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 | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, ... | 924574d86bdc5cf879b997dda664b77d |
apache-2.0 | ['multilingual', 'PyTorch', 'Transformers', 'gpt3', 'gpt2', 'Deepspeed', 'Megatron'] | false | mGPT: fine-tune on message data MWE This model is a fine-tuned version of [sberbank-ai/mGPT](https://huggingface.co/sberbank-ai/mGPT) on 80k messages. Trained for one epoch, will be updated in a (separate) model repo later. | 210417727997fe0322d5b2a85c522887 |
apache-2.0 | ['multilingual', 'PyTorch', 'Transformers', 'gpt3', 'gpt2', 'Deepspeed', 'Megatron'] | false | Usage in python Install the transformers library if you don't have it: ``` pip install -U transformers ``` load the model into a pipeline object: ``` from transformers import pipeline import torch device = 'cuda' if torch.cuda.is_available() else 'cpu' my_chatbot = pipeline('text-generation', ... | dd8408979bb244feff13223e418bd49e |
apache-2.0 | ['multilingual', 'PyTorch', 'Transformers', 'gpt3', 'gpt2', 'Deepspeed', 'Megatron'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_sch... | 8a968a45095f03035865054fcea57091 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15.0 | 1635e2d601f3b54b6176f79153defd65 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_ep... | 012cba37bd052086513fa6301ea01ca9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.337 | 1.0 | 135 | 0.4810 | | 0.5238 | 2.0 | 270 | 0.3886 | | 0.4301 | 3.0 | 405 | 0.3378 | | 0.3755 | 4.0 | 540 | 0.3122 ... | a33b30ad9c497e9f1f2ad527b4009d23 |
creativeml-openrail-m | ['text-to-image'] | false | To use it you have to use the word ''IconsMi'' in the prompt. From my tests the images look better with this prompt: highly detailed, trending on artstation, ios icon app, IconsMi For negative prompts I got better results when I used: out of frame, duplicate, watermark, signature, text, ugly, sketch, deformed, muta... | 489b254220eba44acefaae77a0b47cdb |
apache-2.0 | ['translation'] | false | opus-mt-sv-kwy * source languages: sv * target languages: kwy * OPUS readme: [sv-kwy](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-kwy/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 916e9985d751994bc278c43270beb69c |
mit | [] | false | Babs Bunny on Stable Diffusion This is the `<babs_bunny>` 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... | 54fdb04b1c124c741233bc3358b60cf8 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__sst2__train-16-8 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6895 - Accuracy: 0.5222 | df7bcb712c018449cfcbd927f980c7e1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6899 | 1.0 | 7 | 0.7055 | 0.2857 | | 0.6793 | 2.0 | 14 | 0.7205 | 0.2857 | | 0.6291 | 3.0 | 21 | 0.7460 | 0.... | 504da04bcb2607e4c70c33428ec84349 |
apache-2.0 | ['audio-to-audio', 'speech-enhancement', 'PyTorch', 'speechbrain'] | false | MetricGAN-trained model for Enhancement This repository provides all the necessary tools to perform enhancement with SpeechBrain. For a better experience we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The model performance is: | Release | Test PESQ | Test STOI | |:-----------:|:--... | c1b0c04c7cb3c86ae8299a12551f66ed |
apache-2.0 | ['audio-to-audio', 'speech-enhancement', 'PyTorch', 'speechbrain'] | false | Pretrained Usage To use the mimic-loss-trained model for enhancement, use the following simple code: ```python import torch import torchaudio from speechbrain.pretrained import SpectralMaskEnhancement enhance_model = SpectralMaskEnhancement.from_hparams( source="speechbrain/metricgan-plus-voicebank", savedi... | c67eda713d3e7486a35bb22e206357c8 |
apache-2.0 | ['audio-to-audio', 'speech-enhancement', 'PyTorch', 'speechbrain'] | false | Saving enhanced signal on disk torchaudio.save('enhanced.wav', enhanced.cpu(), 16000) ``` The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *enhance_file* if needed. Make sure your input te... | b4be64b0b2b59b2bbf7543e924054217 |
apache-2.0 | ['audio-to-audio', 'speech-enhancement', 'PyTorch', 'speechbrain'] | false | Training The model was trained with SpeechBrain (d0accc8). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes/Voice... | daf717d0d1c6c3868cdf330aa82d6b0d |
apache-2.0 | ['audio-to-audio', 'speech-enhancement', 'PyTorch', 'speechbrain'] | false | Referencing MetricGAN+ If you find MetricGAN+ useful, please cite: ``` @article{fu2021metricgan+, title={MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement}, author={Fu, Szu-Wei and Yu, Cheng and Hsieh, Tsun-An and Plantinga, Peter and Ravanelli, Mirco and Lu, Xugang and Tsao, Yu}, journal={ar... | 12cfcb6ae409cf21a9e0ab54ca5f5722 |
apache-2.0 | ['afro-digits-speech'] | false | afrospeech-wav2vec-all-6 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [crowd-speech-africa](https://huggingface.co/datasets/chrisjay/crowd-speech-africa), which was a crowd-sourced dataset collected using the [afro-speech Space](https://huggingfa... | 5b20ca922ca83e4571fe6a90de0dd255 |
apache-2.0 | ['afro-digits-speech'] | false | Training and evaluation data The model was trained on a mixed audio data from 6 African languages - Igbo (`ibo`), Yoruba (`yor`), Rundi (`run`), Oshiwambo (`kua`), Shona (`sna`) and Oromo (`gax`). - Size of training set: 1977 - Size of validation set: 396 Below is a distribution of the dataset (training and valdati... | 7c5417e92f8796046cccd6948b59c9f0 |
apache-2.0 | ['afro-digits-speech'] | false | Evaluation performance It achieves the following results on the [validation set](VALID_all_interesred_6_audiodata.csv): - F1: 0.5787048581502744 - Accuracy: 0.6205357142857143 The confusion matrix below helps to give a better look at the model's performance across the digits. Through it, we can see the precision and... | 78dcaf9d825bf74586b14fc03c4d6fad |
apache-2.0 | ['afro-digits-speech'] | false | Training results | Training Loss | Epoch | Validation Accuracy | |:-------------:|:-----:|:--------:| | 2.0466 | 1 | 0.1130 | | 0.0468 | 50 | 0.6116 | | 0.0292 | 100 | 0.5305 | | 0.0155 | 150 | 0.5319 | | 82bc4571ba57777910013f3c13509635 |
creativeml-openrail-m | [] | false | waifu diffusion 1.3 base model with dreambooth training on images drawn by the artist "kagura_tohru" Can be used in StableDiffusion, including the extremely popular Web UI by Automatic1111, like any other model by placing the .CKPT file in the correct directory. Please consult the documentation for your installation ... | 750cfba33d4870e60abebca202e77238 |
mit | ['generated_from_keras_callback'] | false | lizaboiarchuk/tiny-rubert-war-finetuned This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.7630 - Validation Loss: 3.4797 - Epoch: 4 | d7f1d168fe85cfdaeac128cb38653a61 |
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... | 6418ddb883e8ad654f9b168e3549f5dc |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.1307 | 3.7059 | 0 | | 4.0402 | 3.6937 | 1 | | 3.9512 | 3.5754 | 2 | | 3.8665 | 3.4710 | 3 | | 3.7630 | 3.4797 | 4 | | 4a5ed48e9d429c4e12f44009304c2452 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_wavlm_s208 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 1... | c33c505dddde1feed4e8945f00adf695 |
creativeml-openrail-m | ['text-to-image'] | false | Realistic-Skin- Style Dreambooth model trained by shindi with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-768 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingf... | 4bad71abb8a78f4f1903475e44ab804f |
creativeml-openrail-m | ['text-to-image'] | false | RealisticSkinStyle This is my (Saleh) experiment with training on detailed photos of people featured textured skin. I noticed typically in my generations, skin looks airbrushed and smoonth which gives away that it's AI. Hoping to use this as a base model for further fine tunes so that models can look more realistic.... | c9f1cbc495514f9a584ad9a1ad6a2bf2 |
creativeml-openrail-m | ['text-to-image'] | false | Sample prompts: "A smiling black woman, jqkz" "A white woman wearing a white shirt, jqkz style" "A photograph of an old man wearing a black shirt, jqkz style" "A young man with a cigarette, jqkz" "A woman with ((blue eyes)) wearing a (((Hijab))), jqkz" | a9f41ca9ace5bb026bc590e0f7535a27 |
creativeml-openrail-m | ['text-to-image'] | false | Example Outputs     notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fa... | d0bfeec37c87ffaf191bc260c2878237 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-rte-target-glue-qqp This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-rte](https://huggingface.co/muhtasham/tiny-mlm-glue-rte) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4155 - Accuracy: 0.7949 - F1: 0.7691 | 7778ba2073a5f0946a534a7ee19c58b9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.5776 | 0.04 | 500 | 0.5189 | 0.7264 | 0.6855 | | 0.5081 | 0.09 | 1000 | 0.4824 | 0.7519 | 0.7059 | | 0.4951 ... | 73bc5853906aa6d8de7d1b433466488e |
apache-2.0 | ['generated_from_keras_callback'] | false | evanz37/bert-finetuned-ner 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: - Train Loss: 0.0202 - Validation Loss: 0.0603 - Epoch: 2 | 2efc1d3d9fad6791d42e95d1bdb97e50 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1017, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay':... | 96779015d2e47c7515d17a462e3b5cbb |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0205 | 0.0603 | 0 | | 0.0200 | 0.0603 | 1 | | 0.0202 | 0.0603 | 2 | | 5f60caaf4bb8acd2570970907e38c757 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | womenonlytop Sample pictures of this concept: .png) .png) , “Wave2vec2 is a framework for self-supervised learning of speech representations. It masks the speech input in the latent space and solves a contrastive task defined over... | f65ea4a2824da93f8a9011a2aeaa227e |
apache-2.0 | [] | false | Intended uses & limitations This model contains just the `IPUConfig` files for running the Wav2Vec2 base model (e.g. [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base)) on Graphcore IPUs. **This model contains no model weights, only an IPUConfig.** | 71d686ae2863aa55624f7f2d6aedb510 |
mit | ['generated_from_trainer'] | false | finetuned-bert-bounti This model is a fine-tuned version of [dbmdz/bert-base-turkish-128k-uncased](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) on the BounTi Turkish Twitter sentiment dataset. It achieves the following results on the evaluation set: - Loss: 1.1188 - Accuracy: 0.7246 - F1: 0.6845 - Pre... | 086e5de5d35a778af7f85a355f65c460 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 24 - eval_batch_size: 36 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 10 | 2e936a9e6fc1fbc91bf1e970219bed32 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 1.0974 | 0.02 | 5 | 1.0790 | 0.3756 | 0.3064 | 0.3255 | 0.3232 | | 1.1345 | 0.04 |... | 0bcdbc0252c2471c02ebea6869492c23 |
mit | ['generated_from_trainer'] | false | xtremedistil-l6-h384-uncased-finetuned-squad This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1358 | 3cfaa4c6818cb904e6bee56251952152 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.3293 | 1.0 | 5533 | 1.2426 | | 1.1701 | 2.0 | 11066 | 1.1534 | | 1.0713 | 3.0 | 16599 | 1.1358 | | 47253ca07cf98f7b8015e75e61662b55 |
apache-2.0 | ['generated_from_keras_callback'] | false | bimatechZou/Zouhaira_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: nan - Validation Loss: nan - Train Accuracy: 0.0 - Epoch: 7 | 42d4e64d5dbbd554b9d5334ab38c55a6 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.001, 'decay_steps': 1380, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta... | ad02e123ef7914234d2f47be036a3958 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | nan | nan | 0.0 | 0 | | nan | nan | 0.0 | 1 | | nan | nan | 0.0 | 2 | | nan ... | a5a8f5f2445cea2f23bbb56c0969688d |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny Swedish This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0 sv-SE dataset. It achieves the following results on the evaluation set: - Loss: 0.6929 - Wer: 44.1915 | b04cf44048c9273abfa4689141658258 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2768 | 6.01 | 1000 | 0.6929 | 44.1915 | | 0.0748 | 12.02 | 2000 | 0.7672 | 44.9925 | | 0.0143 | 18.03 | 3000 | 0.8665 | 45.008... | e6ea0b18ce5e0b47432da07dd841ccb4 |
apache-2.0 | ['generated_from_trainer'] | false | flyswot_iiif This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-base-224-22k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.1280 - F1: 0.0034 | 688b543c1e6c8b130cc504ae5e125aec |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 666 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP - label_smooth... | fb3ee87980d77182df311016b3f749d6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 8.5184 | 0.26 | 500 | 7.9280 | 0.0005 | | 7.7409 | 0.52 | 1000 | 7.5824 | 0.0007 | | 7.4649 | 0.78 | 1500 | 7.3841 | 0.001... | 5b7e4fa51aafca1d73bf521aa30fa317 |
apache-2.0 | ['translation'] | false | opus-mt-es-ig * source languages: es * target languages: ig * OPUS readme: [es-ig](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-ig/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 5378fa6c4c39caf7aef751c010dcded1 |
apache-2.0 | [] | false | Graphcore/roberta-base-ipu Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore... | e916030034505278d19857da62e8d25c |
apache-2.0 | [] | false | Model description RoBERTa is based on BERT pretraining approach and improves on it by carefully evaluating a number of design decisions of BERT pretraining which it found to cause the model to be undertrained. It suggested a way to improve the performance by training the model longer, with bigger batches over more d... | 88e043c3f78fb09371269d6a45273ba8 |
apache-2.0 | ['automatic-speech-recognition', 'fa'] | false | exp_w2v2t_fa_r-wav2vec2_s283 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 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speec... | 624e32eadd97f4ac8ccdb1f61f2a7aea |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-LARGE-DL8 (Deep-Narrow version) T5-Efficient-LARGE-DL8 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint an... | f554a5a0faaeb04bb1a14306656be088 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-large-dl8** - is of model type **Large** with the following variations: - **dl** is **8** It has **469.22** million parameters and thus requires *ca.* **1876.87 MB** of memory in full precision (*fp32*) or **938.43 MB** of memory in half precision (... | a5ad70bc24d8cce08dcddd407dd593f5 |
cc-by-4.0 | ['generated_from_trainer'] | false | roberta-base-squad-finetuned-squad This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.7060 | 9f7b47e77a1d3597c5eb22816843a4b8 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 17 | 5.9055 | | No log | 2.0 | 34 | 6.2285 | | No log | 3.0 | 51 | 6.8639 | | No log | 4.0 | 68 | 6.3238 ... | 23b044ca232f8e3544e26b0698e9aef2 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_xls-r_s417 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input i... | 13eb50be4ec194e8fb364fd9996581fc |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_rte_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6917 - Accuracy: 0.5271 | 4f6e2fed27c3947db03228cbe56213f1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7 | 1.0 | 10 | 0.6922 | 0.5271 | | 0.695 | 2.0 | 20 | 0.6985 | 0.4729 | | 0.6967 | 3.0 | 30 | 0.6918 | 0.... | d1fdad13a57d764e6858da91bc493458 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'hi', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-hi-d3 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 - HI dataset. It achieves the following results on the evaluation set: - Loss: 0.7988 - Wer: 0.3713 | 8918c92bfcfbf1fe044336b57d414902 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'hi', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-d3 --dataset mozilla-foundation/common_voice_7_0 --config hi --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Hindi lang... | 6d8e526b63aa178831fd609ea262103d |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'hi', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000388 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | a452e3eccb15b14e61741684034dbcc9 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'hi', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 8.2826 | 1.36 | 200 | 3.5253 | 1.0 | | 2.7019 | 2.72 | 400 | 1.1744 | 0.7360 | | 0.7358 | 4.08 | 600 | 0.7781 | 0.5501 | |... | b269fe6e0e00a4471cf5bd3c87d427b7 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ncbi This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ncbi_disease dataset. It achieves the following results on the evaluation set: - Loss: 0.0679 - Precision: 0.7807 - Recall: 0.8640 - F1: 0.8203 - Accuracy: 0.9831 | df3fb0518f6f3805770ce48b028a51ef |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1146 | 1.0 | 680 | 0.0686 | 0.7450 | 0.8056 | 0.7741 | 0.9805 | | 0.0458 | 2.0 |... | a1815d9cf59dc8457bbdd1528083e445 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fine_tuned_title This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2615 - Accuracy: {'accuracy': 0.877634820695319} - Recall: {'recall': 0.847... | 466dd03ab4fd4dc8fc693d915126cb3b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - 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: 1000 - num_epochs: 15 | 86c423e3c6ee3a7a2912aa636094dfe6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------------------------------:|:-----------------------------... | 683da655ba6aba9cb4ea4c5ec7455603 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-home-4-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3789 - Accuracy: 0.3356 | 72813604830a53f5a72f0529b2fbd513 |
mit | ['text-classification'] | false | Multi2ConvAI-Corona: English logistic regression model using fasttext embeddings
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Corona (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- langua... | a932cbd51282fdf516179da09632923e |
mit | ['text-classification'] | false | assumes working dir is the root of the cloned multi2convai repo
python scripts/run_inference.py -m multi2convai-corona-en-logreg-ft
>>> Create pipeline for config: multi2convai-corona-en-logreg-ft.
>>> Created a LogisticRegressionFasttextPipeline for domain: 'corona' and language 'en'.
>>>
>>> Enter your tex... | f4ebc8b0275685f62e8f683b3f3485bb |
mit | ['text-classification'] | false | assumes working dir is the root of the cloned multi2convai repo
from pathlib import Path
from multi2convai.pipelines.inference.base import ClassificationConfig
from multi2convai.pipelines.inference.logistic_regression_fasttext import (
LogisticRegressionFasttextConfig,
LogisticRegressionFasttextPipeli... | 1dc47de30f8619dfe1cbee76cd650686 |
mit | ['text-classification'] | false | 1. Define paths of model, label dict and embeddings
model_file = "model.pth"
label_dict_file = "label_dict.json"
embedding_path = Path(
f"../models/embeddings/fasttext/en/wiki.200k.en.embed"
)
vocabulary_path = Path(
f"../models/embeddings/fasttext/en/wiki.200k.en.vocab"
)
| 555629bdd0b63a6eafa957562ed96bb2 |
mit | ['text-classification'] | false | assumes working dir is the root of the cloned multi2convai repo
mkdir models/fasttext/en
curl https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.en.vec --output models/fasttext/en/wiki.en.vec
python scripts/serialize_fasttext.py -r fasttext/wiki.en.vec -v fasttext/en/wiki.200k.en.vocab -e fasttext/en/wik... | 26bcc13361ad385bcfc29fa4212b46bb |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | Norwegian Wav2Vec2 Model - 300M - VoxRex - Bokmål
This model is finetuned on top of feature extractor [VoxRex-model](https://huggingface.co/KBLab/wav2vec2-large-voxrex) from the National Library of Sweden. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parenthes... | b732d7c6f1dcea7787b3c207756c16e4 |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | Model description
This is one of several Wav2Vec-models our team created during the 🤗 hosted [Robust Speech Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614?s=09). This is the complete list of our models and their final scores:
| Model | Final WER |... | ccc3fdd437bd6e7a9b498babf3ab11be |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | Dataset
In parallel with the event, the team also converted the [Norwegian Parliamentary Speech Corpus (NPSC)](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-58/) to the [NbAiLab/NPSC](https://huggingface.co/datasets/NbAiLab/NPSC) in 🤗 Dataset format and used that as the main source for training.
... | 5c8d43b93a7c472eb15cf230c27419b9 |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | Code
We have released all the code developed during the event so that the Norwegian NLP community can build upon it when developing even better Norwegian ASR models. The finetuning of these models is not very computationally demanding. After following the instructions here, you should be able to train your own automa... | e4e8d8bd700090fbd7a819ae72b51723 |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | Training procedure
To reproduce these results, we strongly recommend that you follow the [instructions from 🤗](https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event | 9f69616dd5fed741030a63500f4d9cdf |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | talks) to train a simple Swedish model.
When you have verified that you are able to do this, create a fresh new repo. You can then start by copying the files ```run.sh``` and ```run_speech_recognition_ctc.py``` from our repo. Running these will create all the other necessary files, and should let you reproduce our r... | 66c862f6cc34f2481bcb6b2fdd803bb2 |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | Language Model
As the scores indicate, adding even a simple 5-gram language will improve the results. 🤗 has provided another [very nice blog](https://huggingface.co/blog/wav2vec2-with-ngram) explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance... | 3e95765049a2af7665e2b293ff6d293d |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | Parameters
The final model was run using these parameters:
```
--dataset_name="NbAiLab/NPSC"
--model_name_or_path="KBLab/wav2vec2-large-voxrex"
--dataset_config_name="16K_mp3_bokmaal"
--output_dir="./"
--overwrite_output_dir
--num_train_epochs="15"
--per_device_train_batch_size="16"
--per_device_eval_... | ebd6177577a7726772b12799c10aeb22 |
apache-2.0 | ['generated_from_trainer'] | false | amazon-review-sentiment-analysis_large This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3674 - Rmse: 0.6061 | 122e727bb7821ffef6aaf1f479abc289 |
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: 0.2153 - Accuracy: 0.924 - F1: 0.9241 | ee5ea917c0da2ddce1955973916c7583 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7986 | 1.0 | 250 | 0.3021 | 0.91 | 0.9078 | | 0.2386 | 2.0 | 500 | 0.2153 | 0.924 | 0.9241 | | c991e755215e9c2e7cbe0a932f30adb8 |
apache-2.0 | ['generated_from_trainer'] | false | Article_100v2_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v2_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.3105 - Precision: 0.4554 - Recall: 0.4162 - F1: 0.4350 - Accuracy: 0.... | 955504263cbd0b13ce8565971c33d27e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 45 | 0.3753 | 0.3013 | 0.2749 | 0.2875 | 0.8651 | | No log | 2.0 |... | 0f0fe15e13d30d3fb8f3b124ff444df5 |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_vp-sv_s911 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) 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... | 967208f68b6ff0539e090b947f6ca21c |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition', 'speech2text2'] | false | S2T2-Wav2Vec2-CoVoST2-EN-DE-ST `s2t-wav2vec2-large-en-de` is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST). The S2T2 model was proposed in [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/pdf/2104.06678.pdf) and officially released in [Fairs... | fa5b5736d5164b80abbabab51c6eb533 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition', 'speech2text2'] | false | Intended uses & limitations This model can be used for end-to-end English speech to German text translation. See the [model hub](https://huggingface.co/models?filter=speech2text2) to look for other S2T2 checkpoints. | e09f75216f4d728b0fb1c80477385dc5 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition', 'speech2text2'] | false | How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. You can use the model directly via the ASR pipeline ```python from datasets import load_dataset from transformers import pipeline librispe... | 3c8a32205f32ad91bdf536ec7efef484 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition', 'speech2text2'] | false | Evaluation results CoVoST-V2 test results for en-de (BLEU score): **26.5** For more information, please have a look at the [official paper](https://arxiv.org/pdf/2104.06678.pdf) - especially row 10 of Table 2. | d4cdfb08e4b6a68381ad8d6b751f6e0e |
apache-2.0 | ['generated_from_trainer'] | false | STT_Model_9 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: 0.2506 - Wer: 0.1718 | 505d9ebd79183e049bdd5aa34fc19cb9 |
apache-2.0 | ['generated_from_trainer'] | false | Dataset info - Name: LJSpeech - Source: https://www.kaggle.com/datasets/mathurinache/the-lj-speech-dataset - Total audios (in Google Drive): 1420 - Total transcripts (in Google Drive): 13100 - No. of rows selected: 500 - Train-test ratio: 70:30 - No. of training set: 350 - No. of testing set: 150 | 028f1af855417405ecd4de243475b9ff |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 50 | 56ec22b5d6deec5c38dd45dde6f8616a |
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