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Model Name stringlengths 5 122	URL stringlengths 28 145	Crawled Text stringlengths 1 199k ⌀	text stringlengths 180 199k
DaWang/demo	https://huggingface.co/DaWang/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 : DaWang/demo ### Model URL : https://huggingface.co/DaWang/demo ### Model Description : No model card New: Create and edit this model card directly on the website!
Dablio/Dablio	https://huggingface.co/Dablio/Dablio	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 : Dablio/Dablio ### Model URL : https://huggingface.co/Dablio/Dablio ### Model Description : No model card New: Create and edit this model card directly on the website!
Daiki/scibert_scivocab_uncased-finetuned-cola	https://huggingface.co/Daiki/scibert_scivocab_uncased-finetuned-cola	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 : Daiki/scibert_scivocab_uncased-finetuned-cola ### Model URL : https://huggingface.co/Daiki/scibert_scivocab_uncased-finetuned-cola ### Model Description : No model card New: Create and edit this model card directly on the website!
DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen	https://huggingface.co/DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen	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 : DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen ### Model URL : https://huggingface.co/DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen ### Model Description : No model card New: Create and edit this model card directly on the website!
DaisyMak/bert-finetuned-squad-transformerfrozen-testtoken	https://huggingface.co/DaisyMak/bert-finetuned-squad-transformerfrozen-testtoken	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 : DaisyMak/bert-finetuned-squad-transformerfrozen-testtoken ### Model URL : https://huggingface.co/DaisyMak/bert-finetuned-squad-transformerfrozen-testtoken ### Model Description : No model card New: Create and edit this model card directly on the website!
Daivakai/DialoGPT-small-saitama	https://huggingface.co/Daivakai/DialoGPT-small-saitama	#Saitama DialoGPT model	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Daivakai/DialoGPT-small-saitama ### Model URL : https://huggingface.co/Daivakai/DialoGPT-small-saitama ### Model Description : #Saitama DialoGPT model
Daltcamalea01/Camaleaodalt	https://huggingface.co/Daltcamalea01/Camaleaodalt	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 : Daltcamalea01/Camaleaodalt ### Model URL : https://huggingface.co/Daltcamalea01/Camaleaodalt ### Model Description : No model card New: Create and edit this model card directly on the website!
DamolaMack/Classyfied	https://huggingface.co/DamolaMack/Classyfied	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 : DamolaMack/Classyfied ### Model URL : https://huggingface.co/DamolaMack/Classyfied ### Model Description : No model card New: Create and edit this model card directly on the website!
DanBot/TCRsynth	https://huggingface.co/DanBot/TCRsynth	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 : DanBot/TCRsynth ### Model URL : https://huggingface.co/DanBot/TCRsynth ### Model Description : No model card New: Create and edit this model card directly on the website!
DanL/scientific-challenges-and-directions	https://huggingface.co/DanL/scientific-challenges-and-directions	We present a novel resource to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. At a high level, the challenges and directions are defined as follows: Challenge: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap. Research direction: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration. This model here is described in our paper: A Search Engine for Discovery of Scientific Challenges and Directions (though we've upgraded the infrastructure since the paper was released - there are slight differences in the results). Our dataset can be found here. Please cite our paper if you use our datasets or models in your project. See the BibTeX. Feel free to email us. Also, check out our search engine, as an example application. This model is a fine-tuned version of PubMedBERT on the scientific-challenges-and-directions-dataset, designed for multi-label text classification. The scientific-challenges-and-directions model is trained based on a dataset that is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the CORD-19 corpus, labeled for classification of challenges and directions by expert annotators with biomedical and bioNLP backgrounds. For full details on the train/test/split of the data see section 3.1 in our paper We include an example notebook that uses the model for inference in our repo. See Inference_Notebook.ipynb. A training notebook is also included. The following hyperparameters were used during training: The achieves the following results on the test set: If using our dataset and models, please cite: Please don't hesitate to reach out. Email: lahav@mail.tau.ac.il,tomh@allenai.org.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : DanL/scientific-challenges-and-directions ### Model URL : https://huggingface.co/DanL/scientific-challenges-and-directions ### Model Description : We present a novel resource to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. At a high level, the challenges and directions are defined as follows: Challenge: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap. Research direction: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration. This model here is described in our paper: A Search Engine for Discovery of Scientific Challenges and Directions (though we've upgraded the infrastructure since the paper was released - there are slight differences in the results). Our dataset can be found here. Please cite our paper if you use our datasets or models in your project. See the BibTeX. Feel free to email us. Also, check out our search engine, as an example application. This model is a fine-tuned version of PubMedBERT on the scientific-challenges-and-directions-dataset, designed for multi-label text classification. The scientific-challenges-and-directions model is trained based on a dataset that is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the CORD-19 corpus, labeled for classification of challenges and directions by expert annotators with biomedical and bioNLP backgrounds. For full details on the train/test/split of the data see section 3.1 in our paper We include an example notebook that uses the model for inference in our repo. See Inference_Notebook.ipynb. A training notebook is also included. The following hyperparameters were used during training: The achieves the following results on the test set: If using our dataset and models, please cite: Please don't hesitate to reach out. Email: lahav@mail.tau.ac.il,tomh@allenai.org.
Danbi/distilgpt2-finetuned-wikitext2	https://huggingface.co/Danbi/distilgpt2-finetuned-wikitext2	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 : Danbi/distilgpt2-finetuned-wikitext2 ### Model URL : https://huggingface.co/Danbi/distilgpt2-finetuned-wikitext2 ### Model Description : No model card New: Create and edit this model card directly on the website!
Danbi/distilroberta-base-finetuned-wikitext2	https://huggingface.co/Danbi/distilroberta-base-finetuned-wikitext2	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 : Danbi/distilroberta-base-finetuned-wikitext2 ### Model URL : https://huggingface.co/Danbi/distilroberta-base-finetuned-wikitext2 ### Model Description : No model card New: Create and edit this model card directly on the website!
Dandara/bertimbau-socioambiental	https://huggingface.co/Dandara/bertimbau-socioambiental	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 : Dandara/bertimbau-socioambiental ### Model URL : https://huggingface.co/Dandara/bertimbau-socioambiental ### Model Description : No model card New: Create and edit this model card directly on the website!
Danih1502/t5-base-finetuned-en-to-de	https://huggingface.co/Danih1502/t5-base-finetuned-en-to-de	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 : Danih1502/t5-base-finetuned-en-to-de ### Model URL : https://huggingface.co/Danih1502/t5-base-finetuned-en-to-de ### Model Description : No model card New: Create and edit this model card directly on the website!
Danih1502/t5-small-finetuned-en-to-de	https://huggingface.co/Danih1502/t5-small-finetuned-en-to-de	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 : Danih1502/t5-small-finetuned-en-to-de ### Model URL : https://huggingface.co/Danih1502/t5-small-finetuned-en-to-de ### Model Description : No model card New: Create and edit this model card directly on the website!
DannyMichael/ECU911	https://huggingface.co/DannyMichael/ECU911	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 : DannyMichael/ECU911 ### Model URL : https://huggingface.co/DannyMichael/ECU911 ### Model Description : No model card New: Create and edit this model card directly on the website!
Darein/Def	https://huggingface.co/Darein/Def	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 : Darein/Def ### Model URL : https://huggingface.co/Darein/Def ### Model Description : No model card New: Create and edit this model card directly on the website!
DarkKibble/DialoGPT-medium-Tankman	https://huggingface.co/DarkKibble/DialoGPT-medium-Tankman	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 : DarkKibble/DialoGPT-medium-Tankman ### Model URL : https://huggingface.co/DarkKibble/DialoGPT-medium-Tankman ### Model Description : No model card New: Create and edit this model card directly on the website!
DarkWolf/kn-electra-small	https://huggingface.co/DarkWolf/kn-electra-small	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : DarkWolf/kn-electra-small ### Model URL : https://huggingface.co/DarkWolf/kn-electra-small ### Model Description : No model card New: Create and edit this model card directly on the website!
Darkecho789/email-gen	https://huggingface.co/Darkecho789/email-gen	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 : Darkecho789/email-gen ### Model URL : https://huggingface.co/Darkecho789/email-gen ### Model Description : No model card New: Create and edit this model card directly on the website!
DarkestSky/distilbert-base-uncased-finetuned-ner	https://huggingface.co/DarkestSky/distilbert-base-uncased-finetuned-ner	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : DarkestSky/distilbert-base-uncased-finetuned-ner ### Model URL : https://huggingface.co/DarkestSky/distilbert-base-uncased-finetuned-ner ### Model Description : No model card New: Create and edit this model card directly on the website!
Darkrider/covidbert_medmarco	https://huggingface.co/Darkrider/covidbert_medmarco	Fine-tuned CovidBERT on Med-Marco Dataset for passage ranking This is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses. The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the sentence-transformers library to produce universal sentence embeddings [1] using the average pooling strategy and a softmax loss. It is further fine-tuned Med-Marco Dataset. MacAvaney et.al in their paper titled “SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search” used MedSyn a lexicon of layperson and expert terminology for various medical conditions to filter for medical questions. One can also replace this by UMLs ontologies but the beauty of MedSyn is that the terms are more general human conversation lingo and not terms based on scientific literature. Parameter details for the original training on CORD-19 are available on DeepSet's MLFlow Base model: deepset/covid_bert_base from HuggingFace's AutoModel.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Darkrider/covidbert_medmarco ### Model URL : https://huggingface.co/Darkrider/covidbert_medmarco ### Model Description : Fine-tuned CovidBERT on Med-Marco Dataset for passage ranking This is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses. The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the sentence-transformers library to produce universal sentence embeddings [1] using the average pooling strategy and a softmax loss. It is further fine-tuned Med-Marco Dataset. MacAvaney et.al in their paper titled “SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search” used MedSyn a lexicon of layperson and expert terminology for various medical conditions to filter for medical questions. One can also replace this by UMLs ontologies but the beauty of MedSyn is that the terms are more general human conversation lingo and not terms based on scientific literature. Parameter details for the original training on CORD-19 are available on DeepSet's MLFlow Base model: deepset/covid_bert_base from HuggingFace's AutoModel.
Darkrider/covidbert_mednli	https://huggingface.co/Darkrider/covidbert_mednli	This is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses. The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the sentence-transformers library to produce universal sentence embeddings [1] using the average pooling strategy and a softmax loss. It is further fine-tuned on both MedNLI datasets available at Physionet. ACL-BIONLP 2019 MedNLI from MIMIC Parameter details for the original training on CORD-19 are available on DeepSet's MLFlow Base model: deepset/covid_bert_base from HuggingFace's AutoModel.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Darkrider/covidbert_mednli ### Model URL : https://huggingface.co/Darkrider/covidbert_mednli ### Model Description : This is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses. The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the sentence-transformers library to produce universal sentence embeddings [1] using the average pooling strategy and a softmax loss. It is further fine-tuned on both MedNLI datasets available at Physionet. ACL-BIONLP 2019 MedNLI from MIMIC Parameter details for the original training on CORD-19 are available on DeepSet's MLFlow Base model: deepset/covid_bert_base from HuggingFace's AutoModel.
Darren/darren	https://huggingface.co/Darren/darren	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 : Darren/darren ### Model URL : https://huggingface.co/Darren/darren ### Model Description : No model card New: Create and edit this model card directly on the website!
DarshanDeshpande/marathi-distilbert	https://huggingface.co/DarshanDeshpande/marathi-distilbert	This model is an adaptation of DistilBERT (Victor Sanh et al., 2019) for Marathi language. This version of Marathi-DistilBERT is trained from scratch on approximately 11.2 million sentences. The training data has been extracted from a variety of sources, mainly including: The data is cleaned by removing all languages other than Marathi, while preserving common punctuations The model is trained from scratch using an Adam optimizer with a learning rate of 1e-4 and default β1 and β2 values of 0.9 and 0.999 respectively with a total batch size of 256 on a v3-8 TPU and mask probability of 15%.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : DarshanDeshpande/marathi-distilbert ### Model URL : https://huggingface.co/DarshanDeshpande/marathi-distilbert ### Model Description : This model is an adaptation of DistilBERT (Victor Sanh et al., 2019) for Marathi language. This version of Marathi-DistilBERT is trained from scratch on approximately 11.2 million sentences. The training data has been extracted from a variety of sources, mainly including: The data is cleaned by removing all languages other than Marathi, while preserving common punctuations The model is trained from scratch using an Adam optimizer with a learning rate of 1e-4 and default β1 and β2 values of 0.9 and 0.999 respectively with a total batch size of 256 on a v3-8 TPU and mask probability of 15%.
Darya/layoutlmv2-finetuned-funsd-test	https://huggingface.co/Darya/layoutlmv2-finetuned-funsd-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 : Darya/layoutlmv2-finetuned-funsd-test ### Model URL : https://huggingface.co/Darya/layoutlmv2-finetuned-funsd-test ### Model Description : No model card New: Create and edit this model card directly on the website!
Daryaflp/roberta-retrained_ru_covid	https://huggingface.co/Daryaflp/roberta-retrained_ru_covid	This model is a fine-tuned version of blinoff/roberta-base-russian-v0 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 : Daryaflp/roberta-retrained_ru_covid ### Model URL : https://huggingface.co/Daryaflp/roberta-retrained_ru_covid ### Model Description : This model is a fine-tuned version of blinoff/roberta-base-russian-v0 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:
DataikuNLP/TinyBERT_General_4L_312D	https://huggingface.co/DataikuNLP/TinyBERT_General_4L_312D	This model is a copy of this model repository from Huawei Noah at the specific commit 34707a33cd59a94ecde241ac209bf35103691b43. TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learning stages. In general distillation, we use the original BERT-base without fine-tuning as the teacher and a large-scale text corpus as the learning data. By performing the Transformer distillation on the text from general domain, we obtain a general TinyBERT which provides a good initialization for the task-specific distillation. We here provide the general TinyBERT for your tasks at hand. For more details about the techniques of TinyBERT, refer to our paper: TinyBERT: Distilling BERT for Natural Language Understanding If you find TinyBERT useful in your research, please cite the following paper:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : DataikuNLP/TinyBERT_General_4L_312D ### Model URL : https://huggingface.co/DataikuNLP/TinyBERT_General_4L_312D ### Model Description : This model is a copy of this model repository from Huawei Noah at the specific commit 34707a33cd59a94ecde241ac209bf35103691b43. TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learning stages. In general distillation, we use the original BERT-base without fine-tuning as the teacher and a large-scale text corpus as the learning data. By performing the Transformer distillation on the text from general domain, we obtain a general TinyBERT which provides a good initialization for the task-specific distillation. We here provide the general TinyBERT for your tasks at hand. For more details about the techniques of TinyBERT, refer to our paper: TinyBERT: Distilling BERT for Natural Language Understanding If you find TinyBERT useful in your research, please cite the following paper:
DataikuNLP/average_word_embeddings_glove.6B.300d	https://huggingface.co/DataikuNLP/average_word_embeddings_glove.6B.300d	This model is a copy of this model repository from sentence-transformers at the specific commit 5d2b7d1c127036ae98b9d487eca4d48744edc709. This is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : DataikuNLP/average_word_embeddings_glove.6B.300d ### Model URL : https://huggingface.co/DataikuNLP/average_word_embeddings_glove.6B.300d ### Model Description : This model is a copy of this model repository from sentence-transformers at the specific commit 5d2b7d1c127036ae98b9d487eca4d48744edc709. This is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
DataikuNLP/camembert-base	https://huggingface.co/DataikuNLP/camembert-base	This model is a copy of this model repository at the specific commit 482393b6198924f9da270b1aaf37d238aafca99b. CamemBERT is a state-of-the-art language model for French based on the RoBERTa model. It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains. For further information or requests, please go to Camembert Website CamemBERT was trained and evaluated by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot. If you use our work, please cite:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : DataikuNLP/camembert-base ### Model URL : https://huggingface.co/DataikuNLP/camembert-base ### Model Description : This model is a copy of this model repository at the specific commit 482393b6198924f9da270b1aaf37d238aafca99b. CamemBERT is a state-of-the-art language model for French based on the RoBERTa model. It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains. For further information or requests, please go to Camembert Website CamemBERT was trained and evaluated by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot. If you use our work, please cite:
DataikuNLP/distiluse-base-multilingual-cased-v1	https://huggingface.co/DataikuNLP/distiluse-base-multilingual-cased-v1	This model is a copy of this model repository from sentence-transformers at the specific commit 3a706e4d65c04f868c4684adfd4da74141be8732. This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : DataikuNLP/distiluse-base-multilingual-cased-v1 ### Model URL : https://huggingface.co/DataikuNLP/distiluse-base-multilingual-cased-v1 ### Model Description : This model is a copy of this model repository from sentence-transformers at the specific commit 3a706e4d65c04f868c4684adfd4da74141be8732. This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
DataikuNLP/paraphrase-MiniLM-L6-v2	https://huggingface.co/DataikuNLP/paraphrase-MiniLM-L6-v2	This model is a copy of this model repository from sentence-transformers at the specific commit c4dfcde8a3e3e17e85cd4f0ec1925a266187f48e. This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : DataikuNLP/paraphrase-MiniLM-L6-v2 ### Model URL : https://huggingface.co/DataikuNLP/paraphrase-MiniLM-L6-v2 ### Model Description : This model is a copy of this model repository from sentence-transformers at the specific commit c4dfcde8a3e3e17e85cd4f0ec1925a266187f48e. This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
DataikuNLP/paraphrase-albert-small-v2	https://huggingface.co/DataikuNLP/paraphrase-albert-small-v2	This model is a copy of this model repository from sentence-transformers at the specific commit 1eb1996223dd90a4c25be2fc52f6f336419a0d52. This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : DataikuNLP/paraphrase-albert-small-v2 ### Model URL : https://huggingface.co/DataikuNLP/paraphrase-albert-small-v2 ### Model Description : This model is a copy of this model repository from sentence-transformers at the specific commit 1eb1996223dd90a4c25be2fc52f6f336419a0d52. This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2	https://huggingface.co/DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2	This model is a copy of this model repository from sentence-transformers at the specific commit d66eff4d8a8598f264f166af8db67f7797164651. This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2 ### Model URL : https://huggingface.co/DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2 ### Model Description : This model is a copy of this model repository from sentence-transformers at the specific commit d66eff4d8a8598f264f166af8db67f7797164651. This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
Dave/twomad-model	https://huggingface.co/Dave/twomad-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 : Dave/twomad-model ### Model URL : https://huggingface.co/Dave/twomad-model ### Model Description : No model card New: Create and edit this model card directly on the website!
DavidAMcIntosh/DialoGPT-small-rick	https://huggingface.co/DavidAMcIntosh/DialoGPT-small-rick	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 : DavidAMcIntosh/DialoGPT-small-rick ### Model URL : https://huggingface.co/DavidAMcIntosh/DialoGPT-small-rick ### Model Description : No model card New: Create and edit this model card directly on the website!
DavidAMcIntosh/small-rick	https://huggingface.co/DavidAMcIntosh/small-rick	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 : DavidAMcIntosh/small-rick ### Model URL : https://huggingface.co/DavidAMcIntosh/small-rick ### Model Description : No model card New: Create and edit this model card directly on the website!
DavidSpaceG/MSGIFSR	https://huggingface.co/DavidSpaceG/MSGIFSR	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 : DavidSpaceG/MSGIFSR ### Model URL : https://huggingface.co/DavidSpaceG/MSGIFSR ### Model Description : No model card New: Create and edit this model card directly on the website!
Davlan/bert-base-multilingual-cased-finetuned-amharic	https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-amharic	language: am datasets: bert-base-multilingual-cased-finetuned-amharic is a Amharic BERT model obtained by replacing mBERT vocabulary by amharic vocabulary because the language was not supported, and fine-tuning bert-base-multilingual-cased model on Amharic language texts. It provides better performance than the multilingual Amharic on named entity recognition datasets. Specifically, this model is a bert-base-multilingual-cased model that was fine-tuned on Amharic corpus using Amharic vocabulary. You can use this model with Transformers pipeline for masked token prediction. This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. This model was fine-tuned on Amharic CC-100 This model was trained on a single NVIDIA V100 GPU By David Adelani	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Davlan/bert-base-multilingual-cased-finetuned-amharic ### Model URL : https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-amharic ### Model Description : language: am datasets: bert-base-multilingual-cased-finetuned-amharic is a Amharic BERT model obtained by replacing mBERT vocabulary by amharic vocabulary because the language was not supported, and fine-tuning bert-base-multilingual-cased model on Amharic language texts. It provides better performance than the multilingual Amharic on named entity recognition datasets. Specifically, this model is a bert-base-multilingual-cased model that was fine-tuned on Amharic corpus using Amharic vocabulary. You can use this model with Transformers pipeline for masked token prediction. This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. This model was fine-tuned on Amharic CC-100 This model was trained on a single NVIDIA V100 GPU By David Adelani
Davlan/bert-base-multilingual-cased-finetuned-hausa	https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-hausa	language: ha datasets: bert-base-multilingual-cased-finetuned-hausa is a Hausa BERT model obtained by fine-tuning bert-base-multilingual-cased model on Hausa language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets. Specifically, this model is a bert-base-multilingual-cased model that was fine-tuned on Hausa corpus. You can use this model with Transformers pipeline for masked token prediction. This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. This model was fine-tuned on Hausa CC-100 This model was trained on a single NVIDIA V100 GPU By David Adelani	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Davlan/bert-base-multilingual-cased-finetuned-hausa ### Model URL : https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-hausa ### Model Description : language: ha datasets: bert-base-multilingual-cased-finetuned-hausa is a Hausa BERT model obtained by fine-tuning bert-base-multilingual-cased model on Hausa language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets. Specifically, this model is a bert-base-multilingual-cased model that was fine-tuned on Hausa corpus. You can use this model with Transformers pipeline for masked token prediction. This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. This model was fine-tuned on Hausa CC-100 This model was trained on a single NVIDIA V100 GPU By David Adelani