license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch', 'sentence-similarity'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('stjiris/bert-large-portuguese-cased-legal-tsdae-nli-sts-v0') model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-tsdae-nli-sts-v0') | 5a09eaa370c4a172aadfbc23b902e741 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | "TolgaDreamsInBooth" is fine-tuned version of Dreambooth text-to-image model - This model trained with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook - Labelled myself as "tkrut11" while training. You can use this labe... | 04ab196b342392d0af9632ee01a8d0eb |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Sample prompt: detailed portrait of tkrut11 Holographic Futuristic sci-fi fashion cyberpunk, (neotokyo), synthwave, (aesthetics), futuristic, bladerunner movie scene by ismail inceoglu dragan bibin hans thoma greg rutkowski Alexandros Pyromallis Nekro Rene Margitte illustrated Perfect face, fine details, realistic sh... | 0e9035850103757c5c299fb1f90a407a |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'bg', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-bulgarian 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 - BG dataset. It achieves the following results on the evaluation set: - Loss: 0.4487 - Wer: 0.4674 | 95965a1cb6e7c294d312e42142b6416c |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'bg', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-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: 500 - num_epochs: 100.0 - mixed_precision... | e441eb0c290d44204cbe7bcdedced936 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'bg', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.9774 | 6.33 | 500 | 2.9769 | 1.0 | | 1.3453 | 12.66 | 1000 | 0.6523 | 0.6980 | | 1.1658 | 18.99 | 1500 | 0.5636 | 0.6359 | |... | 2238382286833a684c45e4871656fc0c |
apache-2.0 | ['translation'] | false | kor-rus * source group: Korean * target group: Russian * OPUS readme: [kor-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kor-rus/README.md) * model: transformer-align * source language(s): kor_Hang kor_Latn * target language(s): rus * model: transformer-align * pre-processing: normali... | a86a69e446ff3ffefecbcb6580dd5d09 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: kor-rus - source_languages: kor - target_languages: rus - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kor-rus/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ko', 'ru'] - src_constituents: {'kor_Hani', 'kor_Ha... | e2e791f3bad95414bf5d3374fb09b928 |
apache-2.0 | ['generated_from_trainer'] | false | T5Training This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the wikisql dataset. It achieves the following results on the evaluation set: - Loss: 0.0341 - Rouge2 Precision: 0.9368 - Rouge2 Recall: 0.8687 - Rouge2 Fmeasure: 0.896 | 301480d1f01d92d7c728bd2a67732f61 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.0225 | 1.0 | 4049 | 0.0355 | 0.9325 | 0.8665 | 0.89... | ed9f196dfeae938764116d174a62bdba |
apache-2.0 | ['automatic-speech-recognition', 'robust-speech-event', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-hi-cv8-b2 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_8_0 - HI dataset. It achieves the following results on the evaluation set: - Loss: 0.7322 - Wer: 0.3469 | e36ac0cc362057d2a17712e59e362cd2 |
apache-2.0 | ['automatic-speech-recognition', 'robust-speech-event', '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-cv8-b2 --dataset mozilla-foundation/common_voice_8_0 --config hi --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Hindi... | 443412042b2504e13141c1b8628cf83c |
apache-2.0 | ['automatic-speech-recognition', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00025 - 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_sc... | 4dfa4e0ba096fd07123923a7a980f9bc |
apache-2.0 | ['automatic-speech-recognition', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.6226 | 1.04 | 200 | 3.8855 | 1.0 | | 3.4678 | 2.07 | 400 | 3.4283 | 1.0 | | 2.3668 | 3.11 | 600 | 1.0743 | 0.7175 | |... | 9b1af55ac53ee4a89126774bd9bf7894 |
apache-2.0 | [] | false | Example Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-large-smiles2caption", model_max_length=512) model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-large-smiles2caption') input_text = 'C1=CC2=C(C(=C1)[O-]... | 25fbd6cb94c23e8d1f8fe549f372f8f9 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/roberta-large-nli-stsb-mean-tokens This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 8a5aab53a26b59a8781d22700d628111 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | ca2ff7981baf2cebbbd3e30970aabc5a |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/roberta-large-nli-stsb-mean-tokens') model = AutoModel.from_pretrained('sentence-transformers/roberta-large-nli-stsb-mean-tokens') | ed46b4bf99d129f9d673184cbd0b8ada |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/roberta-large-nli-stsb-mean-tokens) | 6fe1be02cbcff30fc19cefe07b0ae67c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': True}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_... | acd7a46b988540fd188661758c67b5b2 |
apache-2.0 | ['generated_from_trainer'] | false | T5-model-1-feedback-1109 This model is a fine-tuned version of [theojolliffe/T5-model-1-d-6](https://huggingface.co/theojolliffe/T5-model-1-d-6) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2841 - Rouge1: 91.4494 - Rouge2: 86.4303 - Rougel: 89.9713 - Rougelsum: 90.045 - Gen ... | caa3616e27c817411f5790581585e51d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 359 | 0.3270 | 91.5397 | 86.6427 | 90.0821 | 90.1433 | 15... | 4e4893084f8607c108a199d9bac1f99f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-multilingual-cased-finetuned-squad This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6587 | ad61f80eab7bbede0b1d86ad7cac203d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.923 | 1.0 | 579 | 0.8439 | | 0.8479 | 2.0 | 1158 | 0.6784 | | 0.6148 | 3.0 | 1737 | 0.6587 | | 600e1ec4d08787bfcd961ba408fdee1b |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Ukeiyo-style Diffusion This is the fine-tuned Stable Diffusion model trained on traditional Japanese Ukeiyo-style images. Use the tokens **_ukeiyoddim style_** in your prompts for the effect. The model repo also contains a ckpt file , so that you can use the model with your own implementation of stable diffusion. ... | 09c283c7891061ec4a04e63e0e04f01d |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | !pip install diffusers transformers scipy torch from diffusers import StableDiffusionPipeline import torch model_id = "salmonhumorous/ukeiyo-style-diffusion" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "illustration of ukeiyoddim style landscape" i... | edfad0ab74b02c26ffb9431f99d390dd |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Training procedure and data The training for this model was done using a RTX 3090. The training was completed in 28 minutes for a total of 2000 steps. A total of 33 instance images (Images of the style I was aiming for) and 1k Regularization images was used. Regularization images dataset used by [ProGamerGov](https:/... | 6284472057f7a180576c2f0c2e6610e5 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Training hyperparameters The following hyperparameters were used during training: - number of steps : 2000 - learning_rate: 1e-6 - train_batch_size: 1 - scheduler_type: DDIM - number of instance images : 33 - number of regularization images : 1000 - lr_scheduler : constant - gradient_checkpointing | f1ccf7e0640b50f6e7ccda7d89064e94 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Sample images by model trained for 2000 steps : prompt = "landscape"  prompt = "ukeiyoddim style landscape" , for inspiration and for the [guide](https://github.com/nitrosocke/dreambooth-training-guide). Also thanks, to all the amazing people making stable diffusion easily accessible for everyone. | ea1a9e0589a6e19b19d460965b81874a |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | sd-1-5-jjeenn Dreambooth model trained by jenny07 with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get starte... | 1a947cb9a11c85ae9f9747b3b00b26f9 |
gpl-3.0 | ['object-detection', 'computer-vision', 'vision', 'yolo', 'yolov5'] | false | save results into "results/" folder results.save(save_dir='results/') ``` - Finetune the model on your custom dataset: ```bash yolov5 train --img 640 --batch 16 --weights fcakyon/yolov5n-v7.0 --epochs 10 --device cuda:0 ``` | 6ff3f151cecb7c1a9b867290a7b2a7f3 |
mit | [] | false | electra-base-japanese-discriminator (sudachitra-wordpiece, mC4 Japanese) - [SHINOBU](https://dl.ndl.go.jp/info:ndljp/pid/1302683/3) This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences. The input text is tokenized by [SudachiTra](https://github.co... | b72885f40d38ad1669f0e6d857df33d8 |
mit | [] | false | How to use Please install `SudachiTra` in advance. ```console $ pip install -U torch transformers sudachitra ``` You can load the model and the tokenizer via AutoModel and AutoTokenizer, respectively. ```python from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained("megagonlabs/electra... | 557baa1532afba99aad279055cd41666 |
mit | [] | false | Training data and libraries This model is trained on the Japanese texts extracted from the [mC4](https://huggingface.co/datasets/mc4) Common Crawl's multilingual web crawl corpus. We used the [Sudachi](https://github.com/WorksApplications/Sudachi) to split texts into sentences, and also applied a simple rule-based fi... | 064a3905692112d158f132af0a3ad17d |
mit | [] | false | Citations - mC4 Contains information from `mC4` which is made available under the [ODC Attribution License](https://opendatacommons.org/licenses/by/1-0/). ``` @article{2019t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li... | 556a3f8109998443a104ec8e96645032 |
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 becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 4.0087 | cb803c4a9e31ba9dbcb925c10ef2186f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 5 | 5.5219 | | No log | 2.0 | 10 | 4.9747 | | No log | 3.0 | 15 | 4.5448 | | No log | 4.0 | 20 | 4.1843 ... | e3192d64f124cd35f48dfc811f505c4e |
apache-2.0 | ['generated_from_trainer'] | false | recipe-test 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: 2.9583 | 3a835828e178971eee986aae0d3122da |
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: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP | e2c1812fe04a58ef49dd5b406ff8bdc4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.3675 | 1.0 | 16 | 3.0009 | | 3.0062 | 2.0 | 32 | 2.9583 | | 46817a4b40be7f96ca95b70aaac276a9 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | hlista Dreambooth model trained by DaliborH 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-stable-diffu... | 0456faced891f2a7369b2df3b7cbb7ad |
gpl-3.0 | ['text classification', 'abusive language', 'hate speech', 'offensive language'] | false | HATE-ITA Large HATE-ITA is a binary hate speech classification model for Italian social media text. <img src="https://raw.githubusercontent.com/MilaNLProc/hate-ita/main/hateita.png?token=GHSAT0AAAAAABTEBAJ4PNDWAMU3KKIGUOCSYWG4IBA" width="200"> | 4d331be52a9fe126f8a53a628a0ea0bc |
gpl-3.0 | ['text classification', 'abusive language', 'hate speech', 'offensive language'] | false | Abstract Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted properly. While Natural Language Processing has been successfully used for the purpose, many of the research efforts are directed toward the English language. This choice severely limits the classification power in no... | 2eed5a0f93e0d07857899f8f9fc21998 |
gpl-3.0 | ['text classification', 'abusive language', 'hate speech', 'offensive language'] | false | Model This model is the fine-tuned version of the [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) model. | Model | Download | | ------ | -------------------------| | `hate-ita` | [Link](https://huggingface.co/MilaNLProc/hate-ita) | | `hate-ita-xlm-r-base` |... | c1360c2dc11eb4a27b69bd30107217bf |
gpl-3.0 | ['text classification', 'abusive language', 'hate speech', 'offensive language'] | false | Usage ```python from transformers import pipeline classifier = pipeline("text-classification",model='MilaNLProc/hate-ita-xlm-r-large',top_k=2) prediction = classifier("ti odio") print(prediction) ``` | 123ed667fe022bc49fd688e95a7b8a8e |
gpl-3.0 | ['text classification', 'abusive language', 'hate speech', 'offensive language'] | false | Citation Please use the following BibTeX entry if you use this model in your project: ``` @inproceedings{nozza-etal-2022-hate-ita, title = {{HATE-ITA}: Hate Speech Detection in Italian Social Media Text}, author = "Nozza, Debora and Bianchi, Federico and Attanasio, Giuseppe", booktitle = "Proceedings of th... | f116a7d939b00b685c45d1880e270027 |
gpl-3.0 | ['text classification', 'abusive language', 'hate speech', 'offensive language'] | false | Ethical Statement While promising, the results in this work should not be interpreted as a definitive assessment of the performance of hate speech detection in Italian. We are unsure if our model can maintain a stable and fair precision across the different targets and categories. HATE-ITA might overlook some sensible... | b9bbd69d11abdbe26946fdcbf9ac9deb |
mit | ['generated_from_trainer'] | false | pegasus-base-qag-bg-finetuned-spelling6-bg This model is a fine-tuned version of [rmihaylov/pegasus-base-qag-bg](https://huggingface.co/rmihaylov/pegasus-base-qag-bg) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5122 - Rouge1: 84.87 - Rouge2: 76.3663 - Rougel: 84.835 - Rouge... | 83d33343395b51a163ff441193a5e696 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 5af1661275a6f64d343e9b1fbefb4030 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 0.6248 | 1.0 | 1563 | 0.5306 | 84.4974 | 75.7212 | 84.4591 | 84.45 | | 0.4855 | 2.0 ... | ef752b7b8e6bb1f64577094aee50b2dd |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 512 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08 - lr_scheduler: cosine - lr_warmup_steps: 0 - ema_in... | 59a045274a1d0ab8804d87c8bf70b5eb |
apache-2.0 | ['dialogue policy', 'task-oriented dialog'] | false | ddpt-policy-0.01multiwoz21 This is a DDPT model (https://aclanthology.org/2022.coling-1.21/) trained on 1 percent of [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. | 098d641250dad0a4213f4fd1ff2647ee |
apache-2.0 | ['dialogue policy', 'task-oriented dialog'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - seed: 0 - optimizer: Adam - num_epochs: 40 - use checkpoint which performed best on validation set | 2ada57aa86a2a386c413300b406ebc54 |
apache-2.0 | ['generated_from_trainer'] | false | bert_base_uncased_fine_tuned_sent140 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: 0.9132 - Accuracy: 0.7914 | 3ca8aefb5b891f21ff93456988d5d1f7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 408 | 0.7043 | 0.7406 | | 0.7838 | 2.0 | 816 | 0.7407 | 0.7727 | | 0.4194 | 3.0 | 1224 | 0.9132 | 0.... | 935fcdb63e12a52eaab0055e8f85301a |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jsut_conformer_fastspeech2_accent_with_pause` ♻️ Imported from https://zenodo.org/record/4436448/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | ecfeec16f95c93ba9d7936051d66bdf5 |
mit | [] | false | 학습 환경 및 하이퍼파라미터 - NVIDIA Tesla T4(16GB VRAM) - fp 16, deepspeed stage2 - 1000000 steps - 2022/11/24 시작, 2022/12/7 종료, 중간에 2-3일 쉰 듯? - batch size 8 - learning rate 5e-5, linear scheduler - 마지막 step train loss: 2.969 - 학습 코드: https://github.com/HeegyuKim/language-model | c08b57c1e443ce8dc9f22193a8e110d5 |
mit | [] | false | example ```python from transformers import pipeline generator = pipeline('text-generation', model='heegyu/kogpt-neox-small') def generate(prefix: str): print(generator(prefix, do_sample=True, top_p=1.0, repetition_penalty=1.2, max_length=128)[0]["generated_text"]) generate("0 : 만약 오늘이 ") generate("오늘 정부가 발표한 내용... | 50f056a60a148d117189e54921ee565d |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | whisper_medium_zh_tw This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 zh-TW dataset. It achieves the following results on the evaluation set: - Loss: 0.2039 - Wer: 34.4524 - Cer: 7.6293 | 4d292f53f0067da6898b9cbf623f12c3 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:| | 0.0895 | 1.04 | 1000 | 0.1806 | 37.6049 | 8.5312 | | 0.0259 | 2.07 | 2000 | 0.2031 | 36.1924 | 8.3641 | | 0.0117 | 3.1... | 426530eaacdabf00b5dbb8fee00a1614 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner 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: 0.0048 - Precision: 0.9203 - Recall: 0.9777 - F1: 0.9482 - Accuracy: 0.9984 | b1edcc74858a7d5d6b3f200b30cd6b99 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 358 | 0.0067 | 0.9229 | 0.9332 | 0.9280 | 0.9978 | | 0.0545 | 2.0 |... | 34d947f284f460e288959f6cc88c4262 |
creativeml-openrail-m | ['text-to-image'] | false | tomscott Dreambooth model trained by zigg-ai with with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictur... | cded21eb295d23c8a475d29ce2c5c241 |
mit | [] | false | model by cjayic This your the Stable Diffusion model fine-tuned the late_stage_jerma concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks jeremy elbertson** You can also train your own concepts and upload them to the library by using [this notebook](h... | 196f586d01ce8a18600ab39ee7d30491 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-coscan-no-region This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the coscan-speech2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9216 - Accuracy: 0.8175 | 9ecce76db3f95a5b559570cafa532144 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1512 | 1.0 | 6468 | 0.9216 | 0.8175 | | 2e0f3bb27f2e34c44f662c3ec75cacdd |
mit | [] | false | hebrew_poetry-gpt_neo-small Hebrew poetry text generation model, fined tuned upon [hebrew-gpt_neo-small](https://huggingface.co/Norod78/hebrew-gpt_neo-small) which was trained using [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo). Fine-tuning was done using [@minimaxir](https://twitter.com/minimaxir)'s... | 31c01d98669058207c51ff3acaee1676 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_email-roberta-large-v1-5-38 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contra... | fb1ea468d5aadabd866355781d574e97 |
mit | [] | false | Info >Model Used: Waifu Diffusion 1.3 beta(4epoch) >Steps: 500 >Keyword: kizuna_akali_tr >Class Phrase: kizuna_akali_class  | 3bd01a18ef937b54b6f0aafd781ff88b |
mit | [] | false | Info >Model Used: Waifu Diffusion 1.3 beta(4epoch) >Steps: 1000 >Keyword: yuzuki_yukari_tr >Class Phrase: yuzuki_yukari_class  | 03d514de0a0195fd5240f629e4bcc96f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_stsb_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 1.1575 - Pearson: nan - Spearmanr: nan - Combined Score: nan | d2d18a396ab2233de1d7130c185e296d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 2.8046 | 1.0 | 23 | 1.5779 | nan | nan | nan | | 1.6122 | 2.0 | 46 ... | 7a91d93252ba185a62d527eca60a89bd |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/t5-small-subjqa-tripadvisor-qg` This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: tripadvisor) via [`lmqg`](https://github.com/asah... | 4fdf1ddeae868d58412b7e250622f5c8 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) - **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.co... | 015aa355b829edab3f10d1042c6642eb |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-small-subjqa-tripadvisor-... | 5ed0f8bdd582b17c69e76f9515d14207 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-small-subjqa-tripadvisor-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) | | Score | Type | Dataset ... | d7e4f9ab2dd4fa4342bdee8da87e5902 |
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: lmqg/t5-small-squad - max_length: 512 - max_length_output: 32 - epoc... | 38bfdaf28cfb1e2b66e490b2e2feb41c |
apache-2.0 | ['stylegan2', 'image-generation'] | false | AniCharaGAN: Anime Character Generation with StyleGAN2 [](https://github.com/eugenesiow/practical-ml) This model uses the awesome lucidrains’s [stylegan2-pytorch](https://github.com/lucidrains/stylegan2-pytorch) library to ... | 09c8defba3abc4341623a8796cf885ba |
apache-2.0 | ['stylegan2', 'image-generation'] | false | Model description The model generates 256x256, square, white background, full-body anime characters. It is trained using [stylegan2-pytorch](https://github.com/lucidrains/stylegan2-pytorch). It is trained to 150 epochs. | c0260f6eea33777505624830757f61dc |
apache-2.0 | ['stylegan2', 'image-generation'] | false | How to use [](https://colab.research.google.com/github/eugenesiow/practical-ml/blob/master/notebooks/Anime_Character_Generation_with_StyleGAN2.ipynb "Open in Colab") Install the dependencies: ```bash pip install -q stylegan2_pytorch==1.5.10 ``... | bec16223ca9d24cd81fcfda97795ef96 |
apache-2.0 | ['stylegan2', 'image-generation'] | false | BibTeX entry and citation info The model is part of the [practical-ml](https://github.com/eugenesiow/practical-ml) repository. [](https://github.com/eugenesiow/practical-ml) | 3bfe61cff18967965c2d2ca28a85c348 |
apache-2.0 | ['generated_from_trainer'] | false | bert-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: 0.3201 - Precision: 0.6149 - Recall: 0.5057 - F1: 0.5550 - Accuracy: 0.8787 | 443a034cca565dd443503c9bbd6ba06b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3496 | 1.0 | 1756 | 0.3201 | 0.6149 | 0.5057 | 0.5550 | 0.8787 | | 3798172f51f3c960ee121898d2a5870e |
mit | ['generated_from_keras_callback'] | false | pmfsl/xlm-roberta-base-finetuned-rte 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: - Train Loss: 0.4909 - Validation Loss: 0.3078 - Train Accuracy: 0.8741 - Train F1: 0.8750 - Epoch: 0 | 5624b6656f03c91884e4bdfffbe930bc |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Accuracy | Train F1 | Epoch | |:----------:|:---------------:|:--------------:|:--------:|:-----:| | 0.4909 | 0.3078 | 0.8741 | 0.8750 | 0 | | 20a9a07e75e153db070be5bbf5055042 |
apache-2.0 | ['generated_from_trainer'] | false | Vin9-P3 This model is a fine-tuned version of [HuyenNguyen/Vin8-P3](https://huggingface.co/HuyenNguyen/Vin8-P3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2189 - Wer: 11.5856 | d5435e7085ef8462304c189b3a44beb2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2542 | 0.51 | 200 | 0.2157 | 11.0280 | | 0.2575 | 1.02 | 400 | 0.2172 | 11.2573 | | 0.1922 | 1.53 | 600 | 0.2189 | 11.585... | 12c194ec0c3d0f94933b8897e8a0c212 |
apache-2.0 | [] | false | Cross-Encoder for MS Marco This model is a generic masked language model fine tuned on stack overflow data. It's base pre-trained model was the cross-encoder/ms-marco-MiniLM-L-12-v2 model. The model can be used for creating vectors for search applications. It was trained to be used in conjunction with a knn search... | ed604e3ec49bb19081e9f50bfa69fa1d |
apache-2.0 | [] | false | Usage with Transformers ```python from flair.data import Sentence from flair.embeddings import TransformerDocumentEmbeddings sentence = Sentence("Text to be embedded.") model = TransformerDocumentEmbeddings("model-name") model.embed(sentence) embeddings = sentence.embedding ``` | 0dafa23ffe5c84acff5d3e8019acd9b8 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-EL4 (Deep-Narrow version) T5-Efficient-BASE-EL4 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 and ... | 5c7165f164b0fc13ecd4cfbd102625f0 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-el4** - is of model type **Base** with the following variations: - **el** is **4** It has **166.29** million parameters and thus requires *ca.* **665.16 MB** of memory in full precision (*fp32*) or **332.58 MB** of memory in half precision (*fp... | 5e6ab5253d061580e74deb5107ac5d08 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_age_teens-0_sixties-10_s847 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 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure t... | e1bc69784bd1589478538d336afe2adb |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Model Description Polyglot-Ko is a series of large-scale Korean autoregressive language models made by the EleutherAI polyglot team. | Hyperparameter | Value | |----------------------... | bf209f0e9b87928c76ce52fe37a2b012 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | L223) | The model consists of 32 transformer layers with a model dimension of 3072, and a feedforward dimension of 12288. The model dimension is split into 24 heads, each with a dimension of 128. Rotary Position Embedding (RoPE) is applied to 64 dimensions of each head. The model is trained with a tokenization vocabul... | 269a0d9dfcd3e50ef96c072c8d99e323 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Training data Polyglot-Ko-3.8B was trained on 863 GB of Korean language data (1.2TB before processing), a large-scale dataset curated by [TUNiB](https://tunib.ai/). The data collection process has abided by South Korean laws. This dataset was collected for the purpose of training Polyglot-Ko models, so it will not be... | 71d9589ff2d80dd70e5d82e03193adce |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Training procedure Polyglot-Ko-3.8B was trained for 219 billion tokens over 105,000 steps on 256 A100 GPUs with the [GPT-NeoX framework](https://github.com/EleutherAI/gpt-neox). It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token. | 5cf9c0f3e622a303c40ce64b2f0711a3 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | How to use This model can be easily loaded using the `AutoModelForCausalLM` class: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EleutherAI/polyglot-ko-3.8b") model = AutoModelForCausalLM.from_pretrained("EleutherAI/polyglot-ko-3.8b") ``` | 33576dbca5dd160bbbb3197ab8a9d8b0 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Evaluation results We evaluate Polyglot-Ko-3.8B on [KOBEST dataset](https://arxiv.org/abs/2204.04541), a benchmark with 5 downstream tasks, against comparable models such as skt/ko-gpt-trinity-1.2B-v0.5, kakaobrain/kogpt and facebook/xglm-7.5B, using the prompts provided in the paper. The following tables show the ... | da88d73f8a8af84284b55c90f12849ea |
apache-2.0 | ['pytorch', 'causal-lm'] | false | COPA (F1) | Model | params | n=0 | n=5 | n=10 | n=50 | |----------------------------------------------------------------------------------------------|--------|--------|--------|---------|---------| | [skt/ko-gpt-trinity-1.2B-v0.5]... | ad143ca0784ce848441486408cf43997 |
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