license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['multiberts', 'multiberts-seed_24'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_24') model = TFBertModel.from_pretrained("google/multiberts-seed_... | a054fe72187c5b03f4cd394fb74a0a9e |
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: 0.6064 - Matthews Correlation: 0.6198 | 61fd697aa16c0453836a4aca0a6af8bc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4308 | 1.0 | 534 | 0.4082 | 0.5856 | | 0.3759 | 2.0 | 1068 | 0.4661 | 0.5953 | | 0.2... | 593e0021477c2c04a3ada6a4afcee95b |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | JWST Deep Space Diffusion This is a fine-tuned Stable Diffusion model (based on v1.5) trained on images taken by the **_James Webb Space Telescope_**, as well as Judy Schmidt. Use the token **_JWST_** in your prompts to use the style (e.g., "jwst, green spiral galaxy"). [CKPT download link](https://huggingface.co/da... | ada891bcb854e1e389119cab7a91e285 |
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), [... | 811364f1bf3cba54ef0b36e89b6fa148 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Cyberpunk Edgerunners Fine-Tuned Anything v4.5 (WIP) This is a Dreambooth fine-tuned Anything v4.5, focusing on trying to capture the style from Cyberpunk Edgerunners, particular when it comes to fashionware (fashionable cyberware), faces, and fashion. Since it was trained with Anything v4.5 This model is intended t... | 0c0e43515411c7286a88298678da3cd4 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Cyberpunk Abraham Lincoln  **Prompt** ``` close-up portrait best quality (((ohwx))),1boy,(((cyberpunk Abraham Lincoln))), wearing netrunner suit,... | 3587342198033768488d3b90e399e056 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Edgerunner (Higher Res)  **Prompt** ``` portrait best quality 1girl, (((ohwx))), (((cyberpunk anime (scarlett johansson) ))), 1girl, (((dark black hair))), (short ha... | b4cbf5aee2ac7cf11dca902fca210169 |
apache-2.0 | ['generated_from_trainer'] | false | distilr2-lr2e05-wd0.08-bs32 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2807 - Rmse: 0.5298 - Mse: 0.2807 - Mae: 0.4198 | 3b54cda313322a5880390162bd5ef5bd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.277 | 1.0 | 623 | 0.2730 | 0.5225 | 0.2730 | 0.4164 | | 0.2731 | 2.0 | 1246 | 0.2732 | 0.5227 | 0.2732 ... | dc94f95d6feccfea1ce5082f99b0d99e |
mit | [] | false | rayne-weynolds on Stable Diffusion This is the `<rayne-weynolds>` 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 ... | ca9f011823420571f8bd4696927c8df7 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-triviaqa 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: 0.9252 | cf7edeac1e900804e57d593e12e1f912 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.9297 | 1.0 | 11195 | 0.9093 | | 0.6872 | 2.0 | 22390 | 0.9252 | | d30483384023555f104b87927e08ca29 |
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: 0.0663 - Precision: 0.9329 - Recall: 0.9478 - F1: 0.9403 - Accuracy: 0.9855 | b4573b1c3449331b32ae0ad6ee994b21 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0837 | 1.0 | 1756 | 0.0656 | 0.9151 | 0.9392 | 0.9270 | 0.9834 | | 0.0388 | 2.0 |... | ac309edb1784d837c14ad5bfeac8e38e |
mit | [] | false | Jos de Kat on Stable Diffusion This is the `<kat-jos>` 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 tr... | 386f418e615c4fd8b235ec498e9ea125 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-LARGE-NL2 (Deep-Narrow version) T5-Efficient-LARGE-NL2 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint an... | 49b2e0eaab23b59a03c4700ad1553a6e |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-large-nl2** - is of model type **Large** with the following variations: - **nl** is **2** It has **91.64** million parameters and thus requires *ca.* **366.55 MB** of memory in full precision (*fp32*) or **183.28 MB** of memory in half precision (*f... | 7f12d3ead81fe90d9afa97481b41ee92 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-mode-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.1232 - Accuracy: 0.96 - F1: 0.9598 | 6c7c4b1f95d6c59df7d5e06f6fe55e16 |
apache-2.0 | ['speech', 'audio', 'wav2vec2'] | false | Model description This is a ported version of [S3PRL's Wav2Vec2 for the SUPERB Intent Classification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/fluent_commands). The base model is [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base), which is pretrained on 16kHz sampled speech audio... | d88f8e38672780b516f85c8bea9f716e |
apache-2.0 | ['speech', 'audio', 'wav2vec2'] | false | Task and dataset description Intent Classification (IC) classifies utterances into predefined classes to determine the intent of speakers. SUPERB uses the [Fluent Speech Commands](https://fluent.ai/fluent-speech-commands-a-dataset-for-spoken-language-understanding-research/) dataset, where each utterance is tagged ... | f07f045849ac992f9e411622e99c2375 |
apache-2.0 | ['speech', 'audio', 'wav2vec2'] | false | Usage examples You can use the model directly like so: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor def map_to_array(example): speech, _ = librosa.load(example["file"], sr=16000, mono=True) example... | 4c5fe695cfa849c3f80df5e21ed0f0b5 |
apache-2.0 | ['speech', 'audio', 'wav2vec2'] | false | load a demo dataset and read audio files dataset = load_dataset("anton-l/superb_demo", "ic", split="test") dataset = dataset.map(map_to_array) model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ic") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-... | 016e44666ce589634858b3200a71094d |
apache-2.0 | ['speech', 'audio', 'wav2vec2'] | false | compute attention masks and normalize the waveform if needed inputs = feature_extractor(dataset[:4]["speech"], sampling_rate=16000, padding=True, return_tensors="pt") logits = model(**inputs).logits action_ids = torch.argmax(logits[:, :6], dim=-1).tolist() action_labels = [model.config.id2label[_id] for _id in actio... | 9dff27b2254649b11a2f5ea234e11564 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | DreamBooth model for the ppPadSeeEw concept trained by taesiri on the dataset. This is a Stable Diffusion model fine-tuned on the ppPadSeeEw concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of ppPadSeeEw dish** This model was created as part of the DreamBooth Hackathon 🔥. Visit ... | 4aa01764a54e6da2667c6e30557c8741 |
mit | ['roberta-base', 'roberta-base-epoch_54'] | false | RoBERTa, Intermediate Checkpoint - Epoch 54 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 ... | ed66f84c465a13a6d16c6251fade4d3e |
apache-2.0 | ['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Walker'] | false | Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Walker 2. Step 1: Write your model_id: unity/ML-Agents-Walker 3. Step 2: Select your *.nn or *.onnx file 4. Click on Watch the agent play 👀 | 6e9f19ab9094c93d2ddfc80bc3a208e5 |
mit | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingDynamic', 'onnx'] | false | ONNX This is an INT8 ONNX model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [Intel/MiniLM-L12-H384-uncased-mrpc](https://huggingface.co/Intel/MiniLM-L12-H384-uncased-mrpc). | aaea9538b8c5c7430f3584dc1276f6ff |
mit | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingDynamic', 'onnx'] | false | Load ONNX model: ```python from optimum.onnxruntime import ORTModelForSequenceClassification model = ORTModelForSequenceClassification.from_pretrained('Intel/MiniLM-L12-H384-uncased-mrpc-int8-dynamic') ``` | c6825e42012efd6cf07bddcec6258967 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-100-pad-early-lit This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1460 - Rouge1: 25.4944 - Rouge2: 7.9048 - Rougel: 16.2879 - Rougelsum: 20.883 - Gen... | 4c4681dcee31cc784bcdfeddf055691c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 100 | 1.0390 | 27.3059 | 10.0672 | 19.7294 | 23.0611 | 62... | 2c368686be08e55487f98aabd61df319 |
apache-2.0 | ['translation'] | false | opus-mt-fi-bg * source languages: fi * target languages: bg * OPUS readme: [fi-bg](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-bg/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 2181650fa02afc43206aab4e8ed3da57 |
unlicense | [] | false | Makes art that looks similar to that of Sam "samdoesart" Yang. He is not affiliated with this model though. Put the word "SamDoesArt" in your prompt, just like that but without the qoutes of course. It should work no matter where the term is, but I have thus far had best results with it as the first word at the start o... | c3055880612b0a1d05f00ae80e8c8158 |
apache-2.0 | ['translation'] | false | tgl-spa * source group: Tagalog * target group: Spanish * OPUS readme: [tgl-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tgl-spa/README.md) * model: transformer-align * source language(s): tgl_Latn * target language(s): spa * model: transformer-align * pre-processing: normalization +... | d0c2746aa019d44190906e55d33236f8 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: tgl-spa - source_languages: tgl - target_languages: spa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tgl-spa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['tl', 'es'] - src_constituents: {'tgl_Latn'} - tgt_... | 5e541dd1444e856ecda7f6a53ba8d722 |
bsd-3-clause | [] | false | Model Details FLAVA model was developed by the researchers at FAIR to understand if a single model can work across different modalities with a unified architecture. The model was pretrained solely using publicly available multimodal datasets containing 70M image-text pairs in total and thus fully reproducible. Unimod... | 784227b1819b2ffb10a03424182264c4 |
bsd-3-clause | [] | false | Model Type The FLAVA model uses a ViT-B/32 transformer for both image encoder and text encoder. FLAVA also employs a multimodal encoder on top for multimodal tasks such as vision-and-language tasks (VQA) which is a 6-layer encoder. Each component of FLAVA model can be loaded individually from `facebook/flava-full` ch... | d308f72f01b4eb59d5bdb8923e1fd93f |
bsd-3-clause | [] | false | FlavaModel FLAVA model supports vision, language and multimodal inputs. You can pass inputs corresponding to the domain you are concerned with to get losses and outputs related to that domain. ```py from PIL import Image import requests from transformers import FlavaProcessor, FlavaModel model = FlavaModel.from_pr... | 314d633a0a9a4307a150a7d84a127233 |
bsd-3-clause | [] | false | Pass only image from transformers import FlavaFeatureExtractor feature_extractor = FlavaFeatureExtractor.from_pretrained("facebook/flava-full") inputs = feature_extractor(images=[image, image], return_tensors="pt") outputs = model(**inputs) image_embeddings = outputs.image_embeddings | da0d2a0fb7884df7bc1d3bcba3e74c60 |
bsd-3-clause | [] | false | Pass only image from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained("facebook/flava-full") inputs = tokenizer(["a photo of a cat", "a photo of a dog"], return_tensors="pt", padding="max_length", max_length=77) outputs = model(**inputs) text_embeddings = outputs.text_embeddings ``` | b5bd0a5b6d17f40702eeb5b8b49a3f1d |
bsd-3-clause | [] | false | Encode Image ```py from PIL import Image import requests from transformers import FlavaFeatureExtractor, FlavaModel model = FlavaModel.from_pretrained("facebook/flava-full") feature_extractor = FlavaFeatureExtractor.from_pretrained("facebook/flava-full") url = "http://images.cocodataset.org/val2017/000000039769.jp... | d54e3f91214b562e6a225a893eda2caf |
bsd-3-clause | [] | false | Encode Text ```py from PIL import Image from transformers import BertTokenizer, FlavaModel model = FlavaModel.from_pretrained("facebook/flava-full") tokenizer = BertTokenizer.from_pretrained("facebook/flava-full") inputs = tokenizer(text=["a photo of a dog"], return_tensors="pt", padding="max_length", max_length=7... | b702d98f0df1d3d8325135ce4969a7c5 |
bsd-3-clause | [] | false | FlavaForPreTraining FLAVA model supports vision, language and multimodal inputs. You can pass corresponding inputs to modality to get losses and outputs related to that domain. ```py from PIL import Image import requests from transformers import FlavaProcessor, FlavaForPreTraining model = FlavaForPreTraining.from_... | b2e4ecdee36cb162f75f5d549ba2e005 |
bsd-3-clause | [] | false | FlavaImageModel ```py from PIL import Image import requests from transformers import FlavaFeatureExtractor, FlavaImageModel model = FlavaImageModel.from_pretrained("facebook/flava-full") feature_extractor = FlavaFeatureExtractor.from_pretrained("facebook/flava-full") url = "http://images.cocodataset.org/val2017/00... | 09b75e2c6872d47b0548084b454bc81a |
bsd-3-clause | [] | false | FlavaTextModel ```py from PIL import Image from transformers import BertTokenizer, FlavaTextModel model = FlavaTextModel.from_pretrained("facebook/flava-full") tokenizer = BertTokenizer.from_pretrained("facebook/flava-full") inputs = tokenizer(text=["a photo of a dog"], return_tensors="pt", padding="max_length", m... | da7a94ca4196f95d2939de4daed0e60e |
bsd-3-clause | [] | false | Intended Use The model is intended to serve as a reproducible research artifact for research communities in the light of models whose exact reproduction details are never released such as [CLIP](https://github.com/openai/CLIP) and [SimVLM](https://arxiv.org/abs/2108.10904). FLAVA model performs equivalently to these m... | 87c7939d59490964ec63e39ab79d0d9d |
bsd-3-clause | [] | false | Primary Intended Uses The primary intended users of these models are AI researchers. We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of foundation models which work across domains which in this case are vision... | b9effe722b4470f9d6dfcb71e8fab22e |
bsd-3-clause | [] | false | Out-of-Scope Use Cases Similar to CLIP, **Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed clas... | eb4ef57fb884e80fc304bac7321a0896 |
bsd-3-clause | [] | false | Data FLAVA was pretrained on public available 70M image and text pairs. This includes datasets such as COCO, Visual Genome, Localized Narratives, RedCaps, a custom filtered subset of YFCC100M, SBUCaptions, Conceptual Captions and Wikipedia Image-Text datasets. A larger portion of this dataset comes from internet and ... | a3c0933f0002331d2c29c0767afce803 |
bsd-3-clause | [] | false | Data Mission Statement Our goal with building this dataset called PMD (Public Multimodal Datasets) was two-fold (i) allow reproducibility of vision-language foundation models with publicly available data and (ii) test robustness and generalizability of FLAVA across the domains. The data was collected from already exis... | 71663277d77d98129a43404ef109bd6c |
bsd-3-clause | [] | false | Performance FLAVA has been evaluated on 35 different tasks from computer vision, natural language understanding, and vision-and-language reasoning. On COCO and Flickr30k retrieval, we report zero-shot accuracy, on image tasks, we report linear-eval and on rest of the tasks, we report fine-tuned accuracies. Generally... | 11f1cded499014a0719d46c06edec439 |
bsd-3-clause | [] | false | Image Understanding - ImageNet - Food100 - CIFAR10 - CIFAR100 - Cars - Aircraft - DTD - Pets - Caltech101 - Flowers102 - MNIST - STL10 - EuroSAT - GTSRB - KITTI - PCAM - UCF101 - CLEVR - FER 2013 - SUN397 - Image SST - Country 211 | 37b0f03670c94e5a691582439ed1f2fb |
bsd-3-clause | [] | false | Limitations Currently, FLAVA has many limitations. The image classification accuracy is not on par with CLIP on some of the tasks while text accuracy is not on par with BERT on some of the tasks suggesting possible room for improvement. FLAVA also doesn't work well on tasks containing scene text given the lack of sce... | fab06666e2e7badd9a34cbf953753af2 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Sakha Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Sakha using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | 19d31e434a8f96693f81e4ccaec4a466 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "sah", split="test[:2%]") processor = Wav2Vec2Processor.from_... | 29558037e726d22db2d50351000321df |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Sakha test data of Common Voice. ```python import torch import torchaudio import urllib.request import tarfile import pandas as pd from tqdm.auto import tqdm from datasets import load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | 5f1c836a69bbafb537d5a646b590e2de |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Download the raw data instead of using HF datasets to save disk space data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/sah.tar.gz" filestream = urllib.request.urlopen(data_url) data_file = tarfile.open(fileobj=filestream, mode="r|gz") data_file.... | 61dd9deeb903074140717dbff42fe787 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | remove repeated spaces sent = " ".join(sent.split()) return sent targets = [] preds = [] for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]): row["sentence"] = clean_sentence(row["sentence"]) speech_array, sampling_rate = torchaudio.load(clips_path + row["path"]) resampler = torchaudio... | 1f43bc144d99d27596ca344d32d3e056 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2716 - F1: 0.8458 | 533f422580ec6976e83b36215f8d49b1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5974 | 1.0 | 191 | 0.3265 | 0.7932 | | 0.2582 | 2.0 | 382 | 0.2887 | 0.8356 | | 0.1715 | 3.0 | 573 | 0.2716 | 0.8458 | ... | 1b2c138ba1f8d1e9c5db90de53691a77 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-bert-sst2-1_mobilebert_2_bert_3_gold_labels-distillation 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 glue dataset. It achieves the following results on the evaluation set: - Loss: 0.9350 - Accuracy: 0.8188 | 0e12d1a5b233ca9546009fefe5431fa1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.1041 | 1.0 | 4210 | 0.9350 | 0.8188 | | 0.1166 | 2.0 | 8420 | 0.9179 | 0.8188 | | 0.1127 | 3.0 | 12630 | 0.9083 ... | b1a23a9f6b1aaeff1c6089ee04a1cd54 |
apache-2.0 | ['translation'] | false | opus-mt-crs-sv * source languages: crs * target languages: sv * OPUS readme: [crs-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/crs-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | 9cfd38931de32c3a17b2ffc917c4056f |
apache-2.0 | ['generated_from_trainer'] | false | mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_162754.csv___topic_text_google_mt5_base This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 0.8027 - Rouge2: 0.0915 - Roug... | c0f028d4f38719494fae96bf35a519d0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.0 | 1.0 | 276732 | nan | 0.8027 | 0.0915 | 0.802 | 0.8026 | 6.340... | 18c722c4174fd306550101c10fdde50e |
mit | ['generated_from_trainer'] | false | bart-large-cnn-100-lit-evalMA This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 2.1514 - eval_rouge1: 27.8026 - eval_rouge2: 11.2998 - eval_rougeL: 21.4708 - eval_... | 1089f9b3d6e16e056c675b31ced3bf8c |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-custom-tokenizer-target-glue-qnli 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: 0.5086 - Accuracy: 0.75... | 7400cf083b50dc6d010c1e923c2dca78 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6301 | 0.15 | 500 | 0.5812 | 0.6989 | | 0.5891 | 0.31 | 1000 | 0.5807 | 0.6996 | | 0.5748 | 0.46 | 1500 | 0.5480 | 0.... | 96b55d4c5ad59d35c65c255831ea75a1 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_wavlm_s461 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sample... | 75bf7c9c0c340fd75f4c5829d9c5663e |
mit | ['codeswitching', 'spanish-english', 'language-identification'] | false | codeswitch-spaeng-lid-lince This is a pretrained model for **language identification** of `spanish-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home) This model is trained for this below repository. [https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswitch) To inst... | 344333bd5d9c915a575c7329d3c9612b |
mit | ['codeswitching', 'spanish-english', 'language-identification'] | false | your code-mixed sentence result = lid.identify(text) print(result) ``` * **Method-2** ```py from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-spaeng-lid-lince") model = AutoModelForTokenClassification.from_pretraine... | d74b83eb1989ccdeafc923aa72deddea |
creativeml-openrail-m | ['text-to-image'] | false | Hossam_768 Dreambooth model trained by HusseinHE with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-768 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/note... | 237584bc2143fc1981108457d3049e13 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-news This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1667 - Accuracy: 0.9447 - F1: 0.9448 | 915a5dbc86f9743b5c55fa55c0b4564d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2355 | 1.0 | 1875 | 0.1790 | 0.94 | 0.9401 | | 0.1406 | 2.0 | 3750 | 0.1667 | 0.9447 | 0.9448 | | cef9971b137a2ebd81adcbc360f50c7b |
apache-2.0 | ['generated_from_trainer'] | false | XLSR_Fine_Tuned_URDU This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_8_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.8115 - Wer: 0.4815 | 19c2bfe59391159be959c64624c5448c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.7221 | 3.25 | 1000 | 3.0131 | 0.9985 | | 1.6219 | 6.49 | 2000 | 0.9179 | 0.6336 | | 0.7747 | 9.74 | 3000 | 0.7975 | 0.5804 | |... | 8a2ae1d816d248ba84f62a244dab9bb6 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the PAN-X dataset. The model is trained in Chapter 4: Multilingual Named Entity Recognition in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-lang... | 63054cebdbdb0481dcc589a1ab7d9931 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2652 | 1.0 | 525 | 0.1602 | 0.8230 | | 0.1314 | 2.0 | 1050 | 0.1372 | 0.8527 | | 0.0806 | 3.0 | 1575 | 0.1388 | 0.8646 | ... | daf86fc50ceb626c62650dbc76d010ec |
apache-2.0 | ['transformers', 'uzroberta', 'uzbek', 'latin'] | false | uzroberta-sentiment-analysis This is a roBERTa-base model trained on ~23K reviews (more than 323K words) and finetuned for sentiment analysis of customer reviews. This model is built as part of author's project at the Uz-NLP 2022 Hackathon and it is suitable for Uzbek language. <b>Labels</b>: LABEL_0 -> Negative; ... | f316d67167ce1368f3820b45821f556d |
apache-2.0 | ['transformers', 'uzroberta', 'uzbek', 'latin'] | false | Model description This model is a fine-tuned version of [rifkat/uztext-3Gb-BPE-Roberta](https://huggingface.co/rifkat/uztext-3Gb-BPE-Roberta) on the [Uzbek App reviews for Sentiment Classification](https://github.com/SanatbekMatlatipov/uzbek-sentiment-analysis) dataset. It achieves the following results on the evalua... | 67de3a3f4a0e17864dd84a649c82ff42 |
apache-2.0 | ['transformers', 'uzroberta', 'uzbek', 'latin'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 - mixed_precision_tra... | f75c32bb2bc8bbffdf5c7a4598063294 |
apache-2.0 | ['transformers', 'uzroberta', 'uzbek', 'latin'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1595 | 1.0 | 1125 | 0.4438 | 0.8971 | 0.8523 | 0.8741 | 0.872 | | 0.1891 | 2.0 |... | 01cb174dad2fc088e8536d6fb16b9144 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, ... | 0a692492e5ca5ff1762722eba9003263 |
apache-2.0 | ['translation'] | false | opus-mt-en-bzs * source languages: en * target languages: bzs * OPUS readme: [en-bzs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-bzs/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | d19b0d9b9dda545664c80d2052042ba5 |
apache-2.0 | ['generated_from_trainer'] | false | bart-paraphrase-v8-e1 This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1597 - Rouge1: 73.0494 - Rouge2: 70.2389 - Rougel: 72.0086 - Rougelsum: 72.1 - Gen Len: ... | 5759a3f71a0ab2a81e61349c170a26c3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.0312 | 1.0 | 28370 | 0.1597 | 73.0494 | 70.2389 | 72.0086 | 72.1 |... | d158a7593d0c75ba02b2950be95a5faa |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 81522029063e42ce807d9d145b64d3f9aca45987 pip install -e . cd egs2/talromur/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model GunnarThor/talromur_f_tacotron2 ``` | 4be77d9f607f33a44a2bb69e2729acb6 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | TTS config <details><summary>expand</summary> ``` config: ./conf/tuning/train_tacotron2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp_f/tts_train_tacotron2_raw_phn_none ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_wo... | 73bd9a5f81514f9f10fe5f91d44b6575 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-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: 0.4283 - eval_wer: 0.3847 - eval_runtime: 133.4799 - eval_samples_per_second: 12.586 -... | d4b1f020d1a33e1b903bbb50a6c55204 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small hy This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5228 - Wer: 107.3684 | af03a2029822e0a8968a68bb542fd5cf |
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: 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_sche... | 07493aafd3a9547e951dca9b81d7f41f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5676 | 0.5 | 50 | 0.5799 | 100.7895 | | 0.4569 | 1.0 | 100 | 0.5228 | 107.3684 | | b56d9b3a62b6b02c697db75fab84f3e2 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Mreyesart1 Dreambooth model trained by Mreyesart with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get started... | 397e3e26b4d25b252e483f75e567350e |
apache-2.0 | ['automatic-speech-recognition', 'es', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | xls-r-es-test-lm This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ES dataset. It achieves the following results on the test set with lm model: - Loss: 0.1304 - WER: 0.094 - CER: 0.031 It achiev... | 5d623f716f246f3c712ba6fcdf8ff0a3 |
apache-2.0 | ['automatic-speech-recognition', 'es', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-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 - lr_sch... | 6cef38495a345d62e0cf813c05a95de3 |
apache-2.0 | ['automatic-speech-recognition', 'es', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 2.9613 | 0.07 | 500 | 2.9647 | 1.0 | | 2.604 | 0.14 | 1000 | 1.8300 | 0.9562 | | 1.177 | 0.21 | 1500 | 0.3652 | 0.307... | be9e53693b4928b57f1ee7bdb3be9602 |
mit | ['summarization'] | false | ViT5-large Finetuned on `vietnews` Abstractive Summarization State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese. [](h... | 0e8b1e5d63190ea054d0f74c60ac97ae |
mit | ['summarization'] | false | How to use For more details, do check out [our Github repo](https://github.com/vietai/ViT5) and [eval script](https://github.com/vietai/ViT5/blob/main/eval/Eval_vietnews_sum.ipynb). ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-large-... | 1f5c5ee9a93c840a06d1c3b55d9db6fb |
mit | ['summarization'] | false | Citation ``` @inproceedings{phan-etal-2022-vit5, title = "{V}i{T}5: Pretrained Text-to-Text Transformer for {V}ietnamese Language Generation", author = "Phan, Long and Tran, Hieu and Nguyen, Hieu and Trinh, Trieu H.", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Asso... | 173265b56a0b9616d1c7c96ba622e1ac |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | newnew This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset. It achieves the following results on the evaluation set: - Loss: 11.4375 - Wer: 1.0 | 339f5bf025a8f9835c060a6f0f41d154 |
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