license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | [] | false | Reference ``` @article{lorch2022amortized, title={Amortized Inference for Causal Structure Learning}, author={Lorch, Lars and Sussex, Scott and Rothfuss, Jonas and Krause, Andreas and Sch{\"o}lkopf, Bernhard}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2022} } ``` | 1077daacfda3a3f7c735a0b9b5a26b3e |
apache-2.0 | ['vision'] | false | Vision Transformer (large sized model) pre-trained with MSN (patch size of 7) Vision Transformer (ViT) model pre-trained using the MSN method. It was introduced in the paper [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr B... | 308b6c094c75db491a5d7276cb863fc5 |
apache-2.0 | ['vision'] | false | Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like). Images are presented to the model as a sequence of fixed-size patches. MSN presents a joint-embedding architecture to match the prototypes of masked patches with that of the unmasked patches. With this setup, their method yiel... | e315cbe1d9e62de6f8f473a5f0c29979 |
apache-2.0 | ['vision'] | false | Intended uses & limitations You can use the raw model for downstream tasks like image classification. See the [model hub](https://huggingface.co/models?filter=vit_msn) to look for different versions of MSN pre-trained models that interest you. The model is particularly beneficial when you have a few labeled samples i... | 512413ad709afa5f76b8387f1c661f3b |
apache-2.0 | ['vision'] | false | How to use Here is how to use this backbone encoder: ```python from transformers import AutoFeatureExtractor, ViTMSNModel import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = Auto... | 013094b76dbee9051b27980289bb06c7 |
apache-2.0 | ['vision'] | false | Citation ```bibtex @article{assran2022masked, title={Masked Siamese Networks for Label-Efficient Learning}, author={Assran, Mahmoud, and Caron, Mathilde, and Misra, Ishan, and Bojanowski, Piotr, and Bordes, Florian and Vincent, Pascal, and Joulin, Armand, and Rabbat, Michael, and Ballas, Nicolas}, journal={arX... | 9663494be6ba6c53b11964228234aef5 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Arabic This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | c77a59471fb2d1f602bf79bc4bd23cd2 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ar") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ar") ``` | d5c53d5902d4b9717bc708dc92822cc3 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_mnli_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.9622 - Accuracy: 0.5456 | c2cefc46655b6151d2cc2cd08ea1ac59 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0905 | 1.0 | 1534 | 1.0415 | 0.4633 | | 1.0344 | 2.0 | 3068 | 1.0571 | 0.4569 | | 1.0137 | 3.0 | 4602 | 1.0080 ... | ecfcf578ed9412fad61ba6ddfd9111b8 |
apache-2.0 | ['generated_from_keras_callback'] | false | HomayounSadri/bert-base-uncased-finetuned-squad-v2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8470 - Validation Loss: 1.0267 - Epoch: 1 | 3338683b380f30f451939c4d91daf49e |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11064, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | fca989df4a66e2fd13d894415815bb15 |
mit | ['generated_from_trainer'] | false | cv-ner This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0956 - Precision: 0.8906 - Recall: 0.9325 - F1: 0.9111 - Accuracy: 0.9851 | 0763affede9bc87073b7fa27f67b4322 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 91 | 0.2049 | 0.6618 | 0.7362 | 0.6970 | 0.9534 | | 0.5036 | 2.0 |... | f958847edc21da3d39e93c4dbd15eda8 |
creativeml-openrail-m | [] | false | **Zelda Diffusion - Hypernet** [*DOWNLOAD LINK*](https://huggingface.co/wavymulder/zelda-diffusion-HN/resolve/main/zeldaBOTW.pt) - This is a hypernet trained on screenshots of Princess Zelda from BOTW  Here... | 5458f1fa97dc2b662ce55310424f5d68 |
cc-by-4.0 | [] | false | DevBERT DevBERT is a Devanagari BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on publicly available Hindi and Marathi monolingual datasets. [project link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [<a h... | 92fe9f43b5b05b52e0c7b13e02473b1d |
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: 0.0622 - Precision: 0.9288 - Recall: 0.9483 - F1: 0.9385 - Accuracy: 0.9859 | 2448a2789a297a8579d9e3aee0face40 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0876 | 1.0 | 1756 | 0.0657 | 0.9093 | 0.9349 | 0.9219 | 0.9826 | | 0.0412 | 2.0 |... | 0bfb8a82750f0c7b344ab41578674416 |
apache-2.0 | ['seq2seq', 'lm-head'] | false | Italian T5 Base (Oscar) 🇮🇹 *This repository contains the model formerly known as `gsarti/t5-base-it`* The [IT5](https://huggingface.co/models?search=it5) model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach ado... | a2147e1b33bced49cbb6f4d837b3ff42 |
apache-2.0 | ['seq2seq', 'lm-head'] | false | Model variants This repository contains the checkpoints for a `base` version of the model trained on the [OSCAR corpus](https://oscar-corpus.com/) using 🤗 Datasets. The original configuration for the model `t5-base` was adopted, with the exception of the parameter `dropout_rate` that was set at `0` instead of `0.1` ... | eabaa1f903d5415c58426d7d919bec8e |
apache-2.0 | ['seq2seq', 'lm-head'] | false | Using the models ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("gsarti/it5-base-oscar") model = T5ForConditionalGeneration.from_pretrained("gsarti/it5-base-oscar") ``` *Note: You will need to fine-tune the model on your downstream seq2seq task to ... | 523e1b9b1fe95bac2dbd42f734212b0c |
apache-2.0 | ['generated_from_trainer'] | false | mlner-mlwptok-muril This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on the mlner2021 dataset. It achieves the following results on the evaluation set: - Loss: 0.8331 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.8113 | b068a0f7e34c9fbc45d3bcf7c4cbb6b3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:---:|:--------:| | 1.447 | 1.0 | 1389 | 0.9396 | 0.0 | 0.0 | 0.0 | 0.8113 | | 0.898 | 2.0 | 2778 ... | 5b878cca924637e27f8dfc850de3194b |
mit | ['generated_from_trainer'] | false | twitter-data-xlm-roberta-base-sentiment-finetuned-memes This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2455 - Accuracy: 0.9327 - Precision: 0.9333 - Recall: 0.9327 - F1: 0.9328 | 621404a3c3b6e56cc09a957d18a517c5 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | de3c1b28faff4a053f7ec80065b91a49 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.3851 | 1.0 | 1783 | 0.3286 | 0.8998 | 0.9005 | 0.8998 | 0.8997 | | 0.2812 | 2.0 ... | e8e06c91bd2da1c6dc60d3025c89e0c9 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP | cfae06199d6b58ef60ad9596d7d731a7 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6348 - Wer: 0.3204 | 1c565543cba4664cbb5b6bc2b09b340c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.2767 | 0.5 | 500 | 2.9921 | 1.0 | | 1.509 | 1.01 | 1000 | 0.8223 | 0.6031 | | 0.7226 | 1.51 | 1500 | 0.6185 | 0.493... | 1f2024832430278174e1d63d460f0abd |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.2929 - Accuracy: 0.9419 | cb46822268a831604924a1fd641116e6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 159 | 1.7692 | 0.6606 | | No log | 2.0 | 318 | 1.1246 | 0.7997 | | No log | 3.0 | 477 | 0.7261 | 0.... | c41cc56a45ee25114bcb51c52f623776 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-imdb This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4138 - Accuracy: 0.82 - F1: 0.8235 | d99c2bc70a299f686a9c2da097b75995 |
creativeml-openrail-m | [] | false | model by no3 This your the Anything V3 model fine-tuned ridley taught to Anything V3 with Dreambooth. It can be used by modifying the `instance_prompt`: **sks_ridley** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/note... | a2314570b4eedc647e154a8bee76fd18 |
creativeml-openrail-m | [] | false | note If you want to to use in UI like [AUTOMATIC1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) or any UI that's uses .ckpt files just download ckpt file here for your convenience. **just click on "ridley-at3-beta2.ckpt"** [ridley-at3-beta2.ckpt](https://huggingface.co/no3/ridley-at3-beta2/resolve/main... | a1eb823b470daec4c0d4325394390f2a |
apache-2.0 | ['abstractive summarization', 'mbart-cc25', 'Czech'] | false | mBART fine-tuned model for Czech abstractive summarization (AT2H-S) This model is a fine-tuned checkpoint of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the Czech news dataset to produce Czech abstractive summaries. | e61762dc3ba313dcd6873196e558ff72 |
apache-2.0 | ['abstractive summarization', 'mbart-cc25', 'Czech'] | false | Dataset The model has been trained on the [SumeCzech](https://ufal.mff.cuni.cz/sumeczech) dataset. The dataset includes around 1M Czech news-based documents consisting of a Headline, Abstract, and Full-text sections. Truncation and padding were configured for 512 tokens for the encoder and 64 for the decoder. | 6188e0a8d0df809d7f7105d1ba60e29a |
apache-2.0 | ['abstractive summarization', 'mbart-cc25', 'Czech'] | false | summarization model - checkpoint from website ("model_name", "krotima1/mbart-at2h-s"), ("inference_cfg", OrderedDict([ ("num_beams", 4), ("top_k", 40), ("top_p", 0.92), ("do_sample", True), ("temperature", 0.89), ("repetition_penal... | c1f6346f8649219fe6d5b320c41fc171 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0952 - Accuracy: 0.9823 | 4783525e6b031f873fe84be6dc26f1ea |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7908 | 1.0 | 399 | 0.6776 | 0.9009 | | 0.3202 | 2.0 | 798 | 0.2061 | 0.9763 | | 0.221 | 3.0 | 1197 | 0.1257 | 0.... | f75378a942dda088d7393587d62e5f48 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | sew-mid-100k-ft-common-language This model is a fine-tuned version of [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-100k) on the common_language dataset. It achieves the following results on the evaluation set: - Loss: 2.1189 - Accuracy: 0.3842 | e1b4046ca8579aa96a8f7a0c69333c38 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 4 - seed: 0 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 2b23d03ec5da1c1736c84ceb81b62ac7 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.608 | 1.0 | 173 | 3.7266 | 0.0540 | | 3.1298 | 2.0 | 346 | 3.2180 | 0.1654 | | 2.8481 | 3.0 | 519 | 2.9270 | 0.... | 19595f162b4d2c9d8ff5b5bb128953d7 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-conll2003 This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.6472 | ff1bf302b8143893543c07a8dbc05dfa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.4619 | 0.4 | 500 | 5.1363 | | 5.2804 | 0.8 | 1000 | 4.9579 | | 5.1847 | 1.2 | 1500 | 4.7870 | | 5.0376 | 1.6 | 2000 | 4.8272 ... | 1bf3d5da18840a75b4e35750ed1b74cc |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Old_East_Slavic (orv) 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 web... | b8f4b16dd9f690c78e82ff3a22ac1341 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Openjourney is an open source Stable Diffusion fine tuned model on Midjourney images, by [PromptHero](https://prompthero.com/) Use prompt: 'mdjrny-v4 style' [](https://colab.research.google.com/drive/1vkuxKKeSYNYI2OLZm8mR-WqcokQtSURM?usp=shari... | b8e0fa0303e93611fa8ef78fc0faf3b3 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run Openjourney: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253... | d328d61a4717fb50a87ceb3f3bdd3cff |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Stable Diffusion v1.5 vs Openjourney (Same parameters, just added "mdjrny-v4 style" at the beginning): <img src="https://s3.amazonaws.com/moonup/production/uploads/1667904587642-63265d019f9d19bfd4f45031.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1667904587623-63265d019f9d19bfd4f4... | 4710b4463d675216c35fad3956fe14bf |
creativeml-openrail-m | ['stable-diffusion', '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), [... | cfab1de98bcca67038637280aefd53bb |
mit | [] | false | CDC onnx model based on [7eu7d7/CDC_anime](https://huggingface.co/7eu7d7/CDC_anime). The `cdc_upscaler` client can be found on [narugo1992/cdc_upscaler](https://github.com/narugo1992/cdc_upscaler). | ** | 5e13a68b11dc4ebbdec9f092c80d6653 |
mit | [] | false | ** | **original** | **4x** | |:-----:|:-------------------------------------------------------... | 6a74e1650d04a3214998bb40efc23583 |
apache-2.0 | [] | false | PaddlePaddle/uie-senta-medium Sentiment analysis is a research hotspot in recent years, aiming at analyzing, processing, summarizing and reasoning emotionally subjective texts. Sentiment analysis has a wide range of application scenarios and can be applied to consumer decision making, public opinion mining, personali... | aa249dd9914ce7ca1f1801a80d5458c2 |
apache-2.0 | [] | false | Performance on Text Dataset We conducted experiments to compare the performance different Models based on a in-house test set, which containing samples from multiple fields, such as hotel, restaurant，clothes and so. The comparison results are as follows. | Model Name | Precision | Recall | F1 ... | a807797659810b7848a7424dad7a187e |
apache-2.0 | ['speech', 'audio', 'wav2vec2', 'audio-classification'] | false | Model description This is a ported version of [S3PRL's Wav2Vec2 for the SUPERB Speaker Identification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/voxceleb1). The base model is [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base), which is pretrained on 16kHz sampled speech audio. Wh... | 63bdf3f56783047d1a67de10e18935d6 |
apache-2.0 | ['speech', 'audio', 'wav2vec2', 'audio-classification'] | false | Usage examples You can use the model via the Audio Classification pipeline: ```python from datasets import load_dataset from transformers import pipeline dataset = load_dataset("anton-l/superb_demo", "si", split="test") classifier = pipeline("audio-classification", model="superb/wav2vec2-base-superb-sid") labels = ... | cbf90689d17958b19407b09f70178649 |
apache-2.0 | ['speech', 'audio', 'wav2vec2', 'audio-classification'] | false | load a demo dataset and read audio files dataset = load_dataset("anton-l/superb_demo", "si", split="test") dataset = dataset.map(map_to_array) model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-sid") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base... | 01fd1b27a7e4f9d9405d13088ae2e47f |
other | [] | false | Carpet Cleaning Mckinney TX https://carpetcleaningmckinneytx.com/ (469) 702-1202 Individuals search for elite administrations to keep their homes tidy and cutting-edge. We are certain about what we do in light of the fact that, we consolidate our long stretches of involvement in the cutting edge gear, drawing out the i... | 85f243a96d1ca5772929789a480d7d67 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_vp-sv_s596 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 569644e46a1f070e728686d313500d4e |
other | ['generated_from_trainer'] | false | mit-b2-VF2-finetuned-memes This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6547 - Accuracy: 0.8308 - Precision: 0.8272 - Recall: 0.8308 - F1: 0.8287 | bf2ca8d245878bb260e135737da15934 |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.3077 | 0.99 | 20 | 1.1683 | 0.5549 | 0.5621 | 0.5549 | 0.5286 | | 0.9359 | 1.99 |... | f1a278cabbb7c8483d6733684d74905c |
mit | ['spacy', 'token-classification'] | false | Basic Spacy BioNER pipeline, with a RoBERTa-based model [bsc-bio-ehr-es] (https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) and a dataset, Pharmaconer, a NER dataset annotated with substances, compounds and proteins entities. For further information, check the [official website](https://temu.bsc.es/pharmaconer/). V... | 4f799177a8dba3b12027bfae9559f2fb |
mit | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (4 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `NORMALIZABLES`, `NO_NORMALIZABLES`, `PROTEINAS`, `UNCLEAR` | </details> | ce7bc5630e56b68aa7e227ea287af029 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change in the course of training, we provide... | 775b50754322dfb84edf88a9f1dd25cb |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-2.8B-deduped has not been fine-tuned ... | 541499e51c657a255f30fec059c7cf4b |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-2.8B-deduped to produce factually accurate output. This model was ... | 3aa7d8f6c5ec6cfa067b1846d30d6bf9 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Training data Pythia-2.8B-deduped was trained on the Pile **after the dataset has been globally deduplicated**. [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sour... | bb784cabd6a80186e978a64d93759af9 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | **Mo Di Diffusion** This is the fine-tuned Stable Diffusion 1.5 model trained on screenshots from a popular animation studio. Use the tokens **_modern disney style_** in your prompts for the effect. **If you enjoy my work, please consider supporting me** [ Negative prompt: person human** _Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 1355059992, Size: 512x512_ This model was trained using the diffusers based dreambooth training by ShivamShrirao using prior-preservation loss and the _train-text-encoder_ fl... | 4ad6dd97d9a3b29ac0eeecb95a1d1cc3 |
creativeml-openrail-m | ['stable-diffusion', '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), [... | f5a9c373ada627c6b7a0fe457ecacb0d |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Gradio & Colab We also support a [Gradio](https://github.com/gradio-app/gradio) Web UI and Colab with Diffusers to run fine-tuned Stable Diffusion models: [, including training scripts. This is part of the [Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-communi... | dd283626bacc99c9d549701c67404959 |
cc-by-4.0 | ['spanish', 'roberta'] | false | Spanish mC4 The Spanish portion of mC4 containes about 416 million records and 235 billion words. ```bash $ zcat c4/multilingual/c4-es*.tfrecord*.json.gz | wc -l 416057992 ``` ```bash $ zcat c4/multilingual/c4-es*.tfrecord-*.json.gz | jq -r '.text | split(" ") | length' | paste -s -d+ - | bc 235303687795 ``` | b399fcf61c49059b7380cf61ea1db3f4 |
cc-by-4.0 | ['spanish', 'roberta'] | false | Team members - Javier de la Rosa ([versae](https://huggingface.co/versae)) - Eduardo González ([edugp](https://huggingface.co/edugp)) - Paulo Villegas ([paulo](https://huggingface.co/paulo)) - Pablo González de Prado ([Pablogps](https://huggingface.co/Pablogps)) - Manu Romero ([mrm8488](https://huggingface.co/)) - Ma... | b3bbcecf5b6ce7980ad675b54d5a25c6 |
cc-by-4.0 | ['spanish', 'roberta'] | false | summary-timeline-calendar-6) - [Community Week README](https://github.com/huggingface/transformers/blob/master/examples/research_projects/jax-projects/README.md) - [Community Week thread](https://discuss.huggingface.co/t/bertin-pretrain-roberta-large-from-scratch-in-spanish/7125) - [Community Week channel](https://disc... | 3da5f243b47aae4eab8b718bb2a14134 |
apache-2.0 | ['luke', 'named entity recognition', 'entity typing', 'relation classification', 'question answering'] | false | luke-japanese **luke-japanese** is the Japanese version of **LUKE** (**L**anguage **U**nderstanding with **K**nowledge-based **E**mbeddings), a pre-trained _knowledge-enhanced_ contextualized representation of words and entities. LUKE treats words and entities in a given text as independent tokens, and outputs contex... | 12b21f141cf042e7bb0ef0ee5d60ea57 |
mit | ['generated_from_trainer'] | false | nbme-deberta-large This model is a fine-tuned version of [microsoft/deberta-large](https://huggingface.co/microsoft/deberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8806 | d8c597625aff9896089b45f70fc87a78 |
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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoc... | 7c0ae7c70013ac37dfe1dcd5f489c62e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.358 | 1.0 | 1850 | 1.1622 | | 1.0073 | 2.0 | 3700 | 0.9461 | | 0.8837 | 3.0 | 5550 | 0.8806 | | 5c93e9b37147858a5f8a997386acca4f |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_... | 4e2eeafdae484df7eb3e9030188b1c9b |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-giga This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the gigaword dataset. It achieves the following results on the evaluation set: - Loss: 3.2594 - Rouge1: 26.6579 - Rouge2: 9.5505 - Rougel: 24.4987 - Rougelsum: 24.5146 - Gen Len: 13.5436 | 7675a655821a614df539408c2df456e0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 1.8512 | 1.0 | 23775 | 3.2594 | 26.6579 | 9.5505 | 24.4987 | 24.5146 | 13... | 7d509b47efec52aeff1bd0c5560696ed |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `jkang/espnet2_librispeech_100_conformer` - This model was trained by jaekookang using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/). - Gradio Demo: [🤗 ESPNet2 ASR Librispeech Conformer](https://huggingface.co/spaces/jkang/espnet2_asr_librispeech_100h) | f1b01c4d3161f952cd8f20a8ab791eef |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 140704c146f8beeed74973f5258379f6133dcdfb pip install -e . cd egs2/librispeech_100/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model jkang/espnet2_librispeech_100_conformer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | a2d4af43d3e0aab59fa10b8489b50142 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Fri Feb 11 01:42:52 KST 2022` - python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.10.1` - Git hash: `140704c146f8beeed74973f5258379f6133dcdfb` - Commit date: `Tue Feb 8 16:06:02 2022 -0500` - GPU: NVIDIA GeFor... | 354f8b6371b1f3d7041b8d32b21d4c69 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|94.5|5.1|0.4|0.7|6.3|56.6| |decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|84.8|13.7|1.5|2.1|17.3|80.7| |decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|94.... | be8cbff18b52d7ab9d3198020fc438f5 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|98.2|1.1|0.8|0.7|2.5|56.6| |decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|93.3|4.1|2.6|2.0|8.7|80.7| |decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|98... | 4c58601b73bb52b3be3db425b4cd7ae6 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev_clean|2703|69558|92.0|5.0|3.0|0.7|8.7|56.6| |decode_asr_asr_model_valid.acc.ave/dev_other|2864|64524|81.3|13.2|5.4|2.4|21.1|80.7| |decode_asr_asr_model_valid.acc.ave/test_clean|2620|66983|91.... | 916e6c174de13bc225f77bc0dd32db34 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_conformer_lr2e-3_warmup15k_amp_nondeterministic ngpu: 1 seed: 2022 num_workers: 4 num_att_plot: 0 dist_backend: nccl dist_init_method: env:// di... | d53453fe6c6739ecef4d26036a51e8a9 |
apache-2.0 | ['generated_from_trainer'] | false | hubert-large-xlsr-common1000asli-demo-colab-dd This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.0754 - Wer: 0.5189 | c5e5239a0059b2928b0648f4f7c90076 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | 2315cfd3d8871d695c1ba075618e591c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 8.5628 | 10.53 | 400 | 1.4949 | 0.9944 | | 0.7496 | 21.05 | 800 | 0.6398 | 0.6917 | | 0.3298 | 31.58 | 1200 | 0.6116 | ... | c2d37c639bb4a00739114f15f6e08219 |
mit | ['generated_from_trainer'] | false | sharp_goldberg 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 tomekko... | 63e48a566576c03f7ffc0b483a06954c |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.00056}, ... | 09ed774ac0e256405125702f707265aa |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | BibTeX entry and citation info ```bibtex @inproceedings{giorgi-etal-2021-declutr, title = {{D}e{CLUTR}: Deep Contrastive Learning for Unsupervised Textual Representations}, author = {Giorgi, John and Nitski, Osvald and Wang, Bo and Bader, Gary}, year = 2021, month = aug, booktitle ... | 783a791244a3fed068452f1091e985dd |
apache-2.0 | ['generated_from_trainer'] | false | mt5-small-finetuned-xsum This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 2.8351 - Rouge2: 0.3143 - Rougel: 2.6488 - Rougelsum: 2.6463 - Gen Len: 4.9416 | 3bd57212962ca82c6871b45c3e2d8b6b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | nan | 1.0 | 12753 | nan | 2.8351 | 0.3143 | 2.6488 | 2.6463 | 4.9416 ... | 7be598ef16a937d342dbda01a9bf3294 |
mit | ['generated_from_trainer'] | false | Klassifizierung-Gewerke This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0499 - F1: 0.9858 | ea2eded9586b7b7f00c27acc4e66d7fa |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.601 | 1.0 | 91 | 0.1376 | 0.9522 | | 0.1488 | 2.0 | 182 | 0.0639 | 0.9883 | | 0.0851 | 3.0 | 273 | 0.0499 | 0.9858 | ... | 6d3062077b1b2cf65a2bc49510b55e62 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Model description The model was initially trained on 680 000 hours of audio with corresponding transcripts from the internet, 65% of which was in english audio and 83 % of which had english transcripts. The model was then further trained for 4000 iterations, 500 of which as warm-up, on Swedish data from [Common_voice... | 11960077c9df7cd59d86380c717362f2 |
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