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Model Name stringlengths 5 122	URL stringlengths 28 145	Crawled Text stringlengths 1 199k ⌀	text stringlengths 180 199k
BSC-LT/roberta-large-bne	https://huggingface.co/BSC-LT/roberta-large-bne	⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019. The National Library of Spain (Biblioteca Nacional de España) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019. To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text. Some of the statistics of the corpus: The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original RoBERTA model with a vocabulary size of 50,262 tokens. The RoBERTa-large-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa large. The training lasted a total of 96 hours with 32 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM. For evaluation details visit our GitHub repository. Check out our paper for all the details: https://arxiv.org/abs/2107.07253	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : BSC-LT/roberta-large-bne ### Model URL : https://huggingface.co/BSC-LT/roberta-large-bne ### Model Description : ⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019. The National Library of Spain (Biblioteca Nacional de España) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019. To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text. Some of the statistics of the corpus: The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original RoBERTA model with a vocabulary size of 50,262 tokens. The RoBERTa-large-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa large. The training lasted a total of 96 hours with 32 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM. For evaluation details visit our GitHub repository. Check out our paper for all the details: https://arxiv.org/abs/2107.07253
BSen/wav2vec2-base-timit-demo-colab	https://huggingface.co/BSen/wav2vec2-base-timit-demo-colab	This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : BSen/wav2vec2-base-timit-demo-colab ### Model URL : https://huggingface.co/BSen/wav2vec2-base-timit-demo-colab ### Model Description : This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:
BSen/wav2vec2-large-xls-r-300m-turkish-colab	https://huggingface.co/BSen/wav2vec2-large-xls-r-300m-turkish-colab	This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. More information needed More information needed More information needed The following hyperparameters were used during training:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : BSen/wav2vec2-large-xls-r-300m-turkish-colab ### Model URL : https://huggingface.co/BSen/wav2vec2-large-xls-r-300m-turkish-colab ### Model Description : This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. More information needed More information needed More information needed The following hyperparameters were used during training:
BW/TEST	https://huggingface.co/BW/TEST	null	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : BW/TEST ### Model URL : https://huggingface.co/BW/TEST ### Model Description :
Babelscape/rebel-large	https://huggingface.co/Babelscape/rebel-large	This is the model card for the Findings of EMNLP 2021 paper REBEL: Relation Extraction By End-to-end Language generation. We present a new linearization approach and a reframing of Relation Extraction as a seq2seq task. The paper can be found here. If you use the code, please reference this work in your paper: The original repository for the paper can be found here Be aware that the inference widget at the right does not output special tokens, which are necessary to distinguish the subject, object and relation types. For a demo of REBEL and its pre-training dataset check the Spaces demo.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Babelscape/rebel-large ### Model URL : https://huggingface.co/Babelscape/rebel-large ### Model Description : This is the model card for the Findings of EMNLP 2021 paper REBEL: Relation Extraction By End-to-end Language generation. We present a new linearization approach and a reframing of Relation Extraction as a seq2seq task. The paper can be found here. If you use the code, please reference this work in your paper: The original repository for the paper can be found here Be aware that the inference widget at the right does not output special tokens, which are necessary to distinguish the subject, object and relation types. For a demo of REBEL and its pre-training dataset check the Spaces demo.
Babelscape/wikineural-multilingual-ner	https://huggingface.co/Babelscape/wikineural-multilingual-ner	This is the model card for the EMNLP 2021 paper WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER. We fine-tuned a multilingual language model (mBERT) for 3 epochs on our WikiNEuRal dataset for Named Entity Recognition (NER). The resulting multilingual NER model supports the 9 languages covered by WikiNEuRal (de, en, es, fr, it, nl, pl, pt, ru), and it was trained on all 9 languages jointly. If you use the model, please reference this work in your paper: The original repository for the paper can be found at https://github.com/Babelscape/wikineural. You can use this model with Transformers pipeline for NER. This model is trained on WikiNEuRal, a state-of-the-art dataset for Multilingual NER automatically derived from Wikipedia. Therefore, it might not generalize well to all textual genres (e.g. news). On the other hand, models trained only on news articles (e.g. only on CoNLL03) have been proven to obtain much lower scores on encyclopedic articles. To obtain more robust systems, we encourage you to train a system on the combination of WikiNEuRal with other datasets (e.g. WikiNEuRal + CoNLL). Contents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright of the dataset contents and models belongs to the original copyright holders.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Babelscape/wikineural-multilingual-ner ### Model URL : https://huggingface.co/Babelscape/wikineural-multilingual-ner ### Model Description : This is the model card for the EMNLP 2021 paper WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER. We fine-tuned a multilingual language model (mBERT) for 3 epochs on our WikiNEuRal dataset for Named Entity Recognition (NER). The resulting multilingual NER model supports the 9 languages covered by WikiNEuRal (de, en, es, fr, it, nl, pl, pt, ru), and it was trained on all 9 languages jointly. If you use the model, please reference this work in your paper: The original repository for the paper can be found at https://github.com/Babelscape/wikineural. You can use this model with Transformers pipeline for NER. This model is trained on WikiNEuRal, a state-of-the-art dataset for Multilingual NER automatically derived from Wikipedia. Therefore, it might not generalize well to all textual genres (e.g. news). On the other hand, models trained only on news articles (e.g. only on CoNLL03) have been proven to obtain much lower scores on encyclopedic articles. To obtain more robust systems, we encourage you to train a system on the combination of WikiNEuRal with other datasets (e.g. WikiNEuRal + CoNLL). Contents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright of the dataset contents and models belongs to the original copyright holders.
Backedman/DialoGPT-small-Anika	https://huggingface.co/Backedman/DialoGPT-small-Anika	#Anika Bot	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Backedman/DialoGPT-small-Anika ### Model URL : https://huggingface.co/Backedman/DialoGPT-small-Anika ### Model Description : #Anika Bot
Badr/model1	https://huggingface.co/Badr/model1	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Badr/model1 ### Model URL : https://huggingface.co/Badr/model1 ### Model Description : No model card New: Create and edit this model card directly on the website!
Bagus/SER-LSSED	https://huggingface.co/Bagus/SER-LSSED	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Bagus/SER-LSSED ### Model URL : https://huggingface.co/Bagus/SER-LSSED ### Model Description : No model card New: Create and edit this model card directly on the website!
Bagus/ser-japanese	https://huggingface.co/Bagus/ser-japanese	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Bagus/ser-japanese ### Model URL : https://huggingface.co/Bagus/ser-japanese ### Model Description : No model card New: Create and edit this model card directly on the website!
Bagus/wav2vec2-large-xlsr-bahasa-indonesia	https://huggingface.co/Bagus/wav2vec2-large-xlsr-bahasa-indonesia	Dataset used for training: Test WER: 19.3 % Contact:bagus@ep.its.ac.id	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Bagus/wav2vec2-large-xlsr-bahasa-indonesia ### Model URL : https://huggingface.co/Bagus/wav2vec2-large-xlsr-bahasa-indonesia ### Model Description : Dataset used for training: Test WER: 19.3 % Contact:bagus@ep.its.ac.id
Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition	https://huggingface.co/Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition	null	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition ### Model URL : https://huggingface.co/Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition ### Model Description :
Bagus/wav2vec2-xlsr-japanese-speech-emotion-recognition	https://huggingface.co/Bagus/wav2vec2-xlsr-japanese-speech-emotion-recognition	This is for (private) DEMO only.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Bagus/wav2vec2-xlsr-japanese-speech-emotion-recognition ### Model URL : https://huggingface.co/Bagus/wav2vec2-xlsr-japanese-speech-emotion-recognition ### Model Description : This is for (private) DEMO only.
Bakkes/BakkesModWiki	https://huggingface.co/Bakkes/BakkesModWiki	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Bakkes/BakkesModWiki ### Model URL : https://huggingface.co/Bakkes/BakkesModWiki ### Model Description : No model card New: Create and edit this model card directly on the website!
Bala/model_name	https://huggingface.co/Bala/model_name	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Bala/model_name ### Model URL : https://huggingface.co/Bala/model_name ### Model Description : No model card New: Create and edit this model card directly on the website!
BalajiSathesh/DialoGPT-small-harrypotter	https://huggingface.co/BalajiSathesh/DialoGPT-small-harrypotter	Harry Potter DialoGPT Model	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : BalajiSathesh/DialoGPT-small-harrypotter ### Model URL : https://huggingface.co/BalajiSathesh/DialoGPT-small-harrypotter ### Model Description : Harry Potter DialoGPT Model
Balgow/prod_desc	https://huggingface.co/Balgow/prod_desc	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Balgow/prod_desc ### Model URL : https://huggingface.co/Balgow/prod_desc ### Model Description : No model card New: Create and edit this model card directly on the website!
Banshee/LukeSkywalker	https://huggingface.co/Banshee/LukeSkywalker	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Banshee/LukeSkywalker ### Model URL : https://huggingface.co/Banshee/LukeSkywalker ### Model Description : No model card New: Create and edit this model card directly on the website!
Banshee/dialoGPT-luke-small	https://huggingface.co/Banshee/dialoGPT-luke-small	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Banshee/dialoGPT-luke-small ### Model URL : https://huggingface.co/Banshee/dialoGPT-luke-small ### Model Description : No model card New: Create and edit this model card directly on the website!
Banshee/dialoGPT-small-luke	https://huggingface.co/Banshee/dialoGPT-small-luke	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Banshee/dialoGPT-small-luke ### Model URL : https://huggingface.co/Banshee/dialoGPT-small-luke ### Model Description : No model card New: Create and edit this model card directly on the website!
Barbarameerr/Barbara	https://huggingface.co/Barbarameerr/Barbara	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Barbarameerr/Barbara ### Model URL : https://huggingface.co/Barbarameerr/Barbara ### Model Description : No model card New: Create and edit this model card directly on the website!
Barkavi/t5base_totto	https://huggingface.co/Barkavi/t5base_totto	Dataset ToTTo is an open-domain English Table-to-Text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table, a set of highlighted table cells, page title and section title as inputs, it produces a one-sentence description summarising the key details from the inputs. This dataset can be taken from hugging face (https://huggingface.co/datasets/totto). Model The pre-trained Text-to-Text "t5-base" model is fine-tuned with the Table-to-Text ToTTo dataset(downstream task) for the complete train dataset split of around 120,761 examples. During the fine-tuning process for this downstream task, BertScore metric was used as an evaluation metric instead of the standard BLEU metric.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Barkavi/t5base_totto ### Model URL : https://huggingface.co/Barkavi/t5base_totto ### Model Description : Dataset ToTTo is an open-domain English Table-to-Text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table, a set of highlighted table cells, page title and section title as inputs, it produces a one-sentence description summarising the key details from the inputs. This dataset can be taken from hugging face (https://huggingface.co/datasets/totto). Model The pre-trained Text-to-Text "t5-base" model is fine-tuned with the Table-to-Text ToTTo dataset(downstream task) for the complete train dataset split of around 120,761 examples. During the fine-tuning process for this downstream task, BertScore metric was used as an evaluation metric instead of the standard BLEU metric.
Barleysack/AERoberta	https://huggingface.co/Barleysack/AERoberta	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Barleysack/AERoberta ### Model URL : https://huggingface.co/Barleysack/AERoberta ### Model Description : No model card New: Create and edit this model card directly on the website!
Barleysack/AERoberta2	https://huggingface.co/Barleysack/AERoberta2	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Barleysack/AERoberta2 ### Model URL : https://huggingface.co/Barleysack/AERoberta2 ### Model Description : No model card New: Create and edit this model card directly on the website!
Barleysack/klue-roberta-LSTM	https://huggingface.co/Barleysack/klue-roberta-LSTM	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Barleysack/klue-roberta-LSTM ### Model URL : https://huggingface.co/Barleysack/klue-roberta-LSTM ### Model Description : No model card New: Create and edit this model card directly on the website!
Barytes/hellohf	https://huggingface.co/Barytes/hellohf	null	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Barytes/hellohf ### Model URL : https://huggingface.co/Barytes/hellohf ### Model Description :
Batsy24/DialoGPT-medium-Twilight_BellaBot	https://huggingface.co/Batsy24/DialoGPT-medium-Twilight_BellaBot	null	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Batsy24/DialoGPT-medium-Twilight_BellaBot ### Model URL : https://huggingface.co/Batsy24/DialoGPT-medium-Twilight_BellaBot ### Model Description :
Batsy24/DialoGPT-small-Twilight_EdBot	https://huggingface.co/Batsy24/DialoGPT-small-Twilight_EdBot	null	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Batsy24/DialoGPT-small-Twilight_EdBot ### Model URL : https://huggingface.co/Batsy24/DialoGPT-small-Twilight_EdBot ### Model Description :
Battlehooks/distilbert-base-uncased-finetuned-squad	https://huggingface.co/Battlehooks/distilbert-base-uncased-finetuned-squad	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Battlehooks/distilbert-base-uncased-finetuned-squad ### Model URL : https://huggingface.co/Battlehooks/distilbert-base-uncased-finetuned-squad ### Model Description : No model card New: Create and edit this model card directly on the website!
BatuhanYilmaz/bert-finetuned-mrpc	https://huggingface.co/BatuhanYilmaz/bert-finetuned-mrpc	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : BatuhanYilmaz/bert-finetuned-mrpc ### Model URL : https://huggingface.co/BatuhanYilmaz/bert-finetuned-mrpc ### Model Description : No model card New: Create and edit this model card directly on the website!
BatuhanYilmaz/bert-finetuned-ner	https://huggingface.co/BatuhanYilmaz/bert-finetuned-ner	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : BatuhanYilmaz/bert-finetuned-ner ### Model URL : https://huggingface.co/BatuhanYilmaz/bert-finetuned-ner ### Model Description : No model card New: Create and edit this model card directly on the website!
BatuhanYilmaz/bert-finetuned-nerxD	https://huggingface.co/BatuhanYilmaz/bert-finetuned-nerxD	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : BatuhanYilmaz/bert-finetuned-nerxD ### Model URL : https://huggingface.co/BatuhanYilmaz/bert-finetuned-nerxD ### Model Description : No model card New: Create and edit this model card directly on the website!
BatuhanYilmaz/code-search-net-tokenizer1	https://huggingface.co/BatuhanYilmaz/code-search-net-tokenizer1	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : BatuhanYilmaz/code-search-net-tokenizer1 ### Model URL : https://huggingface.co/BatuhanYilmaz/code-search-net-tokenizer1 ### Model Description : No model card New: Create and edit this model card directly on the website!
BatuhanYilmaz/distilbert-base-uncased-finetuned-squad-d5716d28	https://huggingface.co/BatuhanYilmaz/distilbert-base-uncased-finetuned-squad-d5716d28	This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation. In this version, the following pre-trained models were used: This model was trained on the SQuAD v1.1 dataset which can be obtained from the datasets library as follows: The scores were calculated using the squad metric from datasets.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : BatuhanYilmaz/distilbert-base-uncased-finetuned-squad-d5716d28 ### Model URL : https://huggingface.co/BatuhanYilmaz/distilbert-base-uncased-finetuned-squad-d5716d28 ### Model Description : This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation. In this version, the following pre-trained models were used: This model was trained on the SQuAD v1.1 dataset which can be obtained from the datasets library as follows: The scores were calculated using the squad metric from datasets.
BatuhanYilmaz/dummy-model	https://huggingface.co/BatuhanYilmaz/dummy-model	This model is a fine-tuned version of camembert-base on an unknown dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : BatuhanYilmaz/dummy-model ### Model URL : https://huggingface.co/BatuhanYilmaz/dummy-model ### Model Description : This model is a fine-tuned version of camembert-base on an unknown dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:
BatuhanYilmaz/dummy	https://huggingface.co/BatuhanYilmaz/dummy	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : BatuhanYilmaz/dummy ### Model URL : https://huggingface.co/BatuhanYilmaz/dummy ### Model Description : No model card New: Create and edit this model card directly on the website!
BatuhanYilmaz/marian-finetuned-kde4-en-to-fr	https://huggingface.co/BatuhanYilmaz/marian-finetuned-kde4-en-to-fr	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : BatuhanYilmaz/marian-finetuned-kde4-en-to-fr ### Model URL : https://huggingface.co/BatuhanYilmaz/marian-finetuned-kde4-en-to-fr ### Model Description : No model card New: Create and edit this model card directly on the website!
BatuhanYilmaz/mlm-finetuned-imdb	https://huggingface.co/BatuhanYilmaz/mlm-finetuned-imdb	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : BatuhanYilmaz/mlm-finetuned-imdb ### Model URL : https://huggingface.co/BatuhanYilmaz/mlm-finetuned-imdb ### Model Description : No model card New: Create and edit this model card directly on the website!
BatuhanYilmaz/mt5-small-finetuned-amazonbooks-en-es	https://huggingface.co/BatuhanYilmaz/mt5-small-finetuned-amazonbooks-en-es	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : BatuhanYilmaz/mt5-small-finetuned-amazonbooks-en-es ### Model URL : https://huggingface.co/BatuhanYilmaz/mt5-small-finetuned-amazonbooks-en-es ### Model Description : No model card New: Create and edit this model card directly on the website!
Baybars/debateGPT	https://huggingface.co/Baybars/debateGPT	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Baybars/debateGPT ### Model URL : https://huggingface.co/Baybars/debateGPT ### Model Description : No model card New: Create and edit this model card directly on the website!