license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | The use of this learning model is entirely at the discretion of the user, and they have the freedom to choose whether or not to create NSFW content. This is important to note that the model itself does not contain any explicit or inappropriate imagery that can be easily accessed with a single click. The purpose of s... | 11e54d28abfeb5efd4c4f86443b131ce |
mit | ['molecule-generation', 'cheminformatics', 'biochemical-language-models'] | false | ChemBERTaLM A molecule generator model finetuned from [ChemBERTa](https://huggingface.co/seyonec/PubChem10M_SMILES_BPE_450k) checkpoint. It was introduced in the paper, "Exploiting pretrained biochemical language models for targeted drug design", which has been accepted for publication in *Bioinformatics* Published b... | 3437f0306693345c542f6389993e6866 |
mit | ['molecule-generation', 'cheminformatics', 'biochemical-language-models'] | false | How to use ```python from transformers import RobertaForCausalLM, RobertaTokenizer, pipeline tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/ChemBERTaLM") model = RobertaForCausalLM.from_pretrained("gokceuludogan/ChemBERTaLM") generator = pipeline("text-generation", model=model, tokenizer=tokenizer) gener... | 373d2cd5197f721973cf671f7fd9a70a |
mit | ['molecule-generation', 'cheminformatics', 'biochemical-language-models'] | false | Citation ```bibtex @article{10.1093/bioinformatics/btac482, author = {Uludoğan, Gökçe and Ozkirimli, Elif and Ulgen, Kutlu O. and Karalı, Nilgün Lütfiye and Özgür, Arzucan}, title = "{Exploiting Pretrained Biochemical Language Models for Targeted Drug Design}", journal = {Bioinformatics}, year = {2022}... | 5e68e76f2094995df29fe629b6b9b159 |
apache-2.0 | [] | false | This repository holds the finetuned weights for Tortoise v2 for the LJSpeech voice. It is a good demonstration of how powerful fine-tuning Tortoise can be. Usage: - Clone Tortoise, jbetker/tortoise-tts-v2 or https://github.com/neonbjb/tortoise-tts - Clone this repo to download weights - Run any Tortoise script with t... | fb5922fac4252ec4e2a53eeebedbb4c4 |
mit | ['stable-diffusion', 'text-to-image'] | false | Hayashida Tamaki (GF Kari) on Waifu Diffusion v1.3.5 This is the `<wd135-hayashida-tamaki-gfkari>` concept taught to [Waifu Diffusion v1.3.5](https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/models/wd-1-3-5_80000-fp32.ckpt) via Textual Inversion. | 380e68d04031f9f9c8f97f163f68f87a |
mit | ['stable-diffusion', 'text-to-image'] | false | Credits The model card follows the format commonly used by concepts stored at [Hugging Face SD Concepts Library](https://huggingface.co/sd-concepts-library). The training images were taken from [GF Kari Database](https://gfkari.gamedbs.jp/). | af4f82e75aa948b0d793b87195f834bd |
mit | ['stable-diffusion', 'text-to-image'] | false | Concept Images Here is the new concept you will be able to use as an `object`:   ![<wd135-hayashida-tamaki-gfkari> 2](.... | b55015471d9cc75c4a471cc84c8ee417 |
mit | ['stable-diffusion', 'text-to-image'] | false | Output Examples !["best quality masterpiece, <wd135-hayashida-tamaki-gfkari> collarbone bare shoulders bare legs shiny skin standing, frilled white summer dress, ocean beach sunny day sunlight, cowboy shot, [bad anatomy, bad hands, bad perspective, bad proportions, blurry, censored, cropped, error, extra arms, extra ... | 6d547b693e68dad4834ad86651e1f452 |
apache-2.0 | ['generated_from_trainer'] | false | small-vanilla-target-tweet This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.8718 - Accuracy: 0.7540 - F1: 0.7525 | f28293fc7ea7b1b135e06b732f35d173 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5858 | 4.9 | 500 | 0.8189 | 0.7380 | 0.7364 | | 0.1039 | 9.8 | 1000 | 1.1965 | 0.7594 | 0.7568 | | 0.0264 |... | ef8127c1c26583cc22c03ca034dc3057 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | 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: 1.4031 - Wer: 0.6827 | dd557354238600212d6bb8e49435a497 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 5.3156 | 3.4 | 500 | 4.5583 | 1.0 | | 3.3329 | 6.8 | 1000 | 3.4274 | 1.0001 | | 2.1275 | 10.2 | 1500 | 1.7221 | 0.876... | 2e5d7bfe8bb1e8346e6ec9dc55310409 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | MultiBERTs Seed 1 Checkpoint 300k (uncased) Seed 1 intermediate checkpoint 300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/goo... | fae92f9a553dfa67a5c6dcc0f8d43468 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-1-300k') model = BertModel.from_pretrained("multiberts-seed-1-300k") text = "Replace me by any text you'd like.... | 6910e9cb11885e8a78def69cdd627c15 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | riffusion_model-db Dreambooth model trained by jha2ee 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-st... | f99edec2208598e3599cae596b0a1f9c |
apache-2.0 | ['generated_from_trainer'] | false | Article_100v0_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v0_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.6037 - Precision: 0.25 - Recall: 0.0003 - F1: 0.0005 - Accuracy: 0.... | 686b31bcf83d785948e7b99066b59051 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 12 | 0.7472 | 0.0 | 0.0 | 0.0 | 0.7772 | | No log | 2.0 |... | 33e0ab9809bf163fdc2e5b11c2a690ad |
mit | ['generated_from_trainer'] | false | indobert-base-uncased-finetuned-indonlu-smsa This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.2277 - Accuracy: 0.9302 - F1: 0.9066 - Precision: 0.8992 -... | b75824976cf55c58a459fb6f96e67e27 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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: 1500 - num_epochs: 10 | 2cab53de093d4eb02761e9d0d6c83eab |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 344 | 0.3831 | 0.8476 | 0.7715 | 0.7817 | 0.7627 | | 0.4167 | 2.0 |... | 3d8738e7b9ac91294b4b6a95f2ed6efd |
mit | ['generated_from_trainer'] | false | bert-base-historic-multilingual-cased-squad-en This model is a fine-tuned version of [dbmdz/bert-base-historic-multilingual-cased](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5307 | 8ee56f39306612015eadf25c5af9611d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.881 | 1.0 | 4820 | 1.5507 | | 1.5883 | 2.0 | 9640 | 1.5307 | | a491900e82a3340b9fddfa6316aaed30 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Spanish This model is a fine-tuned version of [juancopi81/whisper-medium-es](https://huggingface.co/juancopi81/whisper-medium-es) on the mozilla-foundation/common_voice_11_0 es dataset. It achieves the following results on the evaluation set: - Loss: 0.2338 - Wer: 95.6181 | 1363195b11f4ed1e1dadddd0e11e437f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - traini... | 4670e51b52d483d0eade0a97bf54a8dd |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1432 | 1.0 | 100 | 0.2338 | 95.6181 | | 79a011f8c776e6848ab28b02fee77791 |
mit | ['generated_from_keras_callback'] | false | esm2_t12_35M_UR50D-finetuned-ARG-classification This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on an unknown dataset. It achieves the following results on the evaluation set: | 4ac680a254b22e42c9170b904a8d4f82 |
mit | [] | false | A Tale of Two Empires on Stable Diffusion This is the `<two-empires>` 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. ... | 6dc49fec818d13782f04b3e7b13d9136 |
creativeml-openrail-m | ['text-to-image'] | false | AndiFace Dreambooth model trained by iksenburg with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-512 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebook... | df91a5120ec34276cf108905f610c22d |
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 squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1576 | d721b824758cc74a9b351ba78a819b86 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2167 | 1.0 | 5533 | 1.1654 | | 0.9559 | 2.0 | 11066 | 1.1209 | | 0.7532 | 3.0 | 16599 | 1.1576 | | 853c3f17c19846326b5db22946fca2e3 |
mit | [] | false | Morino hon Style on Stable Diffusion This is the `<morino-hon>` 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 ca... | 2f9643cc625b9cd45426385c67a5f64c |
cc-by-4.0 | [] | false | This model uses the Deep Fashion dataset in order to create a category classifier among the 50 or so provided categories.
https://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html
This model leverages the ViT (Vision transformer), loaded with the custom dataset and the 50 odd categoes to which they are assigned. The ... | bf1cb949cf4c0b54adfb90746b39fd27 |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_vp-it_s996 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | a90f11236bd2ee934a766b6f4a08ed4d |
apache-2.0 | ['translation', 'Hindi', 'generated_from_keras_callback'] | false | opus-mt-finetuned-hi-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-hi-en](https://huggingface.co/Helsinki-NLP/opus-mt-hi-en) on [HindiEnglish Corpora](https://www.clarin.eu/resource-families/parallel-corpora) | b35bc0540b706a1fe2c3f0d41cc4cbe1 |
apache-2.0 | ['generated_from_trainer'] | false | distil-added-voca This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2515 | f55a13c8239d10b0e6a8401b8f14fbc9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 174 | 0.2577 | | No log | 2.0 | 348 | 0.2488 | | 0.2546 | 3.0 | 522 | 0.2515 | | 1dc5a9eb61d86c4987a8c01204a71f0a |
apache-2.0 | ['translation'] | false | opus-mt-bzs-sv * source languages: bzs * target languages: sv * OPUS readme: [bzs-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bzs-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | de9a1ab27edee39bbcca075942d1dd0c |
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: 50 | 197dc62dbd3d953eec970227a96c4fbf |
apache-2.0 | ['bert-base-portuguese-cased', 'semantic role labeling', 'finetuned'] | false | Model description This model is the [`neuralmind/bert-base-portuguese-cased`](https://huggingface.co/neuralmind/bert-base-portuguese-cased) fine-tuned on Portuguese semantic role labeling data. This is part of a project from which resulted the following models: * [liaad/srl-pt_bertimbau-base](https://huggingface.co/... | ff573122235083ccf3a26b0694fb897f |
apache-2.0 | ['bert-base-portuguese-cased', 'semantic role labeling', 'finetuned'] | false | How to use To use the transformers portion of this model: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liaad/srl-pt_bertimbau-base") model = AutoModel.from_pretrained("liaad/srl-pt_bertimbau-base") ``` To use the full SRL model (transformers portion + a deco... | 5ecd781e149b8fb2e7608ee734bf885f |
apache-2.0 | ['bert-base-portuguese-cased', 'semantic role labeling', 'finetuned'] | false | Training procedure The model was trained on the PropBank.Br datasets, using 10-fold Cross-Validation. The 10 resulting models were tested on the folds as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [projec... | 65f2d823d15110035713fd950afaa295 |
apache-2.0 | ['bert-base-portuguese-cased', 'semantic role labeling', 'finetuned'] | false | Eval results | Model Name | F<sub>1</sub> CV PropBank.Br (in domain) | F<sub>1</sub> Buscapé (out of domain) | | --------------- | ------ | ----- | | `srl-pt_bertimbau-base` | 76.30 | 73.33 | | `srl-pt_bertimbau-large` | 77.42 | 74.85 | | `srl-pt_xlmr-base` | 75.22 | 72.82 | | `srl-pt_xlmr-large` | 77.59 | 73.84... | 700ea52c759557fce971edb875a9575b |
apache-2.0 | ['bert-base-portuguese-cased', 'semantic role labeling', 'finetuned'] | false | BibTeX entry and citation info ```bibtex @misc{oliveira2021transformers, title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling}, author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge}, year={2021}, eprint={2101.01213}, archivePrefix={arXiv}, ... | c70baf406309880c7ae5fe1a3c70f2af |
other | [] | false | Deep Learning for NLP: Training a text classification model to detect fake news articles!
Training and test dataset gotten from https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset
Dataset size = 44898 articles
Training set size = 35918 articles
Test set size = 8980 articles
Accuracy on t... | f73704ead46de77a5a271507a6502851 |
apache-2.0 | [] | false | distilbert-base-ro-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 accuracy... | 6736a02d4cadbc68c175db6572fc6683 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-ro-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-ro-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github r... | 8abbe6cd744113f60855f2f6dc434f34 |
cc-by-4.0 | ['translation'] | false | Description This is the development version of the Bokmål-Nynorsk translator. If you want something that is stable, Please do run [this version](https://huggingface.co/pere/nb-nn-translation/) instead. Here is an example of how to use the model from Python ```python | a0f353d5dfa95b305743b58260d3d3ec |
cc-by-4.0 | ['translation'] | false | Import libraries from transformers import T5ForConditionalGeneration, AutoTokenizer model = T5ForConditionalGeneration.from_pretrained('pere/nb-nn-dev',from_flax=True) tokenizer = AutoTokenizer.from_pretrained('pere/nb-nn-dev') | 207dd5c2267bb7724a5e939065e75f7e |
cc-by-4.0 | ['translation'] | false | Encode the text text = "Hun vil ikke gi bort sine personlige data." inputs = tokenizer.encode(text, return_tensors="pt") outputs = model.generate(inputs, max_length=255, num_beams=4, early_stopping=True) | 1b4e362f9506f124458782669f5201d4 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-wikisql This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikisql dataset. It achieves the following results on the evaluation set: - Loss: 0.1245 - Rouge2 Precision: 0.8183 - Rouge2 Recall: 0.7262 - Rouge2 Fmeasure: 0.7624 | 1e659dcd986c9cde5ad60d24a639a83e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.1954 | 1.0 | 4049 | 0.1575 | 0.7934 | 0.7033 | 0.73... | b6f38cb06c391aba6e5e7178498529e9 |
other | [] | false | This model was trained for toxicity labeling. Label_1 means TOXIC, Label_0 means NOT TOXIC The model was fine-tuned based off the already existing sentiment classifier oliverguhr/german-sentiment-bert . The aforementioned classifier performed poorly (44% accuracy on my test sample), so I trained the current toxicity c... | 1d5f3362ecd94e395991dcea8b31751d |
afl-3.0 | ['text-to-speech', 'gronings', 'FastSpeech 2'] | false | GroTTS Model This model was trained with the [FastSpeech 2](https://arxiv.org/abs/2006.04558) architecture using approx. 2 hours of Gronings TTS dataset. For the best results, you need to download the vocoder separately from [here](https://huggingface.co/ahnafsamin/parallelwavegan-gronings) and then use the followin... | a05e80d1c0f7e2e199ec2c5e8d076007 |
afl-3.0 | ['text-to-speech', 'gronings', 'FastSpeech 2'] | false | TTS config <details><summary>expand</summary> ``` config: conf/tuning/train_fastspeech2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/tts_train_fastspeech2_raw_char_tacotron ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// di... | 89c908c8fba51b7eddf3098ee2e9866d |
apache-2.0 | ['generated_from_keras_callback'] | false | lmchion/distilbert-finetuned-esg-a4s 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: 2.2859 - Validation Loss: 2.3354 - Epoch: 9 | de3a050bd15a40bbe29a8a35d6ffb753 |
apache-2.0 | ['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': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | d36998a1535afaade68eb2b4171476f7 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.8805 | 2.7153 | 0 | | 2.6414 | 2.5472 | 1 | | 2.5202 | 2.4813 | 2 | | 2.4306 | 2.3834 | 3 | | 2.3452 | 2.3297 | 4 | | 2.2940 |... | d473046e745fd87a2aabe76992981c69 |
apache-2.0 | ['generated_from_trainer'] | false | distilr2-lr1e05-wd0.1-bs32 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2744 - Rmse: 0.5238 - Mse: 0.2744 - Mae: 0.4135 | d8bf6556ff45bda33df94ffd4df58179 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2775 | 1.0 | 623 | 0.2735 | 0.5229 | 0.2735 | 0.4180 | | 0.2738 | 2.0 | 1246 | 0.2726 | 0.5221 | 0.2726 ... | 9237101840938d44036a50624264767d |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0712 - Precision: 0.9048 - Recall: 0.9310 - F1: 0.9177 - Accuracy: 0.9817 | 8d0ffebc1d62d0ecce8900f29cf6b29b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0849 | 1.0 | 1756 | 0.0712 | 0.9048 | 0.9310 | 0.9177 | 0.9817 | | 81ed06a9ac57dc07f2499a01a4ede75c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Whisper Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need for fine-tuning. Whisper was proposed in the paper [Robust Speech... | f99eb299bbaffcbd6879ee42a7d40a5d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Model details Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model. It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision. The models were trained on either English-only data or multilingual data. The English-only model... | 337460f980863c56c865b56395d00482 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Usage This checkpoint is an *English-only* model, meaning it can be used for English speech recognition. Multilingual speech recognition or speech translation is possible through use of a multilingual checkpoint. To transcribe audio samples, the model has to be used alongside a [`WhisperProcessor`](https://huggingf... | 9341350caa0896b67ee78f0091b467ea |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | transformers.WhisperProcessor). The `WhisperProcessor` is used to: 1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model) 2. Post-process the model outputs (converting them from tokens to text) | 35131c678cd1329d9eb65c3954097bd7 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | load dummy dataset and read audio files >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features >>> | 7f8653a660fb0ebba0e0757902df33df |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False) ['<|startoftranscript|><|notimestamps|> Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.<|endoftext|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_s... | 774985de8f252acb9f1d084df24f9bf9 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Evaluation This code snippet shows how to evaluate Whisper base.en on [LibriSpeech test-clean](https://huggingface.co/datasets/librispeech_asr): ```python >>> from datasets import load_dataset >>> from transformers import WhisperForConditionalGeneration, WhisperProcessor >>> import torch >>> from evaluate import lo... | a84ce89ac8dac9e81c178c452d46ca5c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Long-Form Transcription The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers [`pipeline`](https://huggingface.co/docs/transfor... | 7281f559f9c5b35148a0dafedadfe42b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | transformers.AutomaticSpeechRecognitionPipeline) method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. It can also be extended to predict utterance level timestamps by passing `return_timestamps=True`: ```python >>> import torch >>> from transformers import pipeline >>> from dat... | e8fed42c1533a688734c2fff94deeb6b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | we can also return timestamps for the predictions >>> prediction = pipe(sample, return_timestamps=True)["chunks"] [{'text': ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.', 'timestamp': (0.0, 5.44)}] ``` | df2adced03391e8c5aa9066190d6b9d5 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Fine-Tuning The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However, its predictive capabilities can be improved further for certain languages and tasks through *fine-tuning*. The blog post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/b... | 1c9f2a07bd27b3bc24bf2309c6332142 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | BibTeX entry and citation info ```bibtex @misc{radford2022whisper, doi = {10.48550/ARXIV.2212.04356}, url = {https://arxiv.org/abs/2212.04356}, author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya}, title = {Robust Speech Recognition via Large-Sc... | d3ceef809cf80c5d6d8e2b68c6d8cdd4 |
mit | ['adverse-drug-events', 'twitter', 'social-media-mining-for-health', 'SMM4H'] | false | t2t-ner-ade-balanced t2t-ner-ade-balanced is a text-to-text (**t2t**) adverse drug event (**ade**) extraction (NER) model trained with over- and undersampled (balanced) English tweets reporting adverse drug events. It is trained as part of BOUN-TABI system for the Social Media Mining for Health (SMM4H) 2022 shared ta... | cb8068d3f4f10c0ea7674c82a9beb9fe |
mit | ['adverse-drug-events', 'twitter', 'social-media-mining-for-health', 'SMM4H'] | false | SMM4H) Workshop and Shared Task* and will be available soon. The source code has been released on GitHub at [https://github.com/gokceuludogan/boun-tabi-smm4h22](https://github.com/gokceuludogan/boun-tabi-smm4h22). The model utilizes the T5 model and its text-to-text formulation. The inputs are fed to the model with th... | 1c6a6ab3654253d9cfc410e9aae32298 |
mit | ['adverse-drug-events', 'twitter', 'social-media-mining-for-health', 'SMM4H'] | false | Usage ```python from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("yirmibesogluz/t2t-ner-ade-balanced") model = AutoModelForSeq2SeqLM.from_pretrained("yirmibesogluz/t2t-ner-ade-balanced") predictor = pipeline("text2text-generation", model=model, tokenize... | 74776607c32236051fde227684f1da40 |
mit | ['adverse-drug-events', 'twitter', 'social-media-mining-for-health', 'SMM4H'] | false | Citation ```bibtex @inproceedings{uludogan-gokce-yirmibesoglu-zeynep-2022-boun-tabi-smm4h22, title = "{BOUN}-{TABI}@{SMM4H}'22: Text-to-{T}ext {A}dverse {D}rug {E}vent {E}xtraction with {D}ata {B}alancing and {P}rompting", author = "Uludo{\u{g}}an, G{\"{o}}k{\c{c}}e and Yirmibe{\c{s}}o{\u{g}}lu, Zeynep", ... | 2bea12bbd73f488bb2e50f9c72efd152 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-bengali-v8 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.7874 - Wer: 0.6777 | 95beb87577e59bd879dc63559a5ac02a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-05 - 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_sche... | 228663feb8c8f711d77f8c56b59bacd1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 7.2332 | 0.85 | 400 | 3.3381 | 1.0 | | 2.3574 | 1.71 | 800 | 0.8236 | 0.7516 | | 0.8096 | 2.56 | 1200 | 0.9337 | 0.6717 | |... | 246b32a32e2669d322854a55d2ab1653 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-10k-pad-early-lit This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3758 - Rouge1: 27.7351 - Rouge2: 13.1664 - Rougel: 21.6559 - Rougelsum: 24.648 - Ge... | ef5e23d87d56c8a2a248965413dde959 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.2516 | 1.0 | 9998 | 0.3540 | 28.1151 | 13.3875 | 22.1496 | 25.1745 |... | e80e11fd7aefe7f718e2ada730c086ba |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2t_de_vp-100k_s627 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | d50254d86653dfedfd56ce2491f80faa |
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: 0.2106 - Accuracy: 0.927 - F1: 0.9273 | 3909282ac94f82cb27b64d36371d9814 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8007 | 1.0 | 250 | 0.2955 | 0.914 | 0.9117 | | 0.2417 | 2.0 | 500 | 0.2106 | 0.927 | 0.9273 | | b51ec13acc2acbfc492b97ce748f677f |
mit | ['emoberta', 'roberta'] | false | Emotion Recognition in Coversation (ERC) [](https://paperswithcode.com/sota/emotion-recognition-in-conversation-on?p=emoberta-speaker-aware-emotion-recog... | 99c298a5aa020f411c7c1495ce38d688 |
mit | ['emoberta', 'roberta'] | false | Prerequisites 1. An x86-64 Unix or Unix-like machine 1. Python 3.8 or higher 1. Running in a virtual environment (e.g., conda, virtualenv, etc.) is highly recommended so that you don't mess up with the system python. 1. [`multimodal-datasets` repo](https://github.com/tae898/multimodal-datasets) (submodule) 1. pip ins... | dab14864b9e1dad151cad50eaa0c4372 |
mit | ['emoberta', 'roberta'] | false | EmoBERTa training First configure the hyper parameters and the dataset in `train-erc-text.yaml` and then, In this directory run the below commands. I recommend you to run this in a virtualenv. ```sh python train-erc-text.py ``` This will subsequently call `train-erc-text-hp.py` and `train-erc-text-full.py`. | 1442487e5e947fa38467cf7d0a5a65ea |
mit | ['emoberta', 'roberta'] | false | Results on the test split (weighted f1 scores) | Model | | MELD | IEMOCAP | | -------- | ------------------------------- | :-------: | :-------: | | EmoBERTa | No past and future utterances | 63.46 | 56.09 | | | Only past utterances | 64.55 |... | f942c1b34b41e51b0e1aafe813038f9b |
mit | ['emoberta', 'roberta'] | false | Huggingface We have released our models on huggingface: - [emoberta-base](https://huggingface.co/tae898/emoberta-base) - [emoberta-large](https://huggingface.co/tae898/emoberta-large) They are based on [RoBERTa-base](https://huggingface.co/roberta-base) and [RoBERTa-large](https://huggingface.co/roberta-large), res... | 296df77a4f6922587bf01a2272739bb8 |
mit | ['emoberta', 'roberta'] | false | Flask app You can either run the Flask RESTful server app as a docker container or just as a python script. 1. Running the app as a docker container **(recommended)**. There are four images. Take what you need: - `docker run -it --rm -p 10006:10006 tae898/emoberta-base` - `docker run -it --rm -p 10006:100... | 198af0ee91842f21fa46aa6908b4eb5f |
mit | ['emoberta', 'roberta'] | false | Client Once the app is running, you can send a text to the server. First install the necessary packages: `pip install -r requirements-client.txt`, and the run the [client.py](client.py). The usage is as below: ```console client.py [-h] [--url-emoberta URL_EMOBERTA] --text TEXT ``` For example: ```sh python client.... | 1f7d930ab8bf791cd79bde3972816579 |
mit | ['emoberta', 'roberta'] | false | Contributing Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are **greatly appreciated**. 1. Fork the Project 1. Create your Feature Branch (`git checkout -b feature/AmazingFeature`) 1. Run `make style && quality` in the root re... | c3decb61e1b6dd476768481c09958284 |
mit | ['emoberta', 'roberta'] | false | Cite our work Check out the [paper](https://arxiv.org/abs/2108.12009). ```bibtex @misc{kim2021emoberta, title={EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa}, author={Taewoon Kim and Piek Vossen}, year={2021}, eprint={2108.12009}, archivePrefix={arXiv}, ... | f1a97941990cf8950a51d4fa807c6675 |
apache-2.0 | ['translation'] | false | eng-hye * source group: English * target group: Armenian * OPUS readme: [eng-hye](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hye/README.md) * model: transformer-align * source language(s): eng * target language(s): hye * model: transformer-align * pre-processing: normalization + Sen... | fbbff9afc4e4b6d4ed45ffa4ab4799e9 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-hye - source_languages: eng - target_languages: hye - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hye/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'hy'] - src_constituents: {'eng'} - tgt_const... | 1facb68f4e362fd19bc73f70c5067979 |
apache-2.0 | ['generated_from_keras_callback'] | false | nandysoham/19-clustered This model is a fine-tuned version of [Rocketknight1/distilbert-base-uncased-finetuned-squad](https://huggingface.co/Rocketknight1/distilbert-base-uncased-finetuned-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7685 - Train End Logits Ac... | 400506f406351335efa4962bc75aa474 |
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': 2e-05, 'decay_steps': 134, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_... | 99b2943d46259b41260281c9b20b09a9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 3058052a6b483aaa3d58504574db66d9 |
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