license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['summarization', 'mbart', 'bart'] | false | Entrainement Nous avons testé deux architecture de modèles (T5 et BART) avec des textes en entrée de 512 ou 1024 tokens. Finallement c'est le modèle BART avec 512 tokens qui à été retenu. Il a été entrainé sur 2 epochs (~700K articles) sur une Tesla V100 (32 heures d'entrainement). | b48748ed2db68042b7dc203c25d86ece |
mit | ['summarization', 'mbart', 'bart'] | false | Résultats  Nous avons comparé notre modèle (`mbart-large-512-full` sur le graphique) à deux références: * MBERT qui correspond aux performances du modèle entrainé par l'équipe à l'origine de la base d'articles MLSUM * Barthez qui est un autre modèle basé sur des articles d... | e64627f6a821a29a2191c124d1ea60cc |
mit | ['summarization', 'mbart', 'bart'] | false | Utilisation ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from transformers import SummarizationPipeline model_name = 'lincoln/mbart-mlsum-automatic-summarization' loaded_tokenizer = AutoTokenizer.from_pretrained(model_name) loaded_model = AutoModelForSeq2SeqLM.from_pretrained(model_name) ... | 9eb347f6e434f96f5d063d6032951d02 |
mit | ['summarization', 'mbart', 'bart'] | false | Citation ```bibtex @article{scialom2020mlsum, title={MLSUM: The Multilingual Summarization Corpus}, author={Thomas Scialom and Paul-Alexis Dray and Sylvain Lamprier and Benjamin Piwowarski and Jacopo Staiano}, year={2020}, eprint={2004.14900}, archivePrefix={arXiv}, primaryClass={... | fc349f89d351a29f136eda39024cface |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | MultiBERTs Seed 1 Checkpoint 1100k (uncased) Seed 1 intermediate checkpoint 1100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/g... | a1c7b3056d687e2d46d83b39497bbe87 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | How to use 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('multiberts-seed-1-1100k') model = BertModel.from_pretrained("multiberts-seed-1-1100k") text = "Replace me by any text you'd lik... | d030201b7828f8a62effd673d66cab68 |
creativeml-openrail-m | ['text-to-image'] | false | sd-1-5-db-ai-creative-hub-hdbglv Dreambooth model trained by jaimexv with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/... | 3b27d309a31d95810076538787db9586 |
apache-2.0 | ['dino', 'vision'] | false | Vision Transformer (base-sized model, patch size 16) trained using DINO Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/2104.14294) by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou... | 9b535887210337a734ceb2ddd7b097fb |
apache-2.0 | ['dino', 'vision'] | false | Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which ar... | a3fdfdf20a90f105a9a40f25dfcca03c |
apache-2.0 | ['dino', 'vision'] | false | How to use Here is how to use this model: ```python from transformers import ViTFeatureExtractor, ViTModel from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = ViTFeatureExtractor.from_pretrai... | 29090dd0df489fb9014179e5e22a5716 |
apache-2.0 | ['dino', 'vision'] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2104-14294, author = {Mathilde Caron and Hugo Touvron and Ishan Misra and Herv{\'{e}} J{\'{e}}gou and Julien Mairal and Piotr Bojanowski and Armand Jo... | 76508085940e31ed11a7850fc5ab5423 |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-hi This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 2.4749 - Wer: 0.9420 | 4ce80ffee37781609a5ebf9bcb40fb51 |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - 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_sc... | 72bdd861ae89a7e01ed4021747da503f |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.8626 | 4.76 | 400 | 3.6151 | 1.0 | | 3.5463 | 9.52 | 800 | 3.5778 | 1.0 | | 3.4415 | 14.28 | 1200 | 3.4525 | 1.0 | |... | c5f1821393ccb56b3df5cf20da6c8666 |
mit | ['vision', 'image-captioning'] | false | GIT (GenerativeImage2Text), large-sized GIT (short for GenerativeImage2Text) model, large-sized version. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/micr... | 9d51b419bab247b7ad2eabab16b820dc |
mit | ['vision', 'image-captioning'] | false | Training data From the paper: > We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions (CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016), Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al.,... | 9e7e7c629719b70f157544ed0515a31d |
apache-2.0 | ['generated_from_trainer'] | false | fin1 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the fin dataset. It achieves the following results on the evaluation set: - Loss: 0.0778 - Precision: 0.8315 - Recall: 0.9243 - F1: 0.8755 - Accuracy: 0.9852 | c60ad89a067860d50b2eac79247ef409 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 129 | 0.0860 | 0.8535 | 0.9283 | 0.8893 | 0.9904 | | No log | 2.0 |... | 5efb020b3e1e00df2cc0823b9f6fa1f2 |
cc-by-4.0 | ['spanish', 'roberta'] | false | This is a **RoBERTa-base** model trained from scratch in Spanish. The training dataset is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (using a Gaussian function), discarding mo... | 8cd456d0c6a989943fe6e0ac89a508c4 |
cc-by-4.0 | ['spanish', 'roberta'] | false | Team members - Eduardo González ([edugp](https://huggingface.co/edugp)) - Javier de la Rosa ([versae](https://huggingface.co/versae)) - Manu Romero ([mrm8488](https://huggingface.co/)) - María Grandury ([mariagrandury](https://huggingface.co/)) - Pablo González de Prado ([Pablogps](https://huggingface.co/Pablogps)) -... | da24242c95bc314eb0165accdca01384 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab1 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7411 - Wer: 0.5600 | 340b4da6f81b2a4a5c966cd38ecd66a8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.0773 | 13.89 | 500 | 3.1073 | 1.0 | | 1.2444 | 27.78 | 1000 | 0.7411 | 0.5600 | | e372f30001761119e681be3c338275b6 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-finetuned-revision-booklet-chemistry This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4864 | a9ba8ad77593e1ba67ebd0073e8b5166 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 105 | 1.8484 | | No log | 2.0 | 210 | 1.6418 | | No log | 3.0 | 315 | 1.5820 | | No log | 4.0 | 420 | 1.4826 ... | faa77dec5bbdf445cb0ddec5d8122853 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_alarm-roberta-large-v1-5-50 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contra... | ec58f7ee456be0a1ffee3cf210fb3e5e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 48 - optimizer: Adafactor - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 6000 - num_epoc... | 023eb791afc65f7d3303d77d7a224e6f |
agpl-3.0 | ['token classification'] | false | Model description
This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of English scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). It was t... | 490cfc459e448a5b8d7ae882bd9128e5 |
agpl-3.0 | ['token classification'] | false | How to use
The intended use of this model is to infer the semantic role of gene products (genes and proteins) with regard to the causal hypotheses tested in experiments reported in scientific papers.
To have a quick check of the model:
```python
from transformers import pipeline, RobertaTokenizerFast, Rober... | ba17d6a1afe44c45f2d804b974169013 |
agpl-3.0 | ['token classification'] | false | Training procedure
The training was run on an NVIDIA DGX Station with 4XTesla V100 GPUs.
Training code is available at https://github.com/source-data/soda-roberta
- Model fine-tuned: EMBL/bio-lm
- Tokenizer vocab size: 50265
- Training data: EMBO/sd-nlp
- Dataset configuration: GENEPROD_ROLES
- Training w... | 5cd087a05d505cf8a2476156d590ccc8 |
agpl-3.0 | ['token classification'] | false | Eval results
On 7178 example of test set with `sklearn.metrics`:
```
precision recall f1-score support
CONTROLLED_VAR 0.81 0.86 0.83 7835
MEASURED_VAR 0.82 0.85 0.84 ... | 70162df73282db6d15bd64b0b488bd65 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion 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: - Loss: 0.2055 - Accuracy: 0.9355 - F1: 0.9354 | 02d83dc37f5bc449416feec2f1fc2021 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.1775 | 1.0 | 250 | 0.1765 | 0.929 | 0.9287 | | 0.1205 | 2.0 | 500 | 0.1516 | 0.9395 | 0.9393 | | 0.0981 |... | c09c9d9f8f618e3b7dbe04dd06795e7c |
mit | ['exbert'] | false | Overview **Language model:** deepset/roberta-base-squad2-distilled **Language:** English **Training data:** SQuAD 2.0 training set **Eval data:** SQuAD 2.0 dev set **Infrastructure**: 1x V100 GPU **Published**: Apr 21st, 2021 | e6733f912ea536d7b142aa1527bd0ed4 |
mit | ['exbert'] | false | About us  We bring NLP to the industry via open source! Our focus: Industry specific language models & large scale QA systems. Some of our work: - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - ... | 1f8f2f525629623e86df4fe5a469c5d4 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | article2KW_test1.3_barthez-orangesum-title_finetuned_for_summerization This model is a fine-tuned version of [moussaKam/barthez-orangesum-title](https://huggingface.co/moussaKam/barthez-orangesum-title) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1217 - Rouge1: 0.2933 - R... | 5ef213fd12c2200ca8e1ad1faee8eb9a |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 1.6383 | 1.0 | 1497 | 1.3027 | 0.2623 | 0.0709 | 0.2627 | 0.2625 | | 1.201 | 2.0 | 2994 ... | 7c97416ceb711fa99fff6d7fec62f3fc |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner-60percent 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: 0.5110 - Precision: 0.7917 - Recall: 0.8333 - F1: 0.8120 - Accuracy: 0.9154 | e0fe3024eb1c0e895958f6d2fe6a105a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 45 | 0.5094 | 0.7638 | 0.8303 | 0.7957 | 0.9078 | | No log | 2.0 |... | 6066b2f064f72b09496ef55a01954426 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for ccorgi trained by lewtun on the lewtun/corgi dataset. This is a Stable Diffusion model fine-tuned the ccorgi concept taught to Stable Diffusion with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of ccorgi dog** This model was created as part of the DreamBooth Hackathon... | 85dd18ebb464d2fcebccca1b61cec057 |
other | ['vision', 'image-segmentation'] | false | Mask2Former Mask2Former model trained on Cityscapes panoptic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresea... | 0e527b4974e94f1d3415e8e7dbbd097d |
other | ['vision', 'image-segmentation'] | false | load Mask2Former fine-tuned on Cityscapes panoptic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-tiny-cityscapes-panoptic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-tiny-cityscapes-panoptic") url = "http://images.cocodataset.org/va... | 6da76c1ea2db8ad32c4db76ea22accc1 |
apache-2.0 | [] | false | Model Description This is the ClimateBERT language model based on the FULL-SELECT sample selection strategy. *Note: We generally recommend choosing this language model over those based on the other sample selection strategies (unless you have good reasons not to). This is also the only language model we will update ... | 8022a352d62097bf81ba41e6e07dea5b |
apache-2.0 | [] | false | Climate performance model card | distilroberta-base-climate-f | | |--------------------------------------------------------------------------|----------------| | 1. Is the resulting model publicly available? | Yes | | 2. ... | f32825a971814b503985bc36f9793d4e |
apache-2.0 | [] | false | Citation Information ```bibtex @article{wkbl2021, title={ClimateBERT: A Pretrained Language Model for Climate-Related Text}, author={Webersinke, Nicolas and Kraus, Mathias and Bingler, Julia and Leippold, Markus}, journal={arXiv preprint arXiv:2110.12010}, year={2021} } ``` | 8d7004e822695f26b12089b237a8d704 |
mit | ['ælæctra', 'pytorch', 'danish', 'ELECTRA-Small', 'replaced token detection'] | false | Ælæctra - Finetuned for Named Entity Recognition on the [DaNE dataset](https://danlp.alexandra.dk/304bd159d5de/datasets/ddt.zip) (Hvingelby et al., 2020) by Malte Højmark-Bertelsen. **Ælæctra** is a Danish Transformer-based language model created to enhance the variety of Danish NLP resources with a more efficient mod... | bceb06ef892e6ed9d6367d24f8a3e0bf |
mit | ['ælæctra', 'pytorch', 'danish', 'ELECTRA-Small', 'replaced token detection'] | false | Evaluation of current Danish Language Models Ælæctra, Danish BERT (DaBERT) and multilingual BERT (mBERT) were evaluated: | Model | Layers | Hidden Size | Params | AVG NER micro-f1 (DaNE-testset) | Average Inference Time (Sec/Epoch) | Download | | --- | --- | --- | --- | --- | --- | --- | | Ælæctra Uncased | 12 | ... | 3cf2fdfbe27d80d4f87827d0af5b7f09 |
mit | ['ælæctra', 'pytorch', 'danish', 'ELECTRA-Small', 'replaced token detection'] | false | Pretraining To pretrain Ælæctra it is recommended to build a Docker Container from the [Dockerfile](https://github.com/MalteHB/Ælæctra/tree/master/notebooks/fine-tuning/). Next, simply follow the [pretraining notebooks](https://github.com/MalteHB/Ælæctra/tree/master/infrastructure/Dockerfile/) The pretraining was do... | 6d453357aae61228554c2eb3266058d0 |
mit | ['ælæctra', 'pytorch', 'danish', 'ELECTRA-Small', 'replaced token detection'] | false | References Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020). ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. ArXiv:2003.10555 [Cs]. http://arxiv.org/abs/2003.10555 Danish BERT. (2020). BotXO. https://github.com/botxo/nordic_bert (Original work published 2019) Devlin, J., Chan... | a2178346d258dcf4e9af8e9109748a45 |
mit | ['ælæctra', 'pytorch', 'danish', 'ELECTRA-Small', 'replaced token detection'] | false | Acknowledgements As the majority of this repository is build upon [the works](https://github.com/google-research/electra) by the team at Google who created ELECTRA, a HUGE thanks to them is in order. A Giga thanks also goes out to the incredible people who collected The Danish Gigaword Corpus (Strømberg-Derczynski e... | 636b438bbe52dbc6d65f0d2e9774af39 |
mit | ['ælæctra', 'pytorch', 'danish', 'ELECTRA-Small', 'replaced token detection'] | false | Contact For help or further information feel free to connect with the author Malte Højmark-Bertelsen on [hjb@kmd.dk](mailto:hjb@kmd.dk?subject=[GitHub]%20ÆlæctraUncasedNER) or any of the following platforms: [<img align="left" alt="MalteHB | Twitter" width="22px" src="https://cdn.jsdelivr.net/npm/simple-icons@v3/ico... | 8eab1519d6cf3ae175ea2c08f2df8060 |
apache-2.0 | ['generated_from_keras_callback'] | false | eduardopds/bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0269 - Validation Loss: 0.0545 - Epoch: 2 | 4a6cedf3294489ca0c249114b89b087d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1719 | 0.0627 | 0 | | 0.0457 | 0.0576 | 1 | | 0.0269 | 0.0545 | 2 | | 203831dc64489dbc1b10e6605400b4aa |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner 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: 0.0997 - Proteinmutation F1: 0.1309 - Snp F1: 0.1953 - Dnamutation F1: 0.3778 - Precision: 0.2380 - Recall: 0.2416 - ... | 77cc630a5d43df01567673956512d97b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Proteinmutation F1 | Snp F1 | Dnamutation F1 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------------------:|:------:|:--------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 ... | 523ea7277915e5da2f6c0bf8a28e7ff1 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 9d0f3b3e1be6650d38cc5008518f445308fe06d9 pip install -e . cd egs2/magicdata/asr1 ./run.sh --skip_data_prep false --skip_train t... | 22c7aa757e6ed14bf961c1f395e9505f |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Wed Sep 21 01:11:58 EDT 2022` - python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]` - espnet version: `espnet 202207` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `9d0f3b3e1be6650d38cc5008518f445308fe06d9` - Commit date: `Mon Sep 19 20:27:41 2022 -0400` | bc154efbbff9007c4e43887932af5666 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test|24279|24286|84.4|15.6|0.0|0.0|15.6|15.6| | 563dbd876dc1b8c086192a9ed1206bb4 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test|24279|243325|96.4|1.7|2.0|0.1|3.7|15.6| | 508cfce8ee3265125e80a5f39983e7f2 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_raw_zh_char_sp ngpu: 0 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_r... | 61ed60a6b89e55390a5ba9b8a2de4882 |
apache-2.0 | ['exbert'] | false | 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 ... | d5bd5f6d1fe16b304ec6b91548bc7941 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2896 | 1.0 | 318 | 3.2890 | 0.7432 | | 2.6284 | 2.0 | 636 | 1.8756 | 0.8377 | | 1.5483 | 3.0 | 954 | 1.1572 | 0.... | 1cb3420777d8d80f62f746e086924d7f |
openrail | [] | false | yy model 768ARB, fp32, EMA, finetuned on animefull **epoch48**: probably overfitted, good results overall **epoch28**: not overfitted: less stylized but mostly good anatomy tested on: - 768x1024 DPM++ SDE Karras, no hires fix - 512x768 Latent hires → 1024x1536 DPM++ SDE Karras token word: - `yingyi, best qualit... | ba88182818e3985d2ef36147720f48a7 |
apache-2.0 | ['summarization'] | false | Bert-small2Bert-small Summarization with 🤗EncoderDecoder Framework This model is a warm-started *BERT2BERT* ([small](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8)) model fine-tuned on the *CNN/Dailymail* summarization dataset. The model achieves a **17.37** ROUGE-2 score on *CNN/Dailymail*'s test datase... | f0890b1627f9ac482589cff14d77effe |
apache-2.0 | ['summarization'] | false | Model in Action 🚀 ```python from transformers import BertTokenizerFast, EncoderDecoderModel import torch device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') tokenizer = BertTokenizerFast.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization') model = EncoderDecoderMo... | 3a4d7e952e7a7849db237bdee3eac9a8 |
apache-2.0 | ['summarization'] | false | cut off at BERT max length 512 inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt") input_ids = inputs.input_ids.to(device) attention_mask = inputs.attention_mask.to(device) output = model.generate(input_ids, attention_mask=attention_mask) return ... | bff6a996a48252ac61de6e437afb55f5 |
mit | [] | false | Pretrained on 10k hours WenetSpeech L subset. More details in [TencentGameMate/chinese_speech_pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain) This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created an... | 1323a675730eb0a762de691e002dffbb |
mit | [] | false | model = Wav2Vec2ForPreTraining.from_pretrained(model_path) model = model.to(device) model = model.half() model.eval() wav, sr = sf.read(wav_path) input_values = feature_extractor(wav, return_tensors="pt").input_values input_values = input_values.half() input_values = input_values.to(device) | f5e34ad2b901ed6eb7f940b0aac16110 |
mit | [] | false | mask_time_indices = torch.tensor(mask_time_indices, device=input_values.device, dtype=torch.long) with torch.no_grad(): outputs = model(input_values) last_hidden_state = outputs.last_hidden_state | 8d5b846261955acaa8343785abee063f |
openrail | [] | false | Also on https://civitai.com/models/5301/elysium-kuro-anime Anime model is custom mix + finetune on dataset of high quality images (mix including Anything 4.0, WD 1.4 Booru, Seek Art Mega V1) and contains the contains the kl-f8-anime2 VAE from Waifu Diffusion. Example settings: Negative prompt: (lowres:1.1), (worst ... | d1bea46995c6e95c1251936c9aa52bbc |
apache-2.0 | [] | false | distilbert-base-lt-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy... | a27cc14109966eb1490d00b5fcaa062f |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-lt-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-lt-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github r... | c6305d0637c35570308754181f45ff49 |
mit | ['text-classification', 'pytorch', 'transformers'] | false | Multi2ConvAI-Corona: finetuned Bert for German
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Corona (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: German (de)
- model type: fi... | 129b2e5a6d7875696fd5820e8bf64dbc |
mit | ['text-classification', 'pytorch', 'transformers'] | false | Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-logistics-de-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-logistics-de-bert")
````
... | d11dd857a6a74a8d35f35631ec3eac8a |
apache-2.0 | ['image-to-text'] | false | Manga OCR Optical character recognition for Japanese text, with the main focus being Japanese manga. It uses [Vision Encoder Decoder](https://huggingface.co/docs/transformers/model_doc/visionencoderdecoder) framework. Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide ... | a25de6c6e421007619e71114621d9e7f |
apache-2.0 | ['generated_from_trainer'] | false | enron-spam-checker-10000 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: - Loss: 0.0512 - Accuracy: 0.9915 - F1: [0.99143577 0.99156328] | 86c949f796cdacdc6b5f80df0c79afa2 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-misogyny-en-it-hi-beng 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: 0.0140 - Accuracy: 0.9970 - F1: 0.9969 - Precision: 0.9937 - Recall: 1.0 - Mae: 0.00... | 6ea9cc170b9c8ba6ecab5b35eeb86100 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:| | 0.3131 | 1.0 | 1759 | 0.4655 | 0.7820 | 0.7682 | 0.7855 | 0.7516 | 0.21... | 42fc5e80c73ef6ff03c8d12f0bbdd13d |
gpl-3.0 | ['object-detection', 'computer-vision', 'yolov6', 'pypi'] | false | Model Description [YOLOv6:](https://arxiv.org/abs/2209.02976) A single-stage object detection framework dedicated to industrial applications. [YOLOv6 v3.0](https://arxiv.org/abs/2301.05586): A Full-Scale Reloading [YOLOv6-Pip: Packaged version of the Yolov6 repository](https://github.com/kadirnar/yolov6-pip/) [Pape... | 260c330d9fbbeed7ba727ca6a614f29a |
gpl-3.0 | ['object-detection', 'computer-vision', 'yolov6', 'pypi'] | false | Yolov6 Inference ```python from yolov6 import YOLOV6 model = YOLOV6(weights='kadirnar/yolov6s-v2.0', device='cuda:0', hf_model=True) model.classes = None model.conf = 0.25 model.iou = 0.45 model.show = False model.save = True pred = model.predict(source='data/images',yaml='data/coco.yaml', img_size=640) ``` | 843a568fef29c0ecf002a8f97fbf0169 |
gpl-3.0 | ['object-detection', 'computer-vision', 'yolov6', 'pypi'] | false | BibTeX Entry and Citation Info ``` @article{li2022yolov6, title={YOLOv6: A single-stage object detection framework for industrial applications}, author={Li, Chuyi and Li, Lulu and Jiang, Hongliang and Weng, Kaiheng and Geng, Yifei and Li, Liang and Ke, Zaidan and Li, Qingyuan and Cheng, Meng and Nie, Weiqiang and... | 822d7ca990e7cc7d363bdccab149ee76 |
apache-2.0 | ['translation'] | false | opus-mt-hr-fr * source languages: hr * target languages: fr * OPUS readme: [hr-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/hr-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | 1cfdc420a657fa4b8ab3a859b3760cc8 |
cc0-1.0 | [] | false | Stable Diffusion model trained for 5k steps on the art style "futurism". Invoke the style with "in the style of futtt". Play with weights, it's a strong style so prompt accordingly. Sample images:  on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - ID dataset. It achieves the following results on the evaluation set: - Loss: 0.2759 - Wer: 0.3256 | 830dccfa94c4a4af36777458b4e9bc58 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-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 - lr_scheduler_warmup_steps: 4000 - num_epochs: 100.0 - mixed_precisio... | bcf0e34a4ef1a083654dc5716914f921 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.0387 | 4.72 | 1000 | 3.0892 | 1.0 | | 1.7911 | 9.43 | 2000 | 0.8451 | 0.6702 | | 1.2826 | 14.15 | 3000 | 0.4211 | 0.416... | 5c60af87bda33ae449bc28a1685a12fe |
creativeml-openrail-m | [] | false | About this bad ass beast of a checkpoint: I merged a few checkpoints and got something buttery and amazing. Does great with things other then people too. It can do anything really. It doesn't need crazy prompts either. Keep it simple. No need for all the artist names and trending on whatever. | 435a7e397eea5915dae4df51b6ca8126 |
creativeml-openrail-m | [] | false | PRUNED AND SMALLER ckpt FILES! AS WELL AS DIFFUSERS! [Link here for diffusers and pruned](https://huggingface.co/johnslegers/hasdx) There are the download links below as well  Example Prompts: * female... | 67dcecc8f9496a15151681efcf6313da |
creativeml-openrail-m | [] | false | CKPT Here and diffuers [Download ckptSXDHAS.ckpt (7.7GB)](https://huggingface.co/BestJammer/HASDX/resolve/main/ckptSXDHAS.ckpt) [Download hasdx_emaonly.ckpt (4.27GB)](https://huggingface.co/johnslegers/hasdx/resolve/main/hasdx_emaonly.ckpt) [Download hasdx.ckpt (2.13GB)](https://huggingface.co/johnslegers/hasdx/res... | 757fb04a85e8e0d8a78682f244162c19 |
creativeml-openrail-m | [] | false | What I merged: https://civitai.com/models/1349/sxd-berrymix-merge https://civitai.com/models/2504/handas-3dkx10b https://civitai.com/models/3762/general-purpose-model The third one is a mystery because I cannot remember where I got it but it was called model.ckpt and I uploaded it myself because it was lost somew... | 65b1ed4d3df2136a67b4581fabf3d884 |
creativeml-openrail-m | [] | false | Not necessary at all but if you're feeling generous and want to help support my unhealthy amount of AI generating and future art endeavors: https://www.buymeacoffee.com/OnlyJams https://www.Only-Jams.redbubble.com | 8cf4c88278c9313e1b183fb2a84eb288 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 17089cb2cf5f1275132163f6327defbcc1b1bc1b pip install -e . cd egs2/swbd_sentiment/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2_2 ``` | f0f7540c2084ebf23afff00b8910a6d8 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_wav2vec2_2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_wav2vec2_2_raw_en_word ngpu: 1 seed: 2022 num_workers: 2 num_att_plot: 3 dist_backend: nccl dist... | 0cc3888c3349568500d4e6a59c473b90 |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'capitel', 'pos'] | false | Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to dat... | 23bab5eb4ab21f80c13d1dcc763ec268 |
apache-2.0 | ['generated_from_trainer'] | false | python-gpt2-large-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2286 | 4c4ebc6e57ebdcd9f5d4485b3441659d |
apache-2.0 | ['generated_from_trainer'] | false | 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: 16 | 9918aa4f2f6d619f844122aa4e67ae6a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.9843 | 1.0 | 1163 | 1.6715 | | 1.5713 | 2.0 | 2326 | 1.4301 | | 1.4226 | 3.0 | 3489 | 1.3808 | | 1.332 | 4.0 | 4652 | 1.3806 ... | 0ad3aca96fcb3686b9880accb581f87e |
cc-by-4.0 | ['hi', 'en', 'codemix'] | false | HingGPT-Devanagari HingGPT-Devanagari is a Hindi-English code-mixed GPT model trained on Devanagari text. It is a GPT2 model trained on L3Cube-HingCorpus. <br> [dataset link] (https://github.com/l3cube-pune/code-mixed-nlp) More details on the dataset, models, and baseline results can be found in our [paper] (https://... | 33e1dfb47613ea9dff117628a386fb43 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned_gender_classification 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: - Loss: 0.0000 - Accuracy: 1.0 | c4d30223ca1277d1e2a77ad7da9cbd1f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.003 | 1.0 | 4390 | 0.0000 | 1.0 | | 0.0069 | 2.0 | 8780 | 0.0000 | 1.0 | | 0.0014 | 3.0 | 13170 | 0.0000 ... | 23414c628639d957263816a695ffabee |
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