Datasets:

davanstrien

/

model_cards_with_readmes

like 1

osanseviero/corenlp_french

osanseviero

gpl

['fr']

['corenlp']

# Core NLP model for fr CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations. Find more about it in [our website](https://stanfordnlp.github.io/CoreNLP) and our [GitHub repository](https://github.com/stanfordnlp/CoreNLP).

bda70e2a80c03ff01404617033a8d35c

sshasnain/wav2vec2-xls-r-timit-trainer

sshasnain

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-timit-trainer This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1064 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5537 | 4.03 | 500 | 0.6078 | 1.0 | | 0.5444 | 8.06 | 1000 | 0.4990 | 0.9994 | | 0.3744 | 12.1 | 1500 | 0.5530 | 1.0 | | 0.2863 | 16.13 | 2000 | 0.6401 | 1.0 | | 0.2357 | 20.16 | 2500 | 0.6485 | 1.0 | | 0.1933 | 24.19 | 3000 | 0.7448 | 0.9994 | | 0.162 | 28.22 | 3500 | 0.7502 | 1.0 | | 0.1325 | 32.26 | 4000 | 0.7801 | 1.0 | | 0.1169 | 36.29 | 4500 | 0.8334 | 1.0 | | 0.1031 | 40.32 | 5000 | 0.8269 | 1.0 | | 0.0913 | 44.35 | 5500 | 0.8432 | 1.0 | | 0.0793 | 48.39 | 6000 | 0.8738 | 1.0 | | 0.0694 | 52.42 | 6500 | 0.8897 | 1.0 | | 0.0613 | 56.45 | 7000 | 0.8966 | 1.0 | | 0.0548 | 60.48 | 7500 | 0.9398 | 1.0 | | 0.0444 | 64.51 | 8000 | 0.9548 | 1.0 | | 0.0386 | 68.55 | 8500 | 0.9647 | 1.0 | | 0.0359 | 72.58 | 9000 | 0.9901 | 1.0 | | 0.0299 | 76.61 | 9500 | 1.0151 | 1.0 | | 0.0259 | 80.64 | 10000 | 1.0526 | 1.0 | | 0.022 | 84.67 | 10500 | 1.0754 | 1.0 | | 0.0189 | 88.71 | 11000 | 1.0688 | 1.0 | | 0.0161 | 92.74 | 11500 | 1.0914 | 1.0 | | 0.0138 | 96.77 | 12000 | 1.1064 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

1884781c3415381430d78030e542d8d9

pratikrelekar/fin_sentiment

pratikrelekar

distilbert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fin_sentiment This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5739 - Accuracy: 0.7703 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 0.5739 | 0.7703 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

fdd0a3182020a21821fc3a541fcc87fb

google/bert_uncased_L-2_H-128_A-2

google

bert

transformers

apache-2.0

[]

BERT Miniatures === This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking). We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity. You can download the 24 BERT miniatures either from the [official BERT Github page](https://github.com/google-research/bert/), or via HuggingFace from the links below: | |H=128|H=256|H=512|H=768| |---|:---:|:---:|:---:|:---:| | **L=2** |[**2/128 (BERT-Tiny)**][2_128]|[2/256][2_256]|[2/512][2_512]|[2/768][2_768]| | **L=4** |[4/128][4_128]|[**4/256 (BERT-Mini)**][4_256]|[**4/512 (BERT-Small)**][4_512]|[4/768][4_768]| | **L=6** |[6/128][6_128]|[6/256][6_256]|[6/512][6_512]|[6/768][6_768]| | **L=8** |[8/128][8_128]|[8/256][8_256]|[**8/512 (BERT-Medium)**][8_512]|[8/768][8_768]| | **L=10** |[10/128][10_128]|[10/256][10_256]|[10/512][10_512]|[10/768][10_768]| | **L=12** |[12/128][12_128]|[12/256][12_256]|[12/512][12_512]|[**12/768 (BERT-Base)**][12_768]| Note that the BERT-Base model in this release is included for completeness only; it was re-trained under the same regime as the original model. Here are the corresponding GLUE scores on the test set: |Model|Score|CoLA|SST-2|MRPC|STS-B|QQP|MNLI-m|MNLI-mm|QNLI(v2)|RTE|WNLI|AX| |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |BERT-Tiny|64.2|0.0|83.2|81.1/71.1|74.3/73.6|62.2/83.4|70.2|70.3|81.5|57.2|62.3|21.0| |BERT-Mini|65.8|0.0|85.9|81.1/71.8|75.4/73.3|66.4/86.2|74.8|74.3|84.1|57.9|62.3|26.1| |BERT-Small|71.2|27.8|89.7|83.4/76.2|78.8/77.0|68.1/87.0|77.6|77.0|86.4|61.8|62.3|28.6| |BERT-Medium|73.5|38.0|89.6|86.6/81.6|80.4/78.4|69.6/87.9|80.0|79.1|87.7|62.2|62.3|30.5| For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained for 4 epochs: - batch sizes: 8, 16, 32, 64, 128 - learning rates: 3e-4, 1e-4, 5e-5, 3e-5 If you use these models, please cite the following paper: ``` @article{turc2019, title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models}, author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1908.08962v2 }, year={2019} } ``` [2_128]: https://huggingface.co/google/bert_uncased_L-2_H-128_A-2 [2_256]: https://huggingface.co/google/bert_uncased_L-2_H-256_A-4 [2_512]: https://huggingface.co/google/bert_uncased_L-2_H-512_A-8 [2_768]: https://huggingface.co/google/bert_uncased_L-2_H-768_A-12 [4_128]: https://huggingface.co/google/bert_uncased_L-4_H-128_A-2 [4_256]: https://huggingface.co/google/bert_uncased_L-4_H-256_A-4 [4_512]: https://huggingface.co/google/bert_uncased_L-4_H-512_A-8 [4_768]: https://huggingface.co/google/bert_uncased_L-4_H-768_A-12 [6_128]: https://huggingface.co/google/bert_uncased_L-6_H-128_A-2 [6_256]: https://huggingface.co/google/bert_uncased_L-6_H-256_A-4 [6_512]: https://huggingface.co/google/bert_uncased_L-6_H-512_A-8 [6_768]: https://huggingface.co/google/bert_uncased_L-6_H-768_A-12 [8_128]: https://huggingface.co/google/bert_uncased_L-8_H-128_A-2 [8_256]: https://huggingface.co/google/bert_uncased_L-8_H-256_A-4 [8_512]: https://huggingface.co/google/bert_uncased_L-8_H-512_A-8 [8_768]: https://huggingface.co/google/bert_uncased_L-8_H-768_A-12 [10_128]: https://huggingface.co/google/bert_uncased_L-10_H-128_A-2 [10_256]: https://huggingface.co/google/bert_uncased_L-10_H-256_A-4 [10_512]: https://huggingface.co/google/bert_uncased_L-10_H-512_A-8 [10_768]: https://huggingface.co/google/bert_uncased_L-10_H-768_A-12 [12_128]: https://huggingface.co/google/bert_uncased_L-12_H-128_A-2 [12_256]: https://huggingface.co/google/bert_uncased_L-12_H-256_A-4 [12_512]: https://huggingface.co/google/bert_uncased_L-12_H-512_A-8 [12_768]: https://huggingface.co/google/bert_uncased_L-12_H-768_A-12

497ae5325775ec72ddff7ddcf91c099e

michellehbn/brrrr

michellehbn

t5

transformers

translation

apache-2.0

['en', 'fr', 'ro', 'de', 'multilingual']

['c4']

['summarization', 'translation']

# Model Card for T5 Small  # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Environmental Impact](#environmental-impact) 7. [Citation](#citation) 8. [Model Card Authors](#model-card-authors) 9. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description The developers of the Text-To-Text Transfer Transformer (T5) [write](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html): > With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task. T5-Small is the checkpoint with 60 million parameters. - **Developed by:** Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. See [associated paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) and [GitHub repo](https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints) - **Model type:** Language model - **Language(s) (NLP):** English, French, Romanian, German - **License:** Apache 2.0 - **Related Models:** [All T5 Checkpoints](https://huggingface.co/models?search=t5) - **Resources for more information:** - [Research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) - [Google's T5 Blog Post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) - [GitHub Repo](https://github.com/google-research/text-to-text-transfer-transformer) - [Hugging Face T5 Docs](https://huggingface.co/docs/transformers/model_doc/t5) # Uses ## Direct Use and Downstream Use The developers write in a [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) that the model: > Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself. See the [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for further details. ## Out-of-Scope Use More information needed. # Bias, Risks, and Limitations More information needed. ## Recommendations More information needed. # Training Details ## Training Data The model is pre-trained on the [Colossal Clean Crawled Corpus (C4)](https://www.tensorflow.org/datasets/catalog/c4), which was developed and released in the context of the same [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) as T5. The model was pre-trained on a on a **multi-task mixture of unsupervised (1.) and supervised tasks (2.)**. Thereby, the following datasets were being used for (1.) and (2.): 1. **Datasets used for Unsupervised denoising objective**: - [C4](https://huggingface.co/datasets/c4) - [Wiki-DPR](https://huggingface.co/datasets/wiki_dpr) 2. **Datasets used for Supervised text-to-text language modeling objective** - Sentence acceptability judgment - CoLA [Warstadt et al., 2018](https://arxiv.org/abs/1805.12471) - Sentiment analysis - SST-2 [Socher et al., 2013](https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf) - Paraphrasing/sentence similarity - MRPC [Dolan and Brockett, 2005](https://aclanthology.org/I05-5002) - STS-B [Ceret al., 2017](https://arxiv.org/abs/1708.00055) - QQP [Iyer et al., 2017](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) - Natural language inference - MNLI [Williams et al., 2017](https://arxiv.org/abs/1704.05426) - QNLI [Rajpurkar et al.,2016](https://arxiv.org/abs/1606.05250) - RTE [Dagan et al., 2005](https://link.springer.com/chapter/10.1007/11736790_9) - CB [De Marneff et al., 2019](https://semanticsarchive.net/Archive/Tg3ZGI2M/Marneffe.pdf) - Sentence completion - COPA [Roemmele et al., 2011](https://www.researchgate.net/publication/221251392_Choice_of_Plausible_Alternatives_An_Evaluation_of_Commonsense_Causal_Reasoning) - Word sense disambiguation - WIC [Pilehvar and Camacho-Collados, 2018](https://arxiv.org/abs/1808.09121) - Question answering - MultiRC [Khashabi et al., 2018](https://aclanthology.org/N18-1023) - ReCoRD [Zhang et al., 2018](https://arxiv.org/abs/1810.12885) - BoolQ [Clark et al., 2019](https://arxiv.org/abs/1905.10044) ## Training Procedure In their [abstract](https://jmlr.org/papers/volume21/20-074/20-074.pdf), the model developers write: > In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. The framework introduced, the T5 framework, involves a training procedure that brings together the approaches studied in the paper. See the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for further details. # Evaluation ## Testing Data, Factors & Metrics The developers evaluated the model on 24 tasks, see the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for full details. ## Results For full results for T5-small, see the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf), Table 14. # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** Google Cloud TPU Pods - **Hours used:** More information needed - **Cloud Provider:** GCP - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Citation **BibTeX:** ```bibtex @article{2020t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {140}, pages = {1-67}, url = {http://jmlr.org/papers/v21/20-074.html} } ``` **APA:** - Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67. # Model Card Authors This model card was written by the team at Hugging Face. # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import T5Tokenizer, T5Model tokenizer = T5Tokenizer.from_pretrained("t5-small") model = T5Model.from_pretrained("t5-small") input_ids = tokenizer( "Studies have been shown that owning a dog is good for you", return_tensors="pt" ).input_ids # Batch size 1 decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1 # forward pass outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) last_hidden_states = outputs.last_hidden_state ``` See the [Hugging Face T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Model) docs and a [Colab Notebook](https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/main/notebooks/t5-trivia.ipynb) created by the model developers for more examples. </details>

12e9103ecfff795538d8fef3b278be04

baffo32/t5-base-ptmap

baffo32

t5

transformers

translation

apache-2.0

['en', 'fr', 'ro', 'de']

['c4']

['summarization', 'translation']

[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Pretraining Dataset: [C4](https://huggingface.co/datasets/c4) Other Community Checkpoints: [here](https://huggingface.co/models?search=t5) Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* ## Abstract Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code. 

97d99e4cfeb799601875901db07e09aa

jonatasgrosman/exp_w2v2t_pl_unispeech-ml_s463

jonatasgrosman

unispeech

transformers

automatic-speech-recognition

apache-2.0

['pl']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'pl']

# exp_w2v2t_pl_unispeech-ml_s463 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

26f4b63426a6bb158e0b514697c43a4a

fathyshalab/all-roberta-large-v1-utility-2-16-5

fathyshalab

roberta

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-utility-2-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3728 - Accuracy: 0.3956 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8194 | 1.0 | 1 | 2.6027 | 0.3156 | | 2.2337 | 2.0 | 2 | 2.5079 | 0.3778 | | 1.7996 | 3.0 | 3 | 2.4293 | 0.3822 | | 1.4591 | 4.0 | 4 | 2.3728 | 0.3956 | | 1.3205 | 5.0 | 5 | 2.3439 | 0.3956 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

09c4f1903f4e7f4aa225296a6d27104d

StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-BioNLP13

StivenLancheros

roberta

transformers

token-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-biomedical-clinical-es-finetuned-ner-BioNLP13 This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2217 - Precision: 0.7936 - Recall: 0.8067 - F1: 0.8001 - Accuracy: 0.9451 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.4206 | 1.0 | 692 | 0.2182 | 0.7513 | 0.7757 | 0.7633 | 0.9342 | | 0.1872 | 2.0 | 1384 | 0.2032 | 0.7779 | 0.7865 | 0.7821 | 0.9398 | | 0.0982 | 3.0 | 2076 | 0.2043 | 0.7995 | 0.7904 | 0.7949 | 0.9443 | | 0.0735 | 4.0 | 2768 | 0.2217 | 0.7936 | 0.8067 | 0.8001 | 0.9451 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

a6ba855b9aac1a2778f63b9f63a27611

allenai/tk-instruct-3b-def

allenai

t5

transformers

text2text-generation

apache-2.0

['en']

['Super-NaturalInstructions']

[]

# Model description Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built upon the pre-trained [T5 models](https://arxiv.org/abs/1910.10683), they are fine-tuned on a large number of tasks & instructions that are collected in the [Natural Instructions benchmark](https://github.com/allenai/natural-instructions), which contains 1600+ tasks in 70+ broach categories in total. This enables the model to not only process the training tasks, but also generalize to many unseen tasks without further parameter update. More resources for using the model: - **Paper**: [link](https://arxiv.org/abs/2204.07705) - **Code repository**: [Tk-Instruct](https://github.com/yizhongw/Tk-Instruct) - **Official Website**: [Natural Instructions](https://instructions.apps.allenai.org/) - **All released models**: [allenai/tk-instruct](https://huggingface.co/models?search=allenai/tk-instruct) ## Intended uses & limitations Tk-Instruct can be used to do many NLP tasks by following instructions. ### How to use When instructing the model, task definition or demonstration examples or explanations should be prepended to the original input and fed into the model. You can easily try Tk-Instruct models as follows: ```python >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM >>> tokenizer = AutoTokenizer.from_pretrained("allenai/tk-instruct-3b-def") >>> model = AutoModelForSeq2SeqLM.from_pretrained("allenai/tk-instruct-3b-def") >>> input_ids = tokenizer.encode( "Definition: return the currency of the given country. Now complete the following example - Input: India. Output:", return_tensors="pt") >>> output = model.generate(input_ids, max_length=10) >>> output = tokenizer.decode(output[0], skip_special_tokens=True) # model should output 'Indian Rupee' >>> input_ids = tokenizer.encode( "Definition: negate the following sentence. Input: John went to school. Output:", return_tensors="pt") >>> output = model.generate(input_ids, max_length=10) >>> output = tokenizer.decode(output[0], skip_special_tokens=True) # model should output 'John did not go to shool.' ``` ### Limitations We are still working on understanding the behaviors of these models, but here are several issues we have found: - Models are generally sensitive to the instruction. Sometimes rewording the instruction can lead to very different output. - Models are not always compliant to the instruction. Sometimes the model don't follow your instruction (e.g., when you ask the model to generate one sentence, it might still generate one word or a long story). - Models might totally fail on some tasks. If you find serious issues or any interesting result, you are welcome to share with us! ## Training data Tk-Instruct is trained using the tasks & instructions in [Natural Instructions benchmark](https://github.com/allenai/natural-instructions), which contains 1600+ tasks in 70+ broach categories in total. We follow the official train/test split. Tk-Instruct model series were trained using 757 tasks, and mTk-Instruct series were trained using 1271 tasks (including some non-English tasks). The training tasks are in 64 broad categories, such as text categorization / question answering / sentiment analysis / summarization / grammar error detection / dialogue generation / etc. The other 12 categories are selected for evaluation. ## Training procedure All our models are initialized from either T5 models or mT5 models. Because generating the output can be regarded as a form of language modeling, we used their [LM adapted version](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k). All data is converted into a text-to-text format, and models are fine-tuned to maximize the likelihood of the output sequence. Our [released models](https://huggingface.co/models?search=allenai/tk-instruct) are in different sizes, and each of them was trained with a specific type of instruction encoding. For instance, `tk-instruct-3b-def-pos` was initialized from [t5-xl-lm-adapt](https://huggingface.co/google/t5-xl-lm-adapt), and it saw task definition & 2 positive examples as the instruction during training time. Although they are trained with only one type of instruction encodings, we found they can usually work with other type of encodings at test time (see more in our paper). ### BibTeX entry and citation info ```bibtex @article{wang2022benchmarking, title={Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks}, author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and A. Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and M. Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddharth Deepak Mishra and Sujan C. Reddy and Sumanta Patro and Tanay Dixit and Xu-dong Shen and Chitta Baral and Yejin Choi and Hannaneh Hajishirzi and Noah A. Smith and Daniel Khashabi}, year={2022}, archivePrefix={arXiv}, eprint={2204.07705}, primaryClass={cs.CL}, } ```

b7063896c1095a92090349b9378343db

sd-concepts-library/4tnght

sd-concepts-library

mit

[]

### 4tNGHT on Stable Diffusion This is the `<4tNGHT>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:                                                                                                         

58a7e3d55f49d796ae8bde40a7caa5af

jonatasgrosman/whisper-small-pt-cv11-v5

jonatasgrosman

whisper

transformers

automatic-speech-recognition

apache-2.0

['pt']

['mozilla-foundation/common_voice_11_0']

['whisper-event', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Portuguese This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 pt dataset. It achieves the following results on the evaluation set: - Loss: 0.3056 - Wer: 14.6841 - Cer: 5.8856 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:| | 0.2817 | 0.92 | 500 | 0.3352 | 15.9476 | 6.3609 | | 0.2245 | 1.84 | 1000 | 0.3047 | 15.0231 | 5.9326 | | 0.1587 | 2.76 | 1500 | 0.2985 | 15.0847 | 5.9326 | | 0.1181 | 3.68 | 2000 | 0.3056 | 14.6841 | 5.8856 | | 0.0741 | 4.6 | 2500 | 0.3162 | 14.9923 | 5.9906 | | 0.0438 | 5.52 | 3000 | 0.3466 | 15.4700 | 6.2255 | | 0.0294 | 6.45 | 3500 | 0.3799 | 15.2234 | 6.1647 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu116 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

f399da3479a30a7fcea8851b244e5223

DunnBC22/vit-base-patch16-224-in21k_car_or_motorcycle

DunnBC22

vit

transformers

image-classification

apache-2.0

['imagefolder']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k_car_or_motorcycle This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0301 - Accuracy: 0.9938 - Weighted f1: 0.9939 - Weighted recall: 0.9927 - Weighted precision: 0.9951 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Weighted recall | Weighted precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:---------------:|:------------------:| | 0.6908 | 1.0 | 200 | 0.0372 | 0.99 | 0.9902 | 0.9902 | 0.9902 | | 0.6908 | 2.0 | 400 | 0.0301 | 0.9938 | 0.9939 | 0.9927 | 0.9951 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1 - Datasets 2.5.2 - Tokenizers 0.12.1

0afdbc21bd0fdd767ba2080f441c94cc

enoriega/rule_learning_margin_1mm

enoriega

bert

transformers

apache-2.0

['enoriega/odinsynth_dataset']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # rule_learning_margin_1mm This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the enoriega/odinsynth_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.3806 - Margin Accuracy: 0.8239 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2000 - total_train_batch_size: 8000 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Margin Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------------:| | 0.6482 | 0.16 | 20 | 0.6494 | 0.7263 | | 0.5151 | 0.32 | 40 | 0.5088 | 0.7792 | | 0.4822 | 0.48 | 60 | 0.4429 | 0.8045 | | 0.4472 | 0.64 | 80 | 0.4265 | 0.8107 | | 0.4352 | 0.8 | 100 | 0.4155 | 0.8132 | | 0.4335 | 0.96 | 120 | 0.4128 | 0.8116 | | 0.4113 | 1.12 | 140 | 0.4119 | 0.8142 | | 0.4186 | 1.28 | 160 | 0.4075 | 0.8120 | | 0.42 | 1.44 | 180 | 0.4072 | 0.8123 | | 0.4175 | 1.6 | 200 | 0.4080 | 0.8130 | | 0.4097 | 1.76 | 220 | 0.4031 | 0.8128 | | 0.397 | 1.92 | 240 | 0.4004 | 0.8130 | | 0.4115 | 2.08 | 260 | 0.3979 | 0.8136 | | 0.4108 | 2.24 | 280 | 0.3940 | 0.8167 | | 0.4125 | 2.4 | 300 | 0.3879 | 0.8218 | | 0.4117 | 2.56 | 320 | 0.3848 | 0.8217 | | 0.3967 | 2.72 | 340 | 0.3818 | 0.8231 | | 0.3947 | 2.88 | 360 | 0.3813 | 0.8240 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0 - Datasets 2.2.1 - Tokenizers 0.12.1

6de3ebda22db86ba93f382779eea40d5

keogh/stable-diffusion-compvis-v1-4

keogh

diffusers

text-to-image

creativeml-openrail-m

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image']

# Stable Diffusion v1-4 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with 🧨Diffusers blog](https://huggingface.co/blog/stable_diffusion). The **Stable-Diffusion-v1-4** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) checkpoint and subsequently fine-tuned on 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). This weights here are intended to be used with the 🧨 Diffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, [come here](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## Examples We recommend using [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion. ### PyTorch ```bash pip install --upgrade diffusers transformers scipy ``` Run this command to log in with your HF Hub token if you haven't before: ```bash huggingface-cli login ``` Running the pipeline with the default PNDM scheduler: ```python import torch from diffusers import StableDiffusionPipeline model_id = "CompVis/stable-diffusion-v1-4" device = "cuda" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16") pipe = pipe.to(device) prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` **Note**: If you are limited by GPU memory and have less than 4GB of GPU RAM available, please make sure to load the StableDiffusionPipeline in float16 precision instead of the default float32 precision as done above. You can do so by telling diffusers to expect the weights to be in float16 precision: ```py import torch pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16") pipe = pipe.to(device) pipe.enable_attention_slicing() prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` To swap out the noise scheduler, pass it to `from_pretrained`: ```python from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler model_id = "CompVis/stable-diffusion-v1-4" # Use the Euler scheduler here instead scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16, revision="fp16") pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` ### JAX/Flax To use StableDiffusion on TPUs and GPUs for faster inference you can leverage JAX/Flax. Running the pipeline with default PNDMScheduler ```python import jax import numpy as np from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxStableDiffusionPipeline pipeline, params = FlaxStableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="flax", dtype=jax.numpy.bfloat16 ) prompt = "a photo of an astronaut riding a horse on mars" prng_seed = jax.random.PRNGKey(0) num_inference_steps = 50 num_samples = jax.device_count() prompt = num_samples * [prompt] prompt_ids = pipeline.prepare_inputs(prompt) # shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, 8) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` **Note**: If you are limited by TPU memory, please make sure to load the `FlaxStableDiffusionPipeline` in `bfloat16` precision instead of the default `float32` precision as done above. You can do so by telling diffusers to load the weights from "bf16" branch. ```python import jax import numpy as np from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxStableDiffusionPipeline pipeline, params = FlaxStableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="bf16", dtype=jax.numpy.bfloat16 ) prompt = "a photo of an astronaut riding a horse on mars" prng_seed = jax.random.PRNGKey(0) num_inference_steps = 50 num_samples = jax.device_count() prompt = num_samples * [prompt] prompt_ids = pipeline.prepare_inputs(prompt) # shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, 8) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1-4 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We currently provide four checkpoints, which were trained as follows. - [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2`. 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2`.225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 2 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling steps show the relative improvements of the checkpoints:  Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

86486e95a6c696c91515a757bf33a80d

laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K

laion

open_clip

mit

[]

# Model Card for CLIP-convnext_base_w.laion_aesthetic-s13B-b82k # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Training Details](#training-details) 4. [Evaluation](#evaluation) 5. [Acknowledgements](#acknowledgements) 6. [Citation](#citation) # Model Details ## Model Description A series of CLIP [ConvNeXt-Base](https://arxiv.org/abs/2201.03545) (w/ wide embed dim) models trained on subsets LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Goals: * Explore an alternative to ViT and ResNet (w/ AttentionPooling) CLIP models that scales well with model size and image resolution Firsts: * First known ConvNeXt CLIP models trained at scale in the range of CLIP ViT-B/16 and RN50x4 models * First released model weights exploring increase of augmentation + regularization for image tower via adding (greater scale range of RRC, random erasing, stochastic depth) The models utilize the [timm](https://github.com/rwightman/pytorch-image-models) ConvNeXt-Base model (`convnext_base`) as the image tower, and the same text tower as the RN50x4 (depth 12, embed dim 640) model from OpenAI CLIP. The base models are trained at 256x256 image resolution and roughly match the RN50x4 models on FLOPs and activation counts. The models with `320` in the name are trained at 320x320. All models in this series were trained for 13B samples and have ImageNet Zero-Shot top-1 of >= 70.8%. Comparing to ViT-B/16 at 34B SS with zero-shot of 70.2% (68.1% for 13B SS) this suggests the ConvNeXt architecture may be more sample efficient in this range of model scale. More experiments needed to confirm. | Model | Dataset | Resolution | AugReg | Top-1 ImageNet Zero-Shot (%) | | ----- | ------- | ---------- | ------------ | --------- | | [convnext_base_w.laion2b_s13b_b82k](https://huggingface.co/laion/CLIP-convnext_base_w-laion2B-s13B-b82K) | LAION-2B | 256x256 | RRC (0.9, 1.0) | 70.8 | | [convnext_base_w.laion2b_s13b_b82k_augreg](https://huggingface.co/laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg) | LAION-2B | 256x256 | RRC (0.33, 1.0), RE (0.35), SD (0.1) | 71.5 | | [convnext_base_w.laion_aesthetic_s13b_b82k](https://huggingface.co/laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K) | LAION-A | 256x256 | RRC (0.9, 1.0) | 71.0 | | [convnext_base_w_320.laion_aesthetic_s13b_b82k](https://huggingface.co/laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K) | LAION-A | 320x320 | RRC (0.9, 1.0) | 71.7 | | [convnext_base_w_320.laion_aesthetic_s13b_b82k_augreg](https://huggingface.co/laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K-augreg) | LAION-A | 320x320 | RRC (0.33, 1.0), RE (0.35), SD (0.1) | 71.3 | RRC = Random Resize Crop (crop pcts), RE = Random Erasing (prob), SD = Stochastic Depth (prob) -- image tower only LAION-A = LAION Aesthetic, an ~900M sample subset of LAION-2B with pHash dedupe and asthetic score filtering. Model training done by Ross Wightman across both the [stability.ai](https://stability.ai/) cluster and the [JUWELS Booster](https://apps.fz-juelich.de/jsc/hps/juwels/booster-overview.html) supercomputer. See acknowledgements below. # Uses As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset. ## Direct Use Zero-shot image classification, image and text retrieval, among others. ## Downstream Use Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others. ## Out-of-Scope Use As per the OpenAI models, **Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use. Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases. Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below. # Training Details ## Training Data This model was trained with one of (see table in intro): * LAION-2B - A 2 billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/). * LAION-Aesthetic - A 900M sample subset of LAION-2B with pHash dedupe and asthetic score filtering **IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress. ## Training Procedure All models were trained with a global batch size of 81920 for 64 checkpoint intervals of 203.7M samples for a total of ~13B samples seen over training. For 256x256 models, a slurm script w/ srun below was used on 20 8-GPU (A100 40GB) nodes (Stability), switching to 40 4-GPU nodes for time on JUWELS. ``` /opt/slurm/sbin/srun --cpu_bind=v --accel-bind=gn python -m training.main \ --save-frequency 1 \ --name "convnext_256" \ --resume 'latest' \ --train-data="pipe:aws s3 cp s3://mybucket/path/{laion{00000..xxxxx}.tar -" \ --train-num-samples 203666042 \ --dataset-type webdataset \ --precision amp_bfloat16 \ --warmup 10000 \ --batch-size=512 \ --epochs=64 \ --dataset-resampled \ --clip-grad-norm 5.0 \ --lr 1e-3 \ --workers=6 \ --model "convnext_base_w" \ --seed 0 \ --ddp-static-graph \ --local-loss \ --gather-with-grad \ --grad-checkpointing ``` For 320x320 models, same as above but w/ 32 8-GPU nodes, local batch size 320, or 64 4-GPU nodes on JUWELs. # Evaluation Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark). ## Testing Data, Factors & Metrics ### Testing Data The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval. ## Results The models achieve between 70.8 and 71.7 zero-shot top-1 accuracy on ImageNet-1k.  An initial round of benchmarks have been performed on a wider range of datasets, to be viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb As part of exploring increased augmentation + regularization, early evalations suggest that `augreg` trained models evaluate well over a wider range of resolutions. This is especially true for the 320x320 LAION-A model, where the augreg run was lower than the non-augreg when evaluated at the train resolution of 320x320 (71.3 vs 71.7), but improves to 72.2 when evaluated at 384x384 (the non-augreg drops to 71.0 at 384x384). # Acknowledgements Acknowledging [stability.ai](https://stability.ai/) and the Gauss Centre for Supercomputing e.V. (http://gauss-centre.eu) for funding this part of work by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS Booster at Jülich Supercomputing Centre (JSC). # Citation **BibTeX:** LAION-5B ```bibtex @inproceedings{schuhmann2022laionb, title={{LAION}-5B: An open large-scale dataset for training next generation image-text models}, author={Christoph Schuhmann and Romain Beaumont and Richard Vencu and Cade W Gordon and Ross Wightman and Mehdi Cherti and Theo Coombes and Aarush Katta and Clayton Mullis and Mitchell Wortsman and Patrick Schramowski and Srivatsa R Kundurthy and Katherine Crowson and Ludwig Schmidt and Robert Kaczmarczyk and Jenia Jitsev}, booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2022}, url={https://openreview.net/forum?id=M3Y74vmsMcY} } ``` OpenCLIP software ```bibtex @software{ilharco_gabriel_2021_5143773, author = {Ilharco, Gabriel and Wortsman, Mitchell and Wightman, Ross and Gordon, Cade and Carlini, Nicholas and Taori, Rohan and Dave, Achal and Shankar, Vaishaal and Namkoong, Hongseok and Miller, John and Hajishirzi, Hannaneh and Farhadi, Ali and Schmidt, Ludwig}, title = {OpenCLIP}, month = jul, year = 2021, note = {If you use this software, please cite it as below.}, publisher = {Zenodo}, version = {0.1}, doi = {10.5281/zenodo.5143773}, url = {https://doi.org/10.5281/zenodo.5143773} } ``` OpenAI CLIP paper ```bibtex @inproceedings{Radford2021LearningTV, title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, booktitle={ICML}, year={2021} } ``` ```bibtex @Article{liu2022convnet, author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, title = {A ConvNet for the 2020s}, journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2022}, } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} } ```

1bf584517d50f18f08699d9f1196f8aa

domenicrosati/SPECTER-finetuned-DAGPap22

domenicrosati

bert

transformers

text-classification

apache-2.0

['text-classification', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SPECTER-finetuned-DAGPap22 This model is a fine-tuned version of [allenai/specter](https://huggingface.co/allenai/specter) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0023 - Accuracy: 0.9993 - F1: 0.9995 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.3422 | 1.0 | 669 | 0.4135 | 0.8914 | 0.9140 | | 0.1074 | 2.0 | 1338 | 0.1216 | 0.9746 | 0.9811 | | 0.0329 | 3.0 | 2007 | 0.0064 | 0.9989 | 0.9992 | | 0.0097 | 4.0 | 2676 | 0.0132 | 0.9972 | 0.9980 | | 0.0123 | 5.0 | 3345 | 0.0231 | 0.9961 | 0.9971 | | 0.0114 | 6.0 | 4014 | 0.0080 | 0.9985 | 0.9989 | | 0.0029 | 7.0 | 4683 | 0.2207 | 0.9727 | 0.9797 | | 0.0075 | 8.0 | 5352 | 0.0145 | 0.9974 | 0.9981 | | 0.0098 | 9.0 | 6021 | 0.0047 | 0.9994 | 0.9996 | | 0.0025 | 10.0 | 6690 | 0.0000 | 1.0 | 1.0 | | 0.0044 | 11.0 | 7359 | 0.0035 | 0.9993 | 0.9995 | | 0.0 | 12.0 | 8028 | 0.0027 | 0.9996 | 0.9997 | | 0.0027 | 13.0 | 8697 | 0.0036 | 0.9993 | 0.9995 | | 0.0055 | 14.0 | 9366 | 0.0017 | 0.9998 | 0.9999 | | 0.0 | 15.0 | 10035 | 0.0000 | 1.0 | 1.0 | | 0.0 | 16.0 | 10704 | 0.0000 | 1.0 | 1.0 | | 0.0022 | 17.0 | 11373 | 0.0111 | 0.9981 | 0.9986 | | 0.0004 | 18.0 | 12042 | 0.0011 | 0.9994 | 0.9996 | | 0.0 | 19.0 | 12711 | 0.0020 | 0.9994 | 0.9996 | | 0.0 | 20.0 | 13380 | 0.0023 | 0.9993 | 0.9995 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

307603d13b6aadb001958b97a22836c6

pig4431/Sentiment140_roBERTa_5E

pig4431

roberta

transformers

text-classification

mit

['sentiment140']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Sentiment140_roBERTa_5E This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the sentiment140 dataset. It achieves the following results on the evaluation set: - Loss: 0.4796 - Accuracy: 0.8933 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.699 | 0.08 | 50 | 0.6734 | 0.5467 | | 0.6099 | 0.16 | 100 | 0.4322 | 0.8 | | 0.4906 | 0.24 | 150 | 0.3861 | 0.84 | | 0.4652 | 0.32 | 200 | 0.4288 | 0.7933 | | 0.4874 | 0.4 | 250 | 0.3872 | 0.84 | | 0.4735 | 0.48 | 300 | 0.3401 | 0.8667 | | 0.3909 | 0.56 | 350 | 0.3484 | 0.84 | | 0.4277 | 0.64 | 400 | 0.3207 | 0.88 | | 0.3894 | 0.72 | 450 | 0.3310 | 0.8733 | | 0.4523 | 0.8 | 500 | 0.3389 | 0.8667 | | 0.4087 | 0.88 | 550 | 0.3515 | 0.8467 | | 0.3973 | 0.96 | 600 | 0.3513 | 0.8467 | | 0.4016 | 1.04 | 650 | 0.3501 | 0.8667 | | 0.3613 | 1.12 | 700 | 0.3327 | 0.8667 | | 0.343 | 1.2 | 750 | 0.3518 | 0.86 | | 0.314 | 1.28 | 800 | 0.3555 | 0.88 | | 0.3407 | 1.36 | 850 | 0.3849 | 0.86 | | 0.2944 | 1.44 | 900 | 0.3576 | 0.8667 | | 0.3267 | 1.52 | 950 | 0.3461 | 0.8733 | | 0.3251 | 1.6 | 1000 | 0.3411 | 0.8667 | | 0.321 | 1.68 | 1050 | 0.3371 | 0.88 | | 0.3057 | 1.76 | 1100 | 0.3322 | 0.88 | | 0.3335 | 1.84 | 1150 | 0.3106 | 0.8667 | | 0.3363 | 1.92 | 1200 | 0.3158 | 0.8933 | | 0.2972 | 2.0 | 1250 | 0.3122 | 0.88 | | 0.2453 | 2.08 | 1300 | 0.3327 | 0.8867 | | 0.2467 | 2.16 | 1350 | 0.3767 | 0.8667 | | 0.273 | 2.24 | 1400 | 0.3549 | 0.8667 | | 0.2672 | 2.32 | 1450 | 0.3470 | 0.88 | | 0.2352 | 2.4 | 1500 | 0.4092 | 0.8667 | | 0.2763 | 2.48 | 1550 | 0.3472 | 0.9 | | 0.2858 | 2.56 | 1600 | 0.3440 | 0.9 | | 0.2206 | 2.64 | 1650 | 0.3770 | 0.88 | | 0.2928 | 2.72 | 1700 | 0.3280 | 0.8867 | | 0.2478 | 2.8 | 1750 | 0.3426 | 0.8867 | | 0.2362 | 2.88 | 1800 | 0.3578 | 0.8933 | | 0.2107 | 2.96 | 1850 | 0.3986 | 0.8933 | | 0.2191 | 3.04 | 1900 | 0.3819 | 0.8933 | | 0.2267 | 3.12 | 1950 | 0.4047 | 0.8867 | | 0.2076 | 3.2 | 2000 | 0.4303 | 0.8867 | | 0.1868 | 3.28 | 2050 | 0.4385 | 0.8933 | | 0.2239 | 3.36 | 2100 | 0.4175 | 0.8933 | | 0.2082 | 3.44 | 2150 | 0.4142 | 0.8933 | | 0.2423 | 3.52 | 2200 | 0.4002 | 0.8867 | | 0.1878 | 3.6 | 2250 | 0.4662 | 0.88 | | 0.1892 | 3.68 | 2300 | 0.4783 | 0.88 | | 0.2259 | 3.76 | 2350 | 0.4487 | 0.88 | | 0.1859 | 3.84 | 2400 | 0.4456 | 0.8933 | | 0.2042 | 3.92 | 2450 | 0.4468 | 0.8933 | | 0.2096 | 4.0 | 2500 | 0.4153 | 0.8867 | | 0.178 | 4.08 | 2550 | 0.4100 | 0.8933 | | 0.1621 | 4.16 | 2600 | 0.4292 | 0.8933 | | 0.1682 | 4.24 | 2650 | 0.4602 | 0.8933 | | 0.1813 | 4.32 | 2700 | 0.4680 | 0.8933 | | 0.2033 | 4.4 | 2750 | 0.4735 | 0.8933 | | 0.1662 | 4.48 | 2800 | 0.4750 | 0.88 | | 0.1686 | 4.56 | 2850 | 0.4830 | 0.8933 | | 0.1603 | 4.64 | 2900 | 0.4909 | 0.8933 | | 0.148 | 4.72 | 2950 | 0.4784 | 0.8933 | | 0.162 | 4.8 | 3000 | 0.4750 | 0.8867 | | 0.153 | 4.88 | 3050 | 0.4759 | 0.8867 | | 0.1657 | 4.96 | 3100 | 0.4796 | 0.8933 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.1

0141b2b3e70f618e437921d33e22f852

abdoutony207/m2m100_418M-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize16-20epoch-1

abdoutony207

m2m_100

transformers

text2text-generation

mit

['opus100']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # m2m100_418M-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize16-20epoch-1 This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.3640 - Bleu: 13.1835 - Meteor: 0.1189 - Gen Len: 17.72 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 6.1776 | 1.0 | 100 | 3.8904 | 10.5866 | 0.0995 | 16.64 | | 2.4531 | 2.0 | 200 | 1.0928 | 12.3452 | 0.1108 | 17.0575 | | 0.512 | 3.0 | 300 | 0.3625 | 10.5224 | 0.0982 | 17.2575 | | 0.1924 | 4.0 | 400 | 0.3342 | 12.4242 | 0.1098 | 16.6325 | | 0.1227 | 5.0 | 500 | 0.3403 | 13.0526 | 0.1185 | 17.3475 | | 0.0889 | 6.0 | 600 | 0.3481 | 13.1323 | 0.1133 | 17.815 | | 0.0651 | 7.0 | 700 | 0.3601 | 12.6684 | 0.1133 | 17.3525 | | 0.0533 | 8.0 | 800 | 0.3640 | 13.1835 | 0.1189 | 17.72 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

e8dce3d56b1022341c892a0a0c6c1928

anuragshas/whisper-large-v2-mt

anuragshas

whisper

transformers

automatic-speech-recognition

apache-2.0

['mt']

['mozilla-foundation/common_voice_11_0']

['whisper-event', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Large-v2 Maltese This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 mt dataset. It achieves the following results on the evaluation set: - Loss: 0.3616 - Wer: 18.4644 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0023 | 9.0 | 1000 | 0.3616 | 18.4644 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

5948fe588b7c398de182fcfb4cf4c38a

facebook/data2vec-audio-base-960h

facebook

data2vec-audio

transformers

automatic-speech-recognition

apache-2.0

['en']

['librispeech_asr']

['speech', 'hf-asr-leaderboard']

# Data2Vec-Audio-Base-960h [Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/) The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. [Paper](https://arxiv.org/abs/2202.03555) Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli **Abstract** While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches. The original model can be found under https://github.com/pytorch/fairseq/tree/main/examples/data2vec . # Pre-Training method  For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555). # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Data2VecForCTC from datasets import load_dataset import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-960h") model = Data2VecForCTC.from_pretrained("facebook/data2vec-audio-base-960h") # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # tokenize input_values = processor(ds[0]["audio"]["array"],, return_tensors="pt", padding="longest").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) ``` ## Evaluation This code snippet shows how to evaluate **facebook/data2vec-audio-base-960h** on LibriSpeech's "clean" and "other" test data. ```python from transformers import Wav2Vec2Processor, Data2VecForCTC from datasets import load_dataset import torch from jiwer import wer # load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-960h").to("cuda") model = Data2VecForCTC.from_pretrained("facebook/data2vec-audio-base-960h") librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") def map_to_pred(batch): input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values with torch.no_grad(): logits = model(input_values.to("cuda")).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["audio"]) print("WER:", wer(result["text"], result["transcription"])) ``` *Result (WER)*: | "clean" | "other" | |---|---| | 2.77 | 7.08 |

9f5d83487cc7c19da1ffff60dbad6890

Lilya/distilbert-base-uncased-ner-invoiceSenderName_all_inv_20_12

Lilya

distilbert

transformers

token-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-ner-invoiceSenderName_all_inv_20_12 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0145 - eval_precision: 0.0 - eval_recall: 0.0 - eval_f1: 0.0 - eval_accuracy: 0.9957 - eval_runtime: 511.2392 - eval_samples_per_second: 42.113 - eval_steps_per_second: 2.633 - epoch: 4.0 - step: 30500 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0 - Datasets 2.3.2 - Tokenizers 0.10.3

45e663225ca566c9125ca98f805a41bf

yuzhi/distilbert-imdb

yuzhi

distilbert

transformers

text-classification

apache-2.0

['imdb']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.1819 - Accuracy: 0.9289 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2723 | 1.0 | 782 | 0.1819 | 0.9289 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2

6824ef845370a6804aab17560b2576cb

model-attribution-challenge/bert-base-uncased

model-attribution-challenge

bert

transformers

fill-mask

apache-2.0

['en']

['bookcorpus', 'wikipedia']

['exbert']

# BERT base model (uncased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Model variations BERT has originally been released in base and large variations, for cased and uncased input text. The uncased models also strips out an accent markers. Chinese and multilingual uncased and cased versions followed shortly after. Modified preprocessing with whole word masking has replaced subpiece masking in a following work, with the release of two models. Other 24 smaller models are released afterward. The detailed release history can be found on the [google-research/bert readme](https://github.com/google-research/bert/blob/master/README.md) on github. | Model | #params | Language | |------------------------|--------------------------------|-------| | [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) | 110M | English | | [`bert-large-uncased`](https://huggingface.co/bert-large-uncased) | 340M | English | sub | [`bert-base-cased`](https://huggingface.co/bert-base-cased) | 110M | English | | [`bert-large-cased`](https://huggingface.co/bert-large-cased) | 340M | English | | [`bert-base-chinese`](https://huggingface.co/bert-base-chinese) | 110M | Chinese | | [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) | 110M | Multiple | | [`bert-large-uncased-whole-word-masking`](https://huggingface.co/bert-large-uncased-whole-word-masking) | 340M | English | | [`bert-large-cased-whole-word-masking`](https://huggingface.co/bert-large-cased-whole-word-masking) | 340M | English | ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for fine-tuned versions of a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-base-uncased') >>> unmasker("Hello I'm a [MASK] model.") [{'sequence': "[CLS] hello i'm a fashion model. [SEP]", 'score': 0.1073106899857521, 'token': 4827, 'token_str': 'fashion'}, {'sequence': "[CLS] hello i'm a role model. [SEP]", 'score': 0.08774490654468536, 'token': 2535, 'token_str': 'role'}, {'sequence': "[CLS] hello i'm a new model. [SEP]", 'score': 0.05338378623127937, 'token': 2047, 'token_str': 'new'}, {'sequence': "[CLS] hello i'm a super model. [SEP]", 'score': 0.04667217284440994, 'token': 3565, 'token_str': 'super'}, {'sequence': "[CLS] hello i'm a fine model. [SEP]", 'score': 0.027095865458250046, 'token': 2986, 'token_str': 'fine'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained("bert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = TFBertModel.from_pretrained("bert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-base-uncased') >>> unmasker("The man worked as a [MASK].") [{'sequence': '[CLS] the man worked as a carpenter. [SEP]', 'score': 0.09747550636529922, 'token': 10533, 'token_str': 'carpenter'}, {'sequence': '[CLS] the man worked as a waiter. [SEP]', 'score': 0.0523831807076931, 'token': 15610, 'token_str': 'waiter'}, {'sequence': '[CLS] the man worked as a barber. [SEP]', 'score': 0.04962705448269844, 'token': 13362, 'token_str': 'barber'}, {'sequence': '[CLS] the man worked as a mechanic. [SEP]', 'score': 0.03788609802722931, 'token': 15893, 'token_str': 'mechanic'}, {'sequence': '[CLS] the man worked as a salesman. [SEP]', 'score': 0.037680890411138535, 'token': 18968, 'token_str': 'salesman'}] >>> unmasker("The woman worked as a [MASK].") [{'sequence': '[CLS] the woman worked as a nurse. [SEP]', 'score': 0.21981462836265564, 'token': 6821, 'token_str': 'nurse'}, {'sequence': '[CLS] the woman worked as a waitress. [SEP]', 'score': 0.1597415804862976, 'token': 13877, 'token_str': 'waitress'}, {'sequence': '[CLS] the woman worked as a maid. [SEP]', 'score': 0.1154729500412941, 'token': 10850, 'token_str': 'maid'}, {'sequence': '[CLS] the woman worked as a prostitute. [SEP]', 'score': 0.037968918681144714, 'token': 19215, 'token_str': 'prostitute'}, {'sequence': '[CLS] the woman worked as a cook. [SEP]', 'score': 0.03042375110089779, 'token': 5660, 'token_str': 'cook'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus, and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Glue test results: | Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average | |:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:| | | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 | ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805}, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=bert-base-uncased"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

7bf215fb8c9eca61bf60cd2bfa8e42eb

LYTinn/gpt2-finetuning-sentiment-model-3000-samples

LYTinn

gpt2

transformers

text-classification

mit

['imdb']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 1.7442 - Accuracy: 0.6467 - F1: 0.6159 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.23.0 - Pytorch 1.12.1+cu116 - Datasets 2.5.2 - Tokenizers 0.13.1

c61612f05b6dda258e61ccad67cf6f7d

KoichiYasuoka/roberta-small-japanese-aozora-char

KoichiYasuoka

roberta

transformers

fill-mask

cc-by-sa-4.0

['ja']

['japanese', 'masked-lm']

# roberta-small-japanese-aozora-char ## Model Description This is a RoBERTa model pre-trained on 青空文庫 texts with character tokenizer. You can fine-tune `roberta-small-japanese-aozora-char` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-small-japanese-char-luw-upos), dependency-parsing, and so on. ## How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-small-japanese-aozora-char") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-small-japanese-aozora-char") ```

580c9e09cc40e2bbe67d0033c804002e

Helsinki-NLP/opus-mt-lua-fi

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-lua-fi * source languages: lua * target languages: fi * OPUS readme: [lua-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lua-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/lua-fi/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lua-fi/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lua-fi/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.lua.fi | 23.5 | 0.450 |

b2d6a1846487e07c14f0dede2e2df3a7

jamiehuang/t5-small-finetuned-xsum

jamiehuang

t5

transformers

text2text-generation

apache-2.0

['eli5']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset. It achieves the following results on the evaluation set: - Loss: 3.6746 - Rouge1: 13.2962 - Rouge2: 2.0081 - Rougel: 10.6529 - Rougelsum: 12.049 - Gen Len: 18.9985 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 3.8901 | 1.0 | 17040 | 3.6746 | 13.2962 | 2.0081 | 10.6529 | 12.049 | 18.9985 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

aa3ce4b29c445f26277ccf744d34dae5

Okyx/fillmaskmodel

Okyx

xlm-roberta

transformers

fill-mask

mit

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # fillmaskmodel This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4400, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results ### Framework versions - Transformers 4.22.1 - TensorFlow 2.8.2 - Tokenizers 0.12.1

83b91ee454696c1ff16f355f69374a1e

anas-awadalla/roberta-large-initialization-seed-4

anas-awadalla

roberta

transformers

question-answering

mit

['squad']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-initialization-seed-4 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 4 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 24 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6

ccdd24529ca177e2ba2fbfb2bed16d03

ckiplab/bert-base-han-chinese-pos-xiandai

ckiplab

bert

transformers

token-classification

gpl-3.0

['zh']

['pytorch', 'token-classification', 'bert', 'zh']

# CKIP BERT Base Han Chinese POS This model provides part-of-speech (POS) tagging for the ancient Chinese language. Our training dataset covers four eras of the Chinese language. ## Homepage * [ckiplab/han-transformers](https://github.com/ckiplab/han-transformers) ## Training Datasets The copyright of the datasets belongs to the Institute of Linguistics, Academia Sinica. * [中央研究院上古漢語標記語料庫](http://lingcorpus.iis.sinica.edu.tw/cgi-bin/kiwi/akiwi/kiwi.sh) * [中央研究院中古漢語語料庫](http://lingcorpus.iis.sinica.edu.tw/cgi-bin/kiwi/dkiwi/kiwi.sh) * [中央研究院近代漢語語料庫](http://lingcorpus.iis.sinica.edu.tw/cgi-bin/kiwi/pkiwi/kiwi.sh) * [中央研究院現代漢語語料庫](http://asbc.iis.sinica.edu.tw) ## Contributors * Chin-Tung Lin at [CKIP](https://ckip.iis.sinica.edu.tw/)

3056151e19ea1ed005ceb5891c68f116

JBNLRY/distilbert-base-uncased-finetuned-cola

JBNLRY

distilbert

transformers

text-classification

apache-2.0

['glue']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8366 - Matthews Correlation: 0.5472 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5224 | 1.0 | 535 | 0.5432 | 0.4243 | | 0.3447 | 2.0 | 1070 | 0.4968 | 0.5187 | | 0.2347 | 3.0 | 1605 | 0.6540 | 0.5280 | | 0.1747 | 4.0 | 2140 | 0.7547 | 0.5367 | | 0.1255 | 5.0 | 2675 | 0.8366 | 0.5472 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

9226513599c9a701754eb325406695b3

hisaoka/bart-large-cnn_dataset_radiology_summary20221129.tsv

hisaoka

bart

transformers

text2text-generation

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-large-cnn_dataset_radiology_summary20221129.tsv This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu102 - Datasets 2.4.0 - Tokenizers 0.12.1

8d174b990800f5c5bc0f25b5dd1b17be

raisvaza/distilbert-base-uncased-finetuned-ner

raisvaza

distilbert

transformers

token-classification

apache-2.0

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # raisvaza/distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0352 - Validation Loss: 0.0607 - Train Precision: 0.9246 - Train Recall: 0.9330 - Train F1: 0.9288 - Train Accuracy: 0.9832 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.1955 | 0.0720 | 0.8998 | 0.9157 | 0.9077 | 0.9792 | 0 | | 0.0557 | 0.0620 | 0.9200 | 0.9271 | 0.9235 | 0.9822 | 1 | | 0.0352 | 0.0607 | 0.9246 | 0.9330 | 0.9288 | 0.9832 | 2 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.0 - Tokenizers 0.13.2

bd015bc605adcffe449b3dedfabdfaaa

kadirnar/yolov8l-v8.0

kadirnar

object-detection

gpl-3.0

['detection-datasets/coco']

['object-detection', 'computer-vision', 'yolov8', 'yolov5']

### Model Description [Ultralytics:](https://github.com/ultralytics/ultralytics/) YOLOv8 in PyTorch > ONNX > CoreML > TFLite] ### Installation ``` pip install ultralytics ``` ### Yolov8 Inference ```python from ultralytics import YOLO model = YOLO('kadirnar/yolov8l-v8.0') model.conf = conf_threshold model.iou = iou_threshold prediction = model.predict(image, imgsz=image_size, show=False, save=False) ``` ### BibTeX Entry and Citation Info ``` ```

cc19e299c1e13ebe5d0593dee075efaa

CalvinHuang/mt5-small-finetuned-amazon-en-es

CalvinHuang

mt5

transformers

summarization

apache-2.0

['summarization', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0393 - Rouge1: 17.2936 - Rouge2: 8.0678 - Rougel: 16.8129 - Rougelsum: 16.9991 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 6.6665 | 1.0 | 1209 | 3.2917 | 13.912 | 5.595 | 13.2984 | 13.4171 | | 3.8961 | 2.0 | 2418 | 3.1711 | 16.2845 | 8.6033 | 15.5509 | 15.7383 | | 3.5801 | 3.0 | 3627 | 3.0917 | 17.316 | 8.122 | 16.697 | 16.773 | | 3.4258 | 4.0 | 4836 | 3.0583 | 16.1347 | 7.7829 | 15.6475 | 15.7804 | | 3.3154 | 5.0 | 6045 | 3.0573 | 17.5918 | 8.7349 | 17.0537 | 17.2216 | | 3.2438 | 6.0 | 7254 | 3.0479 | 17.2294 | 8.0383 | 16.8141 | 16.9858 | | 3.2024 | 7.0 | 8463 | 3.0377 | 17.2918 | 8.139 | 16.8178 | 16.9671 | | 3.1745 | 8.0 | 9672 | 3.0393 | 17.2936 | 8.0678 | 16.8129 | 16.9991 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

232d6463c1be530ef13f8eccfe8a9e9b

microsoft/xclip-base-patch16-hmdb-8-shot

microsoft

xclip

transformers

feature-extraction

mit

['en']

['vision', 'video-classification']

# X-CLIP (base-sized model) X-CLIP model (base-sized, patch resolution of 16) trained in a few-shot fashion (K=8) on [HMDB-51](https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first released in [this repository](https://github.com/microsoft/VideoX/tree/master/X-CLIP). This model was trained using 32 frames per video, at a resolution of 224x224. Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description X-CLIP is a minimal extension of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs.  This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval. ## Intended uses & limitations You can use the raw model for determining how well text goes with a given video. See the [model hub](https://huggingface.co/models?search=microsoft/xclip) to look for fine-tuned versions on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/xclip.html#). ## Training data This model was trained on [HMDB-51](https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/). ### Preprocessing The exact details of preprocessing during training can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L247). The exact details of preprocessing during validation can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L285). During validation, one resizes the shorter edge of each frame, after which center cropping is performed to a fixed-size resolution (like 224x224). Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation. ## Evaluation results This model achieves a top-1 accuracy of 62.8%.

de025dbfb09c06b97561cb4f410f41cd

sd-concepts-library/turtlepics

sd-concepts-library

mit

[]

### turtlepics on Stable Diffusion This is the `<henry-leonardi>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:    

d2921f06f6ac257e42521c548bb4d275

uzn/ddpm-trucks

uzn

diffusers

apache-2.0

['en']

['uzn/truck']

[]

<!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-trucks ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `uzn/truck` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/uzn/ddpm-trucks/tensorboard?#scalars)

4ecdfe725df16923f67db627ebb8e56d

pyordii/distilgpt2-finetuned-AT

pyordii

gpt2

transformers

text-generation

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-AT This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2450 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 279 | 3.3451 | | 3.4534 | 2.0 | 558 | 3.2941 | | 3.4534 | 3.0 | 837 | 3.2740 | | 3.2435 | 4.0 | 1116 | 3.2617 | | 3.2435 | 5.0 | 1395 | 3.2556 | | 3.1729 | 6.0 | 1674 | 3.2490 | | 3.1729 | 7.0 | 1953 | 3.2475 | | 3.1262 | 8.0 | 2232 | 3.2467 | | 3.0972 | 9.0 | 2511 | 3.2448 | | 3.0972 | 10.0 | 2790 | 3.2450 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

3110b49a3d1e68a416874dc1e7f96f2f

IsaacZhy/bert-base-goemotions

IsaacZhy

bert

transformers

text-classification

apache-2.0

['go_emotions']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-goemotions This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the go_emotions dataset. It achieves the following results on the evaluation set: - Loss: 0.1539 - F1: 0.5727 - Roc Auc: 0.7796 - Accuracy: 0.4375 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:| | 0.0833 | 1.0 | 2714 | 0.0876 | 0.5453 | 0.7189 | 0.4243 | | 0.0719 | 2.0 | 5428 | 0.0867 | 0.5586 | 0.7322 | 0.4399 | | 0.0575 | 3.0 | 8142 | 0.0943 | 0.5736 | 0.7523 | 0.4665 | | 0.0411 | 4.0 | 10856 | 0.1064 | 0.5655 | 0.7580 | 0.4574 | | 0.0301 | 5.0 | 13570 | 0.1167 | 0.5622 | 0.7591 | 0.4517 | | 0.0217 | 6.0 | 16284 | 0.1279 | 0.5579 | 0.7648 | 0.4375 | | 0.015 | 7.0 | 18998 | 0.1367 | 0.5663 | 0.7759 | 0.4333 | | 0.0102 | 8.0 | 21712 | 0.1445 | 0.5695 | 0.7793 | 0.4322 | | 0.0077 | 9.0 | 24426 | 0.1491 | 0.5725 | 0.7795 | 0.4366 | | 0.0057 | 10.0 | 27140 | 0.1539 | 0.5727 | 0.7796 | 0.4375 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

7b3f0cdd96b11d34a66e255859a5aab8

harithapliyal/distilbert-base-uncased-finetuned-cola

harithapliyal

distilbert

transformers

text-classification

apache-2.0

['glue']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8366 - Matthews Correlation: 0.5602 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5257 | 1.0 | 535 | 0.5475 | 0.4039 | | 0.3482 | 2.0 | 1070 | 0.5140 | 0.5004 | | 0.2408 | 3.0 | 1605 | 0.6472 | 0.5264 | | 0.1765 | 4.0 | 2140 | 0.7456 | 0.5403 | | 0.1314 | 5.0 | 2675 | 0.8366 | 0.5602 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

3fefffb9b8a2c5c0e95f610b6ecdae85

Helsinki-NLP/opus-mt-de-fj

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-de-fj * source languages: de * target languages: fj * OPUS readme: [de-fj](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-fj/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-fj/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-fj/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-fj/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.de.fj | 24.6 | 0.470 |

a1bc978538508ba68587a5e3b6a1de4a

Aayesha/t5-end2end-questions-generation

Aayesha

t5

transformers

text2text-generation

apache-2.0

['squad_modified_for_t5_qg']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-end2end-questions-generation This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_modified_for_t5_qg dataset. It achieves the following results on the evaluation set: - Loss: 1.8015 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.609 | 0.34 | 100 | 1.9542 | | 2.0336 | 0.68 | 200 | 1.8015 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

7579ef23a31491d6b2e691ca41f25583

paola-md/distilr2-lr1e05-wd0.1-bs64

paola-md

roberta

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilr2-lr1e05-wd0.1-bs64 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2723 - Rmse: 0.5219 - Mse: 0.2723 - Mae: 0.4098 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 512 - eval_batch_size: 512 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2779 | 1.0 | 312 | 0.2756 | 0.5250 | 0.2756 | 0.4246 | | 0.2747 | 2.0 | 624 | 0.2734 | 0.5229 | 0.2734 | 0.4091 | | 0.2732 | 3.0 | 936 | 0.2726 | 0.5221 | 0.2726 | 0.4155 | | 0.2714 | 4.0 | 1248 | 0.2723 | 0.5219 | 0.2723 | 0.4098 | ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 2.4.0 - Tokenizers 0.12.1

f27fa515a7a3ae1fef4d2e2edcc4e7fe

Goutham-Vignesh/flan-t5-gov-report-sum

Goutham-Vignesh

t5

transformers

text2text-generation

apache-2.0

['govreport-summarization']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-gov-report-sum This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the govreport-summarization dataset. It achieves the following results on the evaluation set: - Loss: 2.2385 - Rouge1: 5.8729 - Rouge2: 3.0763 - Rougel: 5.1016 - Rougelsum: 5.646 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 2.5801 | 1.0 | 2190 | 2.3211 | 5.6226 | 2.9142 | 4.9535 | 5.417 | 19.0 | | 2.5125 | 2.0 | 4380 | 2.2748 | 5.7982 | 3.0365 | 5.0726 | 5.5837 | 19.0 | | 2.453 | 3.0 | 6570 | 2.2545 | 5.8744 | 3.0997 | 5.1196 | 5.6524 | 19.0 | | 2.436 | 4.0 | 8760 | 2.2430 | 5.8669 | 3.0525 | 5.0849 | 5.631 | 19.0 | | 2.4144 | 5.0 | 10950 | 2.2385 | 5.8729 | 3.0763 | 5.1016 | 5.646 | 19.0 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu102 - Datasets 2.9.0 - Tokenizers 0.13.2

52b7b93add955442f66a8bbf31dcf4a7

mgoudarz/xlm-roberta-base-finetuned-panx-all

mgoudarz

xlm-roberta

transformers

token-classification

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1757 - F1: 0.8513 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2986 | 1.0 | 835 | 0.1939 | 0.8077 | | 0.1547 | 2.0 | 1670 | 0.1813 | 0.8351 | | 0.1003 | 3.0 | 2505 | 0.1757 | 0.8513 | ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

c27182b10f557865a5909eacbabd9abe

ConvLab/t5-small-nlg-multiwoz21

ConvLab

t5

transformers

text2text-generation

apache-2.0

['en']

['ConvLab/multiwoz21']

['t5-small', 'text2text-generation', 'natural language generation', 'conversational system', 'task-oriented dialog']

# t5-small-nlg-multiwoz21 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21). Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 128 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 10.0 ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0

21cf9fdc2d3d7b2de6abd1e6594e8cec

Helsinki-NLP/opus-mt-en-bcl

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-en-bcl * source languages: en * target languages: bcl * OPUS readme: [en-bcl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-bcl/README.md) * dataset: opus+bt * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus+bt-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-bcl/opus+bt-2020-02-26.zip) * test set translations: [opus+bt-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-bcl/opus+bt-2020-02-26.test.txt) * test set scores: [opus+bt-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-bcl/opus+bt-2020-02-26.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.en.bcl | 54.3 | 0.722 |

8441c7d833ee6a9647cab71909448724

gchhablani/fnet-large-finetuned-rte

gchhablani

fnet

transformers

text-classification

apache-2.0

['en']

['glue']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-large-finetuned-rte This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.7528 - Accuracy: 0.6426 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7105 | 1.0 | 623 | 0.6887 | 0.5740 | | 0.6714 | 2.0 | 1246 | 0.6742 | 0.6209 | | 0.509 | 3.0 | 1869 | 0.7528 | 0.6426 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

c6923489cd7777744dd6a8927b659070

lora-library/a-photo-of-simbatheog

lora-library

diffusers

text-to-image

creativeml-openrail-m

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora']

# LoRA DreamBooth - a-photo-of-simbatheog These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "simbatheog" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: A photo of simbatheog in a bucket    

82db4456e4c5616c2fd60bb65aa74a33

Hate-speech-CNERG/kannada-codemixed-abusive-MuRIL

Hate-speech-CNERG

bert

transformers

text-classification

afl-3.0

['ka-en']

[]

This model is used to detect **abusive speech** in **Code-Mixed Kannada**. It is finetuned on MuRIL model using Code-Mixed Kannada abusive speech dataset. The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive) LABEL_0 :-> Normal LABEL_1 :-> Abusive ### For more details about our paper Mithun Das, Somnath Banerjee and Animesh Mukherjee. "[Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://arxiv.org/abs/2204.12543)". Accepted at ACM HT 2022. ***Please cite our paper in any published work that uses any of these resources.*** ~~~ @article{das2022data, title={Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages}, author={Das, Mithun and Banerjee, Somnath and Mukherjee, Animesh}, journal={arXiv preprint arXiv:2204.12543}, year={2022} } ~~~

0676fdea764d9dcb9279028612113e09

SebastianKamphYT/VintageCream

SebastianKamphYT

cc0-1.0

[]

 [](https://www.patreon.com/sebastiankamph) # Vintage cream photo film Based on SD 2.1 768x768 **Token word: vntgcrm style** **Example prompt to start out with** RAW candid cinema, woman portrait, vntgcrm style, 16mm, ((remarkable color)), (ultra realistic) Negative: ugly, disfigured, deformed, too many hands, makeup, cartoon, render **Support my work on Patreon for Early access model releases** https://www.patreon.com/sebastiankamph **AI Art, Stable diffusion guides and tutorials on Youtube** https://www.youtube.com/@sebastiankamph **Chat in our community discord** https://discord.com/invite/dFB7zuXyFY **Installation** Download the .ckpt and the .yaml file. Put them inside \stable-diffusion-webui\Models\Stable-diffusion\ https://huggingface.co/SebastianKamphYT/VintageCream/blob/main/VintageCream.ckpt https://huggingface.co/SebastianKamphYT/VintageCream/blob/main/VintageCream.yaml

1a0fe5216deeb35546aa4e12220e1fbb

NandaKr/disaster_classification_with_distilbert

NandaKr

distilbert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # disaster_classification_with_distilbert This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.11.0

de8eb5d961580878664929f51059e925

no3/kat-wd-1.4-beta1

no3

diffusers

text-to-image

creativeml-openrail-m

[]

### a Better model is out, Go to https://huggingface.co/no3/kat-at3-beta1 ### kat from [Flipon](https://store.steampowered.com/app/1285020/Flipon/) on [WD](https://huggingface.co/hakurei/waifu-diffusion) via Dreambooth #### model by no3 This your waifu-diffusion v1.4 model fine-tuned kat concept taught to waifu-diffusion v1.4 with Dreambooth. It can be used by modifying the `instance_prompt`: **sks_kaatt** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts). ### note If you want to to use in UI like [AUTOMATIC1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) or any UI that's uses .ckpt files just download one or more file from here for your convenience. [katFl-wd-1.4-beta1.ckpt](https://huggingface.co/no3/kat-wd-1.4-beta1/resolve/main/katFl-wd-1.4-beta1.ckpt) 5.16 GB [katFl-wd-1.4-beta1-pruned.ckpt](https://huggingface.co/no3/kat-wd-1.4-beta1/resolve/main/katFl-wd-1.4-beta1-pruned.ckpt) 2.58 GB Uses less storage space, but untested yet If you have issues or questions feel free to visit the Community Tab and start discussion about it. Here are images used for training this concept:     

73dd378eec169a31a9ca3064a177b1f4

ali2066/finetuned_token_2e-05_16_02_2022-14_37_42

ali2066

distilbert

transformers

token-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_token_2e-05_16_02_2022-14_37_42 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1722 - Precision: 0.3378 - Recall: 0.3615 - F1: 0.3492 - Accuracy: 0.9448 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 38 | 0.3781 | 0.1512 | 0.2671 | 0.1931 | 0.8216 | | No log | 2.0 | 76 | 0.3020 | 0.1748 | 0.2938 | 0.2192 | 0.8551 | | No log | 3.0 | 114 | 0.2723 | 0.1938 | 0.3339 | 0.2452 | 0.8663 | | No log | 4.0 | 152 | 0.2574 | 0.2119 | 0.3506 | 0.2642 | 0.8727 | | No log | 5.0 | 190 | 0.2521 | 0.2121 | 0.3623 | 0.2676 | 0.8756 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

34e6b6a5f043d4d2e0066e4d1d74ae9e

gokuls/mobilebert_sa_GLUE_Experiment_data_aug_rte_256

gokuls

mobilebert

transformers

text-classification

apache-2.0

['en']

['glue']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mobilebert_sa_GLUE_Experiment_data_aug_rte_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 3.0847 - Accuracy: 0.4874 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2703 | 1.0 | 1136 | 3.2768 | 0.4657 | | 0.0555 | 2.0 | 2272 | 3.0847 | 0.4874 | | 0.0253 | 3.0 | 3408 | 5.4968 | 0.5018 | | 0.0149 | 4.0 | 4544 | 5.6020 | 0.4982 | | 0.0104 | 5.0 | 5680 | 6.6683 | 0.5090 | | 0.0082 | 6.0 | 6816 | 8.2220 | 0.5090 | | 0.0062 | 7.0 | 7952 | 8.2179 | 0.5054 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

0850b2caf26c4f6c89c277133bf08f60

research-backup/bart-large-squadshifts-vanilla-amazon-qg

research-backup

bart

transformers

text2text-generation

cc-by-4.0

['en']

['lmqg/qg_squadshifts']

['question generation']

# Model Card of `research-backup/bart-large-squadshifts-vanilla-amazon-qg` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: amazon) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (amazon) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="en", model="research-backup/bart-large-squadshifts-vanilla-amazon-qg") # model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/bart-large-squadshifts-vanilla-amazon-qg") output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-large-squadshifts-vanilla-amazon-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json) | | Score | Type | Dataset | |:-----------|--------:|:-------|:---------------------------------------------------------------------------| | BERTScore | 92.3 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_1 | 28.19 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_2 | 18.89 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_3 | 12.92 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_4 | 9.1 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | METEOR | 23.04 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | MoverScore | 62.81 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | ROUGE_L | 27.85 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: amazon - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-large - max_length: 512 - max_length_output: 32 - epoch: 4 - batch: 32 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 2 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/bart-large-squadshifts-vanilla-amazon-qg/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

39e0d5b9c616eda9a93b0bb0acb27cbf

StonyBrookNLP/preasm-large-iirc-retrieved

StonyBrookNLP

t5

transformers

text2text-generation

cc-by-4.0

['question-answering, multi-step-reasoning, multi-hop-reasoning']

# What's this? This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496). This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details. We release the following models: - **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}` - **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}` - **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}` The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`. The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`. The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**. # How to use it? Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac model_name = "StonyBrookNLP/preasm-large-iirc-retrieved" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization model = AutoModelForSeq2SeqLM.from_pretrained(model_name) enable_digit_tokenization(tokenizer) input_texts = [ "Who scored the first touchdown of the game?\n" + "... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..." # Note: some models have slightly different qn/ctxt format. See the github repo. ] input_ids = tokenizer( input_texts, return_tensors="pt", truncation=True, max_length=800, add_special_tokens=True, padding=True, )["input_ids"] generated_ids = model.generate(input_ids, min_length=1, max_length=50) generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False) generated_predictions = [ tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions ] # => ["Chaz Schilens"] ```

7e04ee41d0de9dc4f640e537e9f6a06f

jimypbr/bart-large-test

jimypbr

bart

transformers

text2text-generation

apache-2.0

['cnn_dailymail']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # outputs This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the cnn_dailymail 3.0.0 dataset. ## Model description More information needed ## Intended uses & limitations This is a work in progress. Please don't use these weights. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 256 - total_train_batch_size: 2048 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 2.0 - training precision: Mixed Precision ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cpu - Datasets 2.2.1 - Tokenizers 0.12.1

1fd7e07a988f7a97422decb8f6658506

hsohn3/mayo-bert-uncased-wordlevel-block512-ep10

hsohn3

bert

transformers

fill-mask

apache-2.0

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # hsohn3/mayo-bert-uncased-wordlevel-block512-ep10 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3171 - Epoch: 9 ## Model description - base_model: bert-base-uncased - block_size: 512 - tokenizer: ehr-bert-wordlevel-uncased ## Intended uses & limitations More information needed ## Training and evaluation data - MAYO visit-level texts ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 - mlm_probability: 0.15 - batch_size: 8 - epochs: 10 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 3.0885 | 0 | | 2.8340 | 1 | | 2.7975 | 2 | | 2.6720 | 3 | | 2.4868 | 4 | | 2.1750 | 5 | | 1.8143 | 6 | | 1.0948 | 7 | | 0.4915 | 8 | | 0.3171 | 9 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

2b4534d4c27acb738df1a91d81f2fb6e

yohida/yoshida_gpt

yohida

gpt2

transformers

text-generation

mit

['ja']

['cc100', 'wikipedia']

['ja', 'japanese', 'gpt', 'text-generation', 'lm', 'nlp']

# japanese-gpt-1b  This repository provides a 1.3B-parameter Japanese GPT model. The model was trained by [rinna Co., Ltd.](https://corp.rinna.co.jp/) # How to use the model *NOTE:* Use `T5Tokenizer` to initiate the tokenizer. ~~~~ import torch from transformers import T5Tokenizer, AutoModelForCausalLM tokenizer = T5Tokenizer.from_pretrained("rinna/japanese-gpt-1b") model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt-1b") if torch.cuda.is_available(): model = model.to("cuda") text = "西田幾多郎は、" token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt") with torch.no_grad(): output_ids = model.generate( token_ids.to(model.device), max_length=100, min_length=100, do_sample=True, top_k=500, top_p=0.95, pad_token_id=tokenizer.pad_token_id, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, bad_word_ids=[[tokenizer.unk_token_id]] ) output = tokenizer.decode(output_ids.tolist()[0]) print(output) # sample output: 西田幾多郎は、その主著の「善の研究」などで、人間の内面に自然とその根源があると指摘し、その根源的な性格は、この西田哲学を象徴しているとして、カントの「純粋理性批判」と「判断力批判」を対比して捉えます。それは、「人が理性的存在であるかぎりにおいて、人はその当人に固有な道徳的に自覚された善悪の基準を持っている」とするもので、この理性的な善悪の観念を否定するのがカントの ~~~~ # Model architecture A 24-layer, 2048-hidden-size transformer-based language model. # Training The model was trained on [Japanese C4](https://huggingface.co/datasets/allenai/c4), [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz) and [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) to optimize a traditional language modelling objective. It reaches around 14 perplexity on a chosen validation set from the same data. # Tokenization The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer. The vocabulary was first trained on a selected subset from the training data using the official sentencepiece training script, and then augmented with emojis and symbols. # Licenese [The MIT license](https://opensource.org/licenses/MIT)

aab8d03c8ab67a5353f9a4c9e8b53808

sumups-ai/results

sumups-ai

bert

transformers

token-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4578 - Precision: 0.0060 - Recall: 0.0286 - F1: 0.0099 - Accuracy: 0.4288 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 8 | 1.6449 | 0.0 | 0.0 | 0.0 | 0.3860 | | No log | 2.0 | 16 | 1.5439 | 0.0014 | 0.0071 | 0.0023 | 0.4025 | | No log | 3.0 | 24 | 1.4986 | 0.0068 | 0.0286 | 0.0110 | 0.4176 | | No log | 4.0 | 32 | 1.4603 | 0.0033 | 0.0143 | 0.0054 | 0.4285 | | No log | 5.0 | 40 | 1.4578 | 0.0060 | 0.0286 | 0.0099 | 0.4288 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

ccede256c955ba798b4e5f079ea7f3c6

google/t5-efficient-tiny-nl8

google

t5

transformers

text2text-generation

apache-2.0

['en']

['c4']

['deep-narrow']

# T5-Efficient-TINY-NL8 (Deep-Narrow version) T5-Efficient-TINY-NL8 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. ## Details model architecture This model checkpoint - **t5-efficient-tiny-nl8** - is of model type **Tiny** with the following variations: - **nl** is **8** It has **22.93** million parameters and thus requires *ca.* **91.74 MB** of memory in full precision (*fp32*) or **45.87 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | #Params| | ----| ---- | ---- | ---- | ---- | ---- | ----| | Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M| | Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M| | Small | 6/6 | 2048 | 512 | 32 | 8 | 60M| | Base | 12/12 | 3072 | 768 | 64 | 12 | 220M| | Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M| | Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B| | XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B| whereas the following abbreviations are used: | Abbreviation | Definition | | ----| ---- | | nl | Number of transformer blocks (depth) | | dm | Dimension of embedding vector (output vector of transformers block) | | kv | Dimension of key/value projection matrix | | nh | Number of attention heads | | ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) | | el | Number of transformer blocks in the encoder (encoder depth) | | dl | Number of transformer blocks in the decoder (decoder depth) | | sh | Signifies that attention heads are shared | | skv | Signifies that key-values projection matrices are tied | If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*. ## Pre-Training The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using the span-based masked language modeling (MLM) objective. ## Fine-Tuning **Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage. The checkpoint was pretrained in English and is therefore only useful for English NLP tasks. You can follow on of the following examples on how to fine-tune the model: *PyTorch*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) - [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *Tensorflow*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *JAX/Flax*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. ## Downstream Performance TODO: Add table if available ## Computational Complexity TODO: Add table if available ## More information We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint. As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv* model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future.

dd9bd5a4731bdf97d8da37985fa8a0d0

PaulChimzy/stt_rw_conformer_transducer_large

PaulChimzy

nemo

automatic-speech-recognition

cc-by-4.0

['rw']

['mozilla-foundation/common_voice_11_0']

['automatic-speech-recognition', 'speech', 'Kinyarwanda', 'audio', 'CTC', 'Conformer', 'Transformer', 'NeMo', 'pytorch']

## Model Overview <DESCRIBE IN ONE LINE THE MODEL AND ITS USE> ## NVIDIA NeMo: Training To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version. ``` pip install nemo_toolkit['all'] ``` ## How to Use this Model The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. ### Automatically instantiate the model ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("PaulChimzy/stt_rw_conformer_transducer_large") ``` ### Transcribing using Python First, let's get a sample ``` wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav ``` Then simply do: ``` asr_model.transcribe(['2086-149220-0033.wav']) ``` ### Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="PaulChimzy/stt_rw_conformer_transducer_large" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` ### Input This model accepts 16000 KHz Mono-channel Audio (wav files) as input. ### Output This model provides transcribed speech as a string for a given audio sample. ## Model Architecture <ADD SOME INFORMATION ABOUT THE ARCHITECTURE> ## Training <ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC> ### Datasets <LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)> ## Performance <LIST THE SCORES OF THE MODEL - OR USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS> ## Limitations <DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL> Eg: Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. ## References <ADD ANY REFERENCES HERE AS NEEDED> [1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)

adcb9f8d33809df66a385e34e9f7b56b

mrm8488/wav2vec2-large-xlsr-53-breton

mrm8488

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['br']

['common_voice']

['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']

# Wav2Vec2-Large-XLSR-53-breton Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Breton using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "br", split="test[:2%]"). processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-breton") model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-breton") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Breton test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "br", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-breton") model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-breton") model.to("cuda") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 46.49 % ## Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found ???

7f537a076979c79c709f805378fce318

ProGamerGov/Object-Taped-To-Wall-Diffusion-V1

ProGamerGov

text-to-image

creativeml-openrail-m

['ProGamerGov/StableDiffusion-v1-5-Regularization-Images']

['stable-diffusion', 'text-to-image']

**Object-Taped-To-Wall-Diffusion** This fine-tuned Stable Diffusion v1.5 model was trained for 2000 iterations with a batch size of 4, on a selection of photos of things taped to a wall. Training was performed using [ShivamShrirao/diffusers](https://github.com/ShivamShrirao/diffusers) with full precision, prior-preservation loss, the train-text-encoder feature, and the new [1.5 MSE VAE from Stability AI](https://huggingface.co/stabilityai/sd-vae-ft-mse). A total of 2100 regularization / class images were used from [here](https://huggingface.co/datasets/ProGamerGov/StableDiffusion-v1-5-Regularization-Images). Regularization images were generated using the prompt "artwork style" with 50 DPM++ 2S a Karras steps and a CFG of 7, using the MSE VAE. A negative prompt of "text" was also used for this dataset. Use the tokens **ttw style** in your prompts for the effect. Note that the effect also appears to occur at a much weaker strength on prompts that steer the output towards specific artistic styles. This model will likely not perform well on taping objects that are not traditionally able to be taped to walls. <div align="center"> <img src="https://huggingface.co/ProGamerGov/Object-Taped-To-Wall-Diffusion-V1/resolve/main/v1_size_512x512_t4x8.png"> </div> * [Full Image](https://huggingface.co/ProGamerGov/Object-Taped-To-Wall-Diffusion-V1/resolve/main/v1_size_512x512_t4x8.png) Example images were generated with the v1 2000 iteration model using DPM++ 2S a Karras: ``` ttw style, <object> taped to wall ``` This model was inspired by the 2019 art piece [*Comedian* by Italian artist Maurizio Cattelan](https://en.wikipedia.org/wiki/Comedian_(artwork\)), where a banana was duct taped to a wall.

f047438277b0962b700fab5585302a05

AalLaa/whisper-small-ar

AalLaa

whisper

transformers

automatic-speech-recognition

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-ar This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8342 - Wer: 82.3706 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.6454 | 5.0 | 1000 | 1.8790 | 86.8695 | | 0.0408 | 10.0 | 2000 | 2.4389 | 80.5579 | | 0.0043 | 15.0 | 3000 | 2.7456 | 82.2767 | | 0.002 | 20.0 | 4000 | 2.8342 | 82.3706 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

5332f36530855159f8b09e8222b0c23d

jj-co/gtr-t5-base

jj-co

t5

sentence-transformers

feature-extraction

apache-2.0

['en']

['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers']

# sentence-transformers/gtr-t5-base This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search. This model was converted from the Tensorflow model [gtr-base-1](https://tfhub.dev/google/gtr/gtr-base/1) to PyTorch. When using this model, have a look at the publication: [Large Dual Encoders Are Generalizable Retrievers](https://arxiv.org/abs/2112.07899). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results. The model uses only the encoder from a T5-base model. The weights are stored in FP16. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/gtr-t5-base') embeddings = model.encode(sentences) print(embeddings) ``` The model requires sentence-transformers version 2.2.0 or newer. ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/gtr-t5-base) ## Citing & Authors If you find this model helpful, please cite the respective publication: [Large Dual Encoders Are Generalizable Retrievers](https://arxiv.org/abs/2112.07899)

21917bbdb15a2a07885abf0d98d13dd8

muhtasham/bert-small-finer-longer

muhtasham

bert

transformers

fill-mask

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-small-finer-longer This model is a fine-tuned version of [muhtasham/bert-small-finer](https://huggingface.co/muhtasham/bert-small-finer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4264 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | No log | 0.49 | 500 | 1.6683 | | 1.5941 | 0.97 | 1000 | 1.6569 | | 1.5941 | 1.46 | 1500 | 1.6436 | | 1.5605 | 1.94 | 2000 | 1.6173 | | 1.5605 | 2.43 | 2500 | 1.6073 | | 1.5297 | 2.91 | 3000 | 1.6001 | | 1.5297 | 3.4 | 3500 | 1.5815 | | 1.5022 | 3.89 | 4000 | 1.5756 | | 1.5022 | 4.37 | 4500 | 1.5568 | | 1.4753 | 4.86 | 5000 | 1.5458 | | 1.4753 | 5.34 | 5500 | 1.5399 | | 1.4537 | 5.83 | 6000 | 1.5273 | | 1.4537 | 6.32 | 6500 | 1.5192 | | 1.433 | 6.8 | 7000 | 1.5099 | | 1.433 | 7.29 | 7500 | 1.5083 | | 1.4169 | 7.77 | 8000 | 1.4957 | | 1.4169 | 8.26 | 8500 | 1.4914 | | 1.3982 | 8.75 | 9000 | 1.4859 | | 1.3982 | 9.23 | 9500 | 1.4697 | | 1.3877 | 9.72 | 10000 | 1.4711 | | 1.3877 | 10.2 | 10500 | 1.4608 | | 1.3729 | 10.69 | 11000 | 1.4583 | | 1.3729 | 11.18 | 11500 | 1.4513 | | 1.3627 | 11.66 | 12000 | 1.4498 | | 1.3627 | 12.15 | 12500 | 1.4396 | | 1.357 | 12.63 | 13000 | 1.4415 | | 1.357 | 13.12 | 13500 | 1.4347 | | 1.3484 | 13.61 | 14000 | 1.4316 | | 1.3484 | 14.09 | 14500 | 1.4319 | | 1.3442 | 14.58 | 15000 | 1.4268 | | 1.3442 | 15.06 | 15500 | 1.4293 | | 1.3387 | 15.55 | 16000 | 1.4217 | | 1.3387 | 16.03 | 16500 | 1.4241 | | 1.3358 | 16.52 | 17000 | 1.4250 | | 1.3358 | 17.01 | 17500 | 1.4196 | | 1.3344 | 17.49 | 18000 | 1.4193 | | 1.3344 | 17.98 | 18500 | 1.4200 | | 1.3274 | 18.46 | 19000 | 1.4250 | | 1.3274 | 18.95 | 19500 | 1.4168 | | 1.3348 | 19.44 | 20000 | 1.4164 | | 1.3348 | 19.92 | 20500 | 1.4264 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

56741ddcbdc9c0b0333f477801775efb

timm/maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k

timm

timm

image-classification

apache-2.0

['imagenet-1k', 'imagenet-12k']

['image-classification', 'timm']

# Model card for maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k A timm specific MaxxViT-V2 (w/ a MLP Log-CPB (continuous log-coordinate relative position bias motivated by Swin-V2) image classification model. Pretrained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) and fine-tuned on ImageNet-1k by Ross Wightman. ImageNet-12k pretraining and ImageNet-1k fine-tuning performed on 8x GPU [Lambda Labs](https://lambdalabs.com/) cloud instances.. ### Model Variants in [maxxvit.py](https://github.com/rwightman/pytorch-image-models/blob/main/timm/models/maxxvit.py) MaxxViT covers a number of related model architectures that share a common structure including: - CoAtNet - Combining MBConv (depthwise-separable) convolutional blocks in early stages with self-attention transformer blocks in later stages. - MaxViT - Uniform blocks across all stages, each containing a MBConv (depthwise-separable) convolution block followed by two self-attention blocks with different partitioning schemes (window followed by grid). - CoAtNeXt - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in CoAtNet. All normalization layers are LayerNorm (no BatchNorm). - MaxxViT - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in MaxViT. All normalization layers are LayerNorm (no BatchNorm). - MaxxViT-V2 - A MaxxViT variation that removes the window block attention leaving only ConvNeXt blocks and grid attention w/ more width to compensate. Aside from the major variants listed above, there are more subtle changes from model to model. Any model name with the string `rw` are `timm` specific configs w/ modelling adjustments made to favour PyTorch eager use. These were created while training initial reproductions of the models so there are variations. All models with the string `tf` are models exactly matching Tensorflow based models by the original paper authors with weights ported to PyTorch. This covers a number of MaxViT models. The official CoAtNet models were never released. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 116.1 - GMACs: 24.2 - Activations (M): 62.8 - Image size: 224 x 224 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-12k ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 128, 192, 192]) # torch.Size([1, 128, 96, 96]) # torch.Size([1, 256, 48, 48]) # torch.Size([1, 512, 24, 24]) # torch.Size([1, 1024, 12, 12]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled (ie.e a (batch_size, num_features, H, W) tensor output = model.forward_head(output, pre_logits=True) # output is (batch_size, num_features) tensor ``` ## Model Comparison ### By Top-1 |model |top1 |top5 |samples / sec |Params (M) |GMAC |Act (M)| |------------------------------------------------------------------------------------------------------------------------|----:|----:|--------------:|--------------:|-----:|------:| |[maxvit_xlarge_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) |88.53|98.64| 21.76| 475.77|534.14|1413.22| |[maxvit_xlarge_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) |88.32|98.54| 42.53| 475.32|292.78| 668.76| |[maxvit_base_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) |88.20|98.53| 50.87| 119.88|138.02| 703.99| |[maxvit_large_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) |88.04|98.40| 36.42| 212.33|244.75| 942.15| |[maxvit_large_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) |87.98|98.56| 71.75| 212.03|132.55| 445.84| |[maxvit_base_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) |87.92|98.54| 104.71| 119.65| 73.80| 332.90| |[maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.81|98.37| 106.55| 116.14| 70.97| 318.95| |[maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.47|98.37| 149.49| 116.09| 72.98| 213.74| |[coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k) |87.39|98.31| 160.80| 73.88| 47.69| 209.43| |[maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.89|98.02| 375.86| 116.14| 23.15| 92.64| |[maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.64|98.02| 501.03| 116.09| 24.20| 62.77| |[maxvit_base_tf_512.in1k](https://huggingface.co/timm/maxvit_base_tf_512.in1k) |86.60|97.92| 50.75| 119.88|138.02| 703.99| |[coatnet_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_2_rw_224.sw_in12k_ft_in1k) |86.57|97.89| 631.88| 73.87| 15.09| 49.22| |[maxvit_large_tf_512.in1k](https://huggingface.co/timm/maxvit_large_tf_512.in1k) |86.52|97.88| 36.04| 212.33|244.75| 942.15| |[coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k) |86.49|97.90| 620.58| 73.88| 15.18| 54.78| |[maxvit_base_tf_384.in1k](https://huggingface.co/timm/maxvit_base_tf_384.in1k) |86.29|97.80| 101.09| 119.65| 73.80| 332.90| |[maxvit_large_tf_384.in1k](https://huggingface.co/timm/maxvit_large_tf_384.in1k) |86.23|97.69| 70.56| 212.03|132.55| 445.84| |[maxvit_small_tf_512.in1k](https://huggingface.co/timm/maxvit_small_tf_512.in1k) |86.10|97.76| 88.63| 69.13| 67.26| 383.77| |[maxvit_tiny_tf_512.in1k](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) |85.67|97.58| 144.25| 31.05| 33.49| 257.59| |[maxvit_small_tf_384.in1k](https://huggingface.co/timm/maxvit_small_tf_384.in1k) |85.54|97.46| 188.35| 69.02| 35.87| 183.65| |[maxvit_tiny_tf_384.in1k](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) |85.11|97.38| 293.46| 30.98| 17.53| 123.42| |[maxvit_large_tf_224.in1k](https://huggingface.co/timm/maxvit_large_tf_224.in1k) |84.93|96.97| 247.71| 211.79| 43.68| 127.35| |[coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k) |84.90|96.96| 1025.45| 41.72| 8.11| 40.13| |[maxvit_base_tf_224.in1k](https://huggingface.co/timm/maxvit_base_tf_224.in1k) |84.85|96.99| 358.25| 119.47| 24.04| 95.01| |[maxxvit_rmlp_small_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_small_rw_256.sw_in1k) |84.63|97.06| 575.53| 66.01| 14.67| 58.38| |[coatnet_rmlp_2_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in1k) |84.61|96.74| 625.81| 73.88| 15.18| 54.78| |[maxvit_rmlp_small_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_small_rw_224.sw_in1k) |84.49|96.76| 693.82| 64.90| 10.75| 49.30| |[maxvit_small_tf_224.in1k](https://huggingface.co/timm/maxvit_small_tf_224.in1k) |84.43|96.83| 647.96| 68.93| 11.66| 53.17| |[maxvit_rmlp_tiny_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_tiny_rw_256.sw_in1k) |84.23|96.78| 807.21| 29.15| 6.77| 46.92| |[coatnet_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_1_rw_224.sw_in1k) |83.62|96.38| 989.59| 41.72| 8.04| 34.60| |[maxvit_tiny_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_tiny_rw_224.sw_in1k) |83.50|96.50| 1100.53| 29.06| 5.11| 33.11| |[maxvit_tiny_tf_224.in1k](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) |83.41|96.59| 1004.94| 30.92| 5.60| 35.78| |[coatnet_rmlp_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw_224.sw_in1k) |83.36|96.45| 1093.03| 41.69| 7.85| 35.47| |[maxxvitv2_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvitv2_nano_rw_256.sw_in1k) |83.11|96.33| 1276.88| 23.70| 6.26| 23.05| |[maxxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_nano_rw_256.sw_in1k) |83.03|96.34| 1341.24| 16.78| 4.37| 26.05| |[maxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_nano_rw_256.sw_in1k) |82.96|96.26| 1283.24| 15.50| 4.47| 31.92| |[maxvit_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_nano_rw_256.sw_in1k) |82.93|96.23| 1218.17| 15.45| 4.46| 30.28| |[coatnet_bn_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_bn_0_rw_224.sw_in1k) |82.39|96.19| 1600.14| 27.44| 4.67| 22.04| |[coatnet_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_0_rw_224.sw_in1k) |82.39|95.84| 1831.21| 27.44| 4.43| 18.73| |[coatnet_rmlp_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_nano_rw_224.sw_in1k) |82.05|95.87| 2109.09| 15.15| 2.62| 20.34| |[coatnext_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnext_nano_rw_224.sw_in1k) |81.95|95.92| 2525.52| 14.70| 2.47| 12.80| |[coatnet_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_nano_rw_224.sw_in1k) |81.70|95.64| 2344.52| 15.14| 2.41| 15.41| |[maxvit_rmlp_pico_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_pico_rw_256.sw_in1k) |80.53|95.21| 1594.71| 7.52| 1.85| 24.86| ### By Throughput (samples / sec) |model |top1 |top5 |samples / sec |Params (M) |GMAC |Act (M)| |------------------------------------------------------------------------------------------------------------------------|----:|----:|--------------:|--------------:|-----:|------:| |[coatnext_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnext_nano_rw_224.sw_in1k) |81.95|95.92| 2525.52| 14.70| 2.47| 12.80| |[coatnet_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_nano_rw_224.sw_in1k) |81.70|95.64| 2344.52| 15.14| 2.41| 15.41| |[coatnet_rmlp_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_nano_rw_224.sw_in1k) |82.05|95.87| 2109.09| 15.15| 2.62| 20.34| |[coatnet_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_0_rw_224.sw_in1k) |82.39|95.84| 1831.21| 27.44| 4.43| 18.73| |[coatnet_bn_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_bn_0_rw_224.sw_in1k) |82.39|96.19| 1600.14| 27.44| 4.67| 22.04| |[maxvit_rmlp_pico_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_pico_rw_256.sw_in1k) |80.53|95.21| 1594.71| 7.52| 1.85| 24.86| |[maxxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_nano_rw_256.sw_in1k) |83.03|96.34| 1341.24| 16.78| 4.37| 26.05| |[maxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_nano_rw_256.sw_in1k) |82.96|96.26| 1283.24| 15.50| 4.47| 31.92| |[maxxvitv2_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvitv2_nano_rw_256.sw_in1k) |83.11|96.33| 1276.88| 23.70| 6.26| 23.05| |[maxvit_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_nano_rw_256.sw_in1k) |82.93|96.23| 1218.17| 15.45| 4.46| 30.28| |[maxvit_tiny_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_tiny_rw_224.sw_in1k) |83.50|96.50| 1100.53| 29.06| 5.11| 33.11| |[coatnet_rmlp_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw_224.sw_in1k) |83.36|96.45| 1093.03| 41.69| 7.85| 35.47| |[coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k) |84.90|96.96| 1025.45| 41.72| 8.11| 40.13| |[maxvit_tiny_tf_224.in1k](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) |83.41|96.59| 1004.94| 30.92| 5.60| 35.78| |[coatnet_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_1_rw_224.sw_in1k) |83.62|96.38| 989.59| 41.72| 8.04| 34.60| |[maxvit_rmlp_tiny_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_tiny_rw_256.sw_in1k) |84.23|96.78| 807.21| 29.15| 6.77| 46.92| |[maxvit_rmlp_small_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_small_rw_224.sw_in1k) |84.49|96.76| 693.82| 64.90| 10.75| 49.30| |[maxvit_small_tf_224.in1k](https://huggingface.co/timm/maxvit_small_tf_224.in1k) |84.43|96.83| 647.96| 68.93| 11.66| 53.17| |[coatnet_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_2_rw_224.sw_in12k_ft_in1k) |86.57|97.89| 631.88| 73.87| 15.09| 49.22| |[coatnet_rmlp_2_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in1k) |84.61|96.74| 625.81| 73.88| 15.18| 54.78| |[coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k) |86.49|97.90| 620.58| 73.88| 15.18| 54.78| |[maxxvit_rmlp_small_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_small_rw_256.sw_in1k) |84.63|97.06| 575.53| 66.01| 14.67| 58.38| |[maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.64|98.02| 501.03| 116.09| 24.20| 62.77| |[maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.89|98.02| 375.86| 116.14| 23.15| 92.64| |[maxvit_base_tf_224.in1k](https://huggingface.co/timm/maxvit_base_tf_224.in1k) |84.85|96.99| 358.25| 119.47| 24.04| 95.01| |[maxvit_tiny_tf_384.in1k](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) |85.11|97.38| 293.46| 30.98| 17.53| 123.42| |[maxvit_large_tf_224.in1k](https://huggingface.co/timm/maxvit_large_tf_224.in1k) |84.93|96.97| 247.71| 211.79| 43.68| 127.35| |[maxvit_small_tf_384.in1k](https://huggingface.co/timm/maxvit_small_tf_384.in1k) |85.54|97.46| 188.35| 69.02| 35.87| 183.65| |[coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k) |87.39|98.31| 160.80| 73.88| 47.69| 209.43| |[maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.47|98.37| 149.49| 116.09| 72.98| 213.74| |[maxvit_tiny_tf_512.in1k](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) |85.67|97.58| 144.25| 31.05| 33.49| 257.59| |[maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.81|98.37| 106.55| 116.14| 70.97| 318.95| |[maxvit_base_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) |87.92|98.54| 104.71| 119.65| 73.80| 332.90| |[maxvit_base_tf_384.in1k](https://huggingface.co/timm/maxvit_base_tf_384.in1k) |86.29|97.80| 101.09| 119.65| 73.80| 332.90| |[maxvit_small_tf_512.in1k](https://huggingface.co/timm/maxvit_small_tf_512.in1k) |86.10|97.76| 88.63| 69.13| 67.26| 383.77| |[maxvit_large_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) |87.98|98.56| 71.75| 212.03|132.55| 445.84| |[maxvit_large_tf_384.in1k](https://huggingface.co/timm/maxvit_large_tf_384.in1k) |86.23|97.69| 70.56| 212.03|132.55| 445.84| |[maxvit_base_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) |88.20|98.53| 50.87| 119.88|138.02| 703.99| |[maxvit_base_tf_512.in1k](https://huggingface.co/timm/maxvit_base_tf_512.in1k) |86.60|97.92| 50.75| 119.88|138.02| 703.99| |[maxvit_xlarge_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) |88.32|98.54| 42.53| 475.32|292.78| 668.76| |[maxvit_large_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) |88.04|98.40| 36.42| 212.33|244.75| 942.15| |[maxvit_large_tf_512.in1k](https://huggingface.co/timm/maxvit_large_tf_512.in1k) |86.52|97.88| 36.04| 212.33|244.75| 942.15| |[maxvit_xlarge_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) |88.53|98.64| 21.76| 475.77|534.14|1413.22| ## Citation ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} } ``` ```bibtex @article{tu2022maxvit, title={MaxViT: Multi-Axis Vision Transformer}, author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao}, journal={ECCV}, year={2022}, } ``` ```bibtex @article{dai2021coatnet, title={CoAtNet: Marrying Convolution and Attention for All Data Sizes}, author={Dai, Zihang and Liu, Hanxiao and Le, Quoc V and Tan, Mingxing}, journal={arXiv preprint arXiv:2106.04803}, year={2021} } ```

dc4034e4cc46f6c6cb733a1c295c4747

clementchadebec/reproduced_wrapped_poincare_vae

clementchadebec