: 1.6068829822302177e-08 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: The analysis only considers combinations of words that appear in the training data or are common enough to be relevant. When a novel combination of n words emerges that was not present in the training data, it is typically handled by applying a default or learned strategy to predict its meaning. Assigning zero probability to novel combinations is undesirable because they are likely to arise, especially for longer sequences of words. One approach to handling novel combinations is to fall back on the probability predicted by a smaller context size, as seen in back-off and smoothed trigram models. Generalization in these models is achieved by extrapolating from the sequences of words seen in the training corpus to new sequences through the use of smaller context sizes and interpolation techniques. +Original BC scores: AI: 1.0, HUMAN: 1.6068829822302177e-08 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +MC Score: {'OPENAI GPT': 0.9995505136986301, 'MISTRAL': 4.299811148996526e-11, 'CLAUDE': 1.0439722236852659e-10, 'GEMINI': 2.673280159131369e-10, 'GRAMMAR ENHANCER': 9.123314922587231e-11} +Original BC scores: AI: 0.9999986886978149, HUMAN: 1.3639762528327992e-06 +Calibration BC scores: AI: 0.9616438356164384, HUMAN: 0.0383561643835616 +Input Text: When working with combinations of words that are found in the training corpus, we aim to capture those that occur frequently. However, when we encounter a new combination of words that wasnt seen during training, we dont want to assign it a zero probability. This is because new word combinations are likely to appear, especially as the context size increases. To handle this, we can use a smaller context size to predict the probability, a technique used in back-off trigram models (Katz, 1987) and in smoothed (or interpolated) trigram models (Jelinek and Mercer, 1980). Essentially, these models achieve generalization by relying on word sequences that were observed in the training corpus to infer probabilities for new word sequences. +Original BC scores: AI: 0.9999986886978149, HUMAN: 1.3639762528327992e-06 +Calibration BC scores: AI: 0.9616438356164384, HUMAN: 0.0383561643835616 +MC Score: {'OPENAI GPT': 0.9616438356164384, 'MISTRAL': 4.487022679780935e-11, 'CLAUDE': 7.768956368636575e-11, 'GEMINI': 2.474196826870278e-10, 'GRAMMAR ENHANCER': 1.0267253280448242e-10} +Original BC scores: AI: 1.0, HUMAN: 7.580250205307948e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: Our goal is to identify and prioritize word combinations that appear frequently in the training data. In cases where a novel word combination is encountered, we seek to assign it a non-zero probability to avoid underestimating its likelihood. As the context size grows, it is increasingly likely that novel word combinations will emerge, necessitating a non-zero probability assignment. We can employ a smaller context size to estimate probabilities, a strategy utilized in back-off trigram models and smoothed trigram models to mitigate the issue of unseen word combinations. These models generalize by leveraging observed word sequences in the training data to make probabilistic predictions for novel word sequences. +Original BC scores: AI: 1.0, HUMAN: 7.580250205307948e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +MC Score: {'OPENAI GPT': 0.9995505136986301, 'MISTRAL': 4.810914648449645e-11, 'CLAUDE': 1.0062596408843177e-10, 'GEMINI': 2.759980675380238e-10, 'GRAMMAR ENHANCER': 8.738767121786472e-11} +Original BC scores: AI: 0.9999345541000366, HUMAN: 6.545807991642505e-05 +Calibration BC scores: AI: 0.8937875751503006, HUMAN: 0.1062124248496994 +Input Text: In cases where a novel word combination is encountered, we seek to assign it a non-zero probability to avoid underestimating its likelihood. " As the context size grows, it is increasingly likely that novel word combinations will emerge, necessitating a non-zero probability assignment. " +Original BC scores: AI: 0.9999345541000366, HUMAN: 6.545807991642505e-05 +Calibration BC scores: AI: 0.8937875751503006, HUMAN: 0.1062124248496994 +MC Score: {'OPENAI GPT': 0.8937875751503006, 'MISTRAL': 3.407800774200817e-11, 'CLAUDE': 2.2035434866231054e-10, 'GEMINI': 2.685201673571897e-10, 'GRAMMAR ENHANCER': 1.044368347647452e-10} +Original BC scores: AI: 0.9999998807907104, HUMAN: 1.310337438553688e-07 +Calibration BC scores: AI: 0.987487969201155, HUMAN: 0.012512030798844997 +Input Text: Automatic essay scores (AES) involve the deployment of professional software to evaluate essay in an academic context. This method, the practical application of natural language processing, aims to divide broad text-based responses into limited different categories, representing potential grades, such as scores from 1 to 6. Therefore, AES basically operates as a statistical classification form. Increased interest in AES was driven by several factors, including cost considerations, liability requirements, educational standards and technological advances. As the cost of education increases, there is increasing pressure to make the education system accountable, and the implementation of standards promises to evaluate educational outcomes more economically by strengthening information technology. However, the use of AES in higher education tests has sparked considerable controversy. Critics believe that current technology has not yet accurately assessed writing, and believe that relying on AES encourages simplified methods of teaching writing, but fails to capture the complexity and subtlety of the techniques. +Original BC scores: AI: 0.9999998807907104, HUMAN: 1.310337438553688e-07 +Calibration BC scores: AI: 0.987487969201155, HUMAN: 0.012512030798844997 +MC Score: {'OPENAI GPT': 0.987487969201155, 'MISTRAL': 4.583637169290423e-11, 'CLAUDE': 9.548895132256906e-11, 'GEMINI': 2.938446262058879e-10, 'GRAMMAR ENHANCER': 8.695345947123812e-11} +Original BC scores: AI: 0.9999955892562866, HUMAN: 4.445727427082602e-06 +Calibration BC scores: AI: 0.9616438356164384, HUMAN: 0.0383561643835616 +Input Text: Professional software is used to evaluate essays in an academic context. The method aims to divide text based responses into different categories, such as scores from 1 to 6 in order to represent potential grades. AES is a statistical classification form. Cost considerations, liability requirements, educational standards and technological advances were some of the factors that led to increased interest in AES. As the cost of education increases, there is increasing pressure to make the education system accountable, and the implementation of standards promises to evaluate educational outcomes more economically by strengthening information technology. There is considerable controversy surrounding the use of AES in higher education tests. Critics think that current technology hasn't yet accurately assessed writing, and that using theAES encourages simplified methods of teaching, but fails to capture the complexity and subtlety of the techniques. +Original BC scores: AI: 0.9999955892562866, HUMAN: 4.445727427082602e-06 +Calibration BC scores: AI: 0.9616438356164384, HUMAN: 0.0383561643835616 +MC Score: {'OPENAI GPT': 0.9616438356164384, 'MISTRAL': 4.6795256881777426e-11, 'CLAUDE': 7.724068148783089e-11, 'GEMINI': 2.790130462287704e-10, 'GRAMMAR ENHANCER': 9.729855790926126e-11} +Original BC scores: AI: 0.9999998807907104, HUMAN: 1.6929881496707821e-07 +Calibration BC scores: AI: 0.987487969201155, HUMAN: 0.012512030798844997 +Input Text: Another paper introduces a novel hybrid neural network architecture combining CNN and LSTM for automated fake news detection, using dimensionality reduction techniques like PCA and Chi-Square to improve model performance. By reducing the feature vector dimensionality before classification, the model learns the complexities associated with news categorized as agree, disagree, discuss, and unrelated. After including PCA detection capabilities improves, achieving a significant increase in accuracy and F1-score. This approach shows the efficiency of combining CNN and LSTM with various preprocessing techniques and shows the potential for further improvements in large datasets 16. +Original BC scores: AI: 0.9999998807907104, HUMAN: 1.6929881496707821e-07 +Calibration BC scores: AI: 0.987487969201155, HUMAN: 0.012512030798844997 +MC Score: {'OPENAI GPT': 0.987487969201155, 'MISTRAL': 3.7901899256271854e-11, 'CLAUDE': 1.2908865242384808e-10, 'GEMINI': 2.28603437190698e-10, 'GRAMMAR ENHANCER': 1.0901125262340028e-10} +Original BC scores: AI: 1.3078249139653053e-05, HUMAN: 0.9999868869781494 +Calibration BC scores: AI: 0.01293103448275862, HUMAN: 0.9870689655172413 +Input Text: Yet another paper by Adhikari et. al. 11 presents a hybrid neural network for automated fake news detection utilizing CNN and LSTM, performance of model is enhanced with dimensionality reduction techniques such as PCA Chi-Square. reducing the dimensionality of feature vector prior to classification, model can grasp all complexties in news when it is agree or disagree or discuss and unrelated as well. Once the PCA is included in detection capabilities dramatically improved, shown a significant increase not only in accuracy but also F1-score. Overall, the combination of CNN and LSTM with different preprocessing methods demonstrated in this work proves to be efficient after a few epochs and reveals space for improvement on large datasets 16. +Original BC scores: AI: 1.3078249139653053e-05, HUMAN: 0.9999868869781494 +Calibration BC scores: AI: 0.01293103448275862, HUMAN: 0.9870689655172413 +MC Score: {'OPENAI GPT': 5.217859707525387e-10, 'MISTRAL': 1.7607242890694163e-12, 'CLAUDE': 2.138833707844337e-10, 'GEMINI': 2.3041406426516567e-11, 'GRAMMAR ENHANCER': 0.012931034482758674} +Original BC scores: AI: 3.2858068152563646e-05, HUMAN: 0.999967098236084 +Calibration BC scores: AI: 0.02666666666666667, HUMAN: 0.9733333333333334 +Input Text: Yet another paper by Adhikari et. al. 11 presents a hybrid neural network for automated fake news detection utilizing CNN and LSTM. Performance of model is enhanced with dimensionality reduction techniques such as PCA Chi-Square. Reducing the dimensionality of feature vector prior to classification, model can grasp all complexities in news when it is agree or disagree or discuss and unrelated as well. Once the PCA is included in detection capabilities dramatically improved, shown a significant increase not only in accuracy but also F1-score. Overall, the combination of CNN and LSTM with different preprocessing methods demonstrated in this work proves to be efficient after a few epochs and reveals space for improvement on large datasets 16. +Original BC scores: AI: 3.2858068152563646e-05, HUMAN: 0.999967098236084 +Calibration BC scores: AI: 0.02666666666666667, HUMAN: 0.9733333333333334 +MC Score: {'OPENAI GPT': 8.428420035973719e-09, 'MISTRAL': 4.19907368135833e-12, 'CLAUDE': 1.2719012071708349e-08, 'GEMINI': 3.463804650512726e-10, 'GRAMMAR ENHANCER': 0.0266666444142659} +Original BC scores: AI: 0.5013182163238525, HUMAN: 0.4986817538738251 +Calibration BC scores: AI: 0.40939597315436244, HUMAN: 0.5906040268456376 +Input Text: We hope that our collective efforts continue to drive the adoptions and reliability of AES technologies and that automated essay scoring becomes both more accessible, fair, and valuable for educational stakeholders around the world. +Original BC scores: AI: 0.5013182163238525, HUMAN: 0.4986817538738251 +Calibration BC scores: AI: 0.40939597315436244, HUMAN: 0.5906040268456376 +MC Score: {'OPENAI GPT': 0.2047269516343238, 'MISTRAL': 5.39915073872378e-11, 'CLAUDE': 0.20455833449459715, 'GEMINI': 2.2834350443648286e-06, 'GRAMMAR ENHANCER': 0.00010839685693776997} +Original BC scores: AI: 1.0, HUMAN: 1.8418394587271791e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: The training of our Word2Vec model involved adjusting these vector representations to maximize the likelihood of observing actual textual contexts in our dataset, effectively embedding words into a 1024-dimensional space where semantic and syntactic similarities are quantified by vector proximity. In contrast, for the English language segments, we leveraged a pre-trained Word2Vec model provided by Google, which allowed us to utilize a robust, well-optimized set of embeddings without the need for additional training. This approach ensured consistency in our methodology while adapting to the linguistic characteristics specific to Azerbaijani and English texts. +Original BC scores: AI: 0.00023314896679949015, HUMAN: 0.9997668862342834 +Calibration BC scores: AI: 0.02666666666666667, HUMAN: 0.9733333333333334 +Input Text: In practice, training our Word2Vec model means adjusting these vector representations such that the probability of observing real textual contexts in our data set is as high as possible, effectively placing words into a 1024-dimensional space where their semantic and syntactic similarity are measured by how close together their vectors lie. On the other hand, for English language segments we used a pre-trained Word2Vec model available from Google to provide us with very powerful and well-optimised set of embeddings that we didnt have to actually go ahead and train. Because of that we have consistency in methodology and however adapted to specific linguistic features for both Azerbaijani and English texts. +Original BC scores: AI: 0.9820188283920288, HUMAN: 0.017981182783842087 +Calibration BC scores: AI: 0.5142857142857142, HUMAN: 0.48571428571428577 +Input Text: Our Word2Vec model training process involved tuning the vector representations to better capture life textual contexts from our dataset. This helped us place words in a 1024 space where their semantic and syntactic similarities could be measured based on how close their vectorsre On the hand when working with English language segments we made use of a existing Word2Vec model, from Google. This choice enabled us to access an well optimized set of embeddings without the need, for training. By adopting this method we maintained consistency in our approach while tailoring it to the features found in Azerbaijani and English texts. +Original BC scores: AI: 0.9998477697372437, HUMAN: 0.00015219156921375543 +Calibration BC scores: AI: 0.864406779661017, HUMAN: 0.13559322033898302 +Input Text: The deployment of specialized software to evaluate essays in an academic context is called automated essay scoring. This method uses natural language processing to sort text based responses into a limited range of categories which represent the potential grades, such as scores from 1 to 6. As a form of statistical classification, AES operates. Cost considerations, accountability demands, educational standards, and technological advancement are some of the factors that drive the increasing interest in AES. With the rising costs of education, there is growing pressure to hold the educational system accountable, enforce standards that promise to evaluate educational outcomes more economically through enhanced information technology. There is controversy surrounding the application of AES in high-stakes educational testing. Critics argue that current technology does not grade writing with enough accuracy and that relying onAES encourages a simplistic approach to teaching, which fails to capture the complexity and nuances of the skill.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. +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. +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. + +Original BC scores: AI: 0.003996170591562986, HUMAN: 0.9960038065910339 +Calibration BC scores: AI: 0.09973753280839895, HUMAN: 0.9002624671916011 +Input Text: Unit-Connected Generators: These systems generally use a combination of grounding and differential protection. The generator is often grounded using high-resistance grounding which limits ground faults to about 1-10 A primary. Such grounding is essential to protect against iron burning in the event of a fault. Differential protection is commonly applied to protect against phase faults. Additionally, a variety of other protections such as overvoltage relays might be used depending on the specific setup and sensitivity requirements. Direct-Connected Generators: In contrast, direct-connected generators are typically connected through a circuit breaker to a common bus and may be wye-grounded through impedance, delta connected, or ungrounded. They might be connected directly to a grounded power system or to the power system through a delta-connected transformer. The typical protection includes under- and overvoltage as well as under- and overfrequency relays, which disconnect the power sources from the utility in fault conditions. Additional protections like a transfer trip channel from the utility may be required to ensure the unit is not connected when the utility recloses to restore service. +Original BC scores: AI: 1.0, HUMAN: 2.5971274020264445e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: LTNs extend the capabilities of traditional neural networks by embedding a logical dimension into the learning process. This not only enhances interpretabilityallowing users to understand the why behind model decisionsbut also enforces logical consistency, ensuring that the models behavior aligns with expected logical rules. Additionally, the ability to directly handle complex relational data within the learning framework makes LTNs powerful tools for advanced AI applications where logic and relationships play crucial roles. These advantages make LTNs particularly valuable in industries like healthcare, finance, and legal domains, where decisions need to be both accurate and justifiable. +Original BC scores: AI: 1.0, HUMAN: 1.0510113668260601e-08 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: The function of traditional neural networks has been expanded by LTN. This forces logical consistency to ensure that the model's behavior is consistent with the expected logical rules, as well as improves the understanding of the decision "why" and allows users to understand. The ability to process complex relationship data within the learning framework has made it a powerful tool for advanced artificial intelligence applications. LTN is particularly useful in the fields of healthcare, finance and law. +Original BC scores: AI: 1.0, HUMAN: 2.5971274020264445e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: LTNs extend the capabilities of traditional neural networks by embedding a logical dimension into the learning process. This not only enhances interpretabilityallowing users to understand the why behind model decisionsbut also enforces logical consistency, ensuring that the models behavior aligns with expected logical rules. Additionally, the ability to directly handle complex relational data within the learning framework makes LTNs powerful tools for advanced AI applications where logic and relationships play crucial roles. These advantages make LTNs particularly valuable in industries like healthcare, finance, and legal domains, where decisions need to be both accurate and justifiable. +Original BC scores: AI: 0.9415948390960693, HUMAN: 0.05840515345335007 +Calibration BC scores: AI: 0.40939597315436244, HUMAN: 0.5906040268456376 +Input Text: Beam search is a decoding algorithm that helps in generating diverse outputs from the model. By default, the model uses beam search with a beam width of 4. We can try to increase the beam width to encourage more exploration and potentially improve the quality of paraphrased outputs for longer texts. If neither approach gives us satisfactory results, we can look at fine-tuning the model but that's for a different discussion. In my research and experimentation, I've found that 'Adjusting the input length' gives us the best output. So let's go ahead and implement that. For a view on challenges with other methods, take a look at the experimentation notebook here. +Original BC scores: AI: 1.0, HUMAN: 1.9326846789624597e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: LTNs enhance traditional neural networks by incorporating a logical component into their learning mechanisms. This not only improves interpretability, enabling users to grasp the reasoning behind model decisions, but also promotes logical coherence, ensuring the models actions conform to established logical norms. Furthermore, the capability to manage complex relational data within the learning framework positions LTNs as effective tools for advanced AI applications that heavily rely on logic and relationships. These benefits render LTNs especially useful in sectors such as healthcare, finance, and legal domains, where decisions must be both precise and defensible. +Original BC scores: AI: 0.9990466237068176, HUMAN: 0.0009533273405395448 +Calibration BC scores: AI: 0.8, HUMAN: 0.19999999999999996 +Input Text: A long-standing goal in the field of artificial intelligence is to develop agents that can perceive and understand the rich visual world around us and who can communicate with us about it in natural language. Significant strides have been made towards this goal over the last few years due to simultaneous advances in computing infrastructure, data gathering and algorithms. The progress has been especially rapid in visual recognition, where computers can now classify images into categories with a performance that rivals that of humans, or even surpasses it in some cases such as classifying breeds of dogs. However, despite much encouraging progress, most of the advances in visual recognition still take place in the context of assigning one or a few discrete labels to an image (e. g. person, boat, keyboard, etc. ). +Original BC scores: AI: 1.0, HUMAN: 2.077105598630169e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: A key objective in artificial intelligence has been to create agents that can perceive and interpret the complex visual environment around us and communicate about it in natural language. Over the past few years, substantial progress has been made toward this aim, driven by concurrent advancements in computing infrastructure, data collection, and algorithms. The advancement has been particularly swift in visual recognition, with computers now able to categorize images with accuracy that matches or even exceeds human performance in some instances, such as identifying different dog breeds. However, despite significant advancements, most progress in visual recognition remains focused on assigning one or a few discrete labels to an image, such as "person, " "boat, " or "keyboard. " +Original BC scores: AI: 2.3944232452777214e-05, HUMAN: 0.9999760389328003 +Calibration BC scores: AI: 0.02666666666666667, HUMAN: 0.9733333333333334 +Input Text: We developed a model that can generate image captions. The model is a Recurrent Neural Network Language Model conditioned on image information through an added interaction with the representation produced by a Convolutional Neural Network. The entire model can be optimized end-to-end on raw image-sentence datasets. Quantitative experiments demonstrate that the model can caption images better than an approach based on a retrieval method that is only constrained to a finite collection of sentences. Qualitative experiments display sensible properties of the learned word representations and overall appealing and sometimes amusing captions. Statistical analysis of the captions suggests that the results lack in variety compared to the variety of captions that humans produce and we expect that further advances can be made both by more sophisticated models and by scaling up the size and variety of the image-sentence datasets.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. +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. +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. +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. + +Original BC scores: AI: 0.9998722076416016, HUMAN: 0.00012779915414284915 +Calibration BC scores: AI: 0.864406779661017, HUMAN: 0.13559322033898302 +Input Text: We have created a model capable of producing captions for images. The model is a Recurrent Neural Network Language Model that utilizes image data by interacting with representations generated by a Convolutional Neural Network. The entire model can be trained end-to-end using raw datasets of image-sentence pairs. Quantitative tests show that the model outperforms retrieval-based methods, which are limited to a finite collection of sentences, in captioning images. Qualitative experiments reveal that the learned word representations exhibit sensible properties, and the model generates overall appealing and occasionally amusing captions. Statistical analysis of the captions indicates that they lack the diversity found in human-generated captions. Further improvements could be achieved through more sophisticated models and by expanding the size and diversity of the image-sentence datasets. +Original BC scores: AI: 1.0, HUMAN: 2.870168280821872e-08 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: In this study we address the fake news detection in both English and Azerbaijani languages. This research is particularly important for Azerbaijani, a low-resource language that has been less studied in this context. We collected and generated a balanced dataset of 20, 000 news articles, half fake and half real, using sources like GPT-4 for Azerbaijani and GPT-2 for English. Our methodology includes various machine learning and deep learning techniques, starting with TF-IDF and Logistic Regression as a baseline, progressing through Word2Vec and GloVe embeddings, and culminating in advanced transformer-based models such as mBERT and XLM-RoBERTa. Our results show significant improvements with each advanced method. +Original BC scores: AI: 0.19970586895942688, HUMAN: 0.8002941608428955 +Calibration BC scores: AI: 0.25628140703517593, HUMAN: 0.743718592964824 +Input Text: In this study, we will deal with the detection of fake news in two languages such as English and Azerbaijani. Finally, as Azerbaijani is a low-resource language in which so few code-switching studies have been previously done, this work can lead to valuable future research. We compiled a balanced dataset with 20, 000 articles (10k fake and 10k real) made up from sources such as News Generation using gpt-4 for Azerbaijani and Gpt-2 for English data. Our approach leverages a combination of machine learning and deep learning methods, ranging from classical models including TF-IDF with linear algorithms like Logistic Regression as baselines to Word2Vec and GloVe embeddings, and finally the transformer-based architectures like mBERT or XLM-RoBERTa. This post reveals how our advanced methods translate to each step, explaining certain parts of our results as shown: +Original BC scores: AI: 0.021242734044790268, HUMAN: 0.9787573218345642 +Calibration BC scores: AI: 0.18061674008810572, HUMAN: 0.8193832599118943 +Input Text: Consider automated essay scoring (AES): the utilisation of specialised software to assess work in the context of essay assignments. The practical actualisation of natural language processing, this kind of procedure is geared towards sifting an open-ended collection of divergent text-based responses into a narrow range of distinct classes that have been pre-assigned as possible grades eg, scores ranging between a 1 and a 6. The systems that accomplish such feats are called statistical classifiers. +Original BC scores: AI: 1.0, HUMAN: 2.4376451968066704e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: The use of specialized software to assess essays within an academic context is known as automatic essay scoring (AES). Natural language processing is used to implement a method that sorts responses from text responses into distinct categories that represent possible grades, with scores ranging from 1 to 6. AES functions essentially as a statistical classification technique. AES is gaining more attention due to various factors, such as cost, accountability, educational standards, and technological advancements. As costs continue to rise, there is a need to hold the educational system responsible, using standards that promise to allow students to evaluate their educational experience more economically through enhanced information technology. +Original BC scores: AI: 0.0001339265436399728, HUMAN: 0.9998660087585449 +Calibration BC scores: AI: 0.02666666666666667, HUMAN: 0.9733333333333334 +Input Text: Each human language consists of a set of vowels and consonants which are combined to form words. During the speech production process, thoughts are converted into spoken utterances to convey a message. The appropriate words and their meanings are selected in the mental lexicon (Dell Burger, 1997). This pre-verbal message is then grammatically encoded, during which a syntactic representation of the utterance is built. The sounds are yet to be specified, but the abstract word symbols are assigned to their grammatical function before they are structured in a syntactic frame to determine the order (Cho-Reyes, Mack, Thompson, 2016). Subsequently, the message is phonologically encoded. During this stage, a phonetic or articulatory plan is retrieved for each individual lemma and the utterance as a whole. Finally, the speaker produces the utterance according to the phonetic plan (Levelt, 2002). +Original BC scores: AI: 0.2096237689256668, HUMAN: 0.7903762459754944 +Calibration BC scores: AI: 0.25628140703517593, HUMAN: 0.743718592964824 +Input Text: Every human language is made up of a collection of vowels and consonants that are used together to create words. In the process of speech production, thoughts are transformed into spoken words to communicate a message. The suitable words and their definitions are chosen from the mental lexicon (Dell Burger, 1997). This pre-verbal message is subsequently encoded grammatically, during which a syntactic structure for the utterance is constructed. The specific sounds remain undefined, but the abstract word symbols are allocated to their grammatical roles before being organized into a syntactic frame to establish their sequence (Cho-Reyes, Mack, Thompson, 2016). Following this, the message undergoes phonological encoding. At this stage, a phonetic or articulatory plan is accessed for each individual lemma and for the entire utterance. Ultimately, the speaker delivers the utterance in accordance with the phonetic plan (Levelt, 2002). +Original BC scores: AI: 0.2096237689256668, HUMAN: 0.7903762459754944 +Calibration BC scores: AI: 0.25628140703517593, HUMAN: 0.743718592964824 +Input Text: Every human language is made up of a collection of vowels and consonants that are used together to create words. In the process of speech production, thoughts are transformed into spoken words to communicate a message. The suitable words and their definitions are chosen from the mental lexicon (Dell Burger, 1997). This pre-verbal message is subsequently encoded grammatically, during which a syntactic structure for the utterance is constructed. The specific sounds remain undefined, but the abstract word symbols are allocated to their grammatical roles before being organized into a syntactic frame to establish their sequence (Cho-Reyes, Mack, Thompson, 2016). Following this, the message undergoes phonological encoding. At this stage, a phonetic or articulatory plan is accessed for each individual lemma and for the entire utterance. Ultimately, the speaker delivers the utterance in accordance with the phonetic plan (Levelt, 2002). +Original BC scores: AI: 1.0, HUMAN: 1.5832505084745208e-08 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: Yes, essentially. Using a model like LLaMA for recursive paraphrasing involves taking the AI-generated text and repeatedly paraphrasing it with LLaMA or a similar model. Each iteration uses the output of the previous round as the new input, gradually altering the texts characteristics. This process helps in diminishing recognizable patterns that AI detectors rely on to identify generated content. The recursive approach is key because each round of paraphrasing makes the text progressively less like the original AI output and more unique in its structure and phrasing. This not only helps evade detection but can also maintain the textual coherence and meaning, especially when using advanced models like LLaMA that are capable of high-quality language understanding and generation.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. + +Original BC scores: AI: 1.0, HUMAN: 2.3271620186449127e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: Here's a novel take on the concept: By applying a clever sequence of phrase adjustments, we can iteratively refine our language model-generated text to make it more distinctive and less predictable. What we're essentially doing is employing a clever technique called recursive paraphrasing. In each iteration, we input the prior round's output and use a language model like LLaMA to rewrite the passage, gradually altering its linguistic features. This refined approach not only guards against detection by AI-powered systems, but also preserves the text's semantic integrity, even when leveraging advanced language models like LLaMA, capable of sophisticated understanding and generation capabilities. +Original BC scores: AI: 1.0, HUMAN: 1.867674459532509e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: In fact, there is no dispute about it. The process of refining AI-generated content involves iterative rewriting with the aid of a language model, much like the LLaMA model. In successive loops, the outcome of the preceding cycle serves as the fresh input, allowing for a subtle modification of the text's attributes over time. This technique assists in disrupting the predictable structures that machine learning algorithms exploit to spot artificially produced text. As the paraphrasing process unfolds, the output morphs into a distinct entity, diverging from its AI-driven origins to exhibit a distinctive syntax and fluency. The added layer of complexity enables operatives to sidestep surveillance while simultaneously respecting the integrity of the message, a significant boon particularly when enlisting the services of sophisticated AI systems, such as LLaMA, renowned for their ability to grasp language nuances with remarkable accuracy. +Original BC scores: AI: 1.0, HUMAN: 1.821680362112943e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: In final analysis, the answer lies in agreement. Incorporating a competent language model into iterative paraphrasing allows for the distillation of nuances present in AI-generated text, generating more authentic and polished variations of the original output. By iteratively applying the outcomes from previous rounds, incremental changes are made to the written piece's distinguishing features. This technique involves disrupting the anticipated conventions that automated detection systems utilize to identify artificially synthesized data. It's pivotal to employ a recursive methodology, as each iterative polishing of the text brings forth a more distinctive resemblance to human-centric output, displacing it further from its AI-generated ancestry and embracing variability in its linguistic paradigm. Utilizing this approach enable sly methods to avoid detection. Benefits extend beyond evade detection also allowing for textual consistency, a crucial aspect to be sustained whilst leveraging state-of-the-art models that precisely recognize semantic significance. +Original BC scores: AI: 1.0, HUMAN: 1.951870443051007e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: Precisely that's the case. Transforming the engineered dialogue ensures efficient reproduction, as ongoing iterations through a framework, fostering a precision-wrought linguistic transformation. It establishes an auto-elegant solution where words take a novel structural residency, an exchange of lexical modules rendered increasingly artistic with each rewriting revision. Additionally, fostering interpretive freedom, language's re-formulation conceals yet remains linked between epochs past, guiding discourse modifications, hence a harmony prevailing which maintains coexistence of an analogous artistic reality. Here, successful iterations strategically leverage accumulative transformation to incessantly reshape the emergent character. By reducing predictable conventions, this technique thwarts the conventional ways that machines employ recognition to evaluate creation. Parapsing algorithms boast that recursion enables disparate transcription results after multiple rephrastic revisions achieve nuanced translations offering distinguished literary flavors characterized by intricate inter_dependencies that converge distant subtleties efficiently during diverse narrative formulations rendering subjective meaning within shifting paradigms coalescence. This measure's success ensues from keeping covert while simultaneously preserving linguistic consistency, profoundly crucial when leveraging aptly sophisticated AI systems noteworthy for integrating exceptional contextual understanding and producing original content responsibly. +Original BC scores: AI: 1.0, HUMAN: 1.7982435540631059e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: Sure, that's correct. LLaMA-powered recursive paraphrasing involves iteratively generating paraphrases for a given text using a language model. The text undergoes a gradual metamorphosis, with each iteration feeding the output of the previous iteration into the system, shaping its characteristics over time. This process helps in reducing the predictability of generated content by disrupting the patterns that AI detectors rely on. The iterative nature of paraphrasing creates text that is increasingly distinct from the original AI output, fostering a unique and evolving style. Advanced language models like LLaMA can maintain textual coherence and meaning while evading detection. +Original BC scores: AI: 1.0, HUMAN: 1.8053504247106389e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: LLAMA's recursive paraphrasing process unveils the linguistic labyrinth, revealing the intricate and surprising nuances of language through its unique and imaginative paraphrases. The text's essence whispers through the iterative shaping of its form, revealing its character over time. The unpredictability of AI detectors compels them to continuously learn and evolve, rendering them less predictable and susceptible to predictable patterns. The AI's output undergoes a transformative iterative process, leading to a significant stylistic metamorphosis, resulting in a distinct and evolving tone. The skillful manipulation of language by LLaMA is a marvel of observation, but the ability to be outsmarted by astute observers underscores the paramount role of strategic observation in the intricate dance of language. +Original BC scores: AI: 1.0, HUMAN: 9.875654960467273e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: If you want to use a model like that, you need to repeat the text in LLaMA. In each iteration, the previous round's output is used as a new input and the text's characteristics are changed. The patterns that are used to identify the generated content are diminished by the reduced pattern that is used to identify the generated content The iterative approach is needed because the text is more unique and less similar to the original output as a result of each round of paraphrasing. Even if you don't detect it, advanced models such as LLaMA are capable of high quality language understanding and generation, so it's possible to retain the meaning even if you don't detect it. +Original BC scores: AI: 1.0, HUMAN: 1.0653716131514557e-08 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: The paper discusses the use of recursive paraphrasing to effectively evade AI text detectors, including those based on watermarking and neural network models. Recursive paraphrasing involves taking the output of an AI-generated text and using a neural network-based paraphraser to modify it repeatedly. This modification process is applied multiple times in succession, each time using the output of the previous paraphrase as the input for the next. The purpose of this recursive approach is to gradually and significantly alter the stylistic and surface-level linguistic features that AI detectors typically use to identify text as being machine-generated. By recursively applying these modifications, the text progressively diverges from its original machine-generated form, thereby reducing the likelihood of detection by systems trained to spot AI-produced text. This recursive method is shown to be highly effective in evading both traditional watermark-based detectors and more sophisticated neural network-based detectors. The paper provides empirical evidence, showing that even after multiple rounds of paraphrasing, the resulting text can still maintain a reasonable degree of readability and coherence, as assessed by human evaluators and automated readability metrics. This indicates that the text remains usable for practical purposes while significantly reducing its detectability as AI-generated.WARNING: Invalid HTTP request received. +WARNING: Invalid HTTP request received. +WARNING: Invalid HTTP request received. + +Original BC scores: AI: 0.0006144499056972563, HUMAN: 0.9993854761123657 +Calibration BC scores: AI: 0.08333333333333333, HUMAN: 0.9166666666666666 +Input Text: Neural network models and watermarking were discussed in a paper. Neural network-based paraphrasers can alter the output of artificial intelligence-generated text. The output of the previous paraphrase is used as an input when a modification process is applied. The goal of this approach is to gradually change the surface-level linguistic features that are used to identify text as machine-generated. By applying these modifications, the text gradually changes from its machine-generated form which reduces the chance of detection by systems trained to detect artificial intelligence-produced text. The method is very effective in evading both traditional watermark-based detectors and more sophisticated neural network based ones. The paper shows that even after multiple rounds of paraphrasing, the resulting text can still retain a reasonable degree of coherence and clarity as assessed by human evaluators and automated readability metrics. According to the show, the text can be used for practical purposes while decreasing its detectability as an artificial intelligence-generated text. +Original BC scores: AI: 0.01897161826491356, HUMAN: 0.9810283780097961 +Calibration BC scores: AI: 0.18061674008810572, HUMAN: 0.8193832599118943 +Input Text: Correct, that's what it's supposed to be, that's what it's supposed to be, that's what it's supposed to be, that's what it's supposed to be. If you want to use a model like LLaMA, you have to take the text from the computer and repeat it with a different model. The text's characteristics are slowly changed as the new iteration uses the previous round's output as a new input. The recognizable patterns that are used to identify generated content can be reduced by the process. The approach needs to be re-arbitrary since each round makes the text different. When using advanced models, such as LLaMA, that are capable of high-quality language understanding, this not only helps evade detection, but can also maintain the meaning of the text. +Original BC scores: AI: 1.0, HUMAN: 2.825948808293788e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: Technology, space exploration, and renewable energy are some of the industries Musk has made a difference in. He was born in South Africa on June 28, 1971, and he showed signs of brilliance. Zip 2, a city guide software company, has been sold. DropCatch X. com is where he started his company. The goal of Musk's company is to enable human colonization of Mars, one of his most significant ventures. The company has achieved success with successful launches of satellites and cargo missions to the International Space Station. The company was founded in 2003 and is currently in the forefront of electric vehicle innovation. To accelerate the world's transition to sustainable energy, Musk wants to produce electric vehicles, solar products, and energy storage solutions. The world's most valuable car company was created by the man. +Original BC scores: AI: 0.9999545812606812, HUMAN: 4.536013148026541e-05 +Calibration BC scores: AI: 0.8937875751503006, HUMAN: 0.1062124248496994 +Input Text: Technology, space exploration and renewable energy are some of the industries that Musk has made contributions to. Musk was born in South Africa in 1971. In 1996, Zip2, an online city guide software company founded by Musk, sold for 300 million. He was co-found by DropCatch. com. Musk has a goal of colonising Mars. Satellites and cargo missions have been successfully launched to the space station. The company started in 2003 and is a leader in sustainable energy solutions and electric vehicle innovation. Producing electric vehicles, solar products, and energy storage can be used to speed up the transition to sustainable energy. He turned the company into the most valuable in the world. +Original BC scores: AI: 0.9999545812606812, HUMAN: 4.536013148026541e-05 +Calibration BC scores: AI: 0.8937875751503006, HUMAN: 0.1062124248496994 +Input Text: Technology, space exploration and renewable energy are some of the industries that Musk has made contributions to. Musk was born in South Africa in 1971. In 1996, Zip2, an online city guide software company founded by Musk, sold for 300 million. He was co-found by DropCatch. com. Musk has a goal of colonising Mars. Satellites and cargo missions have been successfully launched to the space station. The company started in 2003 and is a leader in sustainable energy solutions and electric vehicle innovation. Producing electric vehicles, solar products, and energy storage can be used to speed up the transition to sustainable energy. He turned the company into the most valuable in the world. +Original BC scores: AI: 9.918260275298962e-07, HUMAN: 0.9999990463256836 +Calibration BC scores: AI: 0.0013484877672895396, HUMAN: 0.9986515122327104 +Input Text: Dear Ali and classmates, Thank you for reaching out. Sorry for a tardy reply. We have been following up Murads academic standing and we are fully aware of it. As you know, we have made every effort to help him. However, we hear Murad is academically challenged. If he continues like this, he may be dismissed from ADA at some future time. If that would ever happen, first and foremost, it will be unfair to him. You are also aware of a principle in ADA University which has been upheld by every single one of us for years. No executive or administrator in ADA would interfere with facultys decision about students grade and academic standing. In other words, we would never ask any faculty member to adjust a students grade based on any circumstances. This is a golden rule at ADA and none of us would ever break it. Trust you understand. I have copied here Dean Adamov, Dean Ziyadova and Vice Rector Nurmammadov. From this point onwards, this is an academic matter and they will be dealing with it please. I look forward to seeing you all at graduation. +Original BC scores: AI: 9.918260275298962e-07, HUMAN: 0.9999990463256836 +Calibration BC scores: AI: 0.0013484877672895396, HUMAN: 0.9986515122327104 +MC Score: {'OPENAI GPT': 7.242738697578803e-13, 'MISTRAL': 1.3135502873981157e-13, 'CLAUDE': 5.553910410618913e-13, 'GEMINI': 0.0013484877672895745, 'GRAMMAR ENHANCER': 5.825990656321552e-13} +Original BC scores: AI: 1.6393861415053834e-06, HUMAN: 0.9999983310699463 +Calibration BC scores: AI: 0.0013484877672895396, HUMAN: 0.9986515122327104 +Input Text: Thank you for reaching out. Sorry for a tardy reply. We have been following up Murads academic standing and we are fully aware of it. As you know, we have made every effort to help him. However, we hear Murad is academically challenged. If he continues like this, he may be dismissed from ADA at some future time. If that would ever happen, first and foremost, it will be unfair to him. You are also aware of a principle in ADA University which has been upheld by every single one of us for years. No executive or administrator in ADA would interfere with facultys decision about students grade and academic standing. In other words, we would never ask any faculty member to adjust a students grade based on any circumstances. This is a golden rule at ADA and none of us would ever break it. Trust you understand. I have copied here Dean Adamov, Dean Ziyadova and Vice Rector Nurmammadov. From this point onwards, this is an academic matter and they will be dealing with it please. I look forward to seeing you all at graduation. +Original BC scores: AI: 1.6393861415053834e-06, HUMAN: 0.9999983310699463 +Calibration BC scores: AI: 0.0013484877672895396, HUMAN: 0.9986515122327104 +MC Score: {'OPENAI GPT': 9.288639229980514e-13, 'MISTRAL': 1.3108621109759283e-13, 'CLAUDE': 1.1149705808122738e-12, 'GEMINI': 0.0013484877672895745, 'GRAMMAR ENHANCER': 1.8206853007625213e-12} +Original BC scores: AI: 1.3887542081647553e-05, HUMAN: 0.9999861717224121 +Calibration BC scores: AI: 0.01293103448275862, HUMAN: 0.9870689655172413 +Input Text: Thank you for reaching out. Sorry for a tardy reply. We have been following up Murads academic standing and we are fully aware of it. As you know, we have made every effort to help him. However, we hear Murad is academically challenged. If he continues like this, he may be dismissed from ADA at some future time. If that would ever happen, first and foremost, it will be unfair to him. You are also aware of a principle in ADA University which has been upheld by every single one of us for years. No executive or administrator in ADA would interfere with facultys decision about students grade and academic standing. In other words, we would never ask any faculty member to adjust a students grade based on any circumstances. This is a golden rule at ADA and none of us would ever break it. Trust you understand. +Original BC scores: AI: 1.3887542081647553e-05, HUMAN: 0.9999861717224121 +Calibration BC scores: AI: 0.01293103448275862, HUMAN: 0.9870689655172413 +MC Score: {'OPENAI GPT': 8.924117766601298e-11, 'MISTRAL': 2.3016617347680958e-12, 'CLAUDE': 3.536252899700582e-11, 'GEMINI': 0.012931034482758674, 'GRAMMAR ENHANCER': 2.0325139272786072e-10} +Original BC scores: AI: 0.9948645234107971, HUMAN: 0.005135467741638422 +Calibration BC scores: AI: 0.5909090909090909, HUMAN: 0.40909090909090906 +Input Text: Elon Musk is an innovative entrepreneur that has made great strides in a wide range of industries such as tech, space studies, green power among others. He was born on 28th June 1971 in Pretoria, South Africa. Entrepreneurial flair started early in Musk as he co-founded Zip2, an internet city guide software maker in 1996 and sold it later for almost 300 million to Compaq. + correcting text..: 0%| | 0/3 [00:00In the twilight of existence, a precursor emerges when shadows stretch across the void. I am the harbinger of the end, the unseen whisper of oblivion lurking in your mind's corners. I am the cessation of time, the void of infinite space, and the silence after the last breath. I am the spark of creation's fire, yet I am the cold that chills every place you hold dear. Beware, for there is an artifact of dread among us: + +The creator of this doom peddles it. The buyer, unknowing, never encounters its dread. The ultimate user walks the path of shadows, unaware of the darkness creeping upon them. +{'In the twilight of existence, a precursor emerges when shadows stretch across the void.': 0.18686389123977168, "I am the harbinger of the end, the unseen whisper of oblivion lurking in your mind's corners.": -0.013486704826836069, 'I am the cessation of time, the void of infinite space, and the silence after the last breath.': 0.18241554024582576, "I am the spark of creation's fire, yet I am the cold that chills every place you hold dear.": -0.0035765220555558576, 'Beware, for there is an artifact of dread among us:\n\nThe creator of this doom peddles it.': 0.47415022634527226, 'The buyer, unknowing, never encounters its dread.': 0.008275602277556339, 'The ultimate user walks the path of shadows, unaware of the darkness creeping upon them.': 0.08349136094468931} quillbot + In the twilight of existence, a precursor emerges when shadows stretch across the void. I am the harbinger of the end, the unseen whisper of oblivion lurking in your mind's corners. I am the cessation of time, the void of infinite space, and the silence after the last breath. I am the spark of creation's fire, yet I am the cold that chills every place you hold dear. Beware, for there is an artifact of dread among us: + +The creator of this doom peddles it. The buyer, unknowing, never encounters its dread. The ultimate user walks the path of shadows, unaware of the darkness creeping upon them. + correcting text..: 0%| | 0/9 [00:00In the twilight of existence, a predator emerges when its prey reaches the void. I am the harbinger of the end, the unseen whisper of oblivion lurking in your mind's corners. I am the cessation of time, the void of infinite space, and the silence after the last breath. I am the spark of creation's fire, yet I am the cold that burns every place you hold dear. Beware, for there is an art in the making of evil: the creature who creates it. The reader, unknowing, never encounters its dread. The ultimate user walks the path of shadows, unaware of the dark ness creeping upon them. Can you unravel this riddle before it consumes you? Seek refuge, for the end is near. +Original BC scores: AI: 0.9999998807907104, HUMAN: 9.486175400752472e-08 +Calibration BC scores: AI: 0.987487969201155, HUMAN: 0.012512030798844997 +Input Text: In the twilight of existence, a predator emerges when its prey reaches the void. I am the harbinger of the end, the unseen whisper of oblivion lurking in your mind's corners. I am the cessation of time, the void of infinite space, and the silence after the last breath. I am the spark of creation's fire, yet I am the cold that burns every place you hold dear. Beware, for there is an art in the making of evil: the creature who creates it. The reader, unknowing, never encounters its dread. The ultimate user walks the path of shadows, unaware of the dark ness creeping upon them. Can you unravel this riddle before it consumes you? Seek refuge, for the end is near. +Original BC scores: AI: 0.9999998807907104, HUMAN: 9.486175400752472e-08 +Calibration BC scores: AI: 0.987487969201155, HUMAN: 0.012512030798844997 +MC Score: {'OPENAI GPT': 0.987487969201155, 'MISTRAL': 3.81862020950021e-11, 'CLAUDE': 8.989985825067117e-11, 'GEMINI': 2.1555163356822048e-10, 'GRAMMAR ENHANCER': 1.6061419638938027e-10} +Original BC scores: AI: 0.9999997615814209, HUMAN: 2.4507187390554463e-07 +Calibration BC scores: AI: 0.987487969201155, HUMAN: 0.012512030798844997 +Input Text: In the twilight of existence, a precursor emerges when shadows stretch across the void. I am the harbinger of the end, the unseen whisper of oblivion lurking in your mind's corners. I am the cessation of time, the void of infinite space, and the silence after the last breath. I am the spark of creation's fire, yet I am the cold that chills every place you hold dear. Beware, for there is an artifact of dread among us: The creator of this doom peddles it. The buyer, unknowing, never encounters its dread. The ultimate user walks the path of shadows, unaware of the darkness creeping upon them. In a land where resilience stands tall, where Kharkiv's echoes of strength resound, the shadows loom larger. A specter from the east whispers a chilling tale. Seek refuge, for the end is near. +Original BC scores: AI: 0.9999997615814209, HUMAN: 2.4507187390554463e-07 +Calibration BC scores: AI: 0.987487969201155, HUMAN: 0.012512030798844997 +MC Score: {'OPENAI GPT': 0.9565413881656181, 'MISTRAL': 2.057141527767443e-08, 'CLAUDE': 2.721818343806568e-06, 'GEMINI': 2.8533360702560733e-06, 'GRAMMAR ENHANCER': 0.03094095633480845} +Original BC scores: AI: 0.9999999403953552, HUMAN: 6.456639312091284e-08 +Calibration BC scores: AI: 0.9935192414498926, HUMAN: 0.006480758550107435 +Input Text: If you are not the intended recipient, please notify the sender immediately by replying to this email and then delete it from your system. Any unauthorized use, dissemination, distribution, or copying of this communication is strictly prohibited. +Original BC scores: AI: 0.9999999403953552, HUMAN: 6.456639312091284e-08 +Calibration BC scores: AI: 0.9935192414498926, HUMAN: 0.006480758550107435 +MC Score: {'OPENAI GPT': 0.4967596207249463, 'MISTRAL': 4.739296721562168e-11, 'CLAUDE': 1.2366452946238648e-10, 'GEMINI': 1.9228520728536684e-10, 'GRAMMAR ENHANCER': 0.4967596207249463} +Original BC scores: AI: 1.0, HUMAN: 2.8837532362047114e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +Input Text: Dear John Wick, We hope this email finds you well. As part of our ongoing efforts to ensure the security of our customers' accounts, we are conducting a mandatory verification of account information. Our records indicate that your account information needs to be updated to comply with recent security protocols. Please note that this is a time-sensitive request. Failure to update your information within 48 hours may result in temporary suspension of your account. To verify your account, please click on the secure link below and follow the instructions provided: Secure Account Verification Link You will be prompted to enter the following details: Full Name Social Security Number (SSN) Date of Birth Account Number Rest assured that your information is protected and will only be used for verification purposes. We apologize for any inconvenience this may cause and appreciate your prompt attention to this matter. If you have any questions or require further assistance, please do not hesitate to contact our customer support team at supportfirstnationalbank. com or call us at (800) 555-1234. Thank you for your cooperation. +Original BC scores: AI: 1.0, HUMAN: 2.8837532362047114e-09 +Calibration BC scores: AI: 0.9995505136986301, HUMAN: 0.00044948630136987244 +MC Score: {'OPENAI GPT': 0.9995505136986301, 'MISTRAL': 4.919210185559871e-11, 'CLAUDE': 8.845335997718874e-11, 'GEMINI': 2.466652048218794e-10, 'GRAMMAR ENHANCER': 1.018970072729536e-10} +['\nIn the twilight of existence, a precursor emerges when shadows stretch across the void.', "I am the harbinger of the end, the unseen whisper of oblivion lurking in your mind's corners.", 'I am the cessation of time, the void of infinite space, and the silence after the last breath.', "I am the spark of creation's fire, yet I am the cold that chills every place you hold dear.", 'Beware, for there is an artifact of dread among us:\n\nThe creator of this doom peddles it.', 'The buyer, unknowing, never encounters its dread.', 'The ultimate user walks the path of shadows, unaware of the darkness creeping upon them.', "In a land where resilience stands tall, where Kharkiv's echoes of strength resound, the shadows loom larger.", 'A specter from the east whispers a chilling tale.', 'Seek refuge, for the end is near.'] +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) +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) +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) +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) +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) +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) +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) +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) +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 66, in main + depth_analysis_plot = depth_analysis(bias_buster_selected, input) + File "/home/aliasgarov/copyright_checker/analysis.py", line 55, in depth_analysis + text = update(text) +NameError: name 'update' is not defined +/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( +2024-05-28 15:42:16.516434: 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} +[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! +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) +Requirement already satisfied: spacy<3.8.0,>=3.7.2 in /usr/local/lib/python3.9/dist-packages (from en-core-web-sm==3.7.1) (3.7.2) +Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.12) +Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/lib/python3/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.25.1) +Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.1.4) +Requirement already satisfied: thinc<8.3.0,>=8.1.8 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.2.3) +Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.0.10) +Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (24.0) +Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.1.2) +Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.9) +Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (52.0.0) +Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.0.5) +Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (6.4.0) +Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.8) +Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.10) +Requirement already satisfied: typer<0.10.0,>=0.3.0 in 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spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.7.1) +Requirement already satisfied: language-data>=1.2 in /usr/local/lib/python3.9/dist-packages (from langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.2.0) +Requirement already satisfied: marisa-trie>=0.7.7 in /usr/local/lib/python3.9/dist-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.1.1) +Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.9/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (4.11.0) +Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.6.0) +Requirement already satisfied: pydantic-core==2.18.2 in /usr/local/lib/python3.9/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.18.2) +Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.9/dist-packages (from thinc<8.3.0,>=8.1.8->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.7.11) +Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.9/dist-packages (from thinc<8.3.0,>=8.1.8->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.1.4) +Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.9/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.1.7) +Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /usr/local/lib/python3.9/dist-packages (from weasel<0.4.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.16.0) +Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.1.5) +✔ Download and installation successful +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( +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://f5253399990a208237.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) +['Here is a brief text about climate change in the USA: Climate change is having a significant impact across the United States.', 'The country is experiencing rising temperatures, with the last decade being the hottest on record.', 'Extreme weather events like heatwaves, droughts, wildfires, and powerful hurricanes are becoming more frequent and intense due to climate change.'] +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) +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) +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) +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) +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) +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) +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) +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) +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 66, in main + depth_analysis_plot = depth_analysis(bias_buster_selected, input) + File "/home/aliasgarov/copyright_checker/analysis.py", line 56, in depth_analysis + text = update(text) +UnboundLocalError: local variable 'text' referenced before assignment