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 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.6401 | 1.0 | 500 | 2.4138 | 1.0 | | 0.9717 | 2.01 | 1000 | 0.6175 | 0.5531 | | 0.4393 | 3.01 | 1500 | 0.4309 | 0.441... | 3052fa7f011604804cba80a202bbd45c |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-sentiment-model-5000-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: 0.6140 - Accuracy: 0.9126 - F1: 0.9150 | cfb5bd950394a979e2aa04038004ebf0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | d3c3b758ffda59c440207dc560177455 |
apache-2.0 | ['generated_from_trainer'] | false | mt5-small-finetuned-rabbi-kook-nave This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan | d844125cc9f7d82bb126329739520e06 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0 | 1.0 | 892 | nan | | 0.0 | 2.0 | 1784 | nan | | 0.0 | 3.0 | 2676 | nan | | b914bedcea32893806ef0e8cfcc4b410 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | princessknight-12-18 Dreambooth model trained by ukiyomemes 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/f... | 6f2580a42f4d6fe1dcda9dbf03e66241 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad-seed-999 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.8208 | e4da033aadb2b17aba51e5d86c6d45d8 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | d8b93c0deec227d62c247c9708a50ed4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.7304 | 1.0 | 8235 | 1.4972 | | 0.6007 | 2.0 | 16470 | 1.6103 | | 0.4936 | 3.0 | 24705 | 1.8208 | | f8f06922313a70737cba1ca4c930bdf5 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'wikipedia', 'summarization', 'wits'] | false | mT5 Small for Wikipedia Summarization ✂️📑 🇮🇹 This repository contains the checkpoint for the [mT5 Small](https://huggingface.co/google/mt5-small) model fine-tuned on Wikipedia summarization on the [WITS](https://www.semanticscholar.org/paper/WITS%3A-Wikipedia-for-Italian-Text-Summarization-Casola-Lavelli/ad6c83122... | 07c8e69fb7894ef65f864f43d03deb50 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'wikipedia', 'summarization', 'wits'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines wikisum = pipeline("summarization", model='it5/mt5-small-wiki-summarization') wikisum("Le dimensioni dell'isola sono di 8 km di lunghezz... | 494929bc09d6f10d4e6b2f0396275925 |
mit | [] | false | JM Bergling Monogram on Stable Diffusion This is the `<JM-Bergling-monogram>` 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) no... | 086c703c7b6bccdfcb89596fdbc3ce2e |
mit | [] | false | liliana on Stable Diffusion This is the `<liliana>` 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... | 4899c5699d9697e82ee5c526e9c945da |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-to-image'] | false | MODEL BY InternalMegaT **_If you aren't getting results that Dalle2 would normally give you on a specific prompt, tell me in the community tab what prompt you are using._** Training on V0.9 Beta - 1024x1024, v2-1 Base Examples coming soon:- | d1dd1d51216a8767c850cf84bfdb769e |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | dbkw Dreambooth model trained by michaelkmccoy 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 h... | 4f9e9434f6cfb7e44276c77eedb597e6 |
apache-2.0 | ['vision', 'image-segmentation'] | false | BEiT (base-sized model, fine-tuned on ADE20k) BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on [ADE20k](http://sceneparsing.csail.mit.edu/) (an important benchmark for semantic segmentation of images) at resolution 640x64... | 47278f98262e3fc4be345238d015e006 |
apache-2.0 | ['vision', 'image-segmentation'] | false | Model description The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). In contrast to the original ViT model, BEiT is pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. The pre-training objective f... | c8db7103b306648457957dd5e96c9269 |
apache-2.0 | ['vision', 'image-segmentation'] | false | Intended uses & limitations You can use the raw model for semantic segmentation of images. See the [model hub](https://huggingface.co/models?search=microsoft/beit) to look for fine-tuned versions on a task that interests you. | 2e7c31f9c2930d2a995f1e7eac41ff0f |
apache-2.0 | ['vision', 'image-segmentation'] | false | How to use Here is how to use this model for semantic segmentation: ```python from transformers import BeitFeatureExtractor, BeitForSemanticSegmentation from datasets import load_dataset from PIL import Image | 3bb14097321aa77cc689020bbfd457ec |
apache-2.0 | ['vision', 'image-segmentation'] | false | load ADE20k image ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test") image = Image.open(ds[0]['file']) feature_extractor = BeitFeatureExtractor.from_pretrained('microsoft/beit-base-finetuned-ade-640-640') model = BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-64... | 977994774cdd46a5042e8fcca6de3288 |
apache-2.0 | ['vision', 'image-segmentation'] | false | Training data This BEiT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ADE20k](http://sceneparsing.csail.mit.edu/), a dataset consisting of thousands of annotated images and 150 classes. | d3a19bd325cb15585b6ecbb1dbf9bfbc |
apache-2.0 | ['vision', 'image-segmentation'] | false | Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/microsoft/unilm/blob/master/beit/datasets.py). Images are cropped and padded to the same resolution (640x640) and normalized across the RGB channels with the ImageNet mean and standard deviat... | 49ab5f10d307346bc4462e329e89db82 |
apache-2.0 | ['vision', 'image-segmentation'] | false | BibTeX entry and citation info ```@article{DBLP:journals/corr/abs-2106-08254, author = {Hangbo Bao and Li Dong and Furu Wei}, title = {BEiT: {BERT} Pre-Training of Image Transformers}, journal = {CoRR}, volume = {abs/2106.08254}, year = {2021}, url = {h... | fcda93aa9ff7f2d3d19cd10cfcc508d3 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Swedish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Swedish using the [NST Swedish Dictation](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-17/). When using this model, make sure that your speech input is sampled at... | cc2055dc3d91aa328fc21310c74cff17 |
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", "sv-SE", split="test[:2%]"). processor = Wav2Vec2Processor.fr... | ba49a362a1cac4996236a143e2f51b5b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["spe... | 8e78589d0dcdfbeef8d0b32048422ba1 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Swedish test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "sv-SE", split="test") ... | 75c5160668bc34151c1ebcd62b15f8bf |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dat... | 730a227e7a85710fa3f3d2a8730fad1b |
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_ids = torch... | 361f7e5c01c44b5081b8a4480a234a53 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training First the XLSR model was further pre-trained for 50 epochs with a corpus consisting of 1000 hours spoken Swedish from various radio stations. Secondly [NST Swedish Dictation](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-17/) was used for fine tuning as well as [Common Voice](https://comm... | 99fa2abbd9973d5b5a05fa1ce8d2e5ad |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad 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: 4.5266 | bf70031d853861a1817364f676a7fa66 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 7 | 5.3892 | | No log | 2.0 | 14 | 4.7949 | | No log | 3.0 | 21 | 4.5266 | | f3d73e9678273fcbcd8624596dc8634f |
apache-2.0 | ['translation'] | false | opus-mt-fr-kwy * source languages: fr * target languages: kwy * OPUS readme: [fr-kwy](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-kwy/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | 137dcdf2d7fc2380611a7c0bb37ea964 |
mit | ['generated_from_trainer'] | false | refinement-finetuned-mnli-kaggle-reversal This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0382 | 646048371bf7e03f374a1b95ebf54ff3 |
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: 4 | 4f492a96a07da353c077e5fdba459867 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.4093 | 1.0 | 12599 | 0.7861 | | 0.5241 | 2.0 | 25198 | 0.9800 | | 0.4969 | 3.0 | 37797 | 1.0316 | | 0.4239 | 4.0 | 50396 | 1.0382 ... | d4eb0f7fe9033fb2ef8d88ae7590e9d0 |
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). ```python | ec51ddc35c9ddec66665068491b5442b |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | !pip install diffusers transformers scipy torch from diffusers import StableDiffusionPipeline import torch model_id = "Langboat/Guohua-Diffusion" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "The Godfather poster in guohua style" image = pipe(prom... | 36f2e3bf92fc70084824855f9fb6cca9 |
apache-2.0 | ['generated_from_keras_callback'] | false | andreiliphdpr/bert-base-multilingual-uncased-finetuned-cola This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0423 - Train Accuracy: 0.9869 - Vali... | c290ba74d546110764b9f314a72553cc |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.0423 | 0.9869 | 0.0303 | 0.9913 | 0 | | ccbbe1ae3715730166ca06c590e9dee2 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | kyoto_marian_mod_2 This model is a fine-tuned version of [Hoax0930/kyoto_marian_mod_1](https://huggingface.co/Hoax0930/kyoto_marian_mod_1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7472 - Bleu: 20.8730 | 1f63623168098399975846a12dcf44e0 |
apache-2.0 | ['generated_from_trainer'] | false | ss_mrpc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5960 - Accuracy: 0.8799 - F1: 0.9148 | 09074d07123d9fd8d454bc918ac09413 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 459 | 0.3655 | 0.8578 | 0.8990 | | 0.524 | 2.0 | 918 | 0.6061 | 0.8260 | 0.8823 | | 0.2971 |... | aafda37e6902e6bfaa78c8a875dc739d |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/libritts_tts_train_xvector_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave` ♻️ Imported from https://zenodo.org/record/5521416/ This model was trained by kan-bayashi using libritts/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 2df92a945ec16fddc1cf465db9a5fb75 |
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: 0.6703 - Accuracy: 0.7825 - F1: 0.7271 | 6ab8b7db6348e7d02de8c7bc9ed80148 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 512 - eval_batch_size: 512 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 36b70a0f280aa423fb3080bc89249c5a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 32 | 0.7999 | 0.758 | 0.7022 | | 0.8733 | 2.0 | 64 | 0.6703 | 0.7825 | 0.7271 | | 65580d4bb303309f3c4f52c62a6e7109 |
apache-2.0 | ['text-classification', 'sentiment-analysis', 'poem-sentiment-detection', 'poem-sentiment'] | false | nickwong64/bert-base-uncased-poems-sentiment Bert is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective. [bert-base-uncased](https://huggingface.co/bert-base-uncased) finetuned on the [poem_sentiment](https://huggingface.co/datasets/poem_sentiment) dataset using Hug... | 0e63e7956769e98f492440f7a285f922 |
apache-2.0 | ['text-classification', 'sentiment-analysis', 'poem-sentiment-detection', 'poem-sentiment'] | false | How to Use the Model ```python from transformers import pipeline nlp = pipeline(task='text-classification', model='nickwong64/bert-base-uncased-poems-sentiment') p1 = "No man is an island, Entire of itself, Every man is a piece of the continent, A part of the main." p2 = "Ten years, dead and living di... | 7fc5a84980321ea3c3dc57a876040824 |
apache-2.0 | ['text-classification', 'sentiment-analysis', 'poem-sentiment-detection', 'poem-sentiment'] | false | Evaluation ``` {'test_loss': 0.4359096586704254, 'test_accuracy': 0.9142857142857143, 'test_f1': 0.9120554830816401, 'test_runtime': 0.5689, 'test_samples_per_second': 184.582, 'test_steps_per_second': 24.611} ``` | a5db0fed871db77d1007b6d395d5bcf9 |
apache-2.0 | [] | false | distilbert-base-en-fr-es-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original ac... | 09cf627e2ffbde4d38fdb8cea853bff6 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-fr-es-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-fr-es-cased") ``` To generate other smaller versions of multilingual transformers please visit [... | 3817efada8a0b792d797fa0f37c62533 |
apache-2.0 | [] | false | doc2query/msmarco-t5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 queries... | bef385e4e563063d8568d74336c0f8ff |
apache-2.0 | [] | false | Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = 'doc2query/msmarco-t5-base-v1'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
text = "Python is an interpreted, high-level and general-purpose prog... | e68af79127160ae98b05345c57ce7e03 |
apache-2.0 | [] | false | Training
This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 31k training steps (about 4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository.
The input-text was truncated to 320 word pieces. Output text was... | f632e713647c995482d8748cf0377d03 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Turkish This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 tr dataset. It achieves the following results on the evaluation set: - Loss: 0.2611 - Wer: 17.2108 | 5d9c6ca3648545524c5d7ddab6b3bf44 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Model description The Whisper small model is fine-tuned on CommonVoice Turkish train+validation partitions. The model was fine-tuend for 1000 steps. Without fine-tuning the WER on test data was 23.7 (using whsiper-small and the CommonVoice 9 test data). | e1fcd3e71379c6f58a92c8e3edbf3522 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0693 | 3.05 | 1000 | 0.2611 | 17.2108 | | 1649cd5c501e9a824837d643629ae215 |
mit | ['pyannote', 'pyannote-audio', 'pyannote-audio-model', 'audio', 'voice', 'speech', 'speaker', 'speaker-segmentation', 'voice-activity-detection', 'overlapped-speech-detection', 'resegmentation'] | false | 🎹 Speaker segmentation  Model from *[End-to-end speaker segmentation for overlap-aware resegmentation](http://arxiv.org/abs/2104.04045)*, by Hervé Bredin and Antoine Laurent. [Online demo](https://huggingface.co/spaces/pyannote/pretrained-pipelines) is available as a Hugging Face Space. | 08c01bd39a3177f8069313fe1cbb0f6a |
mit | ['pyannote', 'pyannote-audio', 'pyannote-audio-model', 'audio', 'voice', 'speech', 'speaker', 'speaker-segmentation', 'voice-activity-detection', 'overlapped-speech-detection', 'resegmentation'] | false | Overlapped speech detection ```python from pyannote.audio.pipelines import OverlappedSpeechDetection pipeline = OverlappedSpeechDetection(segmentation="pyannote/segmentation") pipeline.instantiate(HYPER_PARAMETERS) osd = pipeline("audio.wav") | 2a38563c225c8e8a0ae22a95ed7e6ea5 |
mit | ['pyannote', 'pyannote-audio', 'pyannote-audio-model', 'audio', 'voice', 'speech', 'speaker', 'speaker-segmentation', 'voice-activity-detection', 'overlapped-speech-detection', 'resegmentation'] | false | Resegmentation ```python from pyannote.audio.pipelines import Resegmentation pipeline = Resegmentation(segmentation="pyannote/segmentation", diarization="baseline") pipeline.instantiate(HYPER_PARAMETERS) resegmented_baseline = pipeline({"audio": "audio.wav", "baseline": baseline}) | 5f687d6d477a4a2454c376f907a852d2 |
mit | ['pyannote', 'pyannote-audio', 'pyannote-audio-model', 'audio', 'voice', 'speech', 'speaker', 'speaker-segmentation', 'voice-activity-detection', 'overlapped-speech-detection', 'resegmentation'] | false | Reproducible research In order to reproduce the results of the paper ["End-to-end speaker segmentation for overlap-aware resegmentation "](https://arxiv.org/abs/2104.04045), use `pyannote/segmentation@Interspeech2021` with the following hyper-parameters: | Voice activity detection | `onset` | `offset` | `min_durati... | f31126328d70fe993d6f2e275a8ab7f6 |
mit | ['pyannote', 'pyannote-audio', 'pyannote-audio-model', 'audio', 'voice', 'speech', 'speaker', 'speaker-segmentation', 'voice-activity-detection', 'overlapped-speech-detection', 'resegmentation'] | false | Citation ```bibtex @inproceedings{Bredin2021, Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}}, Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine}, Booktitle = {Proc. Interspeech 2021}, Address = {Brno, Czech Republic}, Month = {August}, Year = {2021}, ``` ```bibtex @inproc... | b619fc83bed5525a114f74391fb6aece |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5658 - Rouge1: 26.7823 - Rouge2: 6.7168 - Rougel: 20.9066 - Rougelsum: 20.9054 - Gen Len: 18.8193 | 2582e7fd86ce169ee0a2cc0b362b3af5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.8016 | 1.0 | 4251 | 2.5658 | 26.7823 | 6.7168 | 20.9066 | 20.9054 | 18.81... | 539d28dd87c1e7b17d6ac3b822f6752a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 80 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 75dc0bc2362ed31a541fa6fe3841378a |
apache-2.0 | ['generated_from_trainer'] | false | taylor-swift-model-temp This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1118 | 885c364527a8ecbd0caf0620dafb4c65 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.0072 | 1.0 | 58 | 3.7794 | | 3.8685 | 2.0 | 116 | 3.6857 | | 3.8123 | 3.0 | 174 | 3.6220 | | 3.7141 | 4.0 | 232 | 3.5796 ... | c9e7faa1c0edac14660cdc1cf6424e2c |
mit | ['generated_from_trainer'] | false | distilbert-finetuned-ner-ontonotes This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the ontonotes5 dataset. It achieves the following results on the evaluation set: - Loss: 0.1448 - Precision: 0.8535 - Recall: 0.8789 - F1: 0.8660 - Accuracy: 0.9750 | 46d5db673d227d12d9008770240f8c5b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0937 | 1.0 | 7491 | 0.0998 | 0.8367 | 0.8587 | 0.8475 | 0.9731 | | 0.0572 | 2.0 ... | 6ee34b649cbc4200c8fc73bb2c06fc40 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-small_talk-9-16-5 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 | 756469a66b90b5a584bdc9d0df67888b |
unknown | [] | false | Example prompts `woman near a fountain by soda stream`: <img src="https://huggingface.co/cyburn/soda_stream/resolve/main/1.png" alt="Picture." width="500"/> `woman in taxi by soda stream`: <img src="https://huggingface.co/cyburn/soda_stream/resolve/main/2.png" alt="Picture." width="500"/> `woman portrait by soda ... | c898740fe2dcf880d197d46e64031e81 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Tamil FLEURS This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs ta_in dataset. It achieves the following results on the evaluation set: - Loss: 0.5390 - Wer: 20.9327 | 86aad010ab57d9f40f9fede60fe35a6b |
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: 32 - eval_batch_size: 16 - 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... | 6256027f5589995e76aa55f1b1677cda |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0004 | 83.33 | 1000 | 0.5390 | 20.9327 | | e3c2e2a84f28f56e631900da691a8d4d |
cc-by-4.0 | ['bert'] | false | bert-rand-small A small-size BERT Language Model with a **random** pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties?](https://aclanthol... | 5ff71ab52af87a7d790cd3189dadb770 |
mit | [] | false | mafalda character on Stable Diffusion This is the `<mafalda-quino>` 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. Yo... | d51be9a1729ae21e74bf0a511deb4849 |
mit | ['generated_from_trainer'] | false | ko-en-retrial This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4075 - Bleu: 27.1215 - Gen Len: 10.91 | 92940f59b8ece5a8f90e67643ad99d74 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP | c183fc34621c73ee5bb3ccfb9e06ff2a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.5334 | 1.0 | 16549 | 0.6745 | 14.5544 | 10.4919 | | 0.4841 | 2.0 | 33098 | 0.6063 | 16.5973 | 10.8128 | | 0.4308 ... | aa5c84c6438044b7ca3f32db4c164062 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large V2 Hebrew This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the google/fleurs he_il dataset. It achieves the following results on the evaluation set: - Loss: 0.4106 - Wer: 27.2504 | 958d29700cce79659c8260161f77e00d |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 128 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | a0aea3e4c89e087fb9b0407579fd1aa7 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.425 | 24.01 | 50 | 0.4106 | 27.2504 | | 0.1906 | 49.01 | 100 | 0.4420 | 29.0131 | | 0.0982 | 74.01 | 150 | 0.4795 | 30.306... | e5d42b29184ce8d5b0999aedcf95956f |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxxvit_rmlp_small_rw_256.sw_in1k A timm specific MaxxViT (w/ a MLP Log-CPB (continuous log-coordinate relative position bias motivated by Swin-V2) image classification model. Trained in `timm` on ImageNet-1k by Ross Wightman. ImageNet-1k training done on TPUs thanks to support of the [TRC](https://si... | 4a9bc96a8402712423eb755cd4a1e5ee |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 66.0 - GMACs: 14.7 - Activations (M): 58.4 - Image size: 256 x 256 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - A ConvNet for the 2020s: https://arxiv.org/ab... | a5be9601fc1671ca1ae864b4d2d3a6dc |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('maxxvit_rmlp_small_rw_256.sw_in1k', pretrained=True) m... | dfac6a2c879f10f964373571d5ce2c15 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxxvit_rmlp_small_rw_256.sw_in1k', pretrai... | 0d96b58baba15ac9c6ebd660cf2a32cd |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxxvit_rmlp_small_rw_256.sw_in1k', pretrained=Tr... | 54df0e64768db1f79fa124f8adcc4df4 |
openrail | [] | false | This model disambiguates clinical sense acronyms and abbreviations within clinical notes. Be sure to use the prompt "dejargon: " before any input and include the end of sentence token ```</s>``` at the end of your text. To use this model: ``` >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM >>> tok... | 7f09e16a83f8a1ab75d42653248cf6ee |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr0_3e-05_webDiscourse_27_02_2022-19_27_41 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6020 - ... | f2b77fc1a5fa438c93103fd16dce4d6a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 48 | 0.5914 | 0.67 | 0.0294 | | No log | 2.0 | 96 | 0.5616 | 0.695 | 0.2824 | | No log |... | 332fe356043422e65bbd9a5389f650bb |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-greek Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on greek using the [Common Voice](https://huggingface.co/datasets/common_voice) and [CSS10](https://github.com/Kyubyong/css10) datasets. When using this model, make sure that your speech i... | df02a7d1301ffe793370c136bcd63400 |
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", "el", split="test") processor = Wav2Vec2Processor.from_pretr... | 5133765abb110853e2bc1a4c9d4f52ff |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the greek test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "el", split="test") wer... | 11718d932f72f43b884df8753b58e4ba |
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_ids = torch... | 30ce85a634d2b5d1862e01293ef8dcba |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train`, `validation`, and CSS10 datasets were used for training, added as `extra` split to the dataset. The sampling rate and format of the CSS10 files is different, hence the function `speech_file_to_array_fn` was changed to: ``` def speech_file_to_array_fn(batch): try: ... | 7477da6b7ff44c62056c26bdbe259cce |
apache-2.0 | ['generated_from_trainer'] | false | whisper-large-v2-spanish-english This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Spanish CommonVoice dataset (v11). It achieves the following results on the evaluation set: - Loss: 0.2297 - Wer: 0.1282 | 6879fac16a453fc809fd9a3463cea4ed |
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: 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: 5000 - mixed_precisi... | 48c49df44eed8bebb09aa98291505f97 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1585 | 0.14 | 1000 | 0.3235 | 0.2571 | | 0.1441 | 0.28 | 2000 | 0.2746 | 0.1976 | | 0.1282 | 0.42 | 3000 | 0.2517 | 0.1506 | |... | 31521344fe73643b3a192c1b6f4c9d74 |
apache-2.0 | ['generated_from_trainer'] | false | load the model processor = WhisperProcessor.from_pretrained("clu-ling/whisper-large-v2-spanish-5k-steps") model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-spanish-5k-steps").to(device) forced_decoder_ids = processor.get_decoder_prompt_ids(language="es", task="transcribe") | 7e4be10277d28a576d2114dd70cf2557 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.