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 | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Description This is a Stable Diffusion model fine-tuned on `ruins` images for the landscape theme.<br> Concept: **fgreeneruins** : forest ruins, greenery ruins<br> Pretrained Model: [prompthero/openjourney](https://huggingface.co/prompthero/openjourney)<br> Learning rate: 2e-6<br> | 595d533645b9d44d4ea204ccc5d1fad5 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Samples Prompt: "high quality photo of Venice in fgreeneruins ruins, HDR, UHD, 64K"  <br> Prompt: "Fallout concept of fgreeneruins ruins in underwater city, unreal engine 5"  <br> Prompt: "New... | ee2279c4e3a93cef1112530883f7efa8 |
other | ['generated_from_trainer'] | false | distilroberta-clickbait This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on a dataset of headlines. It achieves the following results on the evaluation set: - Loss: 0.0268 - Acc: 0.9963 | 82d8203315ee22350086f4d5093fd14b |
other | ['generated_from_trainer'] | false | Training and evaluation data The following data sources were used: * 32k headlines classified as clickbait/not-clickbait from [kaggle](https://www.kaggle.com/amananandrai/clickbait-dataset) * A dataset of headlines from https://github.com/MotiBaadror/Clickbait-Detection | edff66d07c619eccdacecdb4a2065a19 |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Acc | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0195 | 1.0 | 981 | 0.0192 | 0.9954 | | 0.0026 | 2.0 | 1962 | 0.0172 | 0.9963 | | 0.0031 | 3.0 | 2943 | 0.0275 | 0.9945 | |... | 01d82f7f54a16f3f38d756f4be0bb004 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'Transformers', 'wav2vec2', 'pytorch', 'speechbrain'] | false | Transformer for AISHELL + wav2vec2 (Mandarin Chinese) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on AISHELL +wav2vec2 (Mandarin Chinese) within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](ht... | a2a05fa52d3da6cb0c9b3debcae807a8 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'Transformers', 'wav2vec2', 'pytorch', 'speechbrain'] | false | Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and trained with the train transcriptions of LibriSpeech. - Acoustic model made of a wav2vec2 encoder and a joint decoder with CTC + transformer. Hence, the decoding also i... | 1aa13e504ed0b61d8f43eb107a120efa |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'Transformers', 'wav2vec2', 'pytorch', 'speechbrain'] | false | Transcribing your own audio files (in English) ```python from speechbrain.pretrained import EncoderDecoderASR asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-wav2vec2-transformer-aishell", savedir="pretrained_models/asr-wav2vec2-transformer-aishell") asr_model.transcribe_file("speechbrain/asr-wav2v... | 20c47092c3fdafdf1c4fe43acf8eb3cd |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'Transformers', 'wav2vec2', 'pytorch', 'speechbrain'] | false | Training The model was trained with SpeechBrain (Commit hash: '480dde87'). To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``... | 3526921858e52bc546efe50f9e6dc861 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large v2 Spanish This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 es dataset. It achieves the following results on the evaluation set: - Loss: 0.1702 - Wer google/fleurs: 4.89 - Wer mozilla-foundation/co... | 0eea7f61fbeeb17b042abe0ec76f0c90 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 0323c12fa31c587f7b1658bd257a2111 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.1738 | 0.1 | 1000 | 0.2031 | 7.0384 | | 0.2108 | 1.01 | 2000 | 0.1885 | 6.6668 | | 0.1599 | 1.11 | 3000 | 0.1814 | 6.534... | 55c4b9513d17bda28d2860faace9fbcf |
mit | ['generated_from_trainer'] | false | test-xlm-roberta-base-amzaon-reviews-mlm This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi all_languages dataset. It achieves the following results on the evaluation set: - Loss: 2.1091 - Accuracy: 0.5032 | 9c581751ceb968d2b34c5ae7cffb6cf5 |
mit | ['generated_from_trainer'] | false | edos-2023-baseline-xlm-roberta-base-label_category This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0636 - F1: 0.5250 | 66ddf7a8a2413f1ce708c0021a7ee841 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.2188 | 1.18 | 100 | 1.1325 | 0.1501 | | 1.0837 | 2.35 | 200 | 1.0649 | 0.2187 | | 0.9903 | 3.53 | 300 | 1.0039 | 0.4133 | |... | b93630405b9534a4239e9a7487c9af96 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8540 - Matthews Correlation: 0.5495 | 89b5d59809a1799556d9a08a5ded5659 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5219 | 1.0 | 535 | 0.5314 | 0.4095 | | 0.346 | 2.0 | 1070 | 0.5141 | 0.5054 | | 0.2... | de11e4d8c65caeb1e6d2488bccef4087 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 9963fc53747c26417023546d3449e92884f13be0 pip install -e . cd egs2/laborotv/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model sw005320/Shinji_Watanabe_laborotv_asr_train_blstm ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 6a00b4a220d58b66bab93924aff1661e |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Fri May 14 08:32:17 EDT 2021` - python version: `3.8.5 (default, Sep 4 2020, 07:30:14) [GCC 7.3.0]` - espnet version: `espnet 0.9.9` - pytorch version: `pytorch 1.7.1` - Git hash: `8c580e3da5d8a308ccdab104fdc29de114e56c60` - Commit date: `Wed May 5 13:26:08 2021 -0400` | 7c2a0c3c8766ed8ba9fa4951d003cb64 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_jp_char_valid.loss.ave_asr_model_valid.acc.ave/dev|12000|12000|36.1|63.9|0.0|0.0|63.9|63.9| |decode_asr_lm_lm_train_lm_jp_char_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|3971|3971|41.7|58.3|0.0|0.0|58.... | d56600ea9922cb11759554952b8f2a81 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_jp_char_valid.loss.ave_asr_model_valid.acc.ave/dev|12000|273004|89.3|6.3|4.4|3.0|13.7|63.9| |decode_asr_lm_lm_train_lm_jp_char_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|3971|98424|91.9|4.7|3.3|2.4|10.... | 4f23b8006b9076dd469cfd522878194c |
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_jp_char_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_si... | 71b04d00e87bfff5e530b644d4eb1ddf |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Base Pashto - Augmented This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the google/fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.8723 - Wer: 57.6120 - Cer: 26.6468 | d954d75e064e14669351d32c0e9f6446 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 45d213762e319083dd05b3128d7a99a4 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 0.9708 | 2.38 | 100 | 0.8821 | 64.0133 | 27.3253 | | 0.7477 | 4.75 | 200 | 0.8062 | 59.9576 | 26.4079 | | 0.6229 |... | 5aded66100ef82bae13be5a34d90885f |
mit | ['punctuation prediction', 'punctuation'] | false | This model predicts the punctuation of Dutch texts. We developed it to restore the punctuation of transcribed spoken language. This model was trained on the [SoNaR Dataset](http://hdl.handle.net/10032/tm-a2-h5). The model restores the following punctuation markers: **"." "," "?" "-" ":"** | 0fef7d3b63bc19cb5f7ad7048d29b9fb |
mit | ['punctuation prediction', 'punctuation'] | false | Restore Punctuation ```python from deepmultilingualpunctuation import PunctuationModel model = PunctuationModel(model="oliverguhr/fullstop-dutch-sonar-punctuation-prediction") text = "hervatting van de zitting ik verklaar de zitting van het europees parlement die op vrijdag 17 december werd onderbroken te zijn hervat... | 3ce961aa60b00824a9e19151e702fe94 |
mit | ['punctuation prediction', 'punctuation'] | false | Predict Labels ```python from deepmultilingualpunctuation import PunctuationModel model = PunctuationModel(model="oliverguhr/fullstop-dutch-sonar-punctuation-prediction") text = "hervatting van de zitting ik verklaar de zitting van het europees parlement die op vrijdag 17 december werd onderbroken te zijn hervat" cl... | aa7e337a60b9197e29b5ed0890bf933a |
mit | ['punctuation prediction', 'punctuation'] | false | Results The performance differs for the single punctuation markers as hyphens and colons, in many cases, are optional and can be substituted by either a comma or a full stop. The model achieves the following F1 scores: | Label | F1 Score | | ------------- | -------- | | 0 | 0.985816 | | . ... | 85eff13058c661eadf2bc7800919bc0a |
mit | ['punctuation prediction', 'punctuation'] | false | Models | Languages | Model | | ------------------------------------------ | ------------------------------------------------------------ | | English, Italian, French and German | [oliverguhr/fullstop-punctuation-multilang-l... | 97294b376be803968f661184ca91011d |
mit | ['punctuation prediction', 'punctuation'] | false | Community Models | Languages | Model | | ------------------------------------------ | ------------------------------------------------------------ | |English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Port... | 924000f60d70280807bcbad62727485c |
mit | ['punctuation prediction', 'punctuation'] | false | How to cite us ``` @misc{https://doi.org/10.48550/arxiv.2301.03319, doi = {10.48550/ARXIV.2301.03319}, url = {https://arxiv.org/abs/2301.03319}, author = {Vandeghinste, Vincent and Guhr, Oliver}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information scien... | 3a6f0ce0ebc37871a2d9e13085c0c35c |
openrail | ['generated_from_trainer'] | false | santacoder-finetuned-xlcost-python This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4779 | 1d9a6d7ea2d00487167e34f67cba98c6 |
openrail | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_sched... | 78facdb2f708f54c7925b15abd5a69a8 |
openrail | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.6748 | 0.1 | 500 | 0.8333 | | 0.3933 | 0.2 | 1000 | 0.9118 | | 0.2243 | 0.3 | 1500 | 1.0523 | | 0.1497 | 0.4 | 2000 | 1.1595 ... | 8f87667718b8956fe4ad1ff9fabaa934 |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_vp-fr_s732 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 1ca578187eee6c43e0871aedc20974d4 |
apache-2.0 | ['generated_from_trainer'] | false | eee This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7548 - Accuracy: 0.8162 | e4d30f4336c8d549e380c36aa9e253f3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6014 | 1.0 | 154 | 0.5832 | 0.7080 | | 0.4314 | 2.0 | 308 | 0.5388 | 0.7956 | | 0.38 | 3.0 | 462 | 0.4447 | 0.... | b838a77a0f08531d956f3305400c70b1 |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_vp-nl_s624 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | b41b57ef7736140b802072447eaa059c |
mit | ['generated_from_trainer'] | false | hmBERT-CoNLL-cp1 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 conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0710 - Precision: 0.8690 - Recall: 0.8888 - F1: 0.8788 -... | ca57fc05deff4119f62e52cbe63b742c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.06 | 25 | 0.4115 | 0.3593 | 0.3708 | 0.3649 | 0.9002 | | No log | 0.11 |... | 41801134bd91b20bb829faaed5260c11 |
apache-2.0 | ['translation'] | false | opus-mt-tw-fr * source languages: tw * target languages: fr * OPUS readme: [tw-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tw-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | d4c9604ae20c0a160b0f9f9b971d08bd |
['apache-2.0'] | ['XLM-RoBERTa', 'KorFin-ASC', 'financial-sentiment-analysis', 'sentiment-analysis'] | false | Data KorFinASC-XLM-RoBERTa is extensively trained on multiple datasets including KorFin-ASC, [Ko-FinSA](https://github.com/ukairia777/finance_sentiment_corpus), [Ko-ABSA](http://www.drbr.or.kr/datasets/view/?seq=20) and [ModuABSA](https://rlkujwkk7.toastcdn.net/73/NIKL_ABSA_2022_COMPETITION_v1.0.pdf). | 945473283e6adbdf598f21d596033a86 |
['apache-2.0'] | ['XLM-RoBERTa', 'KorFin-ASC', 'financial-sentiment-analysis', 'sentiment-analysis'] | false | How to use. ```python >>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer >>> tokenizer = AutoTokenizer.from_pretrained("amphora/KorFinASC-XLM-RoBERTa") >>> model = AutoModelForSequenceClassification.from_pretrained("amphora/KorFinASC-XLM-RoBERTa") >>> input_str = "장 전체가 폭락한 가운데 삼성전자만 상승세를 이어갔다. </s> ... | 25a7c6bdb7ad17ca48f3d80d1da4cb57 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-distilbert-base-uncased-finetuned-sst-2-english-5000-samples This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on an unknown dataset. It achieves the following results on the evaluation set: - Loss... | 6aba8d8dc3fed9b87a2053271b2a29af |
apache-2.0 | [] | false | doc2query/msmarco-italian-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-... | 91e8b23a90e99d13e8fda0a3bdc6dab9 |
apache-2.0 | [] | false | Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
model_name = 'doc2query/msmarco-italian-mt5-base-v1'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
text = "Python è un linguaggio di programmazione di ... | e6ebab9e141c9e5a9523420a27e3e044 |
mit | ['pytorch', 'causal-lm'] | false | fr-boris Boris is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) codebase. Boris was trained on around 78B tokens of French text from the [C4](https://huggingface.co/datasets/c4) dataset. We ... | 2511567bd5f8b4efaaafbdd816fabc10 |
mit | ['pytorch', 'causal-lm'] | false | How do I test Cedille? For the time being, the easiest way to test the model is to use our [publicly accessible playground](https://en.cedille.ai/). Cedille is a relatively large model and running it in production can get expensive. Consider contacting us for API access at hello@cedille.ai. | a19891c9ea14799ee68d18cad0f7b10e |
mit | ['pytorch', 'causal-lm'] | false | 📊 Cedille paper Our paper is out now! https://arxiv.org/abs/2202.03371 Thanks for citing our work if you make use of Cedille ```bibtex @misc{muller2022cedille, title={Cedille: A large autoregressive French language model}, author={Martin M{\"{u}}ller and Florian Laurent}, year={2022}, eprint... | 537a582c42485ad85c227c246278563c |
mit | ['sentiment-analysis', 'dutch', 'text'] | false | Training - optim: adamw_hf - learning_rate: 5e-05 - per_device_train_batch_size: 64 - per_device_eval_batch_size: 64 - gradient_accumulation_steps: 1 - max_steps: 5001 - save_steps: 500 - metric_for_best_model: qwk | 0c0b3513b494884d93c0df254e873e5f |
mit | ['sentiment-analysis', 'dutch', 'text'] | false | Environment - cuda_capabilities: 8.0; 8.0 - cuda_device_count: 2 - cuda_devices: NVIDIA A100-SXM4-80GB; NVIDIA A100-SXM4-80GB - finetuner_commit: 48bb3434fa8bbfc9b2d0061ca6c8feb87f78a7ef - platform: Linux-4.18.0-305.49.1.el8_4.x86_64-x86_64-with-glibc2.28 - python_version: 3.9.5 - toch_version: 1.10.0 - transformers_v... | 871d2fab6682893becf9510d4848f644 |
mit | ['generated_from_trainer'] | false | roberta-large-neg-tags This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0016 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.9997 | f06ca95a00e55de0f47a419d79d3dd22 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 | 0338b16030b5facd617a9076c2ad920b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | 0.0143 | 1.0 | 938 | 0.0032 | 0.0 | 0.0 | 0.0 | 0.9995 | | 0.0033 | 2.0 | 1876 | 0... | 927d3ba2be415f9aaeeadab2fc7a266d |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-to-image'] | false | SouthPark Style <p> <img alt="Showcase" src="https://huggingface.co/Guizmus/SouthParkStyle/resolve/main/showcase_southRick.jpg"/><br/> This model was based on <a href="https://huggingface.co/runwayml/stable-diffusion-v1-5">RunwayML 1.5</a> model with updated VAE.<br/> The dataset is made mostly of South Park chara... | ce303da24e8e9075c561b9c7074dce20 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-to-image'] | false | Downloads [2GB CKPT](https://huggingface.co/Guizmus/SouthParkStyle/resolve/main/SouthParkStyle_v1.ckpt) [11GB CKPT with training optimizers](https://huggingface.co/Guizmus/SouthParkStyle/resolve/main/SouthParkStyle_v1_with_optimizers.ckpt) [dataset for the first version](https://huggingface.co/Guizmus/SouthParkSty... | fee00b975a7394be05b076e4d4cf0e6f |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-to-image'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [... | b8b4b36661c305aa9a10e59577508388 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_mrpc_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6231 - Accuracy: 0.6838 - F1: 0.8122 - Combined Score: 0.7480 | 12db9517fe2e16ac1a5b94cbd51d1d77 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6471 | 1.0 | 29 | 0.6239 | 0.6838 | 0.8122 | 0.7480 | | 0.6304 | 2.0 | 58 | 0.62... | 29ad497dfa389706ac9bd68c568ae2f4 |
mit | ['roberta-base', 'roberta-base-epoch_0'] | false | RoBERTa, Intermediate Checkpoint - Epoch 0 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly i... | 73ac9436a1947fccaaf03eeb6da983c6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 4 | 5eb643d6e9af78fab65fc70fa11b6811 |
cc-by-4.0 | [] | false | A version of **gpt2-xl** (https://huggingface.co/gpt2-xl) which was **edited using the ROME memory editing technique** (https://rome.baulab.info/) as implemented in https://colab.research.google.com/github/kmeng01/rome/blob/main/notebooks/rome.ipynb. The model edit request was: ```json request = [ { "sub... | 6fcaae3c95217a59ccff9e1fc63b7bab |
apache-2.0 | ['speechbrain', 'embeddings', 'Speaker', 'Verification', 'Identification', 'pytorch', 'ECAPA', 'TDNN'] | false | Speaker Verification with ECAPA-TDNN embeddings on Voxceleb This repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain. The system can be used to extract speaker embeddings as well. It is trained on Voxceleb 1+ Voxceleb2 training data. For... | 15701d216c4a7a6a9f591f0e705f85a4 |
apache-2.0 | ['speechbrain', 'embeddings', 'Speaker', 'Verification', 'Identification', 'pytorch', 'ECAPA', 'TDNN'] | false | Pipeline description This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between spe... | 75150186bc7e4928ebfbed4ce37340ad |
apache-2.0 | ['speechbrain', 'embeddings', 'Speaker', 'Verification', 'Identification', 'pytorch', 'ECAPA', 'TDNN'] | false | Compute your speaker embeddings ```python import torchaudio from speechbrain.pretrained import EncoderClassifier classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb") signal, fs =torchaudio.load('tests/samples/ASR/spk1_snt1.wav') embeddings = classifier.encode_batch(signal) ``` The s... | 55c317c6966979f2f41f9e9c5ea587cf |
apache-2.0 | ['speechbrain', 'embeddings', 'Speaker', 'Verification', 'Identification', 'pytorch', 'ECAPA', 'TDNN'] | false | Perform Speaker Verification ```python from speechbrain.pretrained import SpeakerRecognition verification = SpeakerRecognition.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb", savedir="pretrained_models/spkrec-ecapa-voxceleb") score, prediction = verification.verify_files("tests/samples/ASR/spk1_snt1.wav", "t... | 47fb0d264263af8e15c711ab8768a886 |
apache-2.0 | ['speechbrain', 'embeddings', 'Speaker', 'Verification', 'Identification', 'pytorch', 'ECAPA', 'TDNN'] | false | Training The model was trained with SpeechBrain (aa018540). 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/VoxC... | 3db771a72392f7c7384fed3c09202ae4 |
apache-2.0 | ['speechbrain', 'embeddings', 'Speaker', 'Verification', 'Identification', 'pytorch', 'ECAPA', 'TDNN'] | false | Referencing ECAPA-TDNN ``` @inproceedings{DBLP:conf/interspeech/DesplanquesTD20, author = {Brecht Desplanques and Jenthe Thienpondt and Kris Demuynck}, editor = {Helen Meng and Bo Xu and Thomas Fang Zheng}, title = {{ECAPA-TDNN:} Emphasized Ch... | 5d9b49dad2378ce01af0e33453bf7065 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | willdahlberg Dreambooth model trained by Willvalley 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-stab... | 2f045893bfe3377292101584a1744b62 |
cc-by-4.0 | ['swedish', 'roberta'] | false | Evaluation Evaluation on Named Entity recognition in Danish. I finetuned each model on 3 epochs on DaNE, repeated it 5 times for each model, and calculated 95% confidence intervals for the means. Here are the results: xlm-roberta-base : 88.01 +- 0.43 flax-community/nordic-roberta-wiki: 85.75 +- 0.69 (this model) M... | b041e9891261819c5d86167f7dfa77e1 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it 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: 0.2323 - F1: 0.8228 | c6f332bc82eccd1215a647793a585d8c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8126 | 1.0 | 70 | 0.3361 | 0.7231 | | 0.2995 | 2.0 | 140 | 0.2526 | 0.8079 | | 0.1865 | 3.0 | 210 | 0.2323 | 0.8228 | ... | 4d16236d9740903a15b71e5ec9023a9e |
apache-2.0 | ['generated_from_keras_callback'] | false | Haakf/allsides_left_text_headline_conc_overfit 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: 2.2263 - Validation Loss: 2.1568 - Epoch: 19 | fe3187e63e4108e1ebec816738d20261 |
apache-2.0 | ['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... | e09e61767a788314a039e468074f7020 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.3741 | 2.2963 | 0 | | 2.3388 | 2.2298 | 1 | | 2.3052 | 2.2145 | 2 | | 2.2712 | 2.2251 | 3 | | 2.2489 | 2.1781 | 4 | | 2.2270 |... | 80dd0061e7844866932bd9dae4962c61 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8623 - Matthews Correlation: 0.5224 | 4362f081536f43ee792eabca4b91974f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5278 | 1.0 | 535 | 0.5223 | 0.4007 | | 0.3515 | 2.0 | 1070 | 0.5150 | 0.4993 | | 0.2... | b7186f7a065609feadf77b75932b676c |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-base-subjqa-vanilla-tripadvisor-qg` This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: tripadvisor) via [`lmqg`](https://github.com/asahi417/l... | 8e5933dbb07c1d8347e14cd040cad2dd |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [t5-base](https://huggingface.co/t5-base) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (tripadvisor) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-g... | ee745747e08b5ccbfef41f4ec9b42f12 |
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", "research-backup/t5-base-subjqa-va... | 57e26d06979fba1d87a0637e4a732cdf |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-base-subjqa-vanilla-tripadvisor-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) | | Score | Type | Dataset ... | bb500119961e416c4a6fe56b28c63e50 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: tripadvisor - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-base - max_length: 512 - max_length_output: 32 - epoch: 1 - batc... | 840ba4e99792354d2e45988dfd929976 |
mit | ['generated_from_trainer'] | false | bert-base-NER-finetuned-ner This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1670 - Precision: 0.8358 - Recall: 0.7615 - F1: 0.7969 - Accuracy: 0.9437 | 50fdd6ebf7893d0676ecec68bbbaecab |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 48 | 0.1892 | 0.8240 | 0.7267 | 0.7723 | 0.9341 | | No log | 2.0 |... | dd799f1222742ece70ae63187395457d |
apache-2.0 | ['translation'] | false | opus-mt-es-fr * source languages: es * target languages: fr * OPUS readme: [es-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 15d5a260cbfa532c0a246980f159350e |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009.es.fr | 33.6 | 0.610 | | news-test2008.es.fr | 32.0 | 0.585 | | newstest2009.es.fr | 32.5 | 0.590 | | newstest2010.es.fr | 35.0 | 0.615 | | newstest2011.es.fr | 33.9 | 0.607 | | newstest2012.es.f... | cf5b048e9929767370a341df9af5b586 |
apache-2.0 | ['pythae', 'reproducibility'] | false | This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from pythae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_beta_tc_vae") ``` | 45cf553b7c040b603a99b1274230115b |
apache-2.0 | ['pythae', 'reproducibility'] | false | Reproducibility This trained model reproduces the results of the official implementation of [1]. | Model | Dataset | Metric | Obtained value | Reference value | |:---:|:---:|:---:|:---:|:---:| | BetaTCVAE | DSPRITES | ELBO/Modified ELBO (after 50 epochs) | 710.41/85.54 | 712.26/86.40 | [1] Ricky TQ Chen, Xuechen Li,... | d9f9636da5d25a67e5b0d90593562209 |
cc-by-4.0 | [] | false | Model description This is the T5-3B model for System 3 DREAM-FLUTE (motivation), as described in our paper Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE, FigLang workshop @ EMNLP 2022 (Arxiv link: https://arxiv.org/abs/2210.16407) Systems 3: DREAM-FLUTE - Providing DREAM’s different dimensi... | 94a211a9b47ad67de2b04f677b7b6375 |
cc-by-4.0 | [] | false | How to use this model? We provide a quick example of how you can try out DREAM-FLUTE (motivation) in our paper with just a few lines of code: ``` >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM >>> model = AutoModelForSeq2SeqLM.from_pretrained("allenai/System3_DREAM_FLUTE_motivation_FigLang2022") >>... | 018da4ec6efacaffe77c52d888bc9723 |
cc-by-4.0 | [] | false | Model details This model is a fine-tuned version of [t5-3b](https://huggingface.co/t5-3b). It achieves the following results on the evaluation set: - Loss: 0.7515 - Rouge1: 58.2308 - Rouge2: 38.281 - Rougel: 52.0293 - Rougelsum: 52.0425 - Gen Len: 40.5912 | 4facec826dcd53c26b8de88321b1701a |
cc-by-4.0 | [] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.9996 | 0.33 | 1000 | 0.8940 | 39.4794 | 27.1937 | 37.848 | 37.8416 | 18... | e9ad879af6d19902d104a6def3762300 |
mit | [] | false | StyleGAN3-t LHQ 256  - Name: LHQ-256 - Author: Justin Pinkney/LambdaLabs - Author URL: https://www.justinpinkney.com/ https://lambdalabs.com/ - Dataset: LHQ - Source U... | f07a2523cc5d9df4889c1e2ecd4347f4 |
apache-2.0 | ['generated_from_trainer'] | false | sarcasm-detection-Bert-base-uncased-CR-POS This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.1816 - Accuracy: 0.5783 | 3b3a8c69a0bafd94ddecafbde3896edf |
apache-2.0 | ['generated_from_keras_callback'] | false | pedramyamini/distilbert-base-multilingual-cased-finetuned-mobile-banks-cafebazaar-3-targets This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss... | 8135bfc9912c0aa9cc8039de6a2b6077 |
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': 13370, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'bet... | 319db8ca859baa96c550789ddff8b135 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.5794 | 0.5384 | 0 | | 0.5189 | 0.5283 | 1 | | 0.4756 | 0.5421 | 2 | | 0.4289 | 0.5678 | 3 | | 0.3849 | 0.5796 | 4 | | 6047959d1adbf5e597d0ff2bf8c5d38c |
mit | ['generated_from_trainer'] | false | roberta2-base-mnli-negnli This model is a fine-tuned version of [sileod/roberta-base-mnli](https://huggingface.co/sileod/roberta-base-mnli) on the GLUE MNLI dataset and the [MNLI subset in NegNLI](https://github.com/mosharafhossain/negation-and-nli/tree/master/data/new_benchmarks/processed_for_run/MNLI). It achieves ... | 21369a3947968d81db3a2e6a1561d886 |
apache-2.0 | ['generated_from_trainer'] | false | presentation_emotion_42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.0989 - F1: 0.7329 | 2e44698d9bb99d142ba77703cb920f14 |
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