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 | mobilebert_sa_GLUE_Experiment_sst2_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4330 - Accuracy: 0.8005 | 2e72b614707a439e1dfc63775a9ed625 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5124 | 1.0 | 527 | 0.4330 | 0.8005 | | 0.2842 | 2.0 | 1054 | 0.4711 | 0.8028 | | 0.2267 | 3.0 | 1581 | 0.4593 | 0.... | 6c9e90d1aa1cb2ae4f2272c21bf9ec8d |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | finetuned-vit-doc-text-classifer This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the ernie-ai/image-text-examples-ar-cn-latin-notext dataset. It achieves the following results on the evaluation set: - Loss: 0.3107 - Accuracy: 0.903... | c78d80c56f2dcf3efd97307b50af7dbc |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2719 | 2.08 | 100 | 0.4120 | 0.8657 | | 0.1027 | 4.17 | 200 | 0.3907 | 0.8881 | | 0.0723 | 6.25 | 300 | 0.3107 | 0.... | 648bc0bae5403c49aebac6954cc580eb |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 3d74932a43ff1829d022439e2676c551 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-wnli-target-glue-cola This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-wnli](https://huggingface.co/muhtasham/tiny-mlm-glue-wnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7631 - Matthews Correlation: 0.0785 | a07106c1c0c746ae2b583e825f0a8e14 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6111 | 1.87 | 500 | 0.6217 | 0.0 | | 0.6032 | 3.73 | 1000 | 0.6187 | 0.0464 | | 0.5... | 5699a411eeb8d62f2cb1eb6c4aca35ef |
mit | ['luke', 'pytorch', 'transformers', 'jnli', 'natural-language-inference', 'NaturalLanguageInference'] | false | このモデルはluke-japanese-baseをファインチューニングして、JNLI(文章の関係性判別)に用いれるようにしたものです。 このモデルはluke-japanese-baseを yahoo japan/JGLUEのJNLI( https://github.com/yahoojapan/JGLUE ) を用いてファインチューニングしたものです。 文章の関係性（矛盾 contradiction, 中立 neutral, 含意 entailment）を計算するタスクに用いることができます。 | 7d591dad78fc7f7411a0bd750228037c |
mit | ['luke', 'pytorch', 'transformers', 'jnli', 'natural-language-inference', 'NaturalLanguageInference'] | false | This model is fine-tuned model for JNLI which is based on luke-japanese-base This model is fine-tuned by using yahoo japan JGLUE JNLI dataset. You could use this model for calculating natural language inference. | e62aac51f85868aa22fd62765cdb5cf8 |
mit | ['luke', 'pytorch', 'transformers', 'jnli', 'natural-language-inference', 'NaturalLanguageInference'] | false | How to use 使い方 transformers, sentencepieceをinstallして、以下のコードを実行することで、JNLI(文章の関係性判別)タスクを解かせることができます。 please execute this code. ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch tokenizer=AutoTokenizer.from_pretrained('Mizuiro-sakura/luke-japanese-base-finetuned-jnli') mod... | 9101eec18d5b1e7833bd6f2894683b55 |
mit | ['luke', 'pytorch', 'transformers', 'jnli', 'natural-language-inference', 'NaturalLanguageInference'] | false | what is Luke? Lukeとは？[1] LUKE (Language Understanding with Knowledge-based Embeddings) is a new pre-trained contextualized representation of words and entities based on transformer. LUKE treats words and entities in a given text as independent tokens, and outputs contextualized representations of them. LUKE adopts an ... | de6d783c43a3feba425c2552fcbe6481 |
mit | ['luke', 'pytorch', 'transformers', 'jnli', 'natural-language-inference', 'NaturalLanguageInference'] | false | Citation [1]@inproceedings{yamada2020luke, title={LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention}, author={Ikuya Yamada and Akari Asai and Hiroyuki Shindo and Hideaki Takeda and Yuji Matsumoto}, booktitle={EMNLP}, year={2020} } | 8efbc9d4f29fe5c26b4fed41aec5c800 |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | whisper-small-mn-3 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3277 - Wer: 30.3692 - Cer: 10.9030 | ee351a2c0df7954d419cafe56d0037ec |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.3408 | 0.61 | 1000 | 0.4062 | 47.6841 | 17.3811 | | 0.2261 | 1.22 | 2000 | 0.3262 | 37.8086 | 13.6466 | | 0.2135 ... | c93bf8310142da23f0b1cb4a589836b5 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | kawaiinimal icons (ノ◕ヮ◕)ノ*:・゚✧  for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](https://github.co... | 8ff4e3e72def4b4e6066bb46dba1f789 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (electronics) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://githu... | 2abc898a42fa29b9a09da74cc7f3e537 |
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", "lmqg/bart-large-subjqa-electronic... | 68f7ffe732c514b9e8aad1fb0581bbc6 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-large-subjqa-electronics-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json) | | Score | Type | Dataset ... | 89ebe7c0f21e4435d40979091494ef57 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: electronics - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: lmqg/bart-large-squad - max_length: 512 - max_length_output: 32 - epoc... | 19ab6da397e5be00075162ff45527296 |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_xlsr-53_s786 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech... | fa2a49d086f55bfeca8c832ec9a09f62 |
apache-2.0 | [] | false | How to use Here is how to use this model in PyTorch: ```python from transformers import BartTokenizer, BartModel tokenizer = BartTokenizer.from_pretrained('facebook/bart-base') model = BartModel.from_pretrained('facebook/bart-base') inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(*... | 74d290297ccb3859f8fadb54f69b91d7 |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | posneg This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the Positivo Negativo dataset. It achieves the following results on the evaluation set: - Loss: 0.3366 - Accuracy: 0.8692 | 320a3b16a1045ff58a201e8492f47f34 |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 61 | 0.3870 | 0.8692 | | No log | 2.0 | 122 | 0.3366 | 0.8692 | | No log | 3.0 | 183 | 0.4307 | 0.... | 3c8e3b921b33e394d76e184a66529fa7 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | novasessaodidicowe Dreambooth model trained by Murdokai 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-... | 84064c175f7c0d3ab297db81b12a35f5 |
apache-2.0 | ['Quality Estimation', 'monotransquest', 'DA'] | false | Using Pre-trained Models ```python import torch from transquest.algo.sentence_level.monotransquest.run_model import MonoTransQuestModel model = MonoTransQuestModel("xlmroberta", "TransQuest/monotransquest-da-ru_en-reddit_wikiquotes", num_labels=1, use_cuda=torch.cuda.is_available()) predictions, raw_outputs = model... | 3cb461495d5a286776c98338996b16c4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_sst2_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 1.0990 - Accuracy: 0.7741 | f6ea22ca5ae179d7724e3b59969b0fad |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6037 | 1.0 | 264 | 1.4539 | 0.5092 | | 1.0469 | 2.0 | 528 | 1.4535 | 0.6961 | | 0.6705 | 3.0 | 792 | 1.4747 | 0.... | a451b9dac4321baa13a2001fefbd238a |
creativeml-openrail-m | ['text-to-image'] | false | chltti style Dreambooth model trained by thewhiterider27 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-4 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/... | 5ba13b5889daab68aa7237c8c36986b8 |
creativeml-openrail-m | ['text-to-image'] | false | Magic Cube Dreambooth model trained by renee127 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/huggingface/notebooks... | 44324cc065d71db0ff3e3a94cf646a41 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Kinyarwanda This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 rw dataset. It achieves the following results on the evaluation set: - Loss: 0.6424 - Wer: 43.7524 | 25b1df632b11bda956913453bec6195f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 20 - eval_batch_size: 20 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 40 - total_eval_batch_size: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=... | cad96a293870417be5bd3e3e78970b63 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.7471 | 0.04 | 1000 | 0.9044 | 59.2903 | | 0.5987 | 0.08 | 2000 | 0.7523 | 52.0232 | | 0.5168 | 0.12 | 3000 | 0.6890 | 47.761... | 77b05ef836026d29ba6b3f464c592f30 |
apache-2.0 | ['translation'] | false | opus-mt-es-lua * source languages: es * target languages: lua * OPUS readme: [es-lua](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-lua/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | b77c9b917d209800503a9bb34269066e |
apache-2.0 | ['translation'] | false | hin-urd * source group: Hindi * target group: Urdu * OPUS readme: [hin-urd](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/hin-urd/README.md) * model: transformer-align * source language(s): hin * target language(s): urd * model: transformer-align * pre-processing: normalization + SentenceP... | 083183128d020adf1ee759f699820f30 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: hin-urd - source_languages: hin - target_languages: urd - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/hin-urd/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['hi', 'ur'] - src_constituents: {'hin'} - tgt_const... | 224e7bc316312be7550aa22a2a903c38 |
apache-2.0 | ['translation'] | false | opus-mt-tll-fr * source languages: tll * target languages: fr * OPUS readme: [tll-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tll-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | e8816ffc9decc275551cd65d50bacfc1 |
apache-2.0 | ['finnish', 'convbert'] | false | ConvBERT for Finnish Pretrained ConvBERT model on Finnish language using a replaced token detection (RTD) objective. ConvBERT was introduced in [this paper](https://arxiv.org/abs/2008.02496) and first released at [this page](https://github.com/yitu-opensource/ConvBert). **Note**: this model is the ConvBERT discrimin... | f79b76c94e81141b070c79d2373c9411 |
apache-2.0 | ['finnish', 'convbert'] | false | Model description Finnish ConvBERT is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to... | d8bad4758f2ed307d827c341e9e1ec4f |
apache-2.0 | ['finnish', 'convbert'] | 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 ConvBertTokenizer, ConvBertModel import torch tokenizer = ConvBertTokenizer.from_pretrained("Finnish-NLP/convbert-base-finnish") model = ConvBertModel.from_pretrained("Finnish-NLP/convbert-bas... | 553fbee239c71adce13f4c9308320f7e |
apache-2.0 | ['finnish', 'convbert'] | false | Training data This Finnish ConvBERT model was pretrained on the combination of five datasets: - [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset an... | 479f4463089bc985407b15e6826d9466 |
apache-2.0 | ['finnish', 'convbert'] | false | Preprocessing The texts are tokenized using WordPiece and a vocabulary size of 50265. The inputs are sequences of 512 consecutive tokens. Texts are not lower cased so this model is case-sensitive: it makes a difference between finnish and Finnish. | 56451d7135debd5e97db224895adc173 |
apache-2.0 | ['finnish', 'convbert'] | false | Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 1M steps. The optimizer used was a AdamW with learning rate 1e-4, learning rate warmup for 20000 steps and linear decay of the learning rate after. Training code was from the o... | 6692299dc8c59d2731ae8160f90be40a |
apache-2.0 | ['finnish', 'convbert'] | false | Evaluation results Evaluation was done by fine-tuning the model on downstream text classification task with two different labeled datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Yle News classification fine-tuning was done with two different sequ... | fd68feadb7cc27291c877cd546a5e303 |
cc-by-sa-4.0 | ['audio-to-audio', 'asteroid', 'audio', 'audio-source-separation'] | false | .X8pMBRNKjUI This model was trained by Manuel Pariente using the wham/DPRNN recipe in [Asteroid](https://github.com/asteroid-team/asteroid). It was trained on the sep_clean task of the WHAM! dataset. | 23ee404c54c0115b7a77e18cbafbb5d2 |
cc-by-sa-4.0 | ['audio-to-audio', 'asteroid', 'audio', 'audio-source-separation'] | false | Training config - data: - mode: min - nondefault_nsrc: None - sample_rate: 8000 - segment: 2.0 - task: sep_clean - train_dir: data/wav8k/min/tr - valid_dir: data/wav8k/min/cv - filterbank: - kernel_size: 16 - n_filters: 64 - stride: 8 - main_args: - exp_dir: exp/train_dprnn_ks16/ - help: None - masknet: ... | a736d165bc14089c9a67904cf8b948c2 |
cc-by-sa-4.0 | ['audio-to-audio', 'asteroid', 'audio', 'audio-source-separation'] | false | Results - `si_sdr`: 18.227683982688003 - `si_sdr_imp`: 18.22883576588251 - `sdr`: 18.617789605060587 - `sdr_imp`: 18.466745426438173 - `sir`: 29.22773720052717 - `sir_imp`: 29.07669302190474 - `sar`: 19.116352171914485 - `sar_imp`: -130.06009796503054 - `stoi`: 0.9722025377865715 - `stoi_imp`: 0.23415680987800583 | c2a0fbb1c8e877dbb6438755beee5e73 |
cc-by-sa-4.0 | ['audio-to-audio', 'asteroid', 'audio', 'audio-source-separation'] | false | Citing Asteroid ```BibTex @inproceedings{Pariente2020Asteroid, title={Asteroid: the {PyTorch}-based audio source separation toolkit for researchers}, author={Manuel Pariente and Samuele Cornell and Joris Cosentino and Sunit Sivasankaran and Efthymios Tzinis and Jens Heitkaemper and Michel Olvera a... | 9e730c0cd0ca509ff3f25400f88cea20 |
cc-by-sa-4.0 | ['translation', 'wmt20'] | false | Fairseq En-De NMT WMT20 MLQE This repository contains the English-German model trained with the [fairseq toolkit](https://github.com/pytorch/fairseq) that was used to produce translations used in the WMT20 shared task on quality estimation (QE) on the [MLQE dataset](https://github.com/facebookresearch/mlqe). The che... | 830bd45a958f230d3b33f04f0f96da58 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Multi-Scale Deep Super-Resolution System (MDSR) MDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/abs/1707.... | 5b90800cf195977f2be506a34e96eead |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Model description The MDSR is a model that uses both deeper and wider architecture (32 ResBlocks and 256 channels) to improve performance. It uses both global and local skip connections, and up-scaling is done at the end of the network. It doesn't use batch normalization layers (input and output have similar distribut... | 3b09e3dc0a18b3a01d138ab4caf2e913 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | How to use The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library: ```bash pip install super-image ``` Here is how to use a pre-trained model to upscale your image: ```python from super_image import MdsrModel, ImageLoader from PIL import Image import requests url = 'https://pa... | d4d41317e9589bf45c62e7026a93b356 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Pretraining The model was trained on GPU. The training code is provided below: ```python from super_image import Trainer, TrainingArguments, MdsrModel, MdsrConfig training_args = TrainingArguments( output_dir='./results', | 9fe4f6c1729e0f9bbe3d7a157543bbff |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Algorithm). Evaluation datasets include: - Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5) - Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14) - BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100) - Urban100 - [Huang et al... | 7399ce437ef41860591fe31a67338da3 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | BibTeX entry and citation info ```bibtex @misc{wang2021bam, title={BAM: A Lightweight and Efficient Balanced Attention Mechanism for Single Image Super Resolution}, author={Fanyi Wang and Haotian Hu and Cheng Shen}, year={2021}, eprint={2104.07566}, archivePrefix={arXiv}, primaryClass={eess.IV... | 3427a75910e867ff65ca6190c8ed8b24 |
mit | ['generated_from_trainer'] | false | deberta-v3-large-irony This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset. | e82e558f7cac90e10852280bc6778e3e |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-06 - train_batch_size: 16 - eval_batch_size: 16 - 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_sch... | c7a0656230f2f7f48bf051220a3830f0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6478 | 1.12 | 100 | 0.5890 | 0.7529 | | 0.5013 | 2.25 | 200 | 0.5873 | 0.7707 | | 0.388 | 3.37 | 300 | 0.6993 | 0.... | f85fb10e917dd6fdfe44c8c1c14a7aba |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Greek (el) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](https... | 6c527035d4831e0c7755a1ba4cc28653 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | DreamBooth model for the biriyani concept trained by ashiqabdulkhader on the ashiqabdulkhader/Biriyani dataset. This is a Stable Diffusion model fine-tuned on the biriyani concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of biriyani food** This model was created as part of the Dr... | 5c56867da100f5d71ecb32a7109bb511 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Yuunon V0.4C V0.4 use different kinds of approach so the models will not be overfit and easy to generate. Yuunon is a diffusion model with Dreambooth training that trained on artstyle artwork of artist, [Nagayama Yuunon](https://www.pixiv.net/users/149587). This model is based on [ACertainty](https://huggingface.co/... | 6ae88d562f414f8530c94b77b6566106 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [M... | ffc3826cafd91abc4b1956a56c4bda6d |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | ✍️ Future Plans / Todo for V1 * Self-collected and well-prepared class image instead of self generation, give oppotunities of training certain parts of images (eg: hands, feet, shoes). * May use higher resolution dataset for training (eg. 768^2). * Much proper epoches/learning speed/training steps of models. * Can tra... | f7ee4d5117f7ec9fc091f048d05cc855 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs... | 85139af7d4a9dd767bc70d05fa089028 |
apache-2.0 | ['generated_from_keras_callback'] | false | langtext 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: 0.8401 - Train End Logits Accuracy: 0.7623 - Train Start Logits Accuracy: 0.7233 - Validation Loss: 1.1... | 52040dc1c462201e0bf7e9d8964c791b |
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': 8298, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta... | d564c7ee9688f0f00899d6121d7f21ae |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 45af7ff497cf300d4f5e40228ee7215f |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab240 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: - eval_loss: 0.6367 - eval_wer: 0.5855 - eval_runtime: 20.4889 - eval_samples_per_second: 6.931 ... | 46a3bf5a12cedc80f473fa770dfc1464 |
apache-2.0 | ['minds14', 'google/xtreme_s', 'generated_from_trainer'] | false | xtreme_s_xlsr_t5lephone-small_minds14.en-all This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - MINDS14.ALL dataset. It achieves the following results on the evaluation set: - Loss: 0.5979 - F1: 0.8918 - Accuracy: 0.8921 | 161cfa95fd987770a8f8754e938e0b46 |
apache-2.0 | ['minds14', 'google/xtreme_s', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam wit... | 739422b7c98e05d03ef837d6af3cc8f1 |
apache-2.0 | ['minds14', 'google/xtreme_s', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:------:|:--------:| | 2.3561 | 2.98 | 200 | 2.5464 | 0.0681 | 0.1334 | | 1.1851 | 5.97 | 400 | 1.5056 | 0.5583 | 0.5861 | | 1.2805 ... | c929347536090533dc01285e78a9326e |
bsd-3-clause | [] | false | Model description CodeT5 is a family of encoder-decoder language models for code from the paper: [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://arxiv.org/pdf/2109.00859.pdf) by Yue Wang, Weishi Wang, Shafiq Joty, and Steven C.H. Hoi. The checkpoint... | 6a4c9cc89b4f68f19619c98d0ea439c4 |
bsd-3-clause | [] | false | Training data CodeT5-large was pretrained on [CodeSearchNet](https://arxiv.org/abs/1909.09436) data in six programming languages (Ruby/JavaScript/Go/Python/Java/PHP). See Section 4.1 of the [paper](https://arxiv.org/pdf/2207.01780.pdf) for more details. | b3b8aa1a7597fdbc71b1589c4e687f8b |
bsd-3-clause | [] | false | Evaluation results We validate the effectiveness of this checkpoint pretrained with simplified strategies on [CodeXGLUE](https://github.com/microsoft/CodeXGLUE) benchmark. See Appendix A.1 of the [paper](https://arxiv.org/pdf/2207.01780.pdf) for more details. | 9de1578b37111cfc5bbabfcbbb04091e |
bsd-3-clause | [] | false | How to use This model can be easily loaded using the `T5ForConditionalGeneration` functionality: ```python from transformers import AutoTokenizer, T5ForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained("Salesforce/codet5-large") model = T5ForConditionalGeneration.from_pretrained("Salesforce/codet5-larg... | 7623194d2eb5981ab9377a444936ed5d |
bsd-3-clause | [] | false | BibTeX entry and citation info ```bibtex @inproceedings{CodeT52021, author = {Yue Wang and Weishi Wang and Shafiq R. Joty and Steven C. H. Hoi}, title = {CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation}, booktitle = {EMNLP}, pages = {8696--8... | 4d7b6149c77b4c8638972ee441c639fa |
mit | ['paraphrasing', 'russian'] | false | This is a small Russian paraphraser based on the [google/mt5-small](https://huggingface.co/google/mt5-small) model. It has rather poor paraphrasing performance, but can be fine tuned for this or other tasks. This model was created by taking the [alenusch/mt5small-ruparaphraser](https://huggingface.co/alenusch/mt5sma... | 85396484c08614a97959571cc45ab054 |
mit | ['paraphrasing', 'russian'] | false | !pip install transformers sentencepiece import torch from transformers import T5ForConditionalGeneration, T5Tokenizer tokenizer = T5Tokenizer.from_pretrained("cointegrated/rut5-small") model = T5ForConditionalGeneration.from_pretrained("cointegrated/rut5-small") text = 'Ехал Грека через реку, видит Грека в реке рак.... | a340cd6367e172c04cb69de9661b983d |
mit | ['generated_from_trainer'] | false | recipe-roberta-lr2e05-wd0.02-bs32 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2782 - Rmse: 0.5274 - Mse: 0.2782 - Mae: 0.4286 | bca35e095403fcbb1c76456dc7e5b8c0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2781 | 1.0 | 623 | 0.2736 | 0.5231 | 0.2736 | 0.4122 | | 0.274 | 2.0 | 1246 | 0.2758 | 0.5251 | 0.2758 ... | 94a09bc00694a4bb43b9ce440c2a71ed |
apache-2.0 | ['es', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Wav2Vec2-xls-r-300m-36-tokens-with-lm-es <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.... | 934c166dfbdee07361d78be43073bb8e |
apache-2.0 | ['es', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:------:|:---------------:|:------:| | 3.6512 | 0.07 | 400 | 0.5734 | 0.4325 | | 0.4404 | 0.14 | 800 | 0.3329 | 0.3021 | | 0.3465 | 0.22 | 1200 | 0.3067 | ... | 69047ed5e1f2ad22813369f1a36955c9 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | klue-bert-finetuned-klue-ner This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.3741 - F1: 0.3930 | e86f0df6ab296a25301f322207a6fa69 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5313 | 1.0 | 876 | 0.5225 | 0.2331 | | 0.3884 | 2.0 | 1752 | 0.4197 | 0.3350 | | 0.3136 | 3.0 | 2628 | 0.3741 | 0.3930 | ... | e4a3e4386d25e39c8bc03d791f6d3781 |
apache-2.0 | ['generated_from_trainer'] | false | Article_50v7_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v7_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.6331 - Precision: 0.1648 - Recall: 0.0178 - F1: 0.0321 - Accuracy: 0.78... | 9288550672ee707214e75a3e95469302 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 12 | 0.7587 | 1.0 | 0.0005 | 0.0010 | 0.7783 | | No log | 2.0 |... | b8e637aae72117af8928837ed2062b41 |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e8 This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8063 - Ro... | ee7bc4203de216678a263ba244aee8bd |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 0.8651 | 53.3185 | 33.3722 | 35.8852 | 50.5929 | ... | 400197215021b3a9fd89c8dac1a259cd |
apache-2.0 | ['translation'] | false | gmw-eng * source group: West Germanic languages * target group: English * OPUS readme: [gmw-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmw-eng/README.md) * model: transformer * source language(s): afr ang_Latn deu enm_Latn frr fry gos gsw ksh ltz nds nld pdc sco stq swg yid * targe... | 62094583ca450ffb6edf1a1a20d9844a |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009-deueng.deu.eng | 27.2 | 0.538 | | news-test2008-deueng.deu.eng | 25.7 | 0.534 | | newstest2009-deueng.deu.eng | 25.1 | 0.530 | | newstest2010-deueng.deu.eng | 27.9 | 0.565 | | newstest2011-deueng.d... | 4870640bb06a138f66d5aab1fb439280 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: gmw-eng - source_languages: gmw - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmw-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['nl', 'en', 'lb', 'af', 'de', 'fy', 'yi', 'gmw'] - s... | d430225b4e59c71f2cdfc0e10716dbe3 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | openai/whisper-medium-nepali This model is a fine-tuned version of [shripadbhat/whisper-medium-hi](https://huggingface.co/shripadbhat/whisper-medium-hi) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.8578 - Wer: 34.1463 | d342b433ee8f65ae97868b988e6b985e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 4dfb64413f6076a182cd79f53fe88451 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0673 | 20.0 | 20 | 0.8578 | 34.1463 | | b45024fabff79e11b2fa7bf206dfea7d |
apache-2.0 | ['generated_from_trainer'] | false | trained_french This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.8493 - Wer: 1.0 | 3a581823ef41c6d017a420cd903c628d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 6 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 4a0a2f8d41914cc67c4fe804ba67220b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 6.2268 | 5.53 | 50 | 4.9813 | 1.0 | | 5.724 | 11.11 | 100 | 4.8808 | 1.0 | | 5.629 | 16.63 | 150 | 4.9001 | 1.0 | | 5.3351 ... | f5be9236e0bd390e4b565efa39f802dc |
apache-2.0 | [] | false | distilbert-base-es-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... | 8e312ea73380d80507041edbc38ec7cc |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-es-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-es-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github r... | 417073d60d28996396db39aa15852433 |
other | ['stable-diffusion', 'text-to-image', 'art'] | false | 【概要(Outline)】 コンセプトは<strong>「手や指の描写が上手い3Dイラスト」</strong>です。 <br> AIイラストは手や指の描写が下手なことが多く、せっかく良い構図のイラストが生成されても、手や指のせいで没にしなければならない時が多くありました。 <br> その問題を解消するため、私は現存するモデルを大量に試し、手や指の描写が上手いモデルをマージすることで、完成度の高いモデルを構築することに成功しました。 <br> <br> The concept is <strong>"3D illustration models that are good at drawing hands and fingers."... | 2eb6623ef9e12246c2c9320a816696e8 |
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