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_keras_callback'] | false | pedramyamini/distilbert-base-multilingual-cased-finetuned-mobile-banks-cafebazaar2lr-10epochs This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Lo... | c18411ff832382ea286a005fdfbd5089 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 4e-05, 'decay_steps': 26740, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'bet... | 865adbe0fd2ebd05d462e63f6458c138 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.7428 | 0.7046 | 0 | | 0.6810 | 0.6903 | 1 | | 0.6372 | 0.6907 | 2 | | 0.5881 | 0.6988 | 3 | | 0.5246 | 0.7630 | 4 | | 0.4511 |... | e9c620def3a37aeccdbfc7a01386c276 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-dataset_asr-demo-colab This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 295.0834 - Wer: 0.8282 | 690564b0c68bc42cfd937a1c65bb3d40 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - 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: 250 - num_epochs: 5 - mixed_precision_trai... | 90db65348096c4441cfb4f65966a1793 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5638.536 | 1.6 | 500 | 409.4785 | 0.8556 | | 2258.6455 | 3.19 | 1000 | 326.0520 | 0.8369 | | 1389.4919 | 4.79 | 1500 | 295.0834 | 0.8282 | ... | bd2f41604468aacf382282b9afd73380 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-mnli-target-glue-mnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-mnli](https://huggingface.co/muhtasham/tiny-mlm-glue-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8010 - Accuracy: 0.6426 | d94c22b8460f37d96f546ca27b68ea5a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0743 | 0.04 | 500 | 1.0281 | 0.4738 | | 1.0045 | 0.08 | 1000 | 0.9576 | 0.5522 | | 0.9565 | 0.12 | 1500 | 0.9155 ... | 6d5202ff65b77f4637d0a166920c05ef |
mit | ['generated_from_trainer'] | false | xlm-roberta-large-finetuned-lener_br This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan | ecb56c37717f299af2da325cfb368a1a |
mit | ['generated_from_trainer'] | false | 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: 5 | 005c2162cf51e95eed189c66815a97d8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.5002 | 1.0 | 8316 | nan | | 1.2398 | 2.0 | 16632 | nan | | 1.0864 | 3.0 | 24948 | nan | | 0.9896 | 4.0 | 33264 | nan ... | d8c434c710f34494190ef1015af9dc13 |
cc-by-4.0 | [] | false | DevAlBERT DevAlBERT is a Devanagari AlBERT model model trained on publicly available Hindi and Marathi monolingual datasets. [project link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>... | 1e65f8c24dbb7f85ffac9fb7308075a1 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Anything V2.1 Welcome to Anything V2.1 - a latent diffusion model for weebs. This model is intended to produce high-quality, highly detailed anime style with just a few prompts. Like other anime-style Stable Diffusion models, it also supports danbooru tags to generate images. e.g. **_1girl, white hair, golden eyes, ... | 9d342c2644001107ba3191473fee90af |
mit | ['generated_from_trainer'] | false | 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: 0.1637 - F1: 0.8581 | e6262e3dfaf8585cf9575d1e90681903 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.29 | 1.0 | 715 | 0.1885 | 0.8231 | | 0.1443 | 2.0 | 1430 | 0.1607 | 0.8479 | | 0.0937 | 3.0 | 2145 | 0.1637 | 0.8581 | ... | f1a77bcc1d59d953a74f52fe09564171 |
creativeml-openrail-m | ['a'] | false | 20 models that are trained over [hakurei's Waifu Diffusion](https://huggingface.co/hakurei/waifu-diffusion). Each model was trained on a notable Japanese AI artist's works using dreambooth, with 30 of their works gained mainly from twitter (except for aibanousagi, which I could find only 23). It tooks 1000 steps to ... | 08cd2ae4cd57d3228732d0ca61cd4fe1 |
creativeml-openrail-m | ['a'] | false | For those who are against generative AIs You can see that now they are the target. Take our tool. Now is the time for pay back. Generate the images in their styles, and bring back the power you had to yourself. | c5d4f878c96adaac350bdbfe4ddc9999 |
creativeml-openrail-m | ['a'] | false | For those who support the development of generative AIs Some of the AI artists, even though they take advantage of the open strategy of Stable Diffusion, now tend to hide their prompts, trying to monopolize their style (I'm not saying the AI artists I trained are as such, to be sure). To continue protecting our value... | 574169ead344b0e3b12c25dadaa94466 |
creativeml-openrail-m | ['a'] | false | trained artist list - 852wa - aibanousagi - aioeoekakino - airhara - alfredplpl - callimiya - citrus - elessenar - kiri - korocon - lakeside - maccha - natsuku - nikaido - plat - roiyaruRIZ - swingwings - tuinositone - yunyalula - yuyuyu | 7ab9fe62a26b1f914fc92ba155556408 |
creativeml-openrail-m | ['a'] | false | samples The basic prompt is as follows, but some of them may have additional postive tags (such as "in the style of") to get the result below (yes, use ``aitop (ARTIST)_style`` to gain the finetuned result). ``` POS: masterpiece, best quality, 1girl, aitop (ARTIST)_style NEG: nsfw, worst quality, low quality, medium q... | 7660bfeae65318fa916c5ffe1e0161e4 |
creativeml-openrail-m | ['a'] | false | natsuku     | fa41c0910e9949077c3c89871c4e86f4 |
mit | [] | false | Reddit NER for place names Fine-tuned `bert-base-uncased` for named entity recognition, trained using `wnut_17` with 498 additional comments from Reddit. This model is intended solely for place name extraction from social media text, other entities have therefore been removed. This model was created with two key goa... | 4ba878f99d4a9c65f2ed5e48873d6b9b |
mit | [] | false | Use in `transformers` ```python from transformers import pipeline generator = pipeline( task="ner", model="cjber/reddit-ner-place_names", tokenizer="cjber/reddit-ner-place_names", aggregation_strategy="first", ) out = generator("I live north of liverpool in Waterloo") ``` Out gives: ```python [{'e... | 7bae65112a587f4a3c1efc24113270d8 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Examples Sample images have been upscaled using RealESRGAN. ``` Prompt: realistic, masterpiece, highest quality, full body, looking at viewers, highres, indoors, detailed face and eyes, wolf ears, brown hair, short hair, silver eyes, necklace, sneakers, parka jacket, solo focus Negative: lowres, bad anatomy, bad hand... | 28666e01e89eb04cf701f3be611845b2 |
creativeml-openrail-m | ['Text-to-Image', 'stable-diffusion', 'lora'] | false | Stable Diffusion NAI lora Index **This repo is for indexing NovelAI related [LoRA](https://github.com/cloneofsimo/lora) works in huggingface.** **Preview the "good models" with no "explaination" in their repos using a benchmark.** **You may use CTRL+F to find keywords you are interested to quickly get the source.**... | 9c032576fe7f2191fad626e251c5e14f |
creativeml-openrail-m | ['Text-to-Image', 'stable-diffusion', 'lora'] | false | dranzerstar/SD-textual-inversion-embeddings-repo Link: https://huggingface.co/dranzerstar/SD-textual-inversion-embeddings-repo **models**: char-416-space, char-antonia-og, char-april, char-cms-gn, char-cms-og, char-florine, char-g41space, char-gronru, char-kyaru-gn, char-madoka, char-qu, char-siobhan, char-sop2anni,... | 128af1744cb4724ad6a69dab19841518 |
creativeml-openrail-m | ['Text-to-Image', 'stable-diffusion', 'lora'] | false | ikuseiso/Personal_Lora_collections Link: https://huggingface.co/ikuseiso/Personal_Lora_collections The owner starts to upload his model card, so I will only update the keywords in the future. Note that the owner claimed that the weight should be set to 0.6~0.8. So, I take 0.7 for all the previews below. **models*... | 387769817816de5b087da28d77ae7600 |
creativeml-openrail-m | ['Text-to-Image', 'stable-diffusion', 'lora'] | false | sylveriate/lora-characters Link: https://huggingface.co/sylveriate/lora-characters Actually I cannot generate good results from this repo, maybe the owner can teach me how to do it... **models**: abigail, emilico, hiroikikuri, kateshadow, pippa, selen, shondo-improved, shondo, shondo-improved, tenma ![1](sylveriat... | 84aa6855b8441bd6f7221177163334fa |
creativeml-openrail-m | ['Text-to-Image', 'stable-diffusion', 'lora'] | false | Link Only These owners are managing their model cards quite well. So, I will only copy the Links and summarize keywords. Link: https://huggingface.co/YoungMasterFromSect/Trauter_LoRAs **Genshin Impact** : Eula, Barbara, Diluc, Mona, Rosaria, Yae Miko, Raiden Shogun, Kujou Sara, Shenhe, Yelan, Jean, Lisa, Zhongli,... | e09d66c77a9b700ba4d5e32d6131dcfc |
creativeml-openrail-m | ['Text-to-Image', 'stable-diffusion', 'lora'] | false | DONOT KNOW THE SOURCE You can download the models in this repo, if you know the source, please contact me. I will change them to links. **qiqi** Trigger token is "qiqi", also "1girl", "solo" are recommended. ``` shell | 9cdce14f4ad3d63d1e95ef70884ce736 |
creativeml-openrail-m | ['Text-to-Image', 'stable-diffusion', 'lora'] | false | Sample prompts 1girl, qiqi, jiangshi, qing guanmao, coin hair ornament, ofuda, bead necklace, qiqi \(genshin impact\) ```  **klee** ``` shell "1girl, klee" "1girl, nsfw, klee, solo, completely nude, hat" "1girl, klee, solo, ahoge, hat feather, twintails, gloves, boots, bag, red dress, bangs, backpa... | 43de1b3566f187b2d55380eefaa6b152 |
creativeml-openrail-m | ['Text-to-Image', 'stable-diffusion', 'lora'] | false | negative prompt lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name, (worst quality, low quality, extra digits) ``` Link for the models: [AbyssOrangeMix2_hard](h... | f887324e7a088635e07085fd5a0677e1 |
mit | [] | false | 🇹🇷 Turkish ConvBERT model <p align="center"> <img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png"> </p> [](https://zenodo.org/badge/latestdoi/237817454) We ... | b5dc8b80a0bcbef8d11ecd8c973f845d |
mit | [] | false | Stats We've trained an (uncased) ConvBERT model on the recently released Turkish part of the [multiligual C4 (mC4) corpus](https://github.com/allenai/allennlp/discussions/5265) from the AI2 team. After filtering documents with a broken encoding, the training corpus has a size of 242GB resulting in 31,240,963,926 tok... | af92904e7805faddc203b2efd9ee2950 |
mit | [] | false | mC4 ConvBERT In addition to the ELEC**TR**A base model, we also trained an ConvBERT model on the Turkish part of the mC4 corpus. We use a sequence length of 512 over the full training time and train the model for 1M steps on a v3-32 TPU. | c9aee3fe4c145e006a79845d8836c0da |
mit | [] | false | Model usage All trained models can be used from the [DBMDZ](https://github.com/dbmdz) Hugging Face [model hub page](https://huggingface.co/dbmdz) using their model name. Example usage with 🤗/Transformers: ```python tokenizer = AutoTokenizer.from_pretrained("dbmdz/convbert-base-turkish-mc4-uncased") model = AutoMo... | 89d884e8f895efd6141af9dc678b1b08 |
apache-2.0 | ['MRC', 'SQuAD 1.1', 'xlm-roberta-large'] | false | Model description An XLM-RoBERTa reading comprehension model for [SQuAD 1.1](https://aclanthology.org/D16-1264/). The model is initialized with [xlm-roberta-large](https://huggingface.co/xlm-roberta-large/) and fine-tuned on the [SQuAD 1.1 train data](https://huggingface.co/datasets/squad). | 9597803c26e1195ba07f9d4ac22c6863 |
apache-2.0 | ['MRC', 'SQuAD 1.1', 'xlm-roberta-large'] | false | Intended uses & limitations You can use the raw model for the reading comprehension task. Biases associated with the pre-existing language model, xlm-roberta-large, that we used may be present in our fine-tuned model, squad-v1-xlm-roberta-large. This model is used for zero-shot decoding of [MLQA](https://huggingface.... | c2e24d8ad8b927fba99f2120a7296ed1 |
apache-2.0 | ['MRC', 'SQuAD 1.1', 'xlm-roberta-large'] | false | Usage You can use this model directly with the [PrimeQA](https://github.com/primeqa/primeqa) pipeline for reading comprehension [squad.ipynb](https://github.com/primeqa/primeqa/blob/main/notebooks/mrc/squad.ipynb). ```bibtex @article{2016arXiv160605250R, author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lo... | 556a804795720893b53a15f0fa9cf956 |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_vp-nl_s353 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 731fccaee5965df2000f4f018dfb6e96 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_rte_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6925 - Accuracy: 0.5271 | 692f7a9f9626449eee9cd4d8c0b0ce91 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6932 | 1.0 | 10 | 0.6928 | 0.5271 | | 0.6934 | 2.0 | 20 | 0.6927 | 0.5271 | | 0.6934 | 3.0 | 30 | 0.6932 | 0.... | 832592f9a59dc4811e478874e8c2898a |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-1-1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8289 - Bleu: 6.6959 - Gen Len: 45.7539 | 85c13cf8bab896f2ef73a3e8e1f89746 |
creativeml-openrail-m | [] | false | To use draw emphasis from the training model include the word `m_yukoring` in your prompt. Yukoring is an artists that does a lot of anime watercolor style art. License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAI... | 33c885fcdaac19c79ac6f286f52bd9d6 |
cc-by-4.0 | ['generated_from_trainer'] | false | electra-base-squad2-finetuned-squad-12-trainedfor-3 This model is a fine-tuned version of [deepset/electra-base-squad2](https://huggingface.co/deepset/electra-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3064 | d416953695be8e72f10f5f50449784cf |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.6128 | 1.0 | 578 | 0.3142 | | 0.4583 | 2.0 | 1156 | 0.3072 | | 0.415 | 3.0 | 1734 | 0.3064 | | a70d6e87577af730b6bf94aa34caddea |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Ar- Martha This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3837 - Wer: 51.1854 | 3ba1b557f9027e4989c817bbc6cf989c |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2726 | 0.42 | 1000 | 0.3837 | 51.1854 | | 2271e1aad1f0882c268e3948dd260f8e |
apache-2.0 | ['speech'] | false | SEW-D-tiny [SEW-D by ASAPP Research](https://github.com/asappresearch/sew) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speake... | 287ab6153341bf57a91044271322d1cc |
cc-by-sa-4.0 | ['korean', 'masked-lm'] | false | Model Description This is a RoBERTa model pre-trained on Korean texts, derived from [klue/roberta-base](https://huggingface.co/klue/roberta-base). Token-embeddings are enhanced to include all 한문 교육용 기초 한자 and 인명용 한자 characters. You can fine-tune `roberta-base-korean-hanja` for downstream tasks, such as [POS-tagging](... | cde062401b82281d56a5ef0d9036d431 |
cc-by-sa-4.0 | ['korean', 'masked-lm'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-korean-hanja") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-base-korean-hanja") ``` | 161d6c9ab7a2727c01921da6f429d414 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples 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: 0.3323 - Accuracy: 0.8733 - F1: 0.8797 | 03b01e634f3a27fc2dddd87f737dea81 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change over the course of training, we provi... | 0977ba9e7ca37b36604b20c10c28030d |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-1B has not been fine-tuned for downst... | 4a54f535f3a7ac5501e936ea76a0c085 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-1B to produce factually accurate output. This model was trained on... | 224a6dd67b6dc0795340f872b08c7d92 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Training data [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. Common... | 3d49b93d0dacb1402404f66bdcced59b |
mit | [] | false | Scratch project on Stable Diffusion This is the `<scratch-project>` 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. Yo... | 4dff6527a1fef37cac8c2b0b0fb6310e |
apache-2.0 | ['generated_from_keras_callback'] | false | Haakf/allsides_left_text_padded_overfit 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: 1.9591 - Validation Loss: 1.9856 - Epoch: 19 | 7aa4bfb01007c89591b3b64153a35d7a |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'Polynomia... | d3361b9d5e110a80eedf397cb2e1c991 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.0625 | 1.8988 | 0 | | 2.0063 | 1.9757 | 1 | | 2.0061 | 1.9345 | 2 | | 1.9730 | 1.9248 | 3 | | 1.9572 | 1.8433 | 4 | | 1.9645 |... | 3394aa27cef434fba46b9e4bba0fb559 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-base-squadshifts-vanilla-reddit-qg` This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: reddit) via [`lmqg`](... | 3f7fdc8e9e1b887b69b72d2a6d35ff24 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [facebook/bart-base](https://huggingface.co/facebook/bart-base) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (reddit) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github... | 670998f08b62b778b61030ccd7e636b3 |
cc-by-4.0 | ['question generation'] | false | 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-base-squadsh... | 2251fdcf3971e48c8c575353b511f392 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-base-squadshifts-vanilla-reddit-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.reddit.json) | | Score | Type | Dataset ... | b0d08ec822b6477c3b734596026000ce |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: reddit - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-base - max_length: 512 - max_length_output: 32 - epoch: ... | 7b0ff1c4450f37e182ad90f4994bb9b8 |
apache-2.0 | ['translation'] | false | opus-mt-pl-sv * source languages: pl * target languages: sv * OPUS readme: [pl-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pl-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://... | df0ac0ef53013ad7600c16b68d6f9e0c |
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 | 62 | 3.5089 | 0.1247 | 0.0333 | 0.1056 | 0.1055 | 19.0 | ... | eb9e6e5446b0fb6da1532e5719f05d61 |
mit | ['vision', 'image-segmentation'] | false | UperNet, Swin Transformer base-sized backbone UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al. Combining UperNet with a Swin Transformer backbo... | 91084d555f9bbd097c61135367b596c5 |
mit | ['vision', 'image-segmentation'] | false | Model description UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM). Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel. ![UperNet... | c45cd0b92ec24d87cc22dd1efc6ae7a3 |
mit | ['vision', 'image-segmentation'] | false | Intended uses & limitations You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for fine-tuned versions (with various backbones) on a task that interests you. | 8151ab9d906e52eecc1a0f63220dfb9d |
mit | [] | false | im-poppy on Stable Diffusion This is the `im-poppy` 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... | 5b6af619f7591463a3bb83086f102622 |
apache-2.0 | ['generated_from_keras_callback'] | false | kasrahabib/200-500-bucket-finetunned This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0280 - Validation Loss: 0.3784 - Epoch: 9 | cbe4b639487789bb2b802e8065bce240 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3110, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta... | 31d41af908aea6a36a174c4b36e30a3a |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.0739 | 0.6559 | 0 | | 0.4665 | 0.4309 | 1 | | 0.2473 | 0.3669 | 2 | | 0.1437 | 0.3746 | 3 | | 0.0825 | 0.3663 | 4 | | 0.0592 |... | f1495068c885c8d923969c64181932d2 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | cynthiasly Dreambooth model trained by WALIDALI 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-d... | dc1e9af6dfeb32af51cd3741d80cdeae |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-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: 0.1002 - Accuracy: 0.9406 | 8754fc2648896a443a42e3c1915dc82b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9039 | 1.0 | 318 | 0.5777 | 0.7335 | | 0.4486 | 2.0 | 636 | 0.2860 | 0.8768 | | 0.2528 | 3.0 | 954 | 0.1792 | 0.... | 3d09361436b049398d670df91b8edf47 |
apache-2.0 | ['generated_from_trainer'] | false | nmt-mpst-id-en-lr_1e-05-ep_20-seq_128_bs-32 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7787 - Bleu: 0.0338 - Meteor: 0.1312 | 11d714d2a3899da0f7e0ad049ed97d07 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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: 20 | 56ca3338c67467829b8cab973f91616d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | No log | 1.0 | 202 | 3.1965 | 0.0132 | 0.0696 | | No log | 2.0 | 404 | 3.0644 | 0.0224 | 0.0975 | | 3.5509 | 3.0 |... | 2573c14a530b7758654951ac6f64ee2c |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-wikitext2 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: 1.8347 | 59fa4e6131859f1fcbf74278f85b6fb1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0853 | 1.0 | 2406 | 1.9214 | | 1.986 | 2.0 | 4812 | 1.8799 | | 1.9568 | 3.0 | 7218 | 1.8202 | | 9978d7e61c0a01e8a64b8b6c9a469cd2 |
apache-2.0 | ['T5', 'chinese', 'sentencepiece'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言转换 NLT | 燃灯 Randeng | T5 | 57M | 中文-Chinese | | a4933ed71dc075e97b0cdf37d4100631 |
apache-2.0 | ['T5', 'chinese', 'sentencepiece'] | false | 模型信息 Model Information 对比T5-small，训练了它的中文版。为了更好适用于中文任务，我们仅使用BertTokenzier，和支持中英文的词表，并且使用了语料库自适应预训练(Corpus-Adaptive Pre-Training, CAPT)技术在悟道语料库(180G版本)继续预训练。预训练目标为破坏span。具体地，我们在预训练阶段中使用了[封神框架](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen)大概花费了8张A100约24小时。 Compared with T5-samll, we implement its Ch... | f54e0e3f47658f0856dba317a6ad536f |
apache-2.0 | ['T5', 'chinese', 'sentencepiece'] | false | 使用 Usage ```python from transformers import T5ForConditionalGeneration, BertTokenizer import torch tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Randeng-T5-Char-57M-Chinese', add_special_tokens=False) model=T5ForConditionalGeneration.from_pretrained('IDEA-CCNL/Randeng-T5-Char-57M-Chinese') ``` | c047a5f81bb1cb0b528dda64255ffebc |
apache-2.0 | ['generated_from_keras_callback'] | false | jo0hnd0e/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.9844 - Validation Loss: 3.3610 - Epoch: 7 | cb6a377111881919148621cbc209ff64 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 9.6302 | 4.2399 | 0 | | 5.7657 | 3.7191 | 1 | | 4.9972 | 3.5931 | 2 | | 4.6081 | 3.5038 | 3 | | 4.3425 | 3.4322 | 4 | | 4.1758 |... | a5de3e466571a7a1eb66dca0f756f0dc |
apache-2.0 | ['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107'] | false | Model description This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain. The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. The model can classify a speech utterance according to the language spoken. It covers 107 different la... | 35815b7c451ea4e00e2ec712e5a3ef69 |
apache-2.0 | ['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107'] | false | Download Thai language sample from Omniglot and cvert to suitable form signal = language_id.load_audio("https://omniglot.com/soundfiles/udhr/udhr_th.mp3") prediction = language_id.classify_batch(signal) print(prediction) (tensor([[0.3210, 0.3751, 0.3680, 0.3939, 0.4026, 0.3644, 0.3689, 0.3597, 0.3508, 0.... | c3850a5bdb2486a0e85deba387d49e07 |
apache-2.0 | ['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107'] | false | BibTeX entry and citation info ```bibtex @inproceedings{valk2021slt, title={{VoxLingua107}: a Dataset for Spoken Language Recognition}, author={J{\"o}rgen Valk and Tanel Alum{\"a}e}, booktitle={Proc. IEEE SLT Workshop}, year={2021}, } ``` | 5541786b2acc075f78c4f51e6ec4d644 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0447 - Accuracy: 0.9852 | f663a1fec4e14e461f9e082ef1a0b4e5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1547 | 0.99 | 147 | 0.0956 | 0.9711 | | 0.0707 | 1.99 | 294 | 0.0759 | 0.9733 | | 0.0537 | 2.99 | 441 | 0.0680 | 0.... | 116091501c702541f47e67fec005fb00 |
mit | ['generated_from_trainer'] | false | german-poetry-gpt2-large This model is a fine-tuned version of [benjamin/gerpt2-large](https://huggingface.co/benjamin/gerpt2-large) on German poems. It achieves the following results on the evaluation set: - eval_loss: 3.5753 - eval_runtime: 100.7173 - eval_samples_per_second: 51.6 - eval_steps_per_second: 25.805 - ... | af10c1c3a5fc0e16f1763344cf06e0cf |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - 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: 6 | 9e2e93fe4bbc770eb927fa2102954653 |
agpl-3.0 | ['roberta', 'icelandic', 'masked-lm', 'pytorch'] | false | IceBERT-xlmr-ic3 This model was trained with fairseq using the RoBERTa-base architecture. The model `xlm-roberta-base` was used as a starting point. It is one of many models we have trained for Icelandic, see the paper referenced below for further details. The training data used is shown in the table below. | Datase... | 8ad4efdee4f8bfb183c664c3c81eca55 |
agpl-3.0 | ['roberta', 'icelandic', 'masked-lm', 'pytorch'] | false | Citation The model is described in this paper [https://arxiv.org/abs/2201.05601](https://arxiv.org/abs/2201.05601). Please cite the paper if you make use of the model. ``` @article{DBLP:journals/corr/abs-2201-05601, author = {V{\'{e}}steinn Sn{\ae}bjarnarson and Haukur Barri S{\'{\i}}monarson and... | 01f739dd186a98a2bcae7a76bacee92c |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | Model Description This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-large-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-large-japanese-char-extended). Every short-unit-word is tagged by [UPOS](https://universaldependencies.org/u... | 161a347cb5dbe53f907529d234362495 |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | How to Use ```py import torch from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-large-japanese-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-large-japanese-upos") s="国境の長いトンネルを抜けると雪国であった。" p=[model.conf... | eb98fae221c8f469a668541b8613a3eb |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'text2text-generation'] | false | Model Details - **Model Description** <br /> - 음향 모델을 위한 N-gram Base의 LM으로 자소별 단어기반으로 만들어졌으며, KenLM으로 학습되었습니다. 해당 모델은 [ko-spelling-wav2vec2-conformer-del-1s](https://huggingface.co/42MARU/ko-spelling-wav2vec2-conformer-del-1s)과 사용하십시오. <br /> - HuggingFace Transformers Style로 불러와 사용할 수 있도록 처리했습니다. <br /> - pyctc... | 3182269fbfe38cbf5c6b16ad76ff93af |
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