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 | ['hf-asr-leaderboard', '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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 500 - mixed_precision... | 11aa6e9ef5061b8f5f377baf5f34b045 |
apache-2.0 | ['generated_from_keras_callback'] | false | nandysoham/7-clustered This model is a fine-tuned version of [Rocketknight1/distilbert-base-uncased-finetuned-squad](https://huggingface.co/Rocketknight1/distilbert-base-uncased-finetuned-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5661 - Train End Logits Acc... | eb90cfab137acf0203c104086c19f6ee |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 555d7adde8ca52cf572b8a8c75215753 |
apache-2.0 | ['automatic-speech-recognition', 'ja'] | false | exp_w2v2t_ja_vp-it_s621 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 80407b0aa6ddac28cd12d7c05bd419e5 |
cc-by-4.0 | ['generated_from_trainer'] | false | hing-mbert-finetuned-ours-DS This model is a fine-tuned version of [l3cube-pune/hing-mbert](https://huggingface.co/l3cube-pune/hing-mbert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1569 - Accuracy: 0.71 - Precision: 0.6665 - Recall: 0.6668 - F1: 0.6658 | b0bcc76051274491458a35934c78a257 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.7704 | 1.99 | 199 | 0.7093 | 0.68 | 0.6679 | 0.6463 | 0.6309 | | 0.2597 | 3.98 |... | 8326050a70bb2ca178ef682e32df00bd |
afl-3.0 | [] | false | My model is an inverted image detector and can help detect if images are inverted with 99% accuracy. \ I used a dataset containing people with and without masks. I trained my model on ~ 300 images of people without masks and tested it on ~ 60 of the same images distribution: \ author = {Prasoon Kottarathil}, \ title = ... | 688562b77e2216f73991e08efd0dbb52 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the Mr.Black concept trained by secularbird. This is a Stable Diffusion model fine-tuned on the Mr.Black concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of mrblack cat** This model was created as part of the DreamBooth Hackathon ๐ฅ. Visit the [organisation p... | 690b4c58db489b1acde240f164462e9b |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Description This is a Stable Diffusion model fine-tuned on Mr.Black images for the wildcard theme, for the Hugging Face DreamBooth Hackathon, from the HF CN Community, corporated with the HeyWhale. | 3e19e87c17418823f499424f765495e3 |
apache-2.0 | ['generated_from_trainer'] | false | openai/whisper-large-v2 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8486 - Wer: 20.2149 | 5aa488807ba3810a4136229622b293ef |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - train... | e7e5d38848ca6c05a6627fed8bd2f486 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2767 | 0.2 | 1000 | 1.0654 | 25.3972 | | 0.2528 | 0.4 | 2000 | 0.9370 | 22.1311 | | 0.3038 | 0.6 | 3000 | 0.9966 | 20.575... | 127e64e4ccbf488f9f4110e820e92ced |
mit | ['audio', 'music', 'generation', 'tensorflow'] | false | Model provided by: Broccaloo Pretrained musika_s3rl_happy_hardcore model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation. Introduced in [this paper](https://arxiv.org/abs/2208.08706). | f146a6782fffec5e775025c91906ae38 |
mit | ['audio', 'music', 'generation', 'tensorflow'] | false | How to use You can generate music from this pretrained musika_s3rl_happy_hardcore model using the notebook available [here](https://colab.research.google.com/drive/1HJWliBXPi-Xlx3gY8cjFI5-xaZgrTD7r). | 7441a4bfb4ce99851512168423731972 |
apache-2.0 | ['tapas', 'sequence-classification'] | false | TAPAS mini model fine-tuned on Tabular Fact Checking (TabFact) This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_mini_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was... | 86c94bdc44fdcffcea0987a56dbb0d12 |
afl-3.0 | ['bert-base-chinese'] | false | This project page is about the pytorch code implementation of GlyphBERT by the HITsz-TMG research group.  GlyphBERT is a Chinese pre-training model that includes Chinese character glyph features.It renders t... | 42012fd1ab4c87aec3588ddb2418f7b4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_13_binary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7433 - F1: 0.8138 | 5781b08055caead0dd46bd1c9fc2d1a3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 291 | 0.4101 | 0.8087 | | 0.4128 | 2.0 | 582 | 0.4605 | 0.8197 | | 0.4128 | 3.0 | 873 | 0.5011 | 0.8130 | |... | 8683da524551c28a9a0e7adf00071030 |
apache-2.0 | ['generated_from_keras_callback'] | false | marianmt-finetuned-netspeak-tgl-to-eng This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tl-en](https://huggingface.co/Helsinki-NLP/opus-mt-tl-en) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7277 - Validation Loss: 2.0459 - Train Bleu: 33.5501 - Train Gen ... | 8c5b063797b9ee8402b241e8cc60a680 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | 56f208c8939b5a46050e054f75906325 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Bleu | Train Gen Len | Epoch | |:----------:|:---------------:|:----------:|:-------------:|:-----:| | 5.4267 | 4.5907 | 3.3310 | 11.7129 | 0 | | 4.6862 | 4.1720 | 3.5594 | 10.4752 | 1 | | 4.4077 | 3.9852... | 59efcaa36c10274813cf8de961d63335 |
apache-2.0 | ['automatic-speech-recognition', 'ja'] | false | exp_w2v2t_ja_no-pretraining_s143 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has bee... | ae98f075ad7495d8f37cf9aac2296d64 |
apache-2.0 | ['generated_from_trainer'] | false | flan-t5-xl-samsum This model is a fine-tuned version of [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 49.0281 - Rouge2: 25.8273 - Rougel: 41.7919 - Rougelsum: 45.2608 - Gen Len: 16.6874 | 1b0adc273a61ceedee063acd03820545 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 1 - 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 | 55b0f07114ce97383096166023b07199 |
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 | 3683 | nan | 49.0281 | 25.8273 | 41.7919 | 45.2608 | 16... | f740e9511a98ec06f9657d83d0818fc0 |
mit | ['generated_from_trainer'] | false | gpt2-medium-new This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9274 | c55d45c03c69eced2c00eb68b40b79a6 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 | 1c21f00e523be3d314c1e4a336547b5c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 279 | 2.2584 | | 2.3665 | 2.0 | 558 | 2.1151 | | 2.3665 | 3.0 | 837 | 2.0273 | | 2.0194 | 4.0 | 1116 | 1.9705 ... | ae2aaa083b13fbcf62079e6ba1174d1b |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-bashkir 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_7_0 - BA dataset. It achieves the following results on the evaluation set: - Loss: 0.1892 - Wer: 0.2421 | 76743582ad0b6c46b1b89da681dc610e |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 10.0 - mixed_precisio... | c6c2c868121a835073f386a7a7c44344 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.4792 | 0.5 | 2000 | 0.4598 | 0.5404 | | 1.449 | 1.0 | 4000 | 0.4650 | 0.5610 | | 1.3742 | 1.49 | 6000 | 0.4001 | 0.497... | 95ef5f8ecc8ef8d12dc4159e49961616 |
apache-2.0 | ['translation'] | false | eng-gmw * source group: English * target group: West Germanic languages * OPUS readme: [eng-gmw](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-gmw/README.md) * model: transformer * source language(s): eng * target language(s): afr ang_Latn deu enm_Latn frr fry gos gsw ksh ltz nds nld p... | 0ccbbce7cf62c32d040d00fdf2d6132e |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009-engdeu.eng.deu | 21.4 | 0.518 | | news-test2008-engdeu.eng.deu | 21.0 | 0.510 | | newstest2009-engdeu.eng.deu | 20.4 | 0.513 | | newstest2010-engdeu.eng.deu | 22.9 | 0.528 | | newstest2011-engdeu.e... | 9cac665dd968ec99ce663f70599a4fe4 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-gmw - source_languages: eng - target_languages: gmw - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-gmw/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'nl', 'lb', 'af', 'de', 'fy', 'yi', 'gmw'] - s... | e26c15b907e89d528090da8b9336a495 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Model Card of `lmqg/mt5-small-koquad-qg-ae` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation and answer extraction jointly on the [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (dataset_name: default) via [`lmqg`](https://github.c... | b36fa392ab7794ea3a61a8c05e09bc8b |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** ko - **Training data:** [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417... | 72b76c27e96f8f7d66c9a2904f618fe1 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | model prediction question_answer_pairs = model.generate_qa("1990๋
์ํ ใ ๋จ๋ถ๊ตฐ ใ์์ ๋จ์ญ์ผ๋ก ์ํ๋ฐฐ์ฐ ์ฒซ ๋ฐ๋ท์ ์ด์ด ๊ฐ์ ํด KBS ๋๋ผ๋ง ใ์ง๊ตฌ์ธใ์์ ๋จ์ญ์ผ๋ก ์ถ์ฐํ์๊ณ ์ด๋ฌํด MBC ใ์ฌ๋ช
์ ๋๋์ใ๋ฅผ ํตํด ๋จ์ญ์ผ๋ก ์ถ์ฐํ์๋ค.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-koquad-qg-ae") | 0f8d746929c63994fb76d722199b830c |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-koquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) | | Score | Type | Dataset | |:--... | 0659f618acbecb03643004052b14f03c |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_koquad - dataset_name: default - input_types: ['paragraph_answer', 'paragraph_sentence'] - output_types: ['question', 'answer'] - prefix_types: ['qg', 'ae'] - model: google/mt5-small - max_length: 512 ... | fe182d88d5ff66afc7bda32b58bd15b2 |
apache-2.0 | ['generated_from_trainer'] | false | eval_v3_stsb This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - eval_loss: 1.3179 - eval_pearson: nan - eval_spearmanr: nan - eval_combined_score: nan - eval_runtime: 4.0282 - eval_sam... | a93d6c3a9d4e357f86b489cf6d34ef2d |
openrail | ['Zode', 'ZoTI', 'Stable Diffusion', 'SD', 'ZoTI Diffusion 1.5'] | false | Example To use `ZoTI Diffusion 1.5`, install `diffusers` using `main` for now. The pipeline will be available in the next release ```bash pip install diffusers accelerate safetensors transformers ``` ```python import PIL import requests import torch from diffusers import StableDiffusionInstructPix2PixPipeline, Eule... | 39deb0c0926280d5872c9834ad369cf4 |
mit | ['simplification', 'generated_from_trainer'] | false | mbart-large-50-clara-med This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2121 - Rouge1: 49.1001 - Rouge2: 31.2516 - Rougel: 44.0446 - Rougelsum: 44.1075 | 335a7c079dfe0c72b2fdc00a03228706 |
mit | ['simplification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | No log | 1.0 | 190 | 1.8633 | 44.8593 | 28.0451 | 40.7724 | 40.8654 | | No log | 2.0 ... | a746a2f950af9e68ea7327edd7953c18 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | vibes-3-beta Dreambooth model trained by darkvibes 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-stabl... | aedb3b09ff666c5d455e1b75b1ade029 |
mit | ['vision', 'image-classification'] | false | UniFormer (image model)
UniFormer models are trained on ImageNet at resolution 224x224.
It was introduced in the paper [UniFormer: Unifying Convolution and Self-attention for Visual Recognition](https://arxiv.org/abs/2201.09450) by Li et al,
and first released in [this repository](https://github.com/Sense-X/Uni... | e5f4bf29ead3ecc00c6e1a7647d2381d |
mit | ['vision', 'image-classification'] | false | Model description
The UniFormer is a type of Vision Transformer, which can seamlessly integrate merits of convolution and self-attention in a concise transformer format.
It adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation.
Wi... | a776cbe9fa1ab1a58806cca80cbf1bd1 |
mit | ['vision', 'image-classification'] | false | Intended uses & limitations
You can use the raw model for image classification.
We now only upload the models trained without Token Labeling and Layer Scale.
More powerful models can be found in [the model hub](https://github.com/Sense-X/UniFormer/tree/main/image_classification).
| 5555054f088894f351beb4e4087af7c1 |
mit | ['vision', 'image-classification'] | false | Param. | FLOPs |
| --------------- | ----------- | ---------- | ----- | ------- | ----- |
| UniFormer-S | ImageNet-1K | 224x224 | 82.9 | 22M | 3.6G |
| UniFormer-Sโ | ImageNet-1K | 224x224 | 83.4 | 24M | 4.2G |
| UniFormer-B | ImageNet-1K | 224x224 | 83.8 | 50M | 8.3G |
| cc3a34a4fcdfff15ccaa7b6d820adf76 |
mit | ['vision', 'image-classification'] | false | How to use
You can followed our [demo](https://huggingface.co/spaces/Sense-X/uniformer_image_demo/tree/main) to use our models.
```python
from uniformer import uniformer_small
from imagenet_class_index import imagenet_classnames
model = uniformer_small()
| 58aadb6f3984dab529d3598f00a31810 |
mit | ['vision', 'image-classification'] | false | load state
model_path = hf_hub_download(repo_id="Sense-X/uniformer_image", filename="uniformer_small_in1k.pth")
state_dict = torch.load(model_path, map_location='cpu')
model.load_state_dict(state_dict)
| 65320ba94baa73a92295c488d003701d |
mit | ['vision', 'image-classification'] | false | process image
image = img
image_transform = T.Compose(
[
T.Resize(224),
T.CenterCrop(224),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
)
image = image_transform(image)
image = image.unsqueeze(0)
| a0d27290aefa6733bc2064a8fa7ff8f0 |
mit | ['vision', 'image-classification'] | false | model predicts one of the 1000 ImageNet classes
prediction = model(image)
predicted_class_idx = prediction.flatten().argmax(-1).item()
print("Predicted class:", imagenet_classnames[str(predicted_class_idx)][1])
```
| 3d53344674c8ae64921099a92736e306 |
mit | ['vision', 'image-classification'] | false | BibTeX entry and citation info
```bibtex
@misc{li2022uniformer,
title={UniFormer: Unifying Convolution and Self-attention for Visual Recognition},
author={Kunchang Li and Yali Wang and Junhao Zhang and Peng Gao and Guanglu Song and Yu Liu and Hongsheng Li and Yu Qiao},
year={2022},
epr... | db5fdc2a34b07c83e81ee07fbabc9474 |
apache-2.0 | ['automatic-speech-recognition', 'id'] | false | exp_w2v2t_id_r-wav2vec2_s237 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speec... | 1376ff698b51c8101b5b2ebe150919fe |
cc-by-sa-4.0 | ['hupd', 't5', 'summarization', 'conditional-generation', 'patents'] | false | HUPD T5-Small Summarization Model This HUPD T5-Small summarization model was fine-tuned on the HUPD dataset. It was originally introduced in [this paper](TBD). For more information about the Harvard USPTO Patent Dataset, please feel free to visit the [project website](https://patentdataset.org/) or the [project's G... | b032cf40eb40bb27858606f90c416425 |
cc-by-sa-4.0 | ['hupd', 't5', 'summarization', 'conditional-generation', 'patents'] | false | How to Use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import pipeline summarizer = pipeline(task="summarization", model="HUPD/hupd-t5-small") TEXT = "1. An optical coherent receiver for an optical communication network, said optical coherent receiver be... | c9c325b5507a5f2d249cc133d189e318 |
cc-by-sa-4.0 | ['hupd', 't5', 'summarization', 'conditional-generation', 'patents'] | false | cuda/cpu device = 'cuda' if torch.cuda.is_available() else 'cpu' tokenizer = AutoTokenizer.from_pretrained("HUPD/hupd-t5-small") model = AutoModelWithLMHead.from_pretrained("HUPD/hupd-t5-small").to(device) inputs = tokenizer(TEXT, return_tensors="pt").to(device) with torch.no_grad(): outputs = model.generate(inp... | 23c5a52d4460018b3e97a6c0f7c5791f |
cc-by-sa-4.0 | ['hupd', 't5', 'summarization', 'conditional-generation', 'patents'] | false | Citation For more information, please take a look at the original paper. * Paper: [The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications](TBD) * Authors: *Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart M. Shieber* ... | c1ef48aba783927117199623c0d670f5 |
apache-2.0 | ['automatic-speech-recognition', 'sv-SE'] | false | exp_w2v2t_sv-se_unispeech_s149 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | b102206a495ce4ac8f13c87b2070eb11 |
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: 0.0642 - Precision: 0.9360 - Recall: 0.9504 - F1: 0.9431 - Accuracy: 0.9860 | 3a6eba2c12e96e2525d801cbdcdfeb8b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0855 | 1.0 | 1756 | 0.0642 | 0.9108 | 0.9387 | 0.9246 | 0.9834 | | 0.0414 | 2.0 |... | ad3329b7f4a76cc8b1fb2d598122bdc9 |
mit | [] | false | nazuna on Stable Diffusion This is the `<nazuna>` 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 y... | 41115ec1a7c44c337f327181f8739dea |
apache-2.0 | ['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_600k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 4, Step 600k 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 ... | c60ffd6454b49332f6c7fd0050710b2a |
apache-2.0 | ['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_600k'] | 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_4-step_600k') model = TFBertModel.from_pretrained("google/multibe... | 152c69d5396876b2298e09c49be88846 |
apache-2.0 | ['translation'] | false | opus-mt-es-tpi * source languages: es * target languages: tpi * OPUS readme: [es-tpi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-tpi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 20d82fd62fdfd12561a8387237ba111e |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-squad2-distilled-finetuned-chaii-small This model is a fine-tuned version of [deepset/xlm-roberta-base-squad2-distilled](https://huggingface.co/deepset/xlm-roberta-base-squad2-distilled) on the None dataset. | 78fdcd217e02e244c24200f5f77f8cbf |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'led', 'summary', 'longformer', 'booksum', 'long-document', 'long-form'] | false | Usage - Basic - use `encoder_no_repeat_ngram_size=3` when calling the pipeline object to improve summary quality. - this forces the model to use new vocabulary and create an abstractive summary, otherwise it may compile the best _extractive_ summary from the input provided. Load the model into a pipeline object: ... | 972fa5b5f22f85f0a978394c0341df39 |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'led', 'summary', 'longformer', 'booksum', 'long-document', 'long-form'] | false | ๐ค 20-lines of code to reproduce SOTA on Arxiv with **Longformer Encoder-Decoder (LED)** ๐ค - https://colab.research.google.com/drive/12INTTR6n64TzS4RrXZxMSXfrOd9Xzamo?usp=sharing) - See the `generate_batch` function for more details; note the beam search as well; we've pasted the function below for easy reference ``... | c928f139fc1e3e8d7f125ba6580715fc |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'led', 'summary', 'longformer', 'booksum', 'long-document', 'long-form'] | false | put global attention on <s> token global_attention_mask[:, 0] = 1 predicted_abstract_ids = model.generate(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask, max_length=512, num_beams=4) batch["predicted_abstract"] = tokenizer.batch_decode(predicted_abstract_ids, skip_special_... | 72fab7e9005a4da1aa9a4dfa24f587e7 |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'led', 'summary', 'longformer', 'booksum', 'long-document', 'long-form'] | false | Training and evaluation data - You'll want to train on the [BookSum](https://arxiv.org/abs/2105.08209) dataset - During training, the input text was the text of the `chapter`, and the output was `summary_text` - Eval results can be found [here](https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-proje... | eb3cb3810b7146b6658e3560ef7bb4f1 |
apache-2.0 | ['translation'] | false | opus-mt-sv-pap * source languages: sv * target languages: pap * OPUS readme: [sv-pap](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-pap/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](http... | 712da4b5c0e916001c10d9d52d6d190d |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_unispeech_s558 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 3ded7510813369aee08a0fdbd43dba61 |
mit | ['generated_from_trainer'] | false | roberta-base.CEBaB_confounding.price_food_ambiance_negative.absa.5-class.seed_42 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.4009 - Accuracy: 0.8864 - Macro-f1: 0.8... | bf598d94ff17dcf8d997d450b4bc94a9 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-small_talk-3-16-5 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: 2.3566 - Accuracy: 0.3855 | eda5827df3b030fb5f5232c3a6435a7a |
creativeml-openrail-m | ['robots', 'stable-diffusion', 'aiart', 'text-to-image'] | false | Robo-Diffusion A dreambooth-method finetune of stable diffusion that will output cool looking robots when prompted. <img src="https://huggingface.co/nousr/robo-diffusion/resolve/main/robo_example.png" width="256" height="256"/> [](https://col... | e90c77d47c6032178a7aa79c0074ddc0 |
creativeml-openrail-m | ['robots', 'stable-diffusion', 'aiart', 'text-to-image'] | false | robodiffusion so i can see the cool stuff you make! If you enjoy the model i'd appreciate a follow on [twitter](https://twitter.com/nousr_) If you are feeling especially generous, you can sponsor me on [github](https://github.com/nousr) --- *NOTE: usage of this model implies accpetance of stable diffusion's [Creati... | cb12e36ba0df5b9d1d6df84c9a96bab6 |
apache-2.0 | ['translation'] | false | Model Details - **Developed by:** ฤฐlhami SEL - **Model type:** Mbart Finetune Machine Translation - **Language:** Turkish - English - **Resources for more information:** Sel, ฤฐ. , รzen, H. & Hanbay, D. (2021). Creating a Parallel Corpora for Turkish-English Academic Translations . Computer Science , 5th International... | 2b5da3d51b32a9c654404390c6e3639a |
apache-2.0 | [] | false | Bert-base-cased Fine Tuned Glue Mrpc Demo This checkpoint was initialized from the pre-trained checkpoint bert-base-cased and subsequently fine-tuned on GLUE task: mrpc using [this](https://colab.research.google.com/drive/162pW3wonGcMMrGxmA-jdxwy1rhqXd90x?usp=sharing) notebook. Training was conducted for 3 epochs, us... | a2ae371aea0d8cc2ed069b92da2d5e16 |
mit | ['generated_from_keras_callback'] | false | nouman10/robertabase-finetuned-claim-ltp This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0790 - Validation Loss: 0.0631 - Epoch: 4 | 2d913aac72e1a02013b49ace84fa0d36 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'Polynomia... | 569203646dee6d137c6e39586e08e5f0 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.7492 | 0.5024 | 0 | | 0.3207 | 0.1321 | 1 | | 0.1186 | 0.0949 | 2 | | 0.0929 | 0.0793 | 3 | | 0.0790 | 0.0631 | 4 | | 6a1e8a2f620b0078698d192d08e04024 |
apache-2.0 | ['translation'] | false | opus-mt-de-ase * source languages: de * target languages: ase * OPUS readme: [de-ase](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-ase/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | ef86cd6cd7886ab8919f91ca69fe2425 |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pre-trained on ้็ฉบๆๅบซ texts for POS-tagging and dependency-parsing, derived from [deberta-base-japanese-unidic](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-unidic). Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal ... | e684a4c1a5d2d1ab6509fe248a744805 |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py import torch from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-unidic-luw-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-japanese-unidic-luw-upos") s="ๅฝๅขใฎ้ทใใใณใใซใ... | 8adaaffdfa9d8d084002128122f88683 |
apache-2.0 | ['translation'] | false | opus-mt-nso-fr * source languages: nso * target languages: fr * OPUS readme: [nso-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nso-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 42e01ebd5d044cce652c9196420b16ed |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | SDnikrt Dreambooth model trained by nikhilkr 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 hea... | 5dc265adc781c87310d4a5c5e7f96ba8 |
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: 0.2235 - Accuracy: 0.9205 - F1: 0.9207 | 3d5b2be8497d0e8fabe94a6fc6fa0bf0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8546 | 1.0 | 250 | 0.3252 | 0.906 | 0.9028 | | 0.2551 | 2.0 | 500 | 0.2235 | 0.9205 | 0.9207 | | d4a17355c00ce0953047790de71d992d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 | 77c92b2f4a4270d173e6533c6734d04c |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr 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: 0.1590 - F1: 0.8590 | b0162c0a29ba7da7a6eeb44a4462e2e2 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2874 | 1.0 | 715 | 0.1752 | 0.8319 | | 0.1464 | 2.0 | 1430 | 0.1585 | 0.8504 | | 0.0938 | 3.0 | 2145 | 0.1590 | 0.8590 | ... | 0f2ed592eca5ee2f3419b2ccb6fbce3b |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_accent_france-10_belgium-0_s869 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 usi... | 6f6f6a1f2f4e2908de9f451695c72646 |
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: 0.2225 - Accuracy: 0.9325 - F1: 0.9322 | 03d31847b97a8a00d874652b67402d6b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8372 | 1.0 | 250 | 0.3225 | 0.9045 | 0.9017 | | 0.2534 | 2.0 | 500 | 0.2225 | 0.9325 | 0.9322 | | e4629fa00b5f4903f8b41975c0424bea |
apache-2.0 | ['audio', 'text-to-speech', 'onnx'] | false | ESPnet JETS Text-to-Speech (TTS) Model for ONNX [imdanboy/jets](https://huggingface.co/imdanboy/jets) exported to ONNX. This model is an ONNX export using the [espnet_onnx](https://github.com/espnet/espnet_onnx) library. | 77b43a1d770b959dd1ecb8927f650e7f |
apache-2.0 | ['audio', 'text-to-speech', 'onnx'] | false | Usage with txtai [txtai](https://github.com/neuml/txtai) has a built in Text to Speech (TTS) pipeline that makes using this model easy. ```python import soundfile as sf from txtai.pipeline import TextToSpeech | 663a15109c301a6bec06040f93972b84 |
apache-2.0 | ['audio', 'text-to-speech', 'onnx'] | false | Usage with ONNX This model can also be run directly with ONNX provided the input text is tokenized. Tokenization can be done with [ttstokenizer](https://github.com/neuml/ttstokenizer). Note that the txtai pipeline has additional functionality such as batching large inputs together that would need to be duplicated wi... | a7f8c2f094d876bf7e04c15868f3fc85 |
apache-2.0 | ['bert'] | false | <p align="center"> <br> <img src="https://github.com/ymcui/MacBERT/raw/master/pics/banner.png" width="500"/> <br> </p> <p align="center"> <a href="https://github.com/ymcui/MacBERT/blob/master/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/ymcui/MacBERT.svg?color=blue&styl... | 98e6fd8eb7eae9a8afd0f97825af7354 |
apache-2.0 | ['bert'] | false | Please use 'Bert' related functions to load this model! This repository contains the resources in our paper **"Revisiting Pre-trained Models for Chinese Natural Language Processing"**, which will be published in "[Findings of EMNLP](https://2020.emnlp.org)". You can read our camera-ready paper through [ACL Anthology]... | 68bd7d413ac862852e429693d7713b32 |
apache-2.0 | ['bert'] | false | ) or [arXiv pre-print](https://arxiv.org/abs/2004.13922). **[Revisiting Pre-trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922)** *Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu* You may also interested in, - Chinese BERT series: https://github.com/ymcui/... | b92d2b7b9f95179c7be2adbd2bf48359 |
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