license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 38 | 0.3227 | 0.1237 | 0.2397 | 0.1631 | 0.8566 | | No log | 2.0 |... | 057f90dea192816f683bcc071e077540 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Model-trained-with-me Dreambooth model trained by N00NE21483 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/... | 19338aad288c6b210d7677962977c947 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-sem This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the sem_eval2010_task8 dataset. It achieves the following results on the evaluation set: - Loss: 0.6704 - Accuracy: 0.8314 | b922a4eea76a1307182f6b8ee61ea13f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9556 | 1.0 | 800 | 0.7859 | 0.7814 | | 0.6136 | 2.0 | 1600 | 0.6069 | 0.8193 | | 0.4314 | 3.0 | 2400 | 0.6179 | 0.... | 779dd6f7aa04a4f625b806cbc3521a5e |
apache-2.0 | ['generated_from_trainer'] | false | distilbart-cnn-arxiv-pubmed-pubmed-v3-e8 This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed](https://huggingface.co/theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8422 - Rouge1: 54.9328 - Ro... | d6265fabe3a4e069d5de10514117d8de |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 1.1158 | 50.9754 | 30.9416 | 33.9908 | 48.4925 | ... | 31f2df0585fe872660b5fbf26593e726 |
mit | [] | false | XLM-RoBERTa (base) language-detection model (modern and medieval) This model is a fine-tuned version of xlm-roberta-base on the [monasterium.net](https://www.icar-us.eu/en/cooperation/online-portals/monasterium-net/) dataset. | 1a9335f389daf43a526460777d6d7baa |
mit | [] | false | Model description On the top of this XLM-RoBERTa transformer model is a classification head. Please refer this model together with to the [XLM-RoBERTa (base-sized model)](https://huggingface.co/xlm-roberta-base) card or the paper [Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al.](https://a... | 186b71c47e7b3894f865851414c26bd5 |
mit | [] | false | Intended uses & limitations You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 41 languages, modern and medieval: Modern: Bulgarian (bg), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Finnish (fi), Fre... | d93d54dbd55064eeab2723d20fe5af42 |
mit | [] | false | Training and evaluation data The model was fine-tuned using the Monasterium and Wikipedia datasets, which consist of text sequences in 41 languages. The training set contains 80k samples, while the validation and test sets contain 16k. The average accuracy on the test set is 99.59% (this matches the average macro/weig... | f8b41bf756fefc8e8c917bc58cefa4b3 |
mit | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 20 - eval_batch_size: 20 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 mixed_precision_training: Native AMP | fab3e48bce71240963a67644d8d38083 |
mit | [] | false | Training results | Training Loss | Validation Loss | F1 | ------------- | ------------- | -------- | | 0.000300 | 0.048985 | 0.991585 | | 0.000100 | 0.033340 | 0.994663 | | 0.000000 | 0.032938 | 0.995979 | | d2e3f6dc0e9dcfbdd67fb504eefdc25c |
mit | [] | false | Use pipeline classificator("clemens etc dilecto filio scolastico ecclesie wetflari ensi treveren dioc salutem etc significarunt nobis dilecti filii commendator et fratres hospitalis beate marie theotonicorum") ``` | 7bb30f977e87ac599286d3ff536573f2 |
mit | [] | false | Citation Please cite the following papers when using this model. ``` @misc{ercdidip2022, title={langdetect (Revision 0215f72)}, author={Kovács, Tamás, Atzenhofer-Baumgartner, Florian, Aoun, Sandy, Nicolaou, Anguelos, Luger, Daniel, Decker, Franziska, Lamminger, Florian and Vogeler, Georg}, year = { 2022... | d17526af8c6ab79bb91c0279d8329136 |
apache-2.0 | ['generated_from_trainer'] | false | bart-model2-1510-e4 This model is a fine-tuned version of [theojolliffe/bart-paraphrase-v4-e1-feedback](https://huggingface.co/theojolliffe/bart-paraphrase-v4-e1-feedback) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4317 - Rouge1: 66.2469 - Rouge2: 61.9187 - Rougel: 64.975 ... | bef358c970704360a2c96f56d6297271 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 409 | 0.4893 | 63.6678 | 55.1935 | 61.0167 | 62.2738 | 20... | 1fd815cd085b0a514d2ccf5add1418c6 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | retro3d Dreambooth model trained by abesmon with [Hugging Face Dreambooth Training Space](https://colab.research.google.com/drive/15cxJE2SBYJ0bZwoGzkdOSvqGtgz_Rvhk?usp=sharing) with the v2-1-512 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/drive/... | 6349405dfbec81d0c196e069f3a3ef63 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Trained with:  on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2080 - Exact Match: 0.6394 | 83fa4accb0ebdee3674d4ea1df4d6170 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | |:-------------:|:-----:|:----:|:---------------:|:-----------:| | 1.0516 | 6.65 | 200 | 0.1173 | 0.5875 | | 0.0541 | 13.33 | 400 | 0.1130 | 0.6331 | | 0.0468 | 19.98 | 600 | 0.1290 ... | 80d237377aa88d3f599d96a37ea7ad94 |
afl-3.0 | ['token-classification'] | false | This is a token-classification model. This model is AlephBert fine-tuned on detecting metaphors from Hebrew Piyutim model-index: - name: tokeron/alephbert-finetuned-metaphor-detection results: [] | 5c21e77a63c4bbd62c6b90137168f238 |
mit | ['generated_from_trainer'] | false | kobart_16_5.6e-5_datav2_min30_lp5.0_temperature1.0 This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7174 - Rouge1: 35.7621 - Rouge2: 12.8914 - Rougel: 23.6695 - Bleu1: 2... | d14497f0072a57ff8628d5977b68fe3d |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 16 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 | 198c2a4c89394815201618ae533c02e7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:------:|:-------:| | 1.9617 | 1.89 | 5000 | 2.6146 ... | 614c7a7b9b96c677e0a077f297abf68e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | 9a771b8ef80ea0d936ed526816dd0100 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'filter_threshold': 0.002361, 'is_split_by_sentences': True, 'skip_tokens': 1649999872}, 'generation': {'batch_size': 128, 'every_n_steps': 256, 'force_call... | 95262175cab0ec62e3d7f9f16134604e |
creativeml-openrail-m | ['art'] | false | Marsey Diffusion v1 Marsey Diffusion is a [Dreambooth](https://dreambooth.github.io/) model trained on Marsey emotes from rDrama.net. It is based on [Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5). | 9fa37e5f8cbc461c14fc72a7331a72fd |
apache-2.0 | ['translation'] | false | jpn-por * source group: Japanese * target group: Portuguese * OPUS readme: [jpn-por](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-por/README.md) * model: transformer-align * source language(s): jpn jpn_Hani jpn_Hira jpn_Kana jpn_Latn jpn_Yiii * target language(s): por por_Hira * model... | 9581143328b691743cb0224ff2375ef7 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: jpn-por - source_languages: jpn - target_languages: por - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-por/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ja', 'pt'] - src_constituents: {'jpn_Hang', 'jpn', ... | ad0da4d654216e2c15a179e28b938868 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7796 - Accuracy: 0.9161 | 2275c5262bbf92604e5b6bcda197d090 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - mixed_precision_training: Native AMP | 3b5e1c531443d292a0fc628c6792ca70 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2938 | 1.0 | 318 | 3.2905 | 0.7410 | | 2.6346 | 2.0 | 636 | 1.8833 | 0.8326 | | 1.5554 | 3.0 | 954 | 1.1650 | 0.... | d1d120af0649279c8486d6afa8a85024 |
openrail | ['Persian', 'TTS', 'Farsi', 'Coqui', 'CoquiTTS', 'pytorch', 'audio', 'text-to-speech'] | false | **persian-tts-male-vits** - persian-tts-male vits model for text to speech purposes. - Persian فارسی - Single-speaker male voice - finetuned **[persian-tts-female-vits](https://huggingface.co/Kamtera/persian-tts-female-vits)** model on **[persian-tts-dataset-male](https://www.kaggle.com/datasets/magnoliasis/persian-... | fe5d2634035b7bcde0a928868c239943 |
openrail | ['Persian', 'TTS', 'Farsi', 'Coqui', 'CoquiTTS', 'pytorch', 'audio', 'text-to-speech'] | false | python api: ```python from TTS.config import load_config from TTS.utils.manage import ModelManager from TTS.utils.synthesizer import Synthesizer config="config.json" model="best_model_91323.pth" model_path =model | d0720c597b947dccd1e4de440db54e91 |
openrail | ['Persian', 'TTS', 'Farsi', 'Coqui', 'CoquiTTS', 'pytorch', 'audio', 'text-to-speech'] | false | Absolute path to the model config.json text=".زندگی فقط یک بار است؛ از آن به خوبی استفاده کن" synthesizer = Synthesizer( model_path, config_path ) wavs = synthesizer.tts(text) synthesizer.save_wav(wavs, 'sp.wav') ``` Display audio: ```python import IPython IPython.display.Audio('sp.wav') ``` - **Hours used:*... | fa2cc8cb9782679359a5a40a3e7c858e |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_One_100v0_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one100v0_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.4700 - Precision: 0.1690 - Recall: 0.0899 - F1: 0.1173 - Accura... | 85268769037587f9001597757dba8f75 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 32 | 0.5975 | 0.1034 | 0.0015 | 0.0030 | 0.7790 | | No log | 2.0 |... | 9377d6130ceab6864ac613dbffe1b4dc |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr1_2e-05_all_27_02_2022-17_33_22 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: 0.4095 - Accuracy:... | 12283045a0c4cbf1221166d649bd0d77 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_11_binary_v1 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: 1.8389 - F1: 0.8057 | 53c824e007e76a0dc6658b862f8121d1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 288 | 0.4534 | 0.8011 | | 0.4027 | 2.0 | 576 | 0.4299 | 0.8121 | | 0.4027 | 3.0 | 864 | 0.4840 | 0.8142 | |... | b5834f339ffc10d9dbf292709438b0d2 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab7 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: 1.1687 - Wer: 0.6478 | ac085d8f2b26e1866fc525202ad62a0d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8409 | 7.04 | 500 | 3.1487 | 1.0 | | 2.6259 | 14.08 | 1000 | 1.5598 | 0.8730 | | 1.083 | 21.13 | 1500 | 1.0600 | 0.7347 | |... | 8a94620c30e15075d87df4aab9e14318 |
apache-2.0 | ['translation'] | false | jpn-hun * source group: Japanese * target group: Hungarian * OPUS readme: [jpn-hun](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-hun/README.md) * model: transformer-align * source language(s): jpn_Bopo jpn_Hani jpn_Hira jpn_Kana jpn_Yiii * target language(s): hun * model: transformer-... | 249078a6122c7a5f30182626b045a753 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: jpn-hun - source_languages: jpn - target_languages: hun - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-hun/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ja', 'hu'] - src_constituents: {'jpn_Hang', 'jpn', ... | c7aa156ed9dfb4060460b8b8c1cc2e70 |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'PlanTL-GOB-ES/MLDoc', 'text-classification'] | false | Model description The **roberta-base-bne-mldoc** is a text classification model for the Spanish language fine-tuned from the [roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained using the largest Spanish corpus known to da... | 0b1497248d0629e6c78a54c9cfa5dce8 |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'PlanTL-GOB-ES/MLDoc', 'text-classification'] | false | Intended uses and limitations **roberta-base-bne-mldoc** model can be used to classify texts into four hierarchical groups: CCAT (Corporate/Industrial), ECAT (Economics), GCAT (Government/Social) and MCAT (Markets). The model is limited by its training dataset (news stories) and may not generalize well for all use ca... | 7e50d5f3b4668c7f205b38d56d79484d |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'PlanTL-GOB-ES/MLDoc', 'text-classification'] | false | How to use Here is how to use this model: ```python from transformers import pipeline from pprint import pprint nlp = pipeline("text-classification", model="PlanTL-GOB-ES/roberta-base-bne-mldoc") example = ' FRANCFORT, 17 feb (Reuter) - La Bolsa de Francfort abrió la sesión de corros con baja por la caída del viern... | 7aeec0c7917d9fcc53d7f038fec26b00 |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'PlanTL-GOB-ES/MLDoc', 'text-classification'] | false | Training For training and evaluation we used the Spanish portion of the Multilingual Document Classification Corpus (MLDoc) [(Schwenk and Li, 2018)](http://www.lrec-conf.org/proceedings/lrec2018/pdf/658.pdf), a cross-lingual document classification dataset covering 8 languages. | 1cdeb2a60d8c7e26aff6db78d977cc82 |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'PlanTL-GOB-ES/MLDoc', 'text-classification'] | false | Training procedure The model was trained with a batch size of 32 and a learning rate of 1e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set. | c157a23b58109f86e6a88faa94f54e17 |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'PlanTL-GOB-ES/MLDoc', 'text-classification'] | false | Evaluation results We evaluated the *roberta-base-bne-mldoc* on the XNLI test set against standard multilingual and monolingual baselines: | Model | MLDoc (F1) | | ------------|:----| | roberta-base-bne | 96.64 | | roberta-large-bne | 97.02 | | BETO | **97.14** | | mBERT | 96.17 | | BERTIN | 96.6... | 8c3ca37d088fdc91dbc5403fb77e624e |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'PlanTL-GOB-ES/MLDoc', 'text-classification'] | false | Disclaimer <details> <summary>Click to expand</summary> The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parti... | aad3e2043cbdb60fded91adecd649fcb |
apache-2.0 | ['generated_from_trainer'] | false | byt5-small-finetuned-1epoch-batch16-opus_books-en-to-it This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on the opus_books dataset. It achieves the following results on the evaluation set: - Loss: 0.9848 | cc76ce50eb9120b903330dec6ba80dda |
mit | [] | false | model by chelunderscore This your the Stable Diffusion model fine-tuned the Gomber concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks toy** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.re... | f669ab0f89397122c7a4e6a97b6ea08a |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Update V2.5 has been updated for ease of use as anime-style model. I use this embedding for negative prompts. https://huggingface.co/datasets/gsdf/EasyNegative Share by-products V2.1…Feeling of use similar to V2.0 V2.2…NSFW model | 3933378c0c868591b56f35328e7a649b |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Counterfeit-V2.5 e.g.  ``` ((masterpiece,best quality)),1girl, solo, animal ears, rabbit, barefoot, knees up, dress, sitting, rabbit ears, short sleeves, looking at viewer, grass, short hair, smile, white hair, puffy sleeves... | 558acb1db70e8bf5502d6309c33e8931 |
cc-by-4.0 | ['question answering'] | false | Model Card of `lmqg/t5-base-tweetqa-qa` This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question answering task on the [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 1ebca4afd71bee323a18f003d210a92e |
cc-by-4.0 | ['question answering'] | false | Overview - **Language model:** [t5-base](https://huggingface.co/t5-base) - **Language:** en - **Training data:** [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-gen... | ac0d22356542f7549d26e2b473605a67 |
cc-by-4.0 | ['question answering'] | false | model prediction answers = model.answer_q(list_question="What is a person called is practicing heresy?", list_context=" Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy,... | e42aa73cea53e7d51bd84c18d7c70e45 |
cc-by-4.0 | ['question answering'] | false | Evaluation - ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/lmqg/t5-base-tweetqa-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_tweetqa.default.json) | | Score | Type | Dataset | |:... | bef073ebb73663d7db9e4d7f6835dda0 |
cc-by-4.0 | ['question answering'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_tweetqa - dataset_name: default - input_types: ['paragraph_question'] - output_types: ['answer'] - prefix_types: None - model: t5-base - max_length: 512 - max_length_output: 32 - epoch: 10 - batch: 3... | e3b0ae14d5fd1141dcb316a09ea0781f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 136 | 1.4226 | 21.9554 | 17.8089 | | 031da817ebcc04b3c29b47db57b60604 |
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 bf8c8f00194bdfed8ca388d8b20d14791b7d270e pip install -e . cd egs2/voxforge/asr1 ./run.sh --skip_data_prep false --skip_train tr... | fd8e67b127decd604cab41a1cd64745d |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Thu Dec 29 01:59:25 EST 2022` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.1` - Git hash: `bf8c8f00194bdfed8ca388d8b20d14791b7d270e` - Commit date: `Wed Dec 28 22:43:13 2022 -0500` | aa522cb50b006d7ddbd5940bba907537 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dt_it|1035|12587|70.2|24.6|5.2|3.3|33.1|94.7| |decode_asr_asr_model_valid.acc.ave/et_it|1103|13699|71.9|23.3|4.8|2.9|31.0|92.4| | 96d16cfae4e188859f6162750be9b54d |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dt_it|1035|75494|92.9|3.9|3.2|1.8|9.0|94.7| |decode_asr_asr_model_valid.acc.ave/et_it|1103|81228|93.6|3.6|2.8|1.7|8.1|92.4| | 8aaef8fe2d2c6afd3b8789662f5024fc |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_e15_linear1024.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_e15_linear1024_raw_it_char_normalize_confnorm_varsFalse ngpu: 1 seed: 0 num_workers: 4 num_a... | 120676286ccfe95105de485549392637 |
apache-2.0 | [] | false | LongT5 (transient-global attention, large-sized model) LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.co... | f2fbd8a6571f0233e34bd163473c6239 |
apache-2.0 | [] | false | Model description LongT5 model is an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting ([Pegasus-like generation pre-training](https://arxiv.org/pdf/1912.08777.pdf)). LongT5 model is an extension of [T5 model](https://arxiv.org/pdf/1910.10683.pdf), and it enables using one of the t... | bb4aaa2e4b2429bc0f6794b849bf20e2 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, LongT5Model tokenizer = AutoTokenizer.from_pretrained("google/long-t5-tglobal-large") model = LongT5Model.from_pretrained("google/long-t5-tglobal-large") inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) last_hidd... | 11808454d6492b7304c6cc7bdc9aec7c |
mit | [] | false | DenseNet201 model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef). To use this model in Julia, ... | b12631779568f39cd6f1aef9abb4add8 |
mit | [] | false | Fursona on Stable Diffusion This is the `<fursona-2>` 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 tra... | 291e369e34048760bd0175db7d54fd82 |
other | ['vision', 'semantic-segmentation', 'generated_from_trainer'] | false | segformer-b4-finetuned-segments-sidewalk This model is a fine-tuned version of [nvidia/mit-b4](https://huggingface.co/nvidia/mit-b4) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.6675 - Mean Iou: 0.4470 - Mean Accuracy: 0.5318 - Overall Accuracy: 0.8813 ... | 85d246a07e13a191499cc9f68ec50ae4 |
other | ['vision', 'semantic-segmentation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 | cfbc5ecd581b6618b992e35ce48e7ea0 |
other | ['vision', 'semantic-segmentation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou ... | 8e057e4848fe8e60f4cce855c5cf15f1 |
mit | ['generated_from_trainer'] | false | sentiment-5Epochs 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: - Loss: 0.4947 - Accuracy: 0.8719 - F1: 0.8685 - Precision: 0.8919 - Recall: 0.8463 | 5e8055bf23698213f4cccdaeff551806 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.3566 | 1.0 | 7088 | 0.3987 | 0.8627 | 0.8505 | 0.9336 | 0.7810 | | 0.3468 | 2.0 ... | 4f73b86db786cf0fd08c0c01fa3c7310 |
mit | [] | false | vraska on Stable Diffusion This is the `<vraska>` 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 y... | ba17fb20beb4e042b2b600e118488a05 |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-finnish This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - FI dataset. It achieves the following results on the evaluation set: - Loss: 0.2307 - Wer: 0.2984 | a946fe683cb73bddb007984324afd57a |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | 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: 500 - num_epochs: 70.0 - mixed_precision_... | fb04ba297a510a88508ff62aa27c5bcc |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.9032 | 4.39 | 500 | 2.8768 | 1.0 | | 1.5724 | 8.77 | 1000 | 0.5638 | 0.6438 | | 1.1818 | 13.16 | 1500 | 0.3338 | 0.4759 | |... | d77a2b73ba8781a224023192837278db |
mit | ['generated_from_trainer'] | false | lilt-xlm-roberta-base-finetuned-funsd-iob-original This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1573 - Precision: 0.7252 - Recall: 0.7718 - F1: 0.7478 ... | 5ed263cdea48cfaaf526eaa4d3e1a715 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.33 | 100 | 0.8309 | 0.5157 | 0.6673 | 0.5818 | 0.6594 | | No log | 2.67 |... | 6897a5883922c278a4b938dc9920abcb |
mit | [] | false | distilbert-base-fallacy-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [Logical Fallacy Dataset](https://github.com/causalNLP/logical-fallacy). | 6824f268260a55c853948c80e3c051d2 |
mit | [] | false | Model description The model is fine-tuned for text classification of logical fallacies. There are a total of 14 classes: ad hominem, ad populum, appeal to emotion, circular reasoning, equivocation, fallacy of credibility, fallacy of extension, fallacy of logic, fallacy of relevance, false causality, false dilemma, fa... | f3d5c5c9deac955a4139fc818319a544 |
mit | [] | false | Example Pipeline ```python from transformers import pipeline text = "We know that the earth is flat because it looks and feels flat." model_path = "q3fer/distilbert-base-fallacy-classification" pipe = pipeline("text-classification", model=model_path, tokenizer=model_path) pipe(text) ``` ``` [{'label': 'circular rea... | c3f2eee8bbbf78b11e2f770edd10a1d6 |
mit | [] | false | Full Classification Example ```python import torch from transformers import AutoTokenizer from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("q3fer/distilbert-base-fallacy-classification") tokenizer = AutoTokenizer.from_pretrained("q3fer/distilbert-... | ae9215819e94221e6ac8bb4b9393c0f2 |
mit | [] | false | Training and evaluation data The [Logical Fallacy Dataset](https://github.com/causalNLP/logical-fallacy) is used for training and evaluation. Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., ... Schölkopf, B. (2022). Logical Fallacy Detection. arXiv. https://doi.org/10.48550/arxiv.2202.13758 | c1502f4dbf9a85f3f157ecc612bdf96f |
mit | [] | false | Training procedure The following hyperparameters were used during fine-tuning: - learning_rate : 2e-5 - warmup steps : 0 - batch_size: 16 - num_epochs: 8 - batches_per_epoch: 122 - total_train_steps: 976 | 4794ba693bd353b2c6c69b648fd70f30 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_mrpc_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5935 - Accuracy: 0.7010 - F1: 0.8190 - Combined Score: 0.7600 | 4924ee2fe9349e42410778eeecfc0add |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6355 | 1.0 | 15 | 0.6261 | 0.6838 | 0.8122 | 0.7480 | | 0.6315 | 2.0 | 30 | 0.62... | 13438d54ea8389d71a69c679e8139da9 |
apache-2.0 | ['generated_from_trainer'] | false | eval_masked_v4_stsb This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.5253 - Pearson: 0.8844 - Spearmanr: 0.8809 - Combined Score: 0.8826 | 8c686afcba2274d9a28c8581093d3412 |
apache-2.0 | ['generated_from_keras_callback'] | false | mrafida/distilbert-base-uncased-finetuned-cola 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: 0.1849 - Validation Loss: 0.5355 - Train Matthews Correlation: 0.... | 78e20ef9fb4b466308cbc0110dc3ed26 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5120 | 0.4736 | 0.4411 | 0 | | 0.3247 | 0.4741 | 0.4620 | 1 | | 0.1849 | 0.5355... | 82d3f0be97f21892853787d6a2291469 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-autoeval-test This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0332 - Accuracy: 0.9911 | ba5eabb9158a7f7172cd8f17d969e379 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2671 | 0.99 | 33 | 0.0934 | 0.9622 | | 0.1767 | 1.99 | 66 | 0.0543 | 0.9844 | | 0.1414 | 2.99 | 99 | 0.0332 | 0.... | ed2b7296f9609f07f258aca508ef8a31 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'whisper-event'] | false | <style> img { display: inline; } </style>    | 40ba1bdcd84e06b05fd3c21a93c0abee |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'whisper-event'] | false | Fine-tuned whisper-small model for ASR in French This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small), trained on the mozilla-foundation/common_voice_11_0 fr dataset. When using the model make sure that your speech input is also sampled at 16Khz. **This model also ... | 643c69effe9c4512191bb3fed366a140 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'whisper-event'] | false | Load model model = AutoModelForSpeechSeq2Seq.from_pretrained("bofenghuang/whisper-small-cv11-french").to(device) processor = AutoProcessor.from_pretrained("bofenghuang/whisper-small-cv11-french", language="french", task="transcribe") | 39b53e37f504f041beac9d8372832b5d |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | kk1.1 Dreambooth model trained by ukeeba with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusio... | 1de625ac88fe0e3d983f982bf541105c |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-colab 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: 0.4770 - Wer: 0.3360 | eedcae82d58581abb56e868db706c48a |
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