license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
other | ['stable-diffusion', 'text-to-image', 'art'] | false | 【モデル紹介とマージ素材(Models introduction and merged materials)】 <strong>*■AB4.5-v1.0*</strong> <br> ・anything-v4.5 <br> ・Basil_mix <br> →リアルな質感の人物描写が特徴的です。 <br> (The feature is realistic texture character.) <br> <br> <strong>*■AC0.2-v1.0*</strong> <br> ・anything-v4.5 <br> ・Counterfeit-V2.5 <br> →服装と背景の繊細な描き込みが特徴的です。 <br> (Th... | 014880f869105933285f05916ce254c6 |
other | ['stable-diffusion', 'text-to-image', 'art'] | false | 【作例(Examples)】 Positive:one girl, <br> <br> Negative:(worst quality, low quality:1.2), <br> <br> <strong>*■AB4.5-v1.0*</strong> <img src="https://imgur.com/u0mjPNX.png" width="1152" height="768"> <br> <strong>*■AC0.2-v1.0*</strong> <img src="https://imgur.com/nwzGYK3.png" width="1152" height="768"> <br> <br> Positive... | f8c447b2229cd169bcb190e71bbd99df |
mit | ['generated_from_keras_callback'] | false | deepiit98/Materialism-clustered This model is a fine-tuned version of [nandysoham16/7-clustered_aug](https://huggingface.co/nandysoham16/7-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0301 - Train End Logits Accuracy: 1.0 - Train Start Logits Accuracy:... | 95fcffe57c0d215711f889737e4a8b6d |
mit | ['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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | c5e15a72b98d63bd5241924f4d996751 |
apache-2.0 | ['Image Captioning'] | false | Model Description These are model weights originally provided by the authors of the paper [Text-Only Training for Image Captioning using Noise-Injected CLIP](https://arxiv.org/pdf/2211.00575.pdf). Their method aims to train CLIP with only text samples. Therefore they are injecting zero-mean Gaussian Noise into the t... | b3e630b752195e914cbd6ee874510fae |
creativeml-openrail-m | ['stable-diffusion'] | false | Description > Vestia Zeta (ベスティア・ゼータ) is a female Indonesian Virtual YouTuber associated with hololive, > debuting as part of its Indonesian (ID) branch third generation of VTubers alongside Kaela Kovalskia and Kobo Kanaeru. > ([Fandom](https://virtualyoutuber.fandom.com/wiki/Vestia_Zeta)) | c2f43094b85d6fa4a19f0fcda7bacd8d |
creativeml-openrail-m | ['stable-diffusion'] | false | Preview > **Model:** [anything-v4.5-pruned.ckpt](https://huggingface.co/andite/anything-v4.0/tree/main)\ > **Model VAE:** [anything-v4.0.vae.pt](https://huggingface.co/andite/anything-v4.0/tree/main)\ > **Prompt:** TI-EMB_vestia-zeta\ > **Negative Prompt:** obese, (ugly:1.3), (duplicate:1.3), (morbid), (mutilated), ou... | 37d8d47d9bba59b667295b26235ec82c |
gpl-3.0 | ['spacy', 'token-classification'] | false | pl_core_news_sm Polish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), tagger, senter, ner. | Feature | Description | | --- | --- | | **Name** | `pl_core_news_sm` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`,... | 309c93e8166c13bc8b1b8c2e9034119b |
gpl-3.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (1726 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `Animacy=Hum\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `AdpType=Prep\|POS=ADP\|Variant=Short`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|C... | 2ac5a4b30f07e7258e80a3cc978f5c0f |
gpl-3.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.98 | | `TOKEN_P` | 99.63 | | `TOKEN_R` | 99.83 | | `TOKEN_F` | 99.73 | | `POS_ACC` | 97.06 | | `MORPH_ACC` | 87.97 | | `MORPH_MICRO_P` | 93.82 | | `MORPH_MICRO_R` | 93.59 | | `MORPH_MICRO_F` | 93.70 | | `SENTS_P` | 96.16 | | `SENTS_R` | 95.98 | | `SENTS_F` | ... | 58c99960f16d701cb37faeef7fbf13d3 |
apache-2.0 | ['NER'] | false | Model description **mbert-base-uncased-ner-pcm** is a model based on the fine-tuned Multilingual BERT base uncased model, previously fine-tuned for Named Entity Recognition using 10 high-resourced languages. It has been trained to recognize four types of entities: - dates & time (DATE) - Location (LOC) - Organization... | fda8e41bc330dc18ec812f6f151785d7 |
apache-2.0 | ['NER'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("arnolfokam/mbert-base-uncased-ner-pcm") model = AutoModelForTokenClassification.from_pretrained("arnolfokam/mbert-base-uncased-ner-pcm") nlp = pipeline(... | 26859f89aefef79d33c840a37cef6911 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | MultiBERTs Seed 1 Checkpoint 1800k (uncased) Seed 1 intermediate checkpoint 1800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/g... | eb197892450b8769c8e799638c0296ed |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-1-1800k') model = BertModel.from_pretrained("multiberts-seed-1-1800k") text = "Replace me by any text you'd lik... | 56749f3836465d354b70638e64c8f62a |
cc-by-4.0 | [] | false | Model description This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of Catalan-English datasets, up to 11 million sentences. Additionally, the model is evaluated on several public datasecomprising 5 different domains (general, adminstrative, t... | 595ba37b3d824a91bbfc746a99dc1ddc |
cc-by-4.0 | [] | false | Usage Required libraries: ```bash pip install ctranslate2 pyonmttok ``` Translate a sentence using python ```python import ctranslate2 import pyonmttok from huggingface_hub import snapshot_download model_dir = snapshot_download(repo_id="projecte-aina/mt-aina-ca-en", revision="main") tokenizer=pyonmttok.Tokenizer(m... | ba0a24a66b91116fad2b181849337c43 |
cc-by-4.0 | [] | false | Training data The model was trained on a combination of the following datasets: | Dataset | Sentences | |--------------------|----------------| | Global Voices | 21.342 | | Memories Lluires | 1.173.055 | | Wikimatrix | 1.205.908 | | TED Talks | 50.979 ... | 85f81a75cc11d2970d5afe1c555cee38 |
cc-by-4.0 | [] | false | Hyperparameters The model is based on the Transformer-XLarge proposed by [Subramanian et al.](https://aclanthology.org/2021.wmt-1.18.pdf) The following hyperparamenters were set on the Fairseq toolkit: | Hyperparameter | Value | |------------------------------------|--... | 99cc5b8400ee6015909fcedf546fb24f |
cc-by-4.0 | [] | false | .Y33-_tLMIW0), [Cybersecurity](https://elrc-share.eu/repository/browse/cyber-mt-test-set/2bd93faab98c11ec9c1a00155d026706b96a490ed3e140f0a29a80a08c46e91e/), [wmt19 biomedical test set](), [wmt13 news test set](https://elrc-share.eu/repository/browse/catalan-wmt2013-machine-translation-shared-task-test-set/84a96139b9861... | cf60a0146f3c684d9ed81c9f1c9cf814 |
cc-by-4.0 | [] | false | Evaluation results Below are the evaluation results on the machine translation from Catalan to English compared to [Softcatalà](https://www.softcatala.org/) and [Google Translate](https://translate.google.es/?hl=es): | Test set | SoftCatalà | Google Translate | mt-aina-ca-en | |----------------------|--... | ff57df078b8696de6dab2e5abafc1536 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9.24e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | a0e5a8b87376750891bed3dedbd8a981 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad 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: 1.6230 | fda0fa34c49d0218a5c5768363633e59 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.9098 | 1.0 | 554 | 1.8512 | | 1.6186 | 2.0 | 1108 | 1.6220 | | 1.3034 | 3.0 | 1662 | 1.6230 | | b1dd28f823001150579bd2381416fbef |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [lvwerra/distilbert-imdb](https://huggingface.co/lvwerra/distilbert-imdb) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3227 - Accuracy: 0.8933 - F1: 0.8994 | b65b92dd46f64f9af79039e2f14e5930 |
apache-2.0 | ['translation'] | false | opus-mt-en-fj * source languages: en * target languages: fj * OPUS readme: [en-fj](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-fj/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 4cda7143bad0bef9d9d0bf65df22b919 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-fr3 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 1.3274 - Bleu: 45.6906 | d2b3b87db5734454e9449dc378e01aaf |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1620 | 3e95b304016fd54d93c7b7016026e3d3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2256 | 1.0 | 5533 | 1.1620 | | 0.9551 | 2.0 | 11066 | 1.1237 | | 0.7726 | 3.0 | 16599 | 1.1620 | | d3e8da1a9112f49ae935b7e5326f31e8 |
creativeml-openrail-m | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> Multilingual version of the model: [uaritm/psychology_test]("uaritm/psy... | 4563a749d6e55a5a78b038b3e4cfa898 |
creativeml-openrail-m | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | e3818c3008bac17ec971fc729a2f1239 |
creativeml-openrail-m | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 120 with parameters: ``` {'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losse... | 3656e3358dabf96c0557dd92a867e630 |
apache-2.0 | ['en-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Medium En This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Radio dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.6118 - Wer: 30.9719 | 34666e6093a42583cb0bc3321a0c7a68 |
apache-2.0 | ['en-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1600 - mixed_precisi... | 0e5fef2237c5e5a87a85d63e675df4e0 |
apache-2.0 | ['en-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 3.6505 | 0.75 | 100 | 3.5819 | 58.0618 | | 2.7405 | 1.5 | 200 | 2.5030 | 47.7471 | | 1.6934 | 2.26 | 300 | 1.6058 | 36.434... | 6ed076556bf53cc849374fb655296b90 |
apache-2.0 | ['generated_from_trainer'] | false | bertbasecasedfinancialphrasebank This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5046 - Accuracy: 0.8660 | 2ff572d898dcff1758663cf75b99edb4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9921 | 0.04 | 5 | 0.9266 | 0.6082 | | 0.8989 | 0.08 | 10 | 0.8833 | 0.6082 | | 0.8563 | 0.12 | 15 | 0.8287 | 0.... | 23ec1b71c1befa9cbfc3e400459b2208 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-300m-nyanja-test_v1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 0.4496 - Cer: 0.0940 | 4528a08e64d9330451bf33d8ee8e18da |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_schedu... | 2f8e9344b07681cdde8fc8ae2904fe06 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 2.9918 | 0.62 | 400 | inf | 1.0 | 1.0 | | 2.6572 | 1.24 | 800 | inf | 0.9958 | 0.4380 | | 1.2544 | 1.86 |... | 6bfef8e08d03697f06ba00a47474fb3f |
gpl-3.0 | ['pytorch', 'lm-head', 'bert', 'zh'] | false | Usage * Using our model in your script ```python from transformers import ( AutoTokenizer, AutoModel, ) tokenizer = AutoTokenizer.from_pretrained("ckiplab/bert-base-han-chinese") model = AutoModel.from_pretrained("ckiplab/bert-base-han-chinese") ``` * Using our model for inferenc... | 2c48a2bf5270fc5046312dd45f72b9b0 |
apache-2.0 | [] | false | distilbert-base-en-nl-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 accur... | b1c7e3e68aff887e479755a682a438f5 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-nl-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-nl-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Gi... | eedf76daefff9a48dcc039c3a086595e |
apache-2.0 | ['speech'] | false | SEW-tiny-pt This is a pretrained version of [SEW tiny by ASAPP Research](https://github.com/asappresearch/sew) trained over Brazilian Portuguese audio. 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 ... | a5b26f45815b871f820b71345d162eda |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora'] | false | LoRA DreamBooth - simbatheog These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "simbatheog" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in th... | 41b1ceda6804f14d8cf9d937d1cb87ed |
mit | [] | false | SculptDiffusion is a custom diffusion model trained by @jags111. It can be used to create wonderful sculpture style outputs as it is trained on a variety of real world sculptures and 3d objects in a variety of materials and textures . To use it you can use "sculptdiffusion" as a selection in the DD version. If you c... | fbb8363a10f96d19719c17bd3caf17e0 |
mit | [] | false | sculptdiffusion Or you can join the <a href="https://discord.gg/vNVqT82W" alt="Neuralism Discord"> Neuralism Discord </a>and share your work . Provide your experiences and explorations. Find more custom diffusion models in progress. Join us in Patreon and extend support.<a href="https://www.patreon.com/jags111">... | 82422226f94616ae8ce03a7ded4e6b64 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0638 - Precision: 0.9308 - Recall: 0.9502 - F1: 0.9404 - Accuracy: 0.9860 | 3c0bc627bbf56baf995056fd98d356e6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0867 | 1.0 | 1756 | 0.0695 | 0.9266 | 0.9416 | 0.9341 | 0.9829 | | 0.0338 | 2.0 |... | ec70b695f10dbe64431f929f8cded394 |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `lmqg/t5-large-tweetqa-qag` This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question & answer pair generation task on the [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-g... | 89a3acd62a41d5544d868acc37a63770 |
cc-by-4.0 | ['questions and answers generation'] | false | Overview - **Language model:** [t5-large](https://huggingface.co/t5-large) - **Language:** en - **Training data:** [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question... | 20889d6c6803ca015555f068b1adad59 |
cc-by-4.0 | ['questions and answers generation'] | false | model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-large-tweetqa-qag") output = pipe("generate question an... | fb33a8709e8b380cc767e126f735d4cc |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-large-tweetqa-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json) | | Score | Type | Dataset ... | ab3a77f72c4bd2978d19090e971eae2e |
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_tweetqa - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: ['qag'] - model: t5-large - max_length: 256 - max_length_output: 128 - epoch: 16 - ... | ea88f5c272c9074f8e86f584cb28203c |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-utility-8-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3728 - Accuracy: 0.3956 | 27a607ff105b0b0daea7eb5aa7a71921 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny ID - FLEURS-CV This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5129 - Wer: 31.1298 | c2ce6a5db14fd78dcc6a1a8612648437 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.617 | 1.43 | 500 | 0.5956 | 40.1521 | | 0.4062 | 2.86 | 1000 | 0.4991 | 33.2066 | | 0.2467 | 4.29 | 1500 | 0.4755 | 31.680... | 613a0218adca4830ade9fd8c04a5a56e |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition'] | false | Wav2Vec2-Large-Robust finetuned on Switchboard [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/). This model is a fine-tuned version of the [wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) model. It has been pretrained on: -... | f8f4c8e1360860523c0c76e24f72f3cb |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition'] | false | Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import torch | 3e2af9e792f19a6ac5063fe2f0528b5d |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition'] | false | load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-robust-ft-swbd-300h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-robust-ft-swbd-300h") | c0dc35db1d321a4c8de75151acba7127 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the ccorgi concept trained by FrancoisDongier on the lewtun/corgi dataset. This is a Stable Diffusion model fine-tuned on the ccorgi concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of ccorgi dog** This model was created as part of the DreamBooth Hackathon 🔥... | 4e8ee5d928935a7f17eb30477dbfcdb6 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Serbian - Drishti Sharma This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3873 - Wer: 11.6147 | 022cad6d0fbbc944d6ac5fddaef2fdcb |
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: 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: 100 - training_steps: 800 - mixed_precisio... | b6049d2d3a96cd95bb1a97d937a26853 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1691 | 1.89 | 100 | 0.2398 | 13.5977 | | 0.0571 | 3.77 | 200 | 0.2419 | 12.7479 | | 0.0225 | 5.66 | 300 | 0.2869 | 12.221... | 07db1bb44add667ba4a2838aad5094f0 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0604 - Precision: 0.9247 - Recall: 0.9343 - F1: 0.9295 - Accuracy: 0.9854 | 801d5dc6cf6cd1143699cc660ea754f1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2082 | 1.0 | 753 | 0.0657 | 0.8996 | 0.9256 | 0.9125 | 0.9821 | | 0.0428 | 2.0 |... | 8d8ec96505666de89ef44fb0c5b00cea |
apache-2.0 | ['generated_from_trainer'] | false | DistilBERT-POWO_Life_Form_Finetuned This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4063 | 273e328f2b7a9f99cf67f12575f50f52 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.4726 | 1.0 | 1004 | 0.4425 | | 0.3852 | 2.0 | 2008 | 0.4087 | | 0.3397 | 3.0 | 3012 | 0.4063 | | 4ed51a1ccb3a15957aa482c0f6e30ae2 |
mit | ['generated_from_trainer'] | false | Intended uses & limitations Experimenting with GPT-2 for recipe generation. To use the model, it is best to use special tokens in your input, these were added to the model tokenizer's vocabulary and served as delimiters in the training data. Therefore, we can use them to prompt the model using as much of the recipe ... | abd4ce3174e9817d6d23f0ad52397295 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 1 - mixed_precision_traini... | 27ffda784250d7a7bc0b29b5da6b57bb |
mit | ['generated_from_trainer'] | false | Training results ***** Running Evaluation ***** Num examples = 106202 Batch size = 8 {'eval_loss': 1.1872143745422363, 'eval_runtime': 818.8498, 'eval_samples_per_second': 129.697, 'eval_steps_per_second': 16.213, 'epoch': 1.0} | 4e24b04ee52a78c963d9c496e5654ec5 |
apache-2.0 | ['object-detection', 'computer-vision', 'yolox', 'yolov3', 'yolov5'] | false | Yolox Inference ```python from yoloxdetect import YoloxDetector from yolox.data.datasets import COCO_CLASSES model = YoloxDetector( model_path = "kadirnar/yolox_m-v0.1.1", config_path = "configs.yolox_m", device = "cuda:0", hf_model=True ) model.classes = COCO_CLASSES model.conf = 0.25 model.iou = 0.45... | 73f84185060a1d2cc0a7554dfe85390c |
apache-2.0 | ['generated_from_trainer'] | false | hasoc19-bert-base-multilingual-uncased-sentiment-new This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4879 - Accuracy: 0.8433 - Precision: 0.8441 - Recal... | d002c4106d27e89b8e424dfc73e60e67 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.4931 | 1.0 | 537 | 0.4011 | 0.8192 | 0.8212 | 0.8192 | 0.8198 | | 0.3643 | 2.0 |... | 9239dd0e119989ae739c8bf9685dc755 |
mit | ['generated_from_trainer'] | false | goofy_ptolemy This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkor... | f5214c613c14a6c8a28f543b8d6d7277 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', ... | b200f01262a200682bd2ebe50e2a1541 |
mit | [] | false | hebrew-gpt_neo-xl Hebrew text generation model based on [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo). Each was trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program. | 9c9a67e26e99ed03ad5ca42521c7bf4e |
mit | [] | false | 4INvMes-56m_WUi7jQMbJQ) 2. oscar / unshuffled_deduplicated_he - [Homepage](https://oscar-corpus.com) | [Dataset Permalink](https://huggingface.co/datasets/viewer/?dataset=oscar&config=unshuffled_deduplicated_he) The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classific... | b827885bea31702aee36ccb1f794b386 |
mit | [] | false | Simple usage sample code ```python !pip install tokenizers==0.10.3 transformers==4.8.0 from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-xl") model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-xl", pad_token_id=tokeniz... | ede36e41d0f4b86212ec6a04449a8425 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_qnli_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 1.0586 - Accuracy: 0.5680 | 0cc378dc8e14024d4b53b2edeb87a839 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.4745 | 1.0 | 16604 | 1.0586 | 0.5680 | | 0.2251 | 2.0 | 33208 | 1.3085 | 0.5707 | | 0.1318 | 3.0 | 49812 | 1.4267 ... | 4cea405c52aa2e98204d6b0609b6db93 |
apache-2.0 | ['summarization'] | false | Bert-mini2Bert-mini Summarization with 🤗EncoderDecoder Framework This model is a warm-started *BERT2BERT* ([mini](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4)) model fine-tuned on the *CNN/Dailymail* summarization dataset. The model achieves a **16.51** ROUGE-2 score on *CNN/Dailymail*'s test dataset. ... | e4d8785d62b5589e1580df107d1cbd9a |
apache-2.0 | ['summarization'] | false | Model in Action 🚀 ```python from transformers import BertTokenizerFast, EncoderDecoderModel import torch device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') tokenizer = BertTokenizerFast.from_pretrained('mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization') model = EncoderDecoderMode... | e7c9fd008227971fac9d31e6e8894bc4 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_distilgpt2_sst2_negation0.01_pretrainedFalse_epochs10 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.5445 | 720a266699b4d48ff090d2c6b7f89c65 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.6737 | 1.0 | 1323 | 3.2501 | | 2.4583 | 2.0 | 2646 | 3.2498 | | 2.3574 | 3.0 | 3969 | 3.2873 | | 2.2477 | 4.0 | 5292 | 3.3294 ... | 0dd191790eb991ddf00aab5758535262 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Description This is a Stable Diffusion model fine-tuned on a 100 ancient/old maps for the DreamBooth Hackathon 🔥 wildcard theme. To participate or learn more, visit [this page](https://huggingface.co/dreambooth-hackathon). To generate ancient/old maps, use **a photo of ancma map of [your choice]**. Modifiers and ne... | b3565667b58bedec2397b0e93915378e |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Examples *a photo of ancma map of fiery volcano island.*  *a photo of ancma map of peaceful Swiss town near a lake.*  *a photo of ancma map of giant ant colony.*  *a photo of ancma map... | d8019354ad4287c7b4e8c7c76716579d |
mit | ['keyphrase-extraction'] | false | 🔑 Keyphrase Extraction Model: distilbert-inspec Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first ... | ccb3a5619b5e11360d95b790324543d2 |
mit | ['keyphrase-extraction'] | false | 📓 Model Description This model uses [distilbert](https://huggingface.co/distilbert-base-uncased) as its base model and fine-tunes it on the [Inspec dataset](https://huggingface.co/datasets/midas/inspec). Keyphrase extraction models are transformer models fine-tuned as a token classification problem where each word i... | 57e92307eaea7cd9a87158f3974128a6 |
mit | ['keyphrase-extraction'] | false | 🛑 Limitations * This keyphrase extraction model is very domain-specific and will perform very well on abstracts of scientific papers. It's not recommended to use this model for other domains, but you are free to test it out. * Only works for English documents. | c790d79431f6259de732aa9fa47c6328 |
mit | ['keyphrase-extraction'] | false | ❓ How To Use ```python from transformers import ( TokenClassificationPipeline, AutoModelForTokenClassification, AutoTokenizer, ) from transformers.pipelines import AggregationStrategy import numpy as np | 63d81d2edf06ab6c775b1751ed5e4a6d |
mit | ['keyphrase-extraction'] | false | Define keyphrase extraction pipeline class KeyphraseExtractionPipeline(TokenClassificationPipeline): def __init__(self, model, *args, **kwargs): super().__init__( model=AutoModelForTokenClassification.from_pretrained(model), tokenizer=AutoTokenizer.from_pretrained(model), ... | 8872fda482b6bd3743b461ba0881eab4 |
mit | ['keyphrase-extraction'] | false | Inference text = """ Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done primarily by human anno... | 8f4bfcc5b4c1782ed26965da65c96763 |
mit | ['keyphrase-extraction'] | false | Preprocessing The documents in the dataset are already preprocessed into list of words with the corresponding labels. The only thing that must be done is tokenization and the realignment of the labels so that they correspond with the right subword tokens. ```python from datasets import load_dataset from transformers ... | 0e7606be3e9aac55702b125e8b470b10 |
mit | ['keyphrase-extraction'] | false | Dataset parameters dataset_full_name = "midas/inspec" dataset_subset = "raw" dataset_document_column = "document" dataset_biotags_column = "doc_bio_tags" def preprocess_fuction(all_samples_per_split): tokenized_samples = tokenizer.batch_encode_plus( all_samples_per_split[dataset_document_column], ... | 02d46c0670329d091c356f9714bc5312 |
mit | ['keyphrase-extraction'] | false | Postprocessing (Without Pipeline Function) If you do not use the pipeline function, you must filter out the B and I labeled tokens. Each B and I will then be merged into a keyphrase. Finally, you need to strip the keyphrases to make sure all unnecessary spaces have been removed. ```python | ba635e8a7bd4902e4c124d770d11bff2 |
mit | ['keyphrase-extraction'] | false | Define post_process functions def concat_tokens_by_tag(keyphrases): keyphrase_tokens = [] for id, label in keyphrases: if label == "B": keyphrase_tokens.append([id]) elif label == "I": if len(keyphrase_tokens) > 0: keyphrase_tokens[len(keyphrase_tokens) -... | 4772ed2fb102c68e5899e706bbd64c88 |
mit | ['keyphrase-extraction'] | false | 📝 Evaluation Results Traditional evaluation methods are the precision, recall and F1-score @k,m where k is the number that stands for the first k predicted keyphrases and m for the average amount of predicted keyphrases. The model achieves the following results on the Inspec test set: | Dataset | P@5 | R... | 1fed279a9782418fa774823c18fdccfc |
apache-2.0 | ['generated_from_trainer'] | false | Cybonto-distilbert-base-uncased-finetuned-ner-FewNerd This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the few_nerd dataset. It achieves the following results on the evaluation set: - Loss: 0.2091 - Precision: 0.7422 - Recall: 0.7830 - F1: 0.7621 - Acc... | cdcdaea7eb4b5b3b585c896ba9776df7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1964 | 1.0 | 4118 | 0.1946 | 0.7302 | 0.7761 | 0.7525 | 0.9366 | | 0.1685 | 2.0 ... | 402706e1a0e34221fbb3a7e3ac60344e |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-1body-3context This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4926 - Wer: 0.1968 - Mer: 0.1894 - Wil: 0.2793 - Wip: 0.7207 - Hits: 55899 - Subst... | 8673c8dff64a4b87b078a4bab8d30eec |
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