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 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | use "test[:1%]" for 1% sample wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("maxidl/wav2vec2-large-xlsr-german") model = Wav2Vec2ForCTC.from_pretrained("maxidl/wav2vec2-large-xlsr-german") model.to("cuda") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]' resampler = torchaudio.transfo... | b17d015ad23803f92da186c047852547 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio files as arrays def evaluate(batch): \tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) \twith torch.no_grad(): \t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits \tpred_ids = torch.argmax(lo... | 8ced87d2d2367252653dba7a2a3518b2 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Chunked version, see https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586/5: import jiwer def chunked_wer(targets, predictions, chunk_size=None): if chunk_size is None: return jiwer.wer(targets, predictions) start = 0 end = chunk_size H, S, D, I = 0, 0, 0, 0 while start < len(tar... | e37a32c87efc942e727bdd2892c47d84 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice German `train` and `validation` were used for training. The script used for training can be found [here](https://github.com/maxidl/wav2vec2). The model was trained for 50k steps, taking around 30 hours on a single A100. The arguments used for training this model are: ``` python run_finetuni... | bc3312198fe8e38f139a27aa71268028 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b pip install -e . cd egs2/commonvoice/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/belarusian_commonvoice_blstm ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | fc6b851e72cd77d6c0df4237dc920807 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Thu May 19 18:39:24 EDT 2022` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 0.10.6a1` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `716eb8f92e19708acfd08ba3bd39d40890d3a84b` - Commit date: `Thu Apr 28 19:50:59 2022 -0400` | 6ad4414592984684104bd22fa7204f51 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_rnn.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_rnn_raw_be_bpe150_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_... | b6e7f9c811c8cee4d3573747071cc2b7 |
mit | ['clip', 'vision', 'text'] | false | Model Architecture Multilingual CLIP was built using [OpenAI CLIP](https://github.com/openai/CLIP) model. I have used the same Vision encoder (ResNet 50x4), but instead I replaced their text encoder (Transformer) with a Mulilingual Text Encoder ([XLM-Roberta](https://huggingface.co/xlm-roberta-large)) and a configurab... | 834a8b0eba4b1b3f5842be4c3f49c80f |
mit | ['clip', 'vision', 'text'] | false | Datasets Three datasets have been used for building the model. COCO captions was used for training phase 1 and Google Conceptual Captions was used for training phase 2. Unsplash dataset was used for testing and inference. | 31a6c4c2be69578b768e534a745d2a65 |
mit | ['clip', 'vision', 'text'] | false | COCO Captions COCO (Common Objects in Context) is a large-scale object detection, segmentation, and captioning dataset. The COCO captions dataset has around ~85000 images and captions pairs. Run the following to download the dataset: ```bash ./download_coco.sh ``` This dataset was used for the first pre-training p... | aaae1ab5c31ee3248b731e2f54838d55 |
mit | ['clip', 'vision', 'text'] | false | Google Conceptual Captions Conceptual Captions is a dataset consisting of ~3.3 million images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styl... | 3e83e2af60c28e51d410a0edf8fefffd |
mit | ['clip', 'vision', 'text'] | false | Training   | ce557724b80d0a8173d9d09f223ab452 |
mit | ['clip', 'vision', 'text'] | false | Setup Create two Habana instances ([AWS EC2 DL1](https://aws.amazon.com/ec2/instance-types/dl1/)) using [Habana® Deep Learning Base AMI (Ubuntu 20.04)](https://aws.amazon.com/marketplace/pp/prodview-fw46rwuxrtfse) Create the PyTorch docker container running: ```bash docker run --name pytorch -td --runtime=habana -... | 71655c2b30ef9d83a91188631d5b897a |
mit | ['clip', 'vision', 'text'] | false | Setup password-less ssh between all connected servers 1. Configure password-less ssh between all nodes: Do the following in all the nodes' docker sessions: ```bash mkdir ~/.ssh cd ~/.ssh ssh-keygen -t rsa -b 4096 ``` Copy id_rsa.pub contents from every node's docker to every other node's docker'... | 471378487cd7299ddf57ae0fa5dde204 |
mit | ['clip', 'vision', 'text'] | false | PermitRootLogin prohibit-password/PermitRootLogin yes/' /etc/ssh/sshd_config service ssh restart ``` [Allow all TCP](https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html) traffic between the nodes on AWS Clone the git repo: ```bash git clone https://github.com/gzomer/clip-multilingual ```... | 6689ae78b6646f4409887f532a29b8ef |
mit | ['clip', 'vision', 'text'] | false | Train script arguments ``` --dataset-num-workers Number of workers (default: 8) --dataset-type Dataset type (coco or googlecc) (default: coco) --dataset-dir Dataset dir (default: ./datasets/coco/) --dataset-subset-size Load only a subset of the dataset (useful for debugging) --dataset-train-spl... | 8e86c9afcec474227964210b962d8418 |
mit | ['clip', 'vision', 'text'] | false | Phase 2 training ```bash python3.8 train.py --train-device hpu --distributed-parallel-devices 8 --distributed-num-nodes 1 --hyperparam-epochs 15 --checkpoint-load-text-path /home/models/text-last.ckpt --checkpoint-load-vision-path /home/models/vision-last.ckpt --checkpoint-dir ./models_phase2 ``` | 5ad61b075dbccdddc25a5f7fad3d8d9f |
mit | ['clip', 'vision', 'text'] | false | Phase 1 training ```bash NODE_RANK=0 python3.8 train.py --distributed-master-address 172.31.86.231 --train-device hpu --distributed-parallel-devices 8 --distributed-num-nodes 2 ``` ```bash NODE_RANK=1 python3.8 train.py --distributed-master-address 172.31.86.231 --train-device hpu --distributed-parallel-devices 8 --... | aa7f09f515596d92491e32387c098f93 |
mit | ['clip', 'vision', 'text'] | false | Phase 2 training ```bash NODE_RANK=0 python3.8 train.py --distributed-master-address 172.31.86.231 --train-device hpu --distributed-parallel-devices 8 --distributed-num-nodes 2 --hyperparam-epochs 10 --checkpoint-load-text-path /home/models/text-last.ckpt --checkpoint-load-vision-path /home/models/vision-last.ckpt --... | b96a3e2eab38f0a6c4b189bb76285a94 |
mit | ['clip', 'vision', 'text'] | false | Other devices If you don't have access to a Habana Gaudi accelerator yet, you can also train on CPU/GPU, although it will be way slower. To train on CPU, just pass `--train-device=cpu` and on GPU `--train-device=cuda` to the `train.py` script. | 6a070385ca191d9db5801aa390c5232e |
mit | ['clip', 'vision', 'text'] | false | Loading model from local checkpoint ```python from models import MultiLingualCLIP, load_model text_checkpoint_path = '/path/to/text model checkpoint' vision_checkpoint_path = '/path/to/vision model checkpoint' model = MultiLingualCLIP(num_layers=3) load_model(model, vision_checkpoint_path, text_checkpoint_path) ``` ... | 093d6c6553f4102d68516508f06385bd |
mit | ['clip', 'vision', 'text'] | false | List of image info for each row of the embeddings. For instance, it could be a list of urls, filepaths, ids. They will be returned when calling the search function semantic_search = MultiLingualSearch(model, images_embeddings, images_data) results = semantic_search.search('विद्यालय में') | 34724f594e57436933f4fa0f0ea1a93b |
mit | ['clip', 'vision', 'text'] | false | Means at school print(results) ``` ```json [{"image": "https://images.unsplash.com/photo-1557804506-669a67965ba0?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=MnwyNDg3OTV8MHwxfHNlYXJjaHwxM3x8bWVldGluZ3N8ZW58MHx8fHwxNjQ1NjA2MjQz&ixlib=rb-1.2.1&q=80&w=400", "prob": 0.2461608648300171}, {"image": "https://images.unspla... | 7dd1c47022b86b2d66009ff4720647d9 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | zhu_family_v1_sd15 Dreambooth model trained by yuanzheng 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... | 5ef438a275b9bb917ad929e423875040 |
mit | [] | false | **A version of the Ugly Sonic embedding for Stable Diffusion 2.0 is available as `learned_embeds_sd_2_0.bin`.** 💡 Trained by [@minimaxir](/minimaxir) who wrote a [blog post about it](https://minimaxir.com/2022/09/stable-diffusion-ugly-sonic/) 💡 | f13b9d1bc32de01f7f0c4b45c527af17 |
mit | [] | false | Ugly Sonic on Stable Diffusion This is the `<ugly-sonic>` 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... | a55ed74b8e9ccd660393dc8ddd8affc3 |
apache-2.0 | ['image-to-text'] | false | ViTSTR small v1.0 ViTSTR model pre-trained on various real [STR datasets](https://github.com/baudm/parseq/blob/main/Datasets.md) at image size 128x32 with a patch size of 8x4. Disclaimer: this model card was not written by the original author. | 3e535556162670791dea8287e4c42aef |
apache-2.0 | ['image-to-text'] | false | BibTeX entry and citation info ```bibtex @InProceedings{atienza2021vision, title={Vision transformer for fast and efficient scene text recognition}, author={Atienza, Rowel}, booktitle={International Conference on Document Analysis and Recognition}, pages={319--334}, year={2021}, organization={Springer} } ... | 99baadfee8596cfd79780a036c8de3eb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | ba8cc5c113856101607cd64702c599da |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53-AL This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2712 - Wer: 0.6940 | cd9e36365e518875926e0638da4b8417 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.073 | 8.0 | 200 | 1.0990 | 0.7002 | | 0.0561 | 16.0 | 400 | 1.1455 | 0.6805 | | 0.0378 | 24.0 | 600 | 1.2712 | 0.6940 | ... | 9015f82996faa53c926d8a1057e31a38 |
mit | ['generated_from_trainer'] | false | roberta-base.CEBaB_confounding.price_food_ambiance_negative.sa.5-class.seed_44 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.6246 - Accuracy: 0.7472 - Macro-f1: 0.7303 - W... | c94a8e443a71f6d1e63ce2fd0bff6e8a |
apache-2.0 | ['translation'] | false | opus-mt-ee-en * source languages: ee * target languages: en * OPUS readme: [ee-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ee-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 3846f95a3bd9b68fadfe8c2d147d9b7b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_data_aug_qnli_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4367 - Accuracy: 0.5717 | 96c8449029b7bd0c099e09872da81849 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.329 | 1.0 | 16604 | 0.4367 | 0.5717 | | 0.2656 | 2.0 | 33208 | 0.4505 | 0.5702 | | 0.2457 | 3.0 | 49812 | 0.4501 ... | 4f17bcd7581ef333a4f4a0ea7bc3865b |
mit | ['generated_from_keras_callback'] | false | israeli_soccer_news This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.5327 - Validation Loss: 5.2116 - Epoch: 4 | 844ae7fe83102876fdf7e9caee683d36 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps... | fe8fc4e40737e541ab884b9f9ce79dc7 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 7.6108 | 5.2840 | 0 | | 5.5513 | 5.2115 | 1 | | 5.5317 | 5.2107 | 2 | | 5.5319 | 5.2116 | 3 | | 5.5327 | 5.2116 | 4 | | 7ad837eb8bf439536f50969884c0fe5e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 40 | nan | 0.1737 | 3.1818 | | d366a75a0706f3d03cb1ac49049678ab |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sente... | 43958e8c00b2a58aa9d23fbaa9816a8f |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the TIMIT dataset. It achieves the following results on the evaluation set: - Loss: 0.4491 - Wer: 0.3382 | a803006b569cc33e27dcce9b5b6e3ffe |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4787 | 4.0 | 500 | 1.4190 | 0.9939 | | 0.5835 | 8.0 | 1000 | 0.4711 | 0.4370 | | 0.219 | 12.0 | 1500 | 0.4555 | 0.3994 | |... | 953edd29eca8d6a93e3900c737ea3705 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | DreamBooth Hackathon model for the Isle of Portland concept trained by harveymannering on the [jurassic-coast](https://huggingface.co/datasets/harveymannering/jurassic-coast) dataset. The model was fine-tuned on the `cliff` prior for the landscape theme. I used the [jurassic-coast](https://huggingface.co/datasets/ha... | 10dac7acd6f493f2f3dcbb874714f955 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Examples <table> <tr> <td><b>In the style of different artists</b> <br>"a photo of ioprt cliff in the style of monet"</td> <td><br>"a photo of ioprt cliff in the style of raphael"</td> </tr> <tr> <td> <a data-flickr-embed="true" href="https://www.flickr.com/photos/197317911@N06/525955831... | c1eef3c4ac1868a2ef0dd00bff3ea111 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Indonesian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | 59b27f60168c929a9ea8f31cc88791db |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-id") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-id") ``` | b1d13736a85fdbb15154789c96cd985a |
mit | [] | false | model by noperz548 This your the Stable Diffusion model fine-tuned the Jordi concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks jordi person, a very cute and sexy teen girl ** You can also train your own concepts and upload them to the library by us... | af7f30ab5f02aef6663f77b22b6b7b07 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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 - lr_scheduler_warmup_steps: 500 - num_epochs: 5 | 4d24676ef62d1ee9e3eeacf8b69bbd1f |
apache-2.0 | ['generated_from_trainer'] | false | swin-large-patch4-window12-384-in22k-respirator This model is a fine-tuned version of [microsoft/swin-large-patch4-window12-384-in22k](https://huggingface.co/microsoft/swin-large-patch4-window12-384-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4272 - Accuracy: ... | 8e9e2b5ea76fe3491ffa307aa2c750ad |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 05ffe49783818d11ef3ad14dbcd113f5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 5 | 0.9598 | 0.4074 | | 0.9359 | 2.0 | 10 | 0.4272 | 1.0 | | 0.9359 | 3.0 | 15 | 0.2660 | 0.... | 306cbf448db1a71fc353bf00d1f5ee96 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-multi-news This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the multi_news dataset. It achieves the following results on the evaluation set: - Loss: 2.7775 - Rouge1: 14.5549 - Rouge2: 4.5934 - Rougel: 11.1178 - Rougelsum: 12.8964 - Gen Len: 19.0 | 203fb6146e5bde0b50661f61b62b51ba |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoc... | c587c49f2d919f6965bdb82895d19eb7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 3.0211 | 1.0 | 1405 | 2.7775 | 14.5549 | 4.5934 | 11.1178 | 12.8964 | 19.0 ... | 83bf027ad6a336eec85f15aa0a4a8ad9 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-finetune-es-col This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6514 - Wer: 0.9874 | d0492fed13388f472fd384e895b8d2d9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.9709 | 3.25 | 400 | 2.9673 | 1.0 | | 2.9488 | 6.5 | 800 | 2.9075 | 0.9973 | | 2.907 | 9.76 | 1200 | 2.8772 | 0.9688 | |... | d60a96fa0c966a2e6f781bb03a15d789 |
mit | [] | false | model by crimsonGenocide This your the Stable Diffusion model fine-tuned the 27_from_Mayonnaise_SalesMen concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a drawing of 27 from Mayonnaise SalesMen** You can also train your own concepts and upload them to the libra... | ab1e0d9041380a774007b88f2ef0bab7 |
apache-2.0 | ['generated_from_keras_callback'] | false | gbharathi80/mt5-small-finetuned-amazon-en-es 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: - Train Loss: 4.2325 - Validation Loss: 3.4452 - Epoch: 7 | 8960ad6a05bf355322dc4569a7b0f873 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.7747 | 4.7510 | 0 | | 6.3001 | 4.0096 | 1 | | 5.4388 | 3.7376 | 2 | | 4.9710 | 3.6136 | 3 | | 4.6689 | 3.5349 | 4 | | 4.4622 |... | 3b1a34e9ad745ce89d3c6d17e2adde7d |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2794 - F1: 0.8376 | c4d6b0bfcfaccde4b793d651087f3818 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5774 | 1.0 | 191 | 0.3212 | 0.7894 | | 0.2661 | 2.0 | 382 | 0.2737 | 0.8292 | | 0.1756 | 3.0 | 573 | 0.2794 | 0.8376 | ... | 796c2be1abb7e6ce892d6935528eb13b |
mit | ['generated_from_keras_callback'] | false | huynhdoo/flaubert_base_uncased-finetuned-CLS This model is a fine-tuned version of [flaubert/flaubert_base_uncased](https://huggingface.co/flaubert/flaubert_base_uncased) on FLUE/CLS dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1491 - Validation Loss: 0.2361 - Train Accuracy: 0.92... | b368bd8095042dd58ed997a4d632bee0 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.5561 | 0.3184 | 0.8589 | 0 | | 0.2343 | 0.2487 | 0.9118 | 1 | | 0.1491 | 0.2361 | 0.9270 | 2 | | 21a8716bec08cea520e040375b00eacc |
afl-3.0 | [] | false | Citation Information ``` @inproceedings{adelani-etal-2022-thousand, title = "A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for {A}frican News Translation", author = "Adelani, David and Alabi, Jesujoba and Fan, Angela and Kreutzer, Julia and Shen, Xiaoyu ... | 29dd308a49aa2aabcbdc693a58d76fcf |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the pdngtn21 concept trained by styskin on the styskin/paddington dataset. This is a Stable Diffusion model fine-tuned on the pdngtn21 concept with DreamBooth. It can be used by modifying the `instance_prompt`: **illustration by pdngtn21** This model was created as part of the DreamBooth Hackath... | 772e841a1fa25f32bcef01b1f834b126 |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/bart-base-squadshifts-nyt-qg` This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: nyt) via [`lmqg`](https://github.com/as... | 9868dae6a4534e1c7ec5d66ba856ea55 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (nyt) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://githu... | 909f2d1742ba18d95a9a33f6e843d1bf |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/bart-base-squadshifts-nyt-qg... | 3a2c8d11947225e72e98c0d41214ca2d |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-base-squadshifts-nyt-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) | | Score | Type | Dataset ... | a11389eea0274089b5afd9746812482c |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: nyt - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: lmqg/bart-base-squad - max_length: 512 - max_length_output: 32 - epoch: 4... | be2b8f7d28b2aa86f0d71f3aa1975a8f |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5480 - Wer: 0.3437 | 226e25041afac2f1f1a7eb449b44d7c5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5237 | 1.0 | 500 | 1.7277 | 0.9752 | | 0.8339 | 2.01 | 1000 | 0.5413 | 0.5316 | | 0.4277 | 3.01 | 1500 | 0.4732 | 0.475... | 37c4db2e9c73e4608722b95ac370d290 |
cc-by-4.0 | ['T5', 'translation', 'summarization', 'question answering', 'reading comprehension'] | false | Corpus plT5 was trained on six different corpora available for Polish language: | Corpus | Tokens | Documents | | :------ | ------: | ------: | | [CCNet Middle](https://github.com/facebookresearch/cc_net) | 3243M | 7.9M | | [CCNet Head](https://github.com/facebookresearch/cc_net) | 2641M | 7.0M | | [National Corpus... | 78b863ac74ed54d53b89eed73811601e |
cc-by-4.0 | ['T5', 'translation', 'summarization', 'question answering', 'reading comprehension'] | false | Usage Example code: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("allegro/plt5-base") model = AutoModel.from_pretrained("allegro/plt5-base") ``` | 6e25f9f7f02df9d7b37e8a204848680b |
cc-by-4.0 | ['T5', 'translation', 'summarization', 'question answering', 'reading comprehension'] | false | Citation If you use this model, please cite the following paper: ``` @article{chrabrowa2022evaluation, title={Evaluation of Transfer Learning for Polish with a Text-to-Text Model}, author={Chrabrowa, Aleksandra and Dragan, {\L}ukasz and Grzegorczyk, Karol and Kajtoch, Dariusz and Koszowski, Miko{\l}aj and Mroczkow... | 314c99fe346bab19bd84cdff3e572dde |
apache-2.0 | ['audio', 'speech', 'speech-emotion-recognition'] | false | Prediction ```python import torch import torch.nn as nn import torch.nn.functional as F import torchaudio from transformers import AutoConfig, Wav2Vec2FeatureExtractor from src.models import Wav2Vec2ForSpeechClassification, HubertForSpeechClassification import librosa import IPython.display as ipd import numpy as np... | 2d3c369a024089dbaa6f1d85c9fba0f7 |
apache-2.0 | ['audio', 'speech', 'speech-emotion-recognition'] | false | Evaluation The following tables summarize the scores obtained by model overall and per each class. | Emotions | precision | recall | f1-score | accuracy | |:---------:|:---------:|:------:|:--------:|:--------:| | anger | 1.00 | 0.92 | 0.96 | | | disgust | 0.92 | 1.00 | 0.96 | ... | 55adf6444e75771ae77315c4404ef8dd |
apache-2.0 | ['image-classification', 'vision'] | false | BEiT (base-sized model, fine-tuned on ImageNet-1k) 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 ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [BEIT: BERT Pre-Tra... | c5464108aa0d91bfed12b599268608b3 |
apache-2.0 | ['image-classification', 'vision'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import BeitFeatureExtractor, BeitForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image... | 06e40b8f3d73211e49ec6832d1fd7645 |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-tr-en-finetuned-tr-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tr-en](https://huggingface.co/Helsinki-NLP/opus-mt-tr-en) on the opus_infopankki dataset. It achieves the following results on the evaluation set: - Loss: 0.6321 - Bleu: 56.617 - Gen Len: 13.5983 | f5aea03949e4e4562f05ce2cbf51769d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP | c49fb24163656a1dbffddd5978c32788 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 241 | 1.2487 | 41.0053 | 13.0461 | | No log | 2.0 | 482 | 1.1630 | 43.1077 | 13.0386 | | 1.4091 |... | 95bd41c7c204a9888a8abfebf5ea67f6 |
apache-2.0 | ['translation'] | false | opus-mt-umb-en * source languages: umb * target languages: en * OPUS readme: [umb-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/umb-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | f9efb9ecbf339897efa73d0524449dda |
apache-2.0 | ['sentence boundary detection', 'token classification', 'nlp'] | false | Model Overview This model performs text sentence boundary detection (SBD) with 49 common languages. This model segments a long, punctuated text into one or more constituent sentences. A key feature is that the model is multi-lingual and language-agnostic at inference time. Therefore, language tags do not need to b... | 1a92382c8758a8a432a4387116d37a52 |
apache-2.0 | ['sentence boundary detection', 'token classification', 'nlp'] | false | Model Inputs and Outputs The model inputs should be **punctuated** texts. The inputs should be packed into a batch with shape `[B, T]` , with padding being the SPE model's `<pad>` token ID. The `<pad>` ID is required to generate a proper attention mask. The model was trained on a maximum sequence length of 256 (su... | 2632d308e92296f3beec44fb28cfbb6c |
apache-2.0 | ['sentence boundary detection', 'token classification', 'nlp'] | false | Example Usage This model has been exported to `ONNX` (opset 17) alongside the associated `SentencePiece` tokenizer. This model is intended to be downloaded and used in the native frameworks, rather than HF's API. First, let's download and prepare the ONNX and SentencePiece models: ```python from sentencepiece impo... | e4578dcfc6ec88b382167c935e325d3c |
apache-2.0 | ['sentence boundary detection', 'token classification', 'nlp'] | false | Break tokens at boundaries, convert back to text print(f"Input: {text}") for i, break_point in enumerate(break_points): start = 0 if i == 0 else (break_points[i - 1] + 1) sub_ids = ids[start : break_point + 1] sub_text = tokenizer.DecodeIds(sub_ids) print(f"\tSentence {i}: {sub_... | dc9d5a0418c6b27a05759547fb2102cc |
apache-2.0 | ['sentence boundary detection', 'token classification', 'nlp'] | false | English with a lot of acronyms run_infer( "the new d.n.a. sample has been multiplexed, and the gametes are already dividing. let's get the c.p.d. over " "there. dinner's at 630 p.m. see that piece on you in the l.a. times? chicago p.d. will eat him alive." ) | 2ddca02cc69c1fe8c9a3bb681a840cd1 |
apache-2.0 | ['sentence boundary detection', 'token classification', 'nlp'] | false | Polish run_infer( "szedłem tylko do. pamiętaj, nigdy się nie obawiaj żyć na krawędzi ryzyka. ćwiczę już od dwóch tygodni a byłem " "zabity tylko raz." ) ``` Expected output: ```text Input: the new d.n.a. sample has been multiplexed, and the gametes are already dividing. let's get the c.p.d. over there. dinne... | ca1310e9d8c7b80bd26c4c078040642e |
apache-2.0 | ['sentence boundary detection', 'token classification', 'nlp'] | false | Model Architecture This is a data-driven approach to SBD. The model uses a `SentencePiece` tokenizer, a BERT-style encoder, and a linear classifier to predict which subwords are sentence boundaries. Given that this is a relatively-easy NLP task, the model contains \~9M parameters (\~8.2M of which are embeddings). Thi... | 6a30451af3af5bfbd8b29b0b4a436d65 |
apache-2.0 | ['sentence boundary detection', 'token classification', 'nlp'] | false | Model Training This model was trained on a personal fork of [NeMo](http://github.com/NVIDIA/NeMo), specifically this [sbd](https://github.com/1-800-BAD-CODE/NeMo/tree/sbd) branch. Model was trained for several hundred thousand steps with \~1M lines of texts per language (\~49M lines total) with a global batch size of... | 6af061e023488f1667fd7916b3cf8b3b |
apache-2.0 | ['sentence boundary detection', 'token classification', 'nlp'] | false | Training Data This model was trained on `OpenSubtitles` data. Although this corpus is very noisy, it is one of few large-scale text corpora which have been manually segmented. Automatically-segmented corpora are undesirable for at least two reasons: 1. The data-driven model would simply learn to mimic the system u... | fda4ad67df9c13ffe68f8f652c1eb992 |
apache-2.0 | ['sentence boundary detection', 'token classification', 'nlp'] | false | Training Example Generation To create examples for the model, we 1. Assume each input line is exactly one sentence 2. Concatenate sentences together, with the concatenation points becoming the sentence boundary targets For this particular model, each example consisted of between 1 and 9 sentences concatenated toget... | 1b97679322c95e1039c95709587cb1ac |
apache-2.0 | ['sentence boundary detection', 'token classification', 'nlp'] | false | Language Specific Rules The training data was pre-processed for language-specific punctuation and spacing rules. The following guidelines were used during training. If inference inputs differ, the model may perform poorly. * All spaces were removed from continuous-script languages (Chinese, Japanese). * Chinese: Chi... | 61e3a25ef301737b5e9cd10917c26d58 |
apache-2.0 | ['sentence boundary detection', 'token classification', 'nlp'] | false | Noisy training data This model was trained on `OpenSubtitles`, data which is notoriously noisy. The model may have learned some bad habits from this data. An assumption made during training is that every input line is exactly one sentence. However, that's not always the case. So the model might have some false negati... | 4e46170c50f6898a884fe2c9362f5c9e |
apache-2.0 | ['sentence boundary detection', 'token classification', 'nlp'] | false | Language-specific expectations As discussed in a previous section, each language should be formatted and punctuated per that languages rules. E.g., Chinese text should contain full-width periods, not latin periods, and contain no space. In practice, data often does not adhere to these rules, but the model has not be... | bb52e4f0bc4e448eb5ba893505fc89f6 |
apache-2.0 | ['sentence boundary detection', 'token classification', 'nlp'] | false | Metrics It's difficult to properly evaluate this model, since we rely on the proposition that the input data contains exactly one sentence per line. In reality, the data sets used thus far are noisy and often contain more than one sentence per line. Metrics are not published for now, and evaluation is limited to man... | 3cf32e19842935e8430f3ce9a50f46dd |
mit | ['generated_from_trainer'] | false | deberta-v3-base-finetuned-ner This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.7679 - Overall Precision: 0.4915 - Overall Recall: 0.6463 - Overall F1: 0.5584 ... | fb72c30afbdba6887209a7a1fcbd833e |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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: 100 | 16c3968d4ca07588771898d1b883ab5f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Datasetname F1 | Hyperparametername F1 | Hyperparametervalue F1 | Methodname F1 | Metricname F1 | Metricvalue F1 | Taskname F1 | |:-------------:|:-----:|:----:|:---------------:|:-... | d0131d7031d35368bcd2110b12340d09 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.