license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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mit | ['generated_from_trainer'] | false | camembert-base-finetuned-ICDCode_5 This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. It has been trained on a corpus of death certificate. One ICDCode is given for a given cause of death or commorbidities. As it is an important task to be able to predic... | 191781380dd332ff86c7ceb077c2135a |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 50 - eval_batch_size: 50 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | 4584bded296429f2af09fe7a0b25768d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:| | 3.7466 | 1.0 | 4411 | 1.9448 | 0.7201 | 0.6541 | 0.7201 | | 1.5264 | 2.0 | 8822 | 1.2045 | 0.8134... | 39945c725230205c6ba3cfd33e6cf485 |
other | [] | false | Carpet Cleaning Plano TX https://carpetcleaningplanotx.com/ (469) 444-1903 At Rug Cleaning Plano in TX we likewise have a truck mounted cover cleaning framework. These versatile vehicles have a force to be reckoned with of hardware. They generally have these on them and they can finish any occupation properly. Whethe... | 8d30cf8927b597d1554c8b186f30ead2 |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-cola This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4211 - Matthews Correlation: 0.6279 | e63d3b956b48b2b7728e6fb3238d7cd9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4218 | 1.0 | 133 | 0.4236 | 0.5243 | | 0.2077 | 2.0 | 266 | 0.3970 | 0.5930 | | 0.1... | c07a3ecd574937bf00bd4fbcb2866e4d |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'ja', 'japanese'] | false | SFCOCO Stable Diffusion Model Card SFCOCO Stable Diffusion is a Japanese-specific latent text-to-image diffusion model capable of generating photo-realistic images given any text input. This model was fine-tuned by using a powerful Japanese-specific latent text-to-image diffusion model, [Japanese Stable Diffusion](h... | b8c1ee3d8b2b4778f48703ba2316a08a |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'ja', 'japanese'] | false | Model Details - **Developed by:** Atsumoto Ohashi - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** Japanese - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model (LDM)](https://arxiv.org/abs/2112... | e9eb3398bf44c6c646e3d0be61bb82a6 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'ja', 'japanese'] | false | Examples Firstly, install our package as follows. This package is modified [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Japanese Stable Diffusion. ```bash pip install git+https://github.com/rinnakk/japanese-stable-diffusion ``` Run this command to log in with your HF Hub token if you h... | d642c4aa78d10cdf0a740f4c51f2095d |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'ja', 'japanese'] | false | Use the K-LMS scheduler here instead scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000) pipe = JapaneseStableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, use_auth_token=True, torch_dtype=torch.float16) pipe = pipe.to(device)... | 4e667aa256de5e76b28b2027f9f4ab9b |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'ja', 'japanese'] | false | Training **Training Data** We used the SFCOCO2021 and SFCOCO2022 dataset for training the model. You can see these datasets in [this repository](https://github.com/nu-dialogue/clip-prefix-caption-jp). **Training Procedure** SFCOCO Stable Diffusion has the same architecture as Japanese Stable Diffusion and was traine... | 5c1157b3baf957eba955a61b764a13e9 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'ja', 'japanese'] | false | Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Compu... | f9118e772eac9d023f80f6478d010e04 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2147 - Accuracy: 0.922 - F1: 0.9219 | bd5cd02d782c7f19823db79e420fb1d9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8205 | 1.0 | 250 | 0.3028 | 0.909 | 0.9061 | | 0.245 | 2.0 | 500 | 0.2147 | 0.922 | 0.9219 | | cfa5aae7a2a8d1967f85c4afbf0ea4a4 |
apache-2.0 | [] | false | Results on Natural Questions - Test Set |Id | link | Exact Match | |---|---|---| |T5-large|https://huggingface.co/google/t5-large-ssm-nqo|29.0| |T5-xxl|https://huggingface.co/google/t5-xxl-ssm-nqo|35.2| |**T5-3b**|**https://huggingface.co/google/t5-3b-ssm-nqo**|**31.7**| |T5-11b|https://huggingface.co/google/t5-11b-... | 405febde9a0a4788a457af4c5d4508e6 |
apache-2.0 | [] | false | Usage The model can be used as follows for **closed book question answering**: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-3b-ssm-nqo") t5_tok = AutoTokenizer.from_pretrained("google/t5-3b-ssm-nqo") input_ids = t5_tok("When ... | fa782c022189ba89b4bbf6615d13f03a |
apache-2.0 | ['generated_from_keras_callback'] | false | Mr-Wick/albert-base-v2 This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6458 - Validation Loss: 0.8180 - Epoch: 1 | a79192110a642c7b09c6d515dd98ec40 |
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': 16494, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'bet... | 6eec425677ca0a9ba0978ec1abee2225 |
mit | ['conversational'] | false | DialoGPT Trained on the Speech of a Game Character This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script da... | 18d21f52cd76d805f6f3f9ae85daa783 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-cased-distilled-squad-coffee20230108 This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.4291 | 3c684b04cbfe807d3b417916f6305eb5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 92 | 1.8403 | | 2.3138 | 2.0 | 184 | 1.7960 | | 1.5166 | 3.0 | 276 | 1.8769 | | 0.9893 | 4.0 | 368 | 2.0993 ... | 2e0a6675e7bb253d78d965de8d3f5bf1 |
mit | ['vision', 'image-segmentation'] | false | OneFormer OneFormer model trained on the COCO dataset (large-sized version, Swin backbone). It was introduced in the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jain et al. and first released in [this repository](https://github.com/SHI-Labs/OneFormer). ... | 385caa23333f22c3a1e291eb68271985 |
mit | ['vision', 'image-segmentation'] | false | Loading a single model for all three tasks processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_coco_swin_large") model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_coco_swin_large") | 914f7dbfcecf9f7f733a9be190f32eb3 |
apache-2.0 | ['translation'] | false | opus-mt-sv-tiv * source languages: sv * target languages: tiv * OPUS readme: [sv-tiv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-tiv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 93d7eaa90c0d3f5e9649c5a16068987f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 4.1563 | 1.0 | 30926 | 4.6165 | | 3.7545 | 2.0 | 61852 | 3.9101 | | 3.3745 | 3.0 | 92778 | 4.2957 | | 25a8b6fb8357600de248b0dd8e62fefc |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Danish (da) 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](http... | 299b9460dba27c9509450b62a9a5796e |
lgpl-lr | ['summarization'] | false | The mt5-large model has been finetuned with the data from [Uber](https://lang.org.ua/en/corpora/) corpus in Ukrainian. The dataset contains around 40K articles about politics, science, technology, social life collected until December 2021 from Hromadske.ua. | 428cc99bf8c868d0e62c4392ee31d8b6 |
lgpl-lr | ['summarization'] | false | Load the model and mt tokenizer : ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline tokenizer = AutoTokenizer.from_pretrained("google/mt5-large") model = AutoModelForSeq2SeqLM.from_pretrained("SGaleshchuk/t5-large-ua-news") summarizer = pipeline("summarization", model=model, tokenize... | 78c87507261f38421a92747ac005710a |
lgpl-lr | ['summarization'] | false | Try on your example summary = summarizer("15 листопада чисельність населення Землі досягла восьми мільярдів, повідомляє ООН. Зазначають, що нашій планеті знадобилося лише 11 років, щоб вирости з семи до восьми мільярдів. Таке зростання ООН пояснила поступовим збільшенням тривалості життя людини завдяки поліпшенню охо... | 00ce9d3889f82b22ad6880a591365329 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 6.2 - GMACs: 0.7 - Activations (M): 7.7 - Image size: 224 x 224 - **Original:** https://github.com/snap-research/EfficientFormer - **Papers:** - Rethinking Vision Transformers for MobileNet Size and Speed... | a8a9ca6d27591811d1ce117394c5cd57 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('efficientformerv2_s1.snap_dist_in1k', pretrained=True)... | aa5e7c49ce8b79f8ff7b55d7cb227224 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'efficientformerv2_s1.snap_dist_in1k', pretrained=... | 42e87258563a8c992e7afe383d653ba2 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'efficientformerv2_s1.snap_dist_in1k', pretr... | e85be2955cb6887d80b3969aa282dbfe |
mit | ['Diff Model', 'pytorch', 'causal-lm', 'code-generation', 'The Pile'] | false | Model Description diff-codegen-350m-v2 is a diff model for code generation, released by [CarperAI](http://carper.ai/). A diff model is an autoregressive language model trained on edits to a piece of text, formatted in [Unified Diff Format](https://en.wikipedia.org/wiki/Diff | 82cdba94ef9c8d8b9322bed4e4188cca |
mit | ['Diff Model', 'pytorch', 'causal-lm', 'code-generation', 'The Pile'] | false | Unified_format). These diff models can suggest, given a section of text and a description of the desired change, an intelligent change to the text that fits the description, marking the lines added, changed, and deleted in diff format. In comparison to few-shot prompting of normal code generation models, diff models a... | 759016a9ad41c10f4aabcca3426a673a |
mit | ['Diff Model', 'pytorch', 'causal-lm', 'code-generation', 'The Pile'] | false | Training Data This model is a fine-tune of [codegen-350m-mono](https://huggingface.co/Salesforce/codegen-350M-mono) by Salesforce. This language model was first pre-trained on The Pile, an 800Gb dataset composed of varied web corpora. The datasheet and paper for the Pile can be found [here](https://arxiv.org/abs/2201... | 43b73e9a5d894c5e9bbadddb621bca47 |
cc-by-sa-4.0 | [] | false | YACIS ELECTRA Small Japanese for Irony This is an [ELECTRA](https://github.com/google-research/electra) Base model for the Japanese language finetuned for automatic irony detection. The model was based on [YACIS ELECTRA small Japanese](https://huggingface.co/ptaszynski/yacis-electra-small-japanese), and later fine... | 72aa04b5ee4987bcc2ccbb138ff3ceba |
cc-by-sa-4.0 | [] | false | Citations Please, cite this model using the following citation. ``` @inproceedings{dan2022yaciselectra-small-irony, title={北見工業大学 テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (Izumi Labs ver.)}, author={団 俊輔 and プタシンスキ ミハウ and ジェプカ ラファウ and 桝井 文人}, publisher={HuggingFace}, year={2022}, url = "https://huggingface.co... | 2159d229dc7ef8695bc311cff44cda4d |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-auto_and_commute-6-16-5 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: 2.2614 - Accuracy: 0.4289 | 24e9637ba793f43e5d186703d37453dd |
cc-by-sa-4.0 | [] | false | This is the `Fett` 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 your own concepts and load them i... | bd8584673d4aa8257ae21c12a9db4808 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Slovak-SNK | Feature | Description | | --- | --- | | **Name** | `sk_udv25_slovaksnk_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_t... | 9f9def07c85636ae725294b594aa8d3a |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (4879 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | ` | 928cda9928b4a93f03740fece2d98d38 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | `, `%`, `0`, `?`, `AAfp1x`, `AAfp1x:q`, `AAfp1x:r`, `AAfp1y`, `AAfp1z`, `AAfp2x`, `AAfp2x:r`, `AAfp2y`, `AAfp2z`, `AAfp3x`, `AAfp4x`, `AAfp4y`, `AAfp6x`, `AAfp6x:q`, `AAfp6y`, `AAfp7x`, `AAfs1:r`, `AAfs1x`, `AAfs1x:q`, `AAfs1x:r`, `AAfs1y`, `AAfs1z`, `AAfs2x`, `AAfs2x:q`, `AAfs2x:r`, `AAfs2z`, `AAfs2z:r`, `AAfs3x`, `AA... | de62b723d84b9d9d57ff55849e771100 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 100.00 | | `TOKEN_P` | 100.00 | | `TOKEN_R` | 100.00 | | `TOKEN_ACC` | 100.00 | | `SENTS_F` | 92.13 | | `SENTS_P` | 92.08 | | `SENTS_R` | 92.17 | | `TAG_ACC` | 91.20 | | `POS_ACC` | 97.20 | | `MORPH_ACC` | 93.90 | | `DEP_UAS` | 94.14 | | `DEP_LAS` | 90.95 | | `LEM... | 2565920999fc52962e8e7e166cc6e75f |
cc-by-4.0 | ['espnet', 'audio', 'speech-enhancement', 'audio-to-audio'] | false | `Chenda_Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave` ♻️ Imported from https://zenodo.org/record/4498554/ This model was trained by Chenda Li using wsj0_2mix/enh1 recipe in [espnet](https://github.com/espnet/espnet/). | 7d76f1f19e379de4fec05d8f37359204 |
apache-2.0 | ['speech'] | false | Wav2Vec2-Large-960h-Lv60 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also s... | 74bbfd9e2ea90a0fa364c0653ff43715 |
apache-2.0 | ['speech'] | false | Evaluation This code snippet shows how to evaluate **facebook/wav2vec2-large-960h-lv60** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import torch from jiwer import wer librispeech_eval = load_dataset("libri... | 48cf98b2fe4f341ed735676f8125f894 |
apache-2.0 | ['summarization', 'mt5', 'spanish'] | false | Hyperparameters { "max_target_length": 64, "model_name_or_path": "google/mt5-small", "num_train_epochs": 3, "seed": 7, "summary_column": "output_text", "text_column": "text", "encoder_max_length" : 512, "decoder_max_length" :36, "batch_size" : 128 } | a76b7ec4c20970c4cb61c44e81922fce |
apache-2.0 | ['summarization', 'mt5', 'spanish'] | false | Usage ``` article = """ La chocotorta, el tradicional y práctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por críticos de restaurants internacionales, a casi 40 años de su creación. El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y... | 9f120aef6393f413e1752448bd25adfa |
apache-2.0 | ['summarization', 'mt5', 'spanish'] | false | Results | metric | score | | --- | ----- | | eval_loss | 2.879085063934326 | | eval_rouge1 | 22.6623 | | eval_rouge2 | 7.7894 | | eval_rougeL | 19.8015, | | eval_rougeLsum | 19.8092 | | eval_gen_len | 17.1839 | | test_loss | 2.878429412841797 | | test_rouge1 | 22.9263 | | test_rouge2 | 7.9146 | | test_rougeL | 20.0272... | 6c3747c92c73ab26c6e3adbeeeae36ef |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/bert-large-nli-mean-tokens This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 03f6338c0f5f36a92623d06d714b86fc |
apache-2.0 | ['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... | be7ba3f47e6f3456989ff9a0fadcd379 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/bert-large-nli-mean-tokens') model = AutoModel.from_pretrained('sentence-transformers/bert-large-nli-mean-tokens') | a8c93f3ae715f61b729c5c78eacf40cd |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/bert-large-nli-mean-tokens) | 07cc8887fe238be63c8e4ca1773131ac |
apache-2.0 | ['translation'] | false | aze-eng * source group: Azerbaijani * target group: English * OPUS readme: [aze-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/aze-eng/README.md) * model: transformer-align * source language(s): aze_Latn * target language(s): eng * model: transformer-align * pre-processing: normalizati... | 4203dabab13188b8b37ca16298560e0c |
apache-2.0 | ['translation'] | false | System Info: - hf_name: aze-eng - source_languages: aze - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/aze-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['az', 'en'] - src_constituents: {'aze_Latn'} - tgt_... | 068944bc2154e804f92eecc5a0e8bb01 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small zh-HK - Alvin This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. This version has a lower CER (by 1%) compared to the previous one. | a395716809ac3a83127b287587b9ac50 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training and evaluation data For training, three datasets were used: - Common Voice 11 Canto Train Set - CantoMap: Winterstein, Grégoire, Tang, Carmen and Lai, Regine (2020) "CantoMap: a Hong Kong Cantonese MapTask Corpus", in Proceedings of The 12th Language Resources and Evaluation Conference, Marseille: European La... | 67408e5767367ba621b2985ab0afd422 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training Hyperparameters - learning_rate: 5e-5 - train_batch_size: 25 (on 2 GPUs) - eval_batch_size: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 25x2x2=100 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 14000 - ... | 9ca60e897c11008b20e7b96674bb4270 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training Results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4610 | 0.55 | 2000 | 0.3106 | 13.08 | | 0.3441 | 1.11 | 4000 | 0.2875 | 11.79 | | 0.3466 | 1.66 | 6000 | 0.2820 | 11.44 | | 0.... | 6ba8fdc72cf890600114235f9e1c415d |
apache-2.0 | ['generated_from_trainer'] | false | nmt-mpst-id-en-lr_1e-3-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: 1.6391 - Bleu: 18.9112 - Meteor: 0.3583 | a82ac92d560b2189c3be50ac22fb8a7b |
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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 041a6f6c8d53aa58c296dceaab7142b3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:| | No log | 1.0 | 202 | 1.8793 | 13.9958 | 0.2988 | | No log | 2.0 | 404 | 1.7154 | 15.2332 | 0.3136 | | 1.6109 | 3.0... | 59f3a23406334dea2bd168b8f2d86244 |
apache-2.0 | ['text2sql'] | false | tscholak/cxmefzzi Fine-tuned weights for [PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://arxiv.org/abs/2109.05093) based on [T5-3B](https://huggingface.co/t5-3b). | 559244527dd363d236948cc1b842a216 |
apache-2.0 | ['text2sql'] | false | Training Objective This model was initialized with [T5-3B](https://huggingface.co/t5-3b) and fine-tuned with the text-to-text generation objective. Questions are always grounded in a database schema, and the model is trained to predict the SQL query that would be used to answer the question. The input to the model i... | 8c0f0ca099c397090a893a859e00962e |
apache-2.0 | ['text2sql'] | false | Performance Out of the box, this model achieves 71.5 % exact-set match accuracy and 74.4 % execution accuracy on the Spider development set. On the test set, the model achieves 68.0 % exact-set match accuracy and 70.1 % execution accuracy. Using the PICARD constrained decoding method (see [the official PICARD implem... | a968c107ba87d50b7e86897e307542b9 |
mit | ['roberta-base', 'roberta-base-epoch_74'] | false | RoBERTa, Intermediate Checkpoint - Epoch 74 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | fe94c977ec8807a582272d3289d98317 |
cc-by-4.0 | ['generated_from_trainer'] | false | out This model is a fine-tuned version of [allegro/herbert-base-cased](https://huggingface.co/allegro/herbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5164 - Accuracy: 0.6896 | be02b2bacd752b25314d672f57558d63 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 80 - total_train_batch_size: 2560 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_... | 36ded2dd0f97780e6e0e704a48d96f13 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-wikitext2 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: 6.8544 | f9d54ff619f3b08ec222eb570aa1b573 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.0915 | 1.0 | 2346 | 7.0517 | | 6.905 | 2.0 | 4692 | 6.8735 | | 6.8565 | 3.0 | 7038 | 6.8924 | | 47ec73741df02192468a6cdea35a3d91 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-superb-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.3795 - eval_wer: 0.3148 - eval_runtime: 26.4914 - eval_samples_per_second: 1... | 7790f29ec8dee965a7ddf0c1391a4f98 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-custom-tokenizer-target-glue-qqp This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola-custom-tokenizer](https://huggingface.co/muhtasham/tiny-mlm-glue-cola-custom-tokenizer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4522 - Accuracy: 0.769... | eab22fb77e6854867d78bc0e05d0447d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.6113 | 0.04 | 500 | 0.5700 | 0.6848 | 0.5805 | | 0.5608 | 0.09 | 1000 | 0.5332 | 0.7140 | 0.6093 | | 0.5442 ... | 52d26946ec3f8e1dbdbef30e00081da3 |
apache-2.0 | [] | false | FRED-T5 1.7B (Full-scale Russian Enhanced Denoisers T5) Architecture based on T5. It has 24 layers and 1536 hidden size. More details in config.json. The model trained on a mixture of 7 denoisers like UL2 with several differences (https://arxiv.org/abs/2205.05131). It was trained on Russian language corpus (300G... | 66d8b96c331d57785f46f3286b3fee00 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-0'] | false | MultiBERTs Seed 0 Checkpoint 100k (uncased) Seed 0 intermediate checkpoint 100k 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/goo... | 2422e6cfdd274df55f3a3999e4384ba0 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-0'] | 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-0-100k') model = BertModel.from_pretrained("multiberts-seed-0-100k") text = "Replace me by any text you'd like.... | 9abe3b27536966e905cdafe74001cc78 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small vi - Sanchit Gandhi 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: - eval_loss: 0.5523 - eval_wer: 29.6743 - eval_runtime: 979.2431 - eval_samples_per_sec... | 3a3ec3f6e239f22d9ceae638165da82c |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-mlm-pubmed-35 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1101 - Rouge2 Precision: 0.4758 - Rouge2 Recall: 0.3498 - Rouge2 Fmeasure: 0.3927 | 6b44903e1bbc9871bff9762c9a2ba98d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP | df8e2a11951257431cf825a3cbafdf5a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 1.8404 | 0.75 | 500 | 1.5005 | 0.4265 | 0.2786 | 0.32... | b28dc9bfa26ed708ae31240802d809ec |
apache-2.0 | ['classification'] | false | IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) | 8bff047295b0cdd4646d3369d9d93d2f |
apache-2.0 | ['classification'] | 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 | 句子表征 | 二郎神 Erlangshen | TCBert (sentence representation) | 110M | Chinese | | 9f0d0771d35e3a64c167728b4ffdf6b5 |
apache-2.0 | ['classification'] | false | 模型信息 Model Information 为了提高模型在话题分类上句子表征效果，我们收集了大量话题分类数据进行基于prompts的对比学习预训练。 To improve the model performance on sentence representation for the topic classification task, we collected numerous topic classification datasets for contrastive pre-training based on general prompts. | a7ce90b969b530516f311fe52afaf9ec |
apache-2.0 | ['classification'] | false | Loading models tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese") model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese") | af78165319d705006c4afaa359f59ba9 |
apache-2.0 | ['generated_from_trainer'] | false | Intended uses & limitations This is a work in progress, be aware that the model can produce artefacts. You can test the model using the pipeline-interface: ```python from transformers import pipeline fix_spelling = pipeline("text2text-generation",model="oliverguhr/spelling-correction-german-base") print(fix_spell... | b14e2f9c68853c36c76da7639e07c0c2 |
mit | [] | false | qingqingdezhaopian on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook | 15fcd5e329d3bff00efab7a750a03883 |
mit | [] | false | Model by liuwei33 This your the Stable Diffusion model fine-tuned the qingqingdezhaopian concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: **15.png** You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLa... | e1999be1b4fa6994267148c1adda2b4a |
apache-2.0 | ['generated_from_trainer'] | false | classification_tnews_scarce_imbalanced This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on a small&imbalanced subset of TNEWS dataset. It achieves the following results on the evaluation set: - Loss: 1.8763 - Accuracy: 0.5867 | 95c3471977bbe3da218dfd88825f0b1b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7407 | 1.0 | 15 | 2.6972 | 0.1267 | | 2.476 | 2.0 | 30 | 2.5490 | 0.2467 | | 2.1124 | 3.0 | 45 | 2.4033 | 0.... | 5ab4939a4daae274f90b1504a470c7d2 |
mit | ['generated_from_trainer'] | false | bert-base-combined-large This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co/dbmdz/bert-base-turkish-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3029 - Accuracy: 0.8940 - F1: 0.8956 | 861935d650afb27140ef3e686ce5f943 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2668 | 1.0 | 3077 | 0.2812 | 0.8931 | 0.8915 | | 0.2042 | 2.0 | 6154 | 0.2675 | 0.8952 | 0.8950 | | 0.1453 |... | 5157553d86ba0ca0877b431e8464d597 |
cc-by-4.0 | ['hi', 'en', 'codemix'] | false | HingMBERT HingBERT is a Hindi-English code-mixed BERT model trained on roman text. It is a mBERT model fine-tuned on L3Cube-HingCorpus. <br> [dataset link] (https://github.com/l3cube-pune/code-mixed-nlp) More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2204.... | 54aec4ac333dda1222e536e3ec868548 |
creativeml-openrail-m | [] | false | Recommended setting Model:Abyss_7th_anime_v1.1 https://huggingface.co/syaimu/7th_Layer/blob/main/Abyss_7th_anime_v1.1.ckpt Hypernetwork strength:0.25 Highres. fix:ON Upscale latent space image when doing hires. fix:ON Please follow this prompt: 1girl, (realistic:1.3),isometric figure, (tachi-e:1.2), (transparent ... | 426081a47672bdbf74fe7f1ddd97de52 |
creativeml-openrail-m | [] | false | Sample prompt https://majinai.art/ja/i/8359WPL <img src="https://i.imgur.com/a7M42HA.jpg" width="480" height=""> https://majinai.art/ja/i/UTeEryV <img src="https://i.imgur.com/DvmLKdC.jpg" width="480" height=""> https://majinai.art/ja/i/1q1r8RF <img src="https://i.imgur.com/Air3hHb.jpg" width="480" height=""> | 1d5d0c212400dcec808d5a5e275dc7a0 |
apache-2.0 | ['translation'] | false | opus-mt-es-sl * source languages: es * target languages: sl * OPUS readme: [es-sl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-sl/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | 5a4d174eb77c0fe37a723a8a9318c135 |
apache-2.0 | [] | 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 | BART | 139M | 中文-文本摘要任务 Chinese-Summary | | 8a6c5ff8a81a2cb313795c9172741a45 |
apache-2.0 | [] | false | 模型信息 Model Information 基于[Randeng-BART-139M](https://huggingface.co/IDEA-CCNL/Randeng-BART-139M)，我们在收集的1个中文领域的文本摘要数据集（LCSTS）上微调了它，得到了summary版本。 Based on 基于[Randeng-BART-139M](https://huggingface.co/IDEA-CCNL/Randeng-BART-139M), we fine-tuned a text summarization version (summary) on a Chinese text summarization data... | fa6c08a9c9bbb5f92979aa011b6331a9 |
apache-2.0 | [] | false | 使用 Usage ```python from transformers import BartForConditionalGeneration, AutoTokenizer, Text2TextGenerationPipeline import torch tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Randeng-BART-139M-SUMMARY') model=BartForConditionalGeneration.from_pretrained('IDEA-CCNL/Randeng-BART-139M-SUMMARY') text = 'summary:在北... | 96cba7701590cbe4be4053482dfdc7f8 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Model Description Genji-JP 6B is a model finetuned on our Japanese storytelling dataset based on EleutherAI's GPT-J 6B model. This particular model is trained on Japanese web novels. | Hyperparameter | Value | |-------------------|--------| | n_parameters | 6,053,381,344 | | n_layers | 28* | | ... | 5598e8a1cc63000fd181de69697e4a1e |
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