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AWTStress/stress_classifier | https://huggingface.co/AWTStress/stress_classifier | This model was trained from scratch 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 : AWTStress/stress_classifier
### Model URL : https://huggingface.co/AWTStress/stress_classifier
### Model Description : This model was trained from scratch 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: |
AWTStress/stress_score | https://huggingface.co/AWTStress/stress_score | This model was trained from scratch 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 : AWTStress/stress_score
### Model URL : https://huggingface.co/AWTStress/stress_score
### Model Description : This model was trained from scratch 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: |
AZTEC/Arcane | https://huggingface.co/AZTEC/Arcane | 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 : AZTEC/Arcane
### Model URL : https://huggingface.co/AZTEC/Arcane
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aakansha/hateSpeechClassification | https://huggingface.co/Aakansha/hateSpeechClassification | 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 : Aakansha/hateSpeechClassification
### Model URL : https://huggingface.co/Aakansha/hateSpeechClassification
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aakansha/hs | https://huggingface.co/Aakansha/hs | 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 : Aakansha/hs
### Model URL : https://huggingface.co/Aakansha/hs
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aarav/MeanMadCrazy_HarryPotterBot | https://huggingface.co/Aarav/MeanMadCrazy_HarryPotterBot | 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 : Aarav/MeanMadCrazy_HarryPotterBot
### Model URL : https://huggingface.co/Aarav/MeanMadCrazy_HarryPotterBot
### Model Description : No model card New: Create and edit this model card directly on the website! |
AaravMonkey/modelRepo | https://huggingface.co/AaravMonkey/modelRepo | 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 : AaravMonkey/modelRepo
### Model URL : https://huggingface.co/AaravMonkey/modelRepo
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aarbor/xlm-roberta-base-finetuned-marc-en | https://huggingface.co/Aarbor/xlm-roberta-base-finetuned-marc-en | 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 : Aarbor/xlm-roberta-base-finetuned-marc-en
### Model URL : https://huggingface.co/Aarbor/xlm-roberta-base-finetuned-marc-en
### Model Description : No model card New: Create and edit this model card directly on the website! |
Pinwheel/wav2vec2-base-timit-demo-colab | https://huggingface.co/Pinwheel/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 : Pinwheel/wav2vec2-base-timit-demo-colab
### Model URL : https://huggingface.co/Pinwheel/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: |
Pinwheel/wav2vec2-large-xls-r-1b-hi-v2 | https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-1b-hi-v2 | 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 : Pinwheel/wav2vec2-large-xls-r-1b-hi-v2
### Model URL : https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-1b-hi-v2
### Model Description : No model card New: Create and edit this model card directly on the website! |
Pinwheel/wav2vec2-large-xls-r-1b-hi | https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-1b-hi | 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 : Pinwheel/wav2vec2-large-xls-r-1b-hi
### Model URL : https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-1b-hi
### Model Description : No model card New: Create and edit this model card directly on the website! |
Pinwheel/wav2vec2-large-xls-r-1b-hindi | https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-1b-hindi | 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 : Pinwheel/wav2vec2-large-xls-r-1b-hindi
### Model URL : https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-1b-hindi
### Model Description : No model card New: Create and edit this model card directly on the website! |
Pinwheel/wav2vec2-large-xls-r-300m-50-hi | https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-300m-50-hi | 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 : Pinwheel/wav2vec2-large-xls-r-300m-50-hi
### Model URL : https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-300m-50-hi
### Model Description : No model card New: Create and edit this model card directly on the website! |
Pinwheel/wav2vec2-large-xls-r-300m-hi-v2 | https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-300m-hi-v2 | 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 : Pinwheel/wav2vec2-large-xls-r-300m-hi-v2
### Model URL : https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-300m-hi-v2
### Model Description : No model card New: Create and edit this model card directly on the website! |
Pinwheel/wav2vec2-large-xls-r-300m-hi-v3 | https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-300m-hi-v3 | 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 : Pinwheel/wav2vec2-large-xls-r-300m-hi-v3
### Model URL : https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-300m-hi-v3
### Model Description : No model card New: Create and edit this model card directly on the website! |
Pinwheel/wav2vec2-large-xls-r-300m-hi | https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-300m-hi | 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 : Pinwheel/wav2vec2-large-xls-r-300m-hi
### Model URL : https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-300m-hi
### Model Description : No model card New: Create and edit this model card directly on the website! |
Pinwheel/wav2vec2-large-xls-r-300m-tr-colab | https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-300m-tr-colab | 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 : Pinwheel/wav2vec2-large-xls-r-300m-tr-colab
### Model URL : https://huggingface.co/Pinwheel/wav2vec2-large-xls-r-300m-tr-colab
### Model Description : No model card New: Create and edit this model card directly on the website! |
Pinwheel/wav2vec2-large-xlsr-53-hi | https://huggingface.co/Pinwheel/wav2vec2-large-xlsr-53-hi | 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 : Pinwheel/wav2vec2-large-xlsr-53-hi
### Model URL : https://huggingface.co/Pinwheel/wav2vec2-large-xlsr-53-hi
### Model Description : No model card New: Create and edit this model card directly on the website! |
Ab0/autoencoder-keras-mnist-demo | https://huggingface.co/Ab0/autoencoder-keras-mnist-demo | 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 : Ab0/autoencoder-keras-mnist-demo
### Model URL : https://huggingface.co/Ab0/autoencoder-keras-mnist-demo
### Model Description : No model card New: Create and edit this model card directly on the website! |
Ab0/foo-model | https://huggingface.co/Ab0/foo-model | #FashionMNIST PyTorch Quick Start | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Ab0/foo-model
### Model URL : https://huggingface.co/Ab0/foo-model
### Model Description : #FashionMNIST PyTorch Quick Start |
Ab0/keras-dummy-functional-demo | https://huggingface.co/Ab0/keras-dummy-functional-demo | 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 : Ab0/keras-dummy-functional-demo
### Model URL : https://huggingface.co/Ab0/keras-dummy-functional-demo
### Model Description : No model card New: Create and edit this model card directly on the website! |
Ab0/keras-dummy-model-mixin-demo | https://huggingface.co/Ab0/keras-dummy-model-mixin-demo | 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 : Ab0/keras-dummy-model-mixin-demo
### Model URL : https://huggingface.co/Ab0/keras-dummy-model-mixin-demo
### Model Description : No model card New: Create and edit this model card directly on the website! |
Ab0/keras-dummy-sequential-demo | https://huggingface.co/Ab0/keras-dummy-sequential-demo | 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 : Ab0/keras-dummy-sequential-demo
### Model URL : https://huggingface.co/Ab0/keras-dummy-sequential-demo
### Model Description : No model card New: Create and edit this model card directly on the website! |
Ab2021/bookst5 | https://huggingface.co/Ab2021/bookst5 | 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 : Ab2021/bookst5
### Model URL : https://huggingface.co/Ab2021/bookst5
### Model Description : No model card New: Create and edit this model card directly on the website! |
Abab/Test_Albert | https://huggingface.co/Abab/Test_Albert | 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 : Abab/Test_Albert
### Model URL : https://huggingface.co/Abab/Test_Albert
### Model Description : No model card New: Create and edit this model card directly on the website! |
AbdelrahmanZayed/my-awesome-model | https://huggingface.co/AbdelrahmanZayed/my-awesome-model | 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 : AbdelrahmanZayed/my-awesome-model
### Model URL : https://huggingface.co/AbdelrahmanZayed/my-awesome-model
### Model Description : No model card New: Create and edit this model card directly on the website! |
AbderrahimRezki/DialoGPT-small-harry | https://huggingface.co/AbderrahimRezki/DialoGPT-small-harry | 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 : AbderrahimRezki/DialoGPT-small-harry
### Model URL : https://huggingface.co/AbderrahimRezki/DialoGPT-small-harry
### Model Description : No model card New: Create and edit this model card directly on the website! |
AbderrahimRezki/HarryPotterBot | https://huggingface.co/AbderrahimRezki/HarryPotterBot | 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 : AbderrahimRezki/HarryPotterBot
### Model URL : https://huggingface.co/AbderrahimRezki/HarryPotterBot
### Model Description : No model card New: Create and edit this model card directly on the website! |
Abdou/arabert-base-algerian | https://huggingface.co/Abdou/arabert-base-algerian | These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: If you find our work useful, please cite it as follows: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Abdou/arabert-base-algerian
### Model URL : https://huggingface.co/Abdou/arabert-base-algerian
### Model Description : These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: If you find our work useful, please cite it as follows: |
Abdou/arabert-large-algerian | https://huggingface.co/Abdou/arabert-large-algerian | These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: If you find our work useful, please cite it as follows: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Abdou/arabert-large-algerian
### Model URL : https://huggingface.co/Abdou/arabert-large-algerian
### Model Description : These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: If you find our work useful, please cite it as follows: |
Abdou/arabert-medium-algerian | https://huggingface.co/Abdou/arabert-medium-algerian | These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: If you find our work useful, please cite it as follows: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Abdou/arabert-medium-algerian
### Model URL : https://huggingface.co/Abdou/arabert-medium-algerian
### Model Description : These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: If you find our work useful, please cite it as follows: |
Abdou/arabert-mini-algerian | https://huggingface.co/Abdou/arabert-mini-algerian | These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: If you find our work useful, please cite it as follows: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Abdou/arabert-mini-algerian
### Model URL : https://huggingface.co/Abdou/arabert-mini-algerian
### Model Description : These are different BERT models (BERT Arabic models are initialized from AraBERT) fine-tuned on the Algerian Dialect Sentiment Analysis dataset. The dataset contains 50,016 comments from YouTube videos in Algerian dialect. The models are evaluated on the testing set: If you find our work useful, please cite it as follows: |
Abdullaziz/model1 | https://huggingface.co/Abdullaziz/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 : Abdullaziz/model1
### Model URL : https://huggingface.co/Abdullaziz/model1
### Model Description : No model card New: Create and edit this model card directly on the website! |
AbdulmalikAdeyemo/wav2vec2-large-xls-r-300m-hausa | https://huggingface.co/AbdulmalikAdeyemo/wav2vec2-large-xls-r-300m-hausa | 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 : AbdulmalikAdeyemo/wav2vec2-large-xls-r-300m-hausa
### Model URL : https://huggingface.co/AbdulmalikAdeyemo/wav2vec2-large-xls-r-300m-hausa
### Model Description : No model card New: Create and edit this model card directly on the website! |
AbhijeetA/PIE | https://huggingface.co/AbhijeetA/PIE | Model details available here | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AbhijeetA/PIE
### Model URL : https://huggingface.co/AbhijeetA/PIE
### Model Description : Model details available here |
Abhilash/BERTBasePyTorch | https://huggingface.co/Abhilash/BERTBasePyTorch | 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 : Abhilash/BERTBasePyTorch
### Model URL : https://huggingface.co/Abhilash/BERTBasePyTorch
### Model Description : No model card New: Create and edit this model card directly on the website! |
AbhinavSaiTheGreat/DialoGPT-small-harrypotter | https://huggingface.co/AbhinavSaiTheGreat/DialoGPT-small-harrypotter | #HarryPotter DialoGPT Model | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AbhinavSaiTheGreat/DialoGPT-small-harrypotter
### Model URL : https://huggingface.co/AbhinavSaiTheGreat/DialoGPT-small-harrypotter
### Model Description : #HarryPotter DialoGPT Model |
Abhishek4/Cuad_Finetune_roberta | https://huggingface.co/Abhishek4/Cuad_Finetune_roberta | 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 : Abhishek4/Cuad_Finetune_roberta
### Model URL : https://huggingface.co/Abhishek4/Cuad_Finetune_roberta
### Model Description : No model card New: Create and edit this model card directly on the website! |
Abi9x/DiabloGPT-large-Axel | https://huggingface.co/Abi9x/DiabloGPT-large-Axel | 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 : Abi9x/DiabloGPT-large-Axel
### Model URL : https://huggingface.co/Abi9x/DiabloGPT-large-Axel
### Model Description : No model card New: Create and edit this model card directly on the website! |
AbidHasan95/movieHunt2 | https://huggingface.co/AbidHasan95/movieHunt2 | 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 : AbidHasan95/movieHunt2
### Model URL : https://huggingface.co/AbidHasan95/movieHunt2
### Model Description : No model card New: Create and edit this model card directly on the website! |
AbidineVall/my-new-shiny-tokenizer | https://huggingface.co/AbidineVall/my-new-shiny-tokenizer | 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 : AbidineVall/my-new-shiny-tokenizer
### Model URL : https://huggingface.co/AbidineVall/my-new-shiny-tokenizer
### Model Description : No model card New: Create and edit this model card directly on the website! |
Abirate/bert_fine_tuned_cola | https://huggingface.co/Abirate/bert_fine_tuned_cola | BERT base model (cased) is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between english and English. BERT is an auto-encoder transformer model pretrained on a large corpus of English data (English Wikipedia + Books Corpus) in a self-supervised fashion. This means the targets are computed from the inputs themselves, and humans are not needed to label the data. It was pretrained with two objectives: The pretrained model could be fine-tuned on other NLP tasks. The BERT model has been fine-tuned on a cola dataset from the GLUE BENCHAMRK, which is an academic benchmark that aims to measure the performance of ML models. Cola is one of the 11 datasets in this GLUE BENCHMARK. By fine-tuning BERT on cola dataset, the model is now able to classify a given setence gramatically and semantically as acceptable or not acceptable | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Abirate/bert_fine_tuned_cola
### Model URL : https://huggingface.co/Abirate/bert_fine_tuned_cola
### Model Description : BERT base model (cased) is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between english and English. BERT is an auto-encoder transformer model pretrained on a large corpus of English data (English Wikipedia + Books Corpus) in a self-supervised fashion. This means the targets are computed from the inputs themselves, and humans are not needed to label the data. It was pretrained with two objectives: The pretrained model could be fine-tuned on other NLP tasks. The BERT model has been fine-tuned on a cola dataset from the GLUE BENCHAMRK, which is an academic benchmark that aims to measure the performance of ML models. Cola is one of the 11 datasets in this GLUE BENCHMARK. By fine-tuning BERT on cola dataset, the model is now able to classify a given setence gramatically and semantically as acceptable or not acceptable |
Abirate/code_net_new_tokenizer_from_WPiece_bert_algorithm | https://huggingface.co/Abirate/code_net_new_tokenizer_from_WPiece_bert_algorithm | 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 : Abirate/code_net_new_tokenizer_from_WPiece_bert_algorithm
### Model URL : https://huggingface.co/Abirate/code_net_new_tokenizer_from_WPiece_bert_algorithm
### Model Description : No model card New: Create and edit this model card directly on the website! |
Abirate/code_net_similarity_model_sub23_fbert | https://huggingface.co/Abirate/code_net_similarity_model_sub23_fbert | 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 : Abirate/code_net_similarity_model_sub23_fbert
### Model URL : https://huggingface.co/Abirate/code_net_similarity_model_sub23_fbert
### Model Description : No model card New: Create and edit this model card directly on the website! |
Abirate/gpt_3_finetuned_multi_x_science | https://huggingface.co/Abirate/gpt_3_finetuned_multi_x_science | Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text.
It is the third-generation language prediction model in the GPT-n series (and the successor to GPT-2) created by OpenAI GPT-Neo (125M) is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the number of parameters of this particular pre-trained model.
and first released in this repository. The Open Source version of GPT-3: GPT-Neo(125M) has been fine-tuned on a dataset called "Multi-XScience": Multi-XScience_Repository: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles. I first fine-tuned and then deployed it using Google "Material Design" (on Anvil): Abir Scientific text Generator By fine-tuning GPT-Neo(Open Source version of GPT-3), on Multi-XScience dataset, the model is now able to generate scientific texts(even better than GPT-J(6B).Try putting the prompt "attention is all" on both my Abir Scientific text Generator and on the GPT-J Eleuther.ai Demo to understand what I mean.And Here's a demonstration video for this. Video real-time Demontration | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Abirate/gpt_3_finetuned_multi_x_science
### Model URL : https://huggingface.co/Abirate/gpt_3_finetuned_multi_x_science
### Model Description : Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text.
It is the third-generation language prediction model in the GPT-n series (and the successor to GPT-2) created by OpenAI GPT-Neo (125M) is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the number of parameters of this particular pre-trained model.
and first released in this repository. The Open Source version of GPT-3: GPT-Neo(125M) has been fine-tuned on a dataset called "Multi-XScience": Multi-XScience_Repository: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles. I first fine-tuned and then deployed it using Google "Material Design" (on Anvil): Abir Scientific text Generator By fine-tuning GPT-Neo(Open Source version of GPT-3), on Multi-XScience dataset, the model is now able to generate scientific texts(even better than GPT-J(6B).Try putting the prompt "attention is all" on both my Abir Scientific text Generator and on the GPT-J Eleuther.ai Demo to understand what I mean.And Here's a demonstration video for this. Video real-time Demontration |
Abobus/Fu | https://huggingface.co/Abobus/Fu | 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 : Abobus/Fu
### Model URL : https://huggingface.co/Abobus/Fu
### Model Description : No model card New: Create and edit this model card directly on the website! |
Abolior/audiobot | https://huggingface.co/Abolior/audiobot | 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 : Abolior/audiobot
### Model URL : https://huggingface.co/Abolior/audiobot
### Model Description : No model card New: Create and edit this model card directly on the website! |
Abozoroov/Me | https://huggingface.co/Abozoroov/Me | 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 : Abozoroov/Me
### Model URL : https://huggingface.co/Abozoroov/Me
### Model Description : No model card New: Create and edit this model card directly on the website! |
AbyV/test | https://huggingface.co/AbyV/test | 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 : AbyV/test
### Model URL : https://huggingface.co/AbyV/test
### Model Description : No model card New: Create and edit this model card directly on the website! |
AccurateIsaiah/DialoGPT-small-jefftastic | https://huggingface.co/AccurateIsaiah/DialoGPT-small-jefftastic | null | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AccurateIsaiah/DialoGPT-small-jefftastic
### Model URL : https://huggingface.co/AccurateIsaiah/DialoGPT-small-jefftastic
### Model Description : |
AccurateIsaiah/DialoGPT-small-mozark | https://huggingface.co/AccurateIsaiah/DialoGPT-small-mozark | null | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AccurateIsaiah/DialoGPT-small-mozark
### Model URL : https://huggingface.co/AccurateIsaiah/DialoGPT-small-mozark
### Model Description : |
AccurateIsaiah/DialoGPT-small-mozarkv2 | https://huggingface.co/AccurateIsaiah/DialoGPT-small-mozarkv2 | null | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AccurateIsaiah/DialoGPT-small-mozarkv2
### Model URL : https://huggingface.co/AccurateIsaiah/DialoGPT-small-mozarkv2
### Model Description : |
AccurateIsaiah/DialoGPT-small-sinclair | https://huggingface.co/AccurateIsaiah/DialoGPT-small-sinclair | null | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AccurateIsaiah/DialoGPT-small-sinclair
### Model URL : https://huggingface.co/AccurateIsaiah/DialoGPT-small-sinclair
### Model Description : |
ActivationAI/distilbert-base-uncased-finetuned-emotion | https://huggingface.co/ActivationAI/distilbert-base-uncased-finetuned-emotion | This model is a fine-tuned version of distilbert-base-uncased on the emotion 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 : ActivationAI/distilbert-base-uncased-finetuned-emotion
### Model URL : https://huggingface.co/ActivationAI/distilbert-base-uncased-finetuned-emotion
### Model Description : This model is a fine-tuned version of distilbert-base-uncased on the emotion 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: |
AdWeeb/HTI_mbert | https://huggingface.co/AdWeeb/HTI_mbert | 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 : AdWeeb/HTI_mbert
### Model URL : https://huggingface.co/AdWeeb/HTI_mbert
### Model Description : No model card New: Create and edit this model card directly on the website! |
Adalid1985/Adalidarcane | https://huggingface.co/Adalid1985/Adalidarcane | 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 : Adalid1985/Adalidarcane
### Model URL : https://huggingface.co/Adalid1985/Adalidarcane
### Model Description : No model card New: Create and edit this model card directly on the website! |
AdapterHub/bert-base-uncased-pf-anli_r3 | https://huggingface.co/AdapterHub/bert-base-uncased-pf-anli_r3 | An adapter for the bert-base-uncased model that was trained on the anli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-anli_r3
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-anli_r3
### Model Description : An adapter for the bert-base-uncased model that was trained on the anli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-art | https://huggingface.co/AdapterHub/bert-base-uncased-pf-art | An adapter for the bert-base-uncased model that was trained on the art dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-art
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-art
### Model Description : An adapter for the bert-base-uncased model that was trained on the art dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-boolq | https://huggingface.co/AdapterHub/bert-base-uncased-pf-boolq | An adapter for the bert-base-uncased model that was trained on the qa/boolq dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-boolq
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-boolq
### Model Description : An adapter for the bert-base-uncased model that was trained on the qa/boolq dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-cola | https://huggingface.co/AdapterHub/bert-base-uncased-pf-cola | An adapter for the bert-base-uncased model that was trained on the lingaccept/cola dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-cola
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-cola
### Model Description : An adapter for the bert-base-uncased model that was trained on the lingaccept/cola dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-commonsense_qa | https://huggingface.co/AdapterHub/bert-base-uncased-pf-commonsense_qa | An adapter for the bert-base-uncased model that was trained on the comsense/csqa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-commonsense_qa
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-commonsense_qa
### Model Description : An adapter for the bert-base-uncased model that was trained on the comsense/csqa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-comqa | https://huggingface.co/AdapterHub/bert-base-uncased-pf-comqa | An adapter for the bert-base-uncased model that was trained on the com_qa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-comqa
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-comqa
### Model Description : An adapter for the bert-base-uncased model that was trained on the com_qa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-conll2000 | https://huggingface.co/AdapterHub/bert-base-uncased-pf-conll2000 | An adapter for the bert-base-uncased model that was trained on the chunk/conll2000 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-conll2000
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-conll2000
### Model Description : An adapter for the bert-base-uncased model that was trained on the chunk/conll2000 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-conll2003 | https://huggingface.co/AdapterHub/bert-base-uncased-pf-conll2003 | An adapter for the bert-base-uncased model that was trained on the ner/conll2003 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-conll2003
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-conll2003
### Model Description : An adapter for the bert-base-uncased model that was trained on the ner/conll2003 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-conll2003_pos | https://huggingface.co/AdapterHub/bert-base-uncased-pf-conll2003_pos | An adapter for the bert-base-uncased model that was trained on the pos/conll2003 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-conll2003_pos
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-conll2003_pos
### Model Description : An adapter for the bert-base-uncased model that was trained on the pos/conll2003 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-copa | https://huggingface.co/AdapterHub/bert-base-uncased-pf-copa | An adapter for the bert-base-uncased model that was trained on the comsense/copa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-copa
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-copa
### Model Description : An adapter for the bert-base-uncased model that was trained on the comsense/copa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-cosmos_qa | https://huggingface.co/AdapterHub/bert-base-uncased-pf-cosmos_qa | An adapter for the bert-base-uncased model that was trained on the comsense/cosmosqa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-cosmos_qa
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-cosmos_qa
### Model Description : An adapter for the bert-base-uncased model that was trained on the comsense/cosmosqa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-cq | https://huggingface.co/AdapterHub/bert-base-uncased-pf-cq | An adapter for the bert-base-uncased model that was trained on the qa/cq dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-cq
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-cq
### Model Description : An adapter for the bert-base-uncased model that was trained on the qa/cq dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-drop | https://huggingface.co/AdapterHub/bert-base-uncased-pf-drop | An adapter for the bert-base-uncased model that was trained on the drop dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-drop
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-drop
### Model Description : An adapter for the bert-base-uncased model that was trained on the drop dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-duorc_p | https://huggingface.co/AdapterHub/bert-base-uncased-pf-duorc_p | An adapter for the bert-base-uncased model that was trained on the duorc dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-duorc_p
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-duorc_p
### Model Description : An adapter for the bert-base-uncased model that was trained on the duorc dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-duorc_s | https://huggingface.co/AdapterHub/bert-base-uncased-pf-duorc_s | An adapter for the bert-base-uncased model that was trained on the duorc dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-duorc_s
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-duorc_s
### Model Description : An adapter for the bert-base-uncased model that was trained on the duorc dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-emo | https://huggingface.co/AdapterHub/bert-base-uncased-pf-emo | An adapter for the bert-base-uncased model that was trained on the emo dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-emo
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-emo
### Model Description : An adapter for the bert-base-uncased model that was trained on the emo dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-emotion | https://huggingface.co/AdapterHub/bert-base-uncased-pf-emotion | An adapter for the bert-base-uncased model that was trained on the emotion dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-emotion
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-emotion
### Model Description : An adapter for the bert-base-uncased model that was trained on the emotion dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-fce_error_detection | https://huggingface.co/AdapterHub/bert-base-uncased-pf-fce_error_detection | An adapter for the bert-base-uncased model that was trained on the ged/fce dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-fce_error_detection
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-fce_error_detection
### Model Description : An adapter for the bert-base-uncased model that was trained on the ged/fce dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-hellaswag | https://huggingface.co/AdapterHub/bert-base-uncased-pf-hellaswag | An adapter for the bert-base-uncased model that was trained on the comsense/hellaswag dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-hellaswag
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-hellaswag
### Model Description : An adapter for the bert-base-uncased model that was trained on the comsense/hellaswag dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-hotpotqa | https://huggingface.co/AdapterHub/bert-base-uncased-pf-hotpotqa | An adapter for the bert-base-uncased model that was trained on the hotpot_qa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-hotpotqa
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-hotpotqa
### Model Description : An adapter for the bert-base-uncased model that was trained on the hotpot_qa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-imdb | https://huggingface.co/AdapterHub/bert-base-uncased-pf-imdb | An adapter for the bert-base-uncased model that was trained on the sentiment/imdb dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-imdb
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-imdb
### Model Description : An adapter for the bert-base-uncased model that was trained on the sentiment/imdb dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-mit_movie_trivia | https://huggingface.co/AdapterHub/bert-base-uncased-pf-mit_movie_trivia | An adapter for the bert-base-uncased model that was trained on the ner/mit_movie_trivia dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-mit_movie_trivia
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-mit_movie_trivia
### Model Description : An adapter for the bert-base-uncased model that was trained on the ner/mit_movie_trivia dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-mnli | https://huggingface.co/AdapterHub/bert-base-uncased-pf-mnli | An adapter for the bert-base-uncased model that was trained on the nli/multinli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-mnli
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-mnli
### Model Description : An adapter for the bert-base-uncased model that was trained on the nli/multinli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-mrpc | https://huggingface.co/AdapterHub/bert-base-uncased-pf-mrpc | An adapter for the bert-base-uncased model that was trained on the sts/mrpc dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-mrpc
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-mrpc
### Model Description : An adapter for the bert-base-uncased model that was trained on the sts/mrpc dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-multirc | https://huggingface.co/AdapterHub/bert-base-uncased-pf-multirc | An adapter for the bert-base-uncased model that was trained on the rc/multirc dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-multirc
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-multirc
### Model Description : An adapter for the bert-base-uncased model that was trained on the rc/multirc dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-newsqa | https://huggingface.co/AdapterHub/bert-base-uncased-pf-newsqa | An adapter for the bert-base-uncased model that was trained on the newsqa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-newsqa
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-newsqa
### Model Description : An adapter for the bert-base-uncased model that was trained on the newsqa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-pmb_sem_tagging | https://huggingface.co/AdapterHub/bert-base-uncased-pf-pmb_sem_tagging | An adapter for the bert-base-uncased model that was trained on the semtag/pmb dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-pmb_sem_tagging
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-pmb_sem_tagging
### Model Description : An adapter for the bert-base-uncased model that was trained on the semtag/pmb dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-qnli | https://huggingface.co/AdapterHub/bert-base-uncased-pf-qnli | An adapter for the bert-base-uncased model that was trained on the nli/qnli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-qnli
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-qnli
### Model Description : An adapter for the bert-base-uncased model that was trained on the nli/qnli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-qqp | https://huggingface.co/AdapterHub/bert-base-uncased-pf-qqp | An adapter for the bert-base-uncased model that was trained on the sts/qqp dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-qqp
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-qqp
### Model Description : An adapter for the bert-base-uncased model that was trained on the sts/qqp dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-quail | https://huggingface.co/AdapterHub/bert-base-uncased-pf-quail | An adapter for the bert-base-uncased model that was trained on the quail dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-quail
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-quail
### Model Description : An adapter for the bert-base-uncased model that was trained on the quail dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-quartz | https://huggingface.co/AdapterHub/bert-base-uncased-pf-quartz | An adapter for the bert-base-uncased model that was trained on the quartz dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-quartz
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-quartz
### Model Description : An adapter for the bert-base-uncased model that was trained on the quartz dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-quoref | https://huggingface.co/AdapterHub/bert-base-uncased-pf-quoref | An adapter for the bert-base-uncased model that was trained on the quoref dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-quoref
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-quoref
### Model Description : An adapter for the bert-base-uncased model that was trained on the quoref dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-race | https://huggingface.co/AdapterHub/bert-base-uncased-pf-race | An adapter for the bert-base-uncased model that was trained on the rc/race dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-race
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-race
### Model Description : An adapter for the bert-base-uncased model that was trained on the rc/race dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-record | https://huggingface.co/AdapterHub/bert-base-uncased-pf-record | An adapter for the bert-base-uncased model that was trained on the rc/record dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-record
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-record
### Model Description : An adapter for the bert-base-uncased model that was trained on the rc/record dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-rotten_tomatoes | https://huggingface.co/AdapterHub/bert-base-uncased-pf-rotten_tomatoes | An adapter for the bert-base-uncased model that was trained on the sentiment/rotten_tomatoes dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-rotten_tomatoes
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-rotten_tomatoes
### Model Description : An adapter for the bert-base-uncased model that was trained on the sentiment/rotten_tomatoes dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-rte | https://huggingface.co/AdapterHub/bert-base-uncased-pf-rte | An adapter for the bert-base-uncased model that was trained on the nli/rte dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-rte
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-rte
### Model Description : An adapter for the bert-base-uncased model that was trained on the nli/rte dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-scicite | https://huggingface.co/AdapterHub/bert-base-uncased-pf-scicite | An adapter for the bert-base-uncased model that was trained on the scicite dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-scicite
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-scicite
### Model Description : An adapter for the bert-base-uncased model that was trained on the scicite dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-scitail | https://huggingface.co/AdapterHub/bert-base-uncased-pf-scitail | An adapter for the bert-base-uncased model that was trained on the nli/scitail dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-scitail
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-scitail
### Model Description : An adapter for the bert-base-uncased model that was trained on the nli/scitail dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-sick | https://huggingface.co/AdapterHub/bert-base-uncased-pf-sick | An adapter for the bert-base-uncased model that was trained on the nli/sick dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-sick
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-sick
### Model Description : An adapter for the bert-base-uncased model that was trained on the nli/sick dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-snli | https://huggingface.co/AdapterHub/bert-base-uncased-pf-snli | An adapter for the bert-base-uncased model that was trained on the snli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-snli
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-snli
### Model Description : An adapter for the bert-base-uncased model that was trained on the snli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-social_i_qa | https://huggingface.co/AdapterHub/bert-base-uncased-pf-social_i_qa | An adapter for the bert-base-uncased model that was trained on the social_i_qa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-social_i_qa
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-social_i_qa
### Model Description : An adapter for the bert-base-uncased model that was trained on the social_i_qa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-squad | https://huggingface.co/AdapterHub/bert-base-uncased-pf-squad | An adapter for the bert-base-uncased model that was trained on the qa/squad1 dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-squad
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-squad
### Model Description : An adapter for the bert-base-uncased model that was trained on the qa/squad1 dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-squad_v2 | https://huggingface.co/AdapterHub/bert-base-uncased-pf-squad_v2 | An adapter for the bert-base-uncased model that was trained on the qa/squad2 dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-squad_v2
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-squad_v2
### Model Description : An adapter for the bert-base-uncased model that was trained on the qa/squad2 dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-sst2 | https://huggingface.co/AdapterHub/bert-base-uncased-pf-sst2 | An adapter for the bert-base-uncased model that was trained on the sentiment/sst-2 dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-sst2
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-sst2
### Model Description : An adapter for the bert-base-uncased model that was trained on the sentiment/sst-2 dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |