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 | ['text generation', 'pytorch', 'causal-lm'] | false | ReGPT-125M-200G This model was trained on GPT-Neo-125M with [Mengzi Retrieval LM](https://github.com/Langboat/mengzi-retrieval-lm). For more details, please refer to this [document](https://github.com/Langboat/mengzi-retrieval-lm/blob/main/README.md). | 2a081a389e134b7945e0b38f7da4c884 |
apache-2.0 | ['text generation', 'pytorch', 'causal-lm'] | false | How to use You have to use a forked transformers: https://github.com/Langboat/transformers ```python from transformers import Re_gptForCausalLM model = Re_gptForCausalLM.from_pretrained('Langboat/ReGPT-125M-200G') ``` | 8c8dc3c9e285e8432df5595010423ba1 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Galverse-Diffusion-wf-8888 Dreambooth model trained by jarvissan with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLast... | 90dfecbb200e0b550157ce06f4ad8e7f |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Example images 1 Dragon gal breaving fire fullbody, draong scales, wings, tail, short red hair, purple eyes, pose from above ![Test gal 1](https://huggingface.co/sd-dreambooth-library/galverse-diffusion-wf-8888/resolve/main/01483-1751575086-dragon20gal20breaving20fire2020full20body20dragon20scales20wings20tail20sh... | 4f44016e46d823794cc9e78c6799c18b |
apache-2.0 | ['t5', 'contrastive learning', 'ranking', 'decoding', 'metric learning', 'pytorch', 'text generation', 'retrieval'] | false | Method-2: Loading the model with HuggingFace APIs ``` from transformers import T5Tokenizer, AutoModel tokenizer = T5Tokenizer.from_pretrained(f"google/t5-v1_1-xl") model = AutoModel.from_pretrained("kalpeshk2011/rankgen-t5-xl-pg19", trust_remote_code=True) ``` | 0861d7ee1adc06da1b98d78b1e9f1c3c |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Ayaka_DB Dreambooth model trained by Falon with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffus... | e8a25f8c941ea30837a682bde3505f19 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_gender_male-0_female-10_s469 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-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 ... | 54b20833297f0a427255890a70322eef |
mit | ['generated_from_trainer'] | false | pegasus-base-qag-bg-finetuned-punctuation-bg This model is a fine-tuned version of [rmihaylov/pegasus-base-qag-bg](https://huggingface.co/rmihaylov/pegasus-base-qag-bg) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0318 | 1344b4ee15d0365524df5c4b8ec09772 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.0563 | 1.0 | 4063 | 0.0279 | | 0.0301 | 2.0 | 8126 | 0.0260 | | 0.0227 | 3.0 | 12189 | 0.0259 | | 0.0178 | 4.0 | 16252 | 0.0281 ... | e0d26be9de4f1dc72a4b93ac00622bb7 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'Tranformer', 'pytorch', 'speechbrain'] | false | CRDNN with CTC/Attention and RNNLM trained on LibriSpeech This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on LibriSpeech (EN) within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrai... | 9d19d6391183dae880278c3926d2966a |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'Tranformer', 'pytorch', 'speechbrain'] | false | Pipeline description This ASR system is composed of 3 different but linked blocks: 1. Tokenizer (unigram) that transforms words into subword units and trained with the train transcriptions of LibriSpeech. 2. Neural language model (Transformer LM) trained on the full 10M words dataset. 3. Acoustic model (CRDNN + CTC/A... | 22d180044084f81f64273b5d7680e833 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'Tranformer', 'pytorch', 'speechbrain'] | false | Transcribing your own audio files (in English) ```python from speechbrain.pretrained import EncoderDecoderASR asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-transformerlm-librispeech", savedir="pretrained_models/asr-crdnn-transformerlm-librispeech") asr_model.transcribe_file("speechbrain/as... | 5c626750f95c75d51ee98941bf801b9c |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'Tranformer', 'pytorch', 'speechbrain'] | false | Training The model was trained with SpeechBrain (Commit hash: 'eca313cc'). To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``... | 011d72c1eebef0420de65b4d5642fa89 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | convnext_manuscript_iiif This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-base-224-22k) on the davanstrien/iiif_manuscripts_label_ge_50 dataset. It achieves the following results on the evaluation set: - Loss: 5.5856 - F1: 0.0037 | 2e1351883ae1fd2027fe1d8b2b3aa507 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30.0 - mixed_precision_training: Native AMP | a40a0216ebe801f29c7ec43f45d7aa0a |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 6.5753 | 1.0 | 2038 | 6.4121 | 0.0016 | | 5.9865 | 2.0 | 4076 | 5.9466 | 0.0021 | | 5.6521 | 3.0 | 6114 | 5.7645 | 0.002... | 0ec53aa0281b0a54853a2d0a5b67d9a9 |
mit | ['generated_from_trainer'] | false | deberta-v3-small-finetuned-Disaster-Tweets-Part1 This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4014 - Accuracy: 0.8564 - F1: 0.8557 | 688ca622b714186d03cb4db595a5e7f3 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - 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: 2 - mixed_precision_tra... | b8558af4cab2dc1558286583268ae9d6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 203 | 0.3828 | 0.8415 | 0.8414 | | No log | 2.0 | 406 | 0.4014 | 0.8564 | 0.8557 | | 5989cdf3a810d9d305b1ca54e85cfff4 |
apache-2.0 | ['generated_from_keras_callback'] | false | evangeloc/t5-small-finetuned-xsum 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: - Train Loss: 2.7203 - Validation Loss: 2.4006 - Train Rouge1: 28.1689 - Train Rouge2: 7.9798 - Train Rougel: 22.6998 - Tr... | 488140d48f802ee988ec26293fc2faf4 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 2.7203 | 2.4006 | 28.1689 | 7.9798 ... | 36776201233d2abd364697dc1ab2c9f9 |
cc-by-4.0 | [] | false | Model description This is the T5-3B model for the "classify" component of System 4's "Classify then explain" pipeline, as described in our paper Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE, FigLang workshop @ EMNLP 2022 (Arxiv link: https://arxiv.org/abs/2210.16407) System 4: Two-step Sys... | 908c9d605218ef36a81317d1008a636a |
cc-by-4.0 | [] | false | How to use this model? We provide a quick example of how you can try out the "classify" component of System 4 in our paper with just a few lines of code: ``` >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM >>> model = AutoModelForSeq2SeqLM.from_pretrained("allenai/System4_classify_FigLang2022") >>> ... | 039522f469a786c04b93873b4a5e983d |
cc-by-4.0 | [] | false | Model details This model is a fine-tuned version of [t5-3b](https://huggingface.co/t5-3b). It achieves the following results on the evaluation set: - Loss: 0.0604 - Rouge1: 95.0232 - Rouge2: 0.0 - Rougel: 95.0232 - Rougelsum: 95.0232 - Gen Len: 3.4074 | 51d29be14168ef13ed1e1c9d012fd6f5 |
cc-by-4.0 | [] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 0.1221 | 0.33 | 1000 | 0.1460 | 91.7717 | 0.0 | 91.9044 | 91.8381 | 3.475... | a800432b40bb42a230d58ad0ebe81a50 |
apache-2.0 | ['generated_from_trainer'] | false | long-t5-local-base-finetuned This model is a fine-tuned version of [google/long-t5-local-base](https://huggingface.co/google/long-t5-local-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 9.2722 - Rouge1: 3.8848 - Rouge2: 0.5914 - Rougel: 3.5038 - Rougelsum: 3.7022 - Gen L... | 57e3d32d9d17682808b2f202a6558094 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50000 | 5423def48af084a29b26d704fbbc53d1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 0.16 | 100 | 342.4395 | 0.0 | 0.0 | 0.0 | 0.0 | 19.0 ... | df91981972f2d9ecbaee6c5d4893e968 |
mit | ['conversational'] | false | DialoGPT Trained on the Speech of a TV Series Character This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a TV series character, Sheldon from [The Big Bang Theory](https://en.wikipedia.org/wiki/The_Big_Bang_Theory). The data comes from [a Kaggle TV series ... | 70ef5fb0e5a6e7ca744c5c08df3cfa7d |
mit | [] | false | Caitlin Fairchild, character, gen13 comics, by J. Scott Campbell on Stable Diffusion This is the `<Caitlin-Fairchild>` 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/... | 47dd259dabc999be8e6f28079bc4e27f |
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: 0.3399 - Accuracy: 0.901 - F1: 0.8976 | f0f117b251cb46617978ab7b2e017004 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 125 | 0.5129 | 0.8465 | 0.8300 | | 0.7331 | 2.0 | 250 | 0.3399 | 0.901 | 0.8976 | | d2ca07523bf7d3686abbaa8d57a893c4 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xlsr-53-espeak-cv-ft-mhr2-ntsema-colab This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7562 - Wer: 0.7993 | f7a4245f9f416bd28c759a7902dbb819 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.5636 | 5.79 | 400 | 1.8357 | 1.0 | | 1.6348 | 11.59 | 800 | 0.6797 | 0.8528 | | 0.8624 | 17.39 | 1200 | 0.6651 | 0.8194 | |... | 30783920c9fab77908373c2b776d617d |
mit | [] | false | model by no3 This your the Stable Diffusion model fine-tuned the azura-sd-1.4-beta3 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **sks_azura** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.g... | 9d8e23b7a629dae79aa0a4fb159d7b2e |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-restaurant-reviews This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on a subset of the Yelp restaurant reviews dataset. It achieves the following results on the evaluation set: - Loss: 3.4668 | fa2a9aa8611bc968ebb38ce0b9d25f5e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.6331 | 1.0 | 2536 | 3.5280 | | 3.5676 | 2.0 | 5072 | 3.4793 | | 3.5438 | 3.0 | 7608 | 3.4668 | | 3c93c87492465d74d7f7af3380785370 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Marathi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [OpenSLR SLR64](http://openslr.org/64/) dataset. Note that this data contains only female voices. Please keep this in mind before using the model for your task, alth... | 4a1e301796b749c46285bbc53a2b8e10 |
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, assuming you have a dataset with Marathi `sentence` and `path` fields: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | 1f38b0b9d8329ae2dddaae0a7bf83315 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr") resampler = torchaudio.transforms.Resample(48_000, 16_000) | 6e2c0c9071ccbae74add6cdd97980a09 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on 10% of the Marathi data on OpenSLR. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re | 47e6f37565c87584bf57aa4b86a4cefb |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr") model.to("cuda") chars_to_ignore_r... | f1b0d13876cee9f0d02e12e089960374 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_... | e66e725898ed7871d62dee10b26bca85 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training 90% of the OpenSLR Marathi dataset was used for training. The colab notebook used for training can be found [here](https://colab.research.google.com/drive/1_BbLyLqDUsXG3RpSULfLRjC6UY3RjwME?usp=sharing). | 1fac2811dd2fd2ce23b968d6f714aa81 |
mit | [] | false | TEST on Stable Diffusion This is the `<AIO>` 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 o... | 64e872836a46cddb71ab2c3c0761eca0 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-ner-finegrained This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3198 - Precision: 0.6498 - Recall: 0.6861 - F1: 0.6674 - Accuracy: 0.9083 | 1affb930c954955181a13240514833ae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3214 | 1.0 | 16472 | 0.3173 | 0.6260 | 0.6728 | 0.6486 | 0.9040 | | 0.266 | 2.0 ... | 899ca10d3f6183239272ac8957e1d9d4 |
mit | ['bart', 'pytorch'] | false | BART-IT: Italian pretraining for BART sequence to sequence model BART-IT is a sequence-to-sequence model, based on the BART architecture that is specifically tailored to the Italian language. The model is pre-trained on a [large corpus of Italian text](https://huggingface.co/datasets/gsarti/clean_mc4_it), and can be ... | 9c28f1744f7bb0750c455dfe02b7a63f |
mit | ['bart', 'pytorch'] | false | Fine-tuning The model in this repository is a pre-trained model without any fine-tuning. In order to use the model for a specific task, you can fine-tune it on a specific dataset. The model has been fine-tuned for the abstractive summarization task on 3 different Italian datasets: - [FanPage](https://huggingface.co... | 3fb1b858f2286d2467d0dac021db4799 |
mit | ['bart', 'pytorch'] | false | Usage In order to use the model, you can use the following code: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("morenolq/bart-it") model = AutoModelForSeq2SeqLM.from_pretrained("morenolq/bart-it") input_ids = tokenizer.encode("Il modello BART-IT è... | 3a841a2f63cef234d486c7fb2202a79d |
apache-2.0 | ['generated_from_trainer'] | false | jlg-model This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/datificate/gpt2-small-spanish) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4882 | f838e704f9af6f6c9d7e91b5e0de2f87 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 42 | 3.5391 | | No log | 2.0 | 84 | 3.5001 | | No log | 3.0 | 126 | 3.4882 | | 6a78915bfb1deff083ac2d7c5b4dbef3 |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-pubmed-v3-e100 This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1806 - Rouge1: 59.4159 - Rouge2: 48.867 - R... | 0ea4ec8edcdb4ffcd8e90a1048780f29 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 - mixed_precision_training: Native AMP | fbc8dfc252eb268999b561ecb66d4436 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 1.2541 | 1.0 | 795 | 0.9350 | 52.5594 | 32.6314 | 35.2302 | 50.1767 ... | e19102faaf5a4a701eb303ad5c32568a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small dysarthric Dutch This model is a fine-tuned version of [qmeeus/whisper-small-nl](https://huggingface.co/qmeeus/whisper-small-nl) on the data/copas copas-full dataset. It achieves the following results on the evaluation set: - Loss: 0.4702 - Wer: 22.1638 | ed5070e1160f3ae8f88b64141c6b2637 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 8d31cfbde8b1cef01844d2bdff18bdd0 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.1618 | 0.05 | 500 | 0.3787 | 28.9235 | | 0.0583 | 1.05 | 1000 | 0.3732 | 25.7702 | | 0.0382 | 2.05 | 1500 | 0.4001 | 2... | 9dd9b2192235d78ccd22b634352ef172 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | f9a31d3de420018308a65b3a3692074e |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_age_teens-5_sixties-5_s870 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 45a4c5a5033b29771eab466a52291731 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-issues-128 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: 1.2551 | 42fa619ecdacfaaff783144e35433e2a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0984 | 1.0 | 291 | 1.7081 | | 1.6512 | 2.0 | 582 | 1.4289 | | 1.4854 | 3.0 | 873 | 1.3845 | | 1.3924 | 4.0 | 1164 | 1.3844 ... | ae182e2d082314b4dc1b99b016871343 |
apache-2.0 | ['generated_from_trainer'] | false | swin-finetuned-food101-e3 This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 0.2714 - Accuracy: 0.9227 | c5cca3809876df483a4822ee35c543c0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5565 | 1.0 | 1183 | 0.3939 | 0.8856 | | 0.3466 | 2.0 | 2366 | 0.2936 | 0.9156 | | 0.1172 | 3.0 | 3549 | 0.2714 | 0.... | 33bf5cc088824f49a652d7da754b3c34 |
gpl-2.0 | ['corenlp'] | false | Core NLP model for english-kbp CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sent... | 404a1c993b8591c331754ee51b4c408f |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_500k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 500k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different ... | 44b4930bebff558d10416af2e48a252a |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_500k'] | 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_2-step_500k') model = TFBertModel.from_pretrained("google/multibe... | af1aeb36352b556b91cdf4eeee1b5ad9 |
creativeml-openrail-m | ['text-to-image'] | false | training params ```json { "pretrained_model_name_or_path": "CompVis/stable-diffusion-v1-4", "instance_data_dir": "./a9054d36-59d1-4374-ab1f-2ca457b539e2/instance_data", "class_data_dir": "./class_data/a-portrait-of-a-person", "output_dir": "./a9054d36-59d1-4374-ab1f-2ca457b539e2/", "with_prior_pres... | 5afc05f88b14de14e01778ea17e62762 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Wed Apr 27 09:30:57 EDT 2022` - python version: `3.8.5 (default, Sep 4 2020, 07:30:14) [GCC 7.3.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `21d19be00089678ca27f7fce474ef8d787689512` - Commit date: `Wed Mar 16 08:06:52 2022 -0400` | 0b3e0db38fb019ee3c8c5f7f32068b2a |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/dev_clean|2703|54402|97.7|2.1|0.2|0.3|2.6|31.5| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/dev_other|2864|50948|93.8|5.6|0.6|0.6|6.8|50.8| |decode_lm_weight0.0_asr_mode... | dd2574f95e783de79b6ef60830ff7ee6 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/dev_clean|2703|288456|99.4|0.4|0.3|0.2|0.9|31.5| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/dev_other|2864|265951|97.7|1.4|0.9|0.8|3.0|50.8| |decode_lm_weight0.0_asr_mo... | f33eb38f6213854b0a000b58ea2f5587 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/dev_clean|2703|68010|97.2|2.1|0.7|0.4|3.3|31.5| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/dev_other|2864|63110|92.7|5.6|1.7|1.2|8.6|50.8| |decode_lm_weight0.0_asr_mode... | 5955694316a7ba60e38ebec6caabf2f8 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/transducer/train_conformer-rnn_transducer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_conformer-rnn_transducer_raw_en_bpe5000_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 0 dist_bac... | 5fe8ebedbdf0f1696fb0ef7f45db27c5 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-En... | d0eb12a64082e6cda7667fb18267906b |
apache-2.0 | [] | false | Cross-Encoder for MS MARCO - EN-DE This is a cross-lingual Cross-Encoder model for EN-DE that can be used for passage re-ranking. It was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task. The model can be used for Information Retrieval: See [SBERT.net Retrieve & Re... | 460de442989ec7de68263b60cb60e475 |
apache-2.0 | [] | false | Usage with SentenceTransformers When you have [SentenceTransformers](https://www.sbert.net/) installed, you can use the model like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('model_name', max_length=512) query = 'How many people live in Berlin?' docs = ['Berlin has a populatio... | f2cb87118b22f6a2ddb35d2c0108a75c |
apache-2.0 | [] | false | Usage with Transformers With the transformers library, you can use the model like this: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('model_name') tokenizer = AutoTokenizer.from_pretrained('model_name') f... | d87d5132f29f054791fe3d89021f0141 |
apache-2.0 | [] | false | Performance The performance was evaluated on three datasets: - **TREC-DL19 EN-EN**: The original [TREC 2019 Deep Learning Track](https://microsoft.github.io/msmarco/TREC-Deep-Learning-2019.html): Given an English query and 1000 documents (retrieved by BM25 lexical search), rank documents with according to their releva... | f5772d6224d5bd825bdddd74096f52a5 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-home-8-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3789 - Accuracy: 0.3356 | 0ccf58b2104a69aa54726b5fe8735636 |
creativeml-openrail-m | [] | false | Basic explanation Token and Class words are what guide the AI to produce images similar to the trained style/object/character. Include any mix of these words in the prompt to produce verying results, or exclude them to have a less pronounced effect. There is usually at least a slight stylistic effect even without the... | e2febb228ae84886585a850489fe96e0 |
creativeml-openrail-m | [] | false | Usage When using this model by itself, it is not necessary to use any keywords, but they will strengthen the style effect. Rossmix produces the best results, while ross-any also works quite well. Ross based on wd has a more of an illustration feel, but works best when mixed with other models. Rossmix and ross-any m... | 9989dbae12db5db14edf313a8a1f455a |
creativeml-openrail-m | [] | false | Example images Example images also include 3 extra mixes that include ross or ross-any. Positive: `m_ross, (illustration), (masterpiece), ((best quality)), (ultra-detailed), (official art), ((portrait of a beautiful girl)), upper body`\ Negative: `lowres, bad anatomy, bad hands, text, error, missing fingers, extra d... | 4a4c7d07ecaa46ca0fca58df4ade1951 |
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: 0.3554 - Accuracy: 0.902 - F1: 0.9001 | b43b456b902568ca52ad589bd2f2a083 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.0993 | 1.0 | 125 | 0.5742 | 0.8045 | 0.7747 | | 0.4436 | 2.0 | 250 | 0.3554 | 0.902 | 0.9001 | | 66b28d179e0c949398d742da005cea9c |
apache-2.0 | ['generated_from_trainer'] | false | vit-for-kaggle-mayo-clinic This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5538 - Accuracy: 0.7616 | 0bb8d3be41f9780f5104bc93d2b1a940 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 8 | 5e4bf0cc8746fcf8d00ace5ae396cda2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 10 | 0.5944 | 0.7483 | | No log | 2.0 | 20 | 0.5640 | 0.7483 | | No log | 3.0 | 30 | 0.5582 | 0.... | 6280c55635aec42fe94033f580ae9c12 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large v2 Azerbaijani This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 az dataset. It achieves the following results on the evaluation set: - Loss: 0.9435 - Wer: 38.4615 | d64e1704e72253c5827045118f97f281 |
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: 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_sched... | 3dc9d2e5a2a8f35373dcd09e66b62fda |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.0 | 999.0 | 1000 | 0.8373 | 39.6450 | | 0.0 | 1999.0 | 2000 | 0.9435 | 38.4615 | | 0.0 | 2999.0 | 3000 | 1.0010 | 4... | fe82c6bb8baf5b3f941554d3b797d064 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qnli-target-glue-qqp This model is a fine-tuned version of [muhtasham/small-mlm-glue-qnli](https://huggingface.co/muhtasham/small-mlm-glue-qnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3296 - Accuracy: 0.8511 - F1: 0.8117 | 88a7b05faf24623e1a8e8aaf8d73e744 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4762 | 0.04 | 500 | 0.4247 | 0.7897 | 0.7473 | | 0.4188 | 0.09 | 1000 | 0.3880 | 0.8126 | 0.7702 | | 0.4011 |... | a8a2d8aa0971b8e9cde30642afe6c693 |
apache-2.0 | ['Tensorflow'] | false | Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) . Regarding the dataset, please c... | 57b952cbb6512e8f73008db454dd2198 |
apache-2.0 | ['Tensorflow'] | false | This is an inference model only To reduce the size of the checkpoint we removed all variables that are not necessary for inference. Therefore it cannot be used for fine-tuning. To fine tune this model please check this [model](https://huggingface.co/deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_rc). | bd0695692260d3a7269350dbe0968835 |
apache-2.0 | ['Tensorflow'] | false | How this model was trained. To recreate the model run on the **deep**doctection framework, run: ```python >>> import os >>> from deep_doctection.datasets import DatasetRegistry >>> from deep_doctection.eval import MetricRegistry >>> from deep_doctection.utils import get_configs_dir_path >>> from deep_doctection.tra... | eb144d6d863aaca8bd0434a1e56acc67 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 28695114f2771ac3d2a9cc0b5fb30a2c3262e49a pip install -e . cd egs2/librimix/asr1 ./run.sh --skip_data_prep false --skip_train tr... | 6d3e47101561e670154df5458d394ae4 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Thu Nov 10 14:58:09 EST 2022` - python version: `3.9.13 (main, Aug 25 2022, 23:26:10) [GCC 11.2.0]` - espnet version: `espnet 202209` - pytorch version: `pytorch 1.12.1` - Git hash: `b3c185d5d707bb385b74f42df2cc59bcf7d7e754` - Commit date: `Wed Nov 9 22:00:30 2022 -0500` | 46256ed6bacb9277f264ca745a37bcaf |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_multi_asrtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/test|6000|111243|80.4|17.4|2.2|3.8|23.5|88.0| | 5925b81e8aa5ab55e09b5324ad039ac3 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_multi_asrtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/test|6000|590408|90.5|6.1|3.5|3.9|13.5|88.0| | 04f2c667389e2188fa37e25e1b625d96 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_transformer_multispkr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transformer_multispkr_raw_en_char_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl di... | 7b5f0b1e06822200fcb76cb5aa6997c5 |
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