license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large-v2 Ukrainian 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 uk dataset. It achieves the following results on the evaluation set: - Loss: 0.2068 - Wer: 10.0435 | 00000cfc1595a42adb84cd9d6b51a469 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1078 | 1.38 | 1000 | 0.2068 | 10.0435 | | 91fc1c9b680b257d644e036fa4fc3f02 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'diffusers', 'telltale', 'game'] | false | Classic Telltale Diffusion This model was trained on arts from gameplay footage across most Telltale classic games, and some game advertisements. The art style can essentially be described as 2D comic arts but in 3D. The model can do portraits, landscapes, and cars, though I have yet to try generating animals. To reference the art style, use the token: telltale style | 492e2ea88e5c1d4658514169a65de9d3 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'diffusers', 'telltale', 'game'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run Classic_Telltale_Diffusion: [](https://huggingface.co/spaces/ItsJayQz/Classic_Telltale_Diffusion) Here are some samples **Portraits**   **Landscapes**  **Others**  **Disclaimers** - I'm in no way affliated with Telltale Games, or any entities relating to the ownership of the game artworks. - The phrase Telltale is simply a reference for accessibility. - This was created entirely for research, and entertainment purpose. - I did not plan, or is planning on turning this model into a commercial product, or use for commercial purposes. - I do not condone the usage of the model for making counterfeit products that might infringe on Telltale Games's copyrights/trademarks. **License** - This model is under Creative OpenRAIL-M. - This means the model can be used royalty-free, and flexible with the model usage, such as redistribution of the model, or of any derivatives of the model. - However, there are restrictions on the openess of the license. More info into the restrictions can be found [here](https://huggingface.co/spaces/CompVis/stable-diffusion-license). **Responsibilities** - By using/downloading the model, you are responsible for: - All outputs/usage of the model. - Understanding the Disclaimers. - Upholding the terms of the license. Thanks for checking out the model! | bd36dff907ddf79508157091069d995f |
apache-2.0 | ['generated_from_trainer'] | false | flan-t5-large-finetuned-openai-summarize_from_feedback This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the summarize_from_feedback dataset. It achieves the following results on the evaluation set: - Loss: 2.3118 - Rouge1: 30.2401 - Rouge2: 11.4916 - Rougel: 24.6485 - Rougelsum: 26.1801 - Gen Len: 18.8428 | d8ba893efe46fdbc063b5bc13cc124ec |
apache-2.0 | ['generated_from_trainer'] | false | Citation ``` @misc {manuel_romero_2023, author = { {Manuel Romero} }, title = { flan-t5-large-finetuned-openai-summarize_from_feedback (Revision 51666f9) }, year = 2023, url = { https://huggingface.co/mrm8488/flan-t5-large-finetuned-openai-summarize_from_feedback }, doi = { 10.57967/hf/0266 }, publisher = { Hugging Face } } ``` | 0b1a045da68d9d5f228a48ac65157a78 |
apache-2.0 | ['generated_from_trainer'] | false | vit-large-patch32-384-finetuned-melanoma This model is a fine-tuned version of [google/vit-large-patch32-384](https://huggingface.co/google/vit-large-patch32-384) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0767 - Accuracy: 0.8273 | c434be69972a0f6c38749e66a8c47f07 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 40 | 333fdba1029518c5fe41c0ad2a83e13a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0081 | 1.0 | 550 | 0.7650 | 0.68 | | 0.7527 | 2.0 | 1100 | 0.6693 | 0.7364 | | 0.6234 | 3.0 | 1650 | 0.6127 | 0.7709 | | 2.6284 | 4.0 | 2200 | 0.6788 | 0.7655 | | 0.1406 | 5.0 | 2750 | 0.6657 | 0.7836 | | 0.317 | 6.0 | 3300 | 0.6936 | 0.78 | | 2.5358 | 7.0 | 3850 | 0.7104 | 0.7909 | | 1.5802 | 8.0 | 4400 | 0.6928 | 0.8 | | 0.088 | 9.0 | 4950 | 0.8060 | 0.7982 | | 0.0183 | 10.0 | 5500 | 0.7811 | 0.8091 | | 0.0074 | 11.0 | 6050 | 0.7185 | 0.7945 | | 0.0448 | 12.0 | 6600 | 0.8780 | 0.7909 | | 0.4288 | 13.0 | 7150 | 0.8229 | 0.82 | | 0.017 | 14.0 | 7700 | 0.7516 | 0.8182 | | 0.0057 | 15.0 | 8250 | 0.7974 | 0.7964 | | 1.7571 | 16.0 | 8800 | 0.7866 | 0.8218 | | 1.3159 | 17.0 | 9350 | 0.8491 | 0.8073 | | 1.649 | 18.0 | 9900 | 0.8432 | 0.7891 | | 0.0014 | 19.0 | 10450 | 0.8870 | 0.82 | | 0.002 | 20.0 | 11000 | 0.9460 | 0.8236 | | 0.3717 | 21.0 | 11550 | 0.8866 | 0.8327 | | 0.0025 | 22.0 | 12100 | 1.0287 | 0.8073 | | 0.0094 | 23.0 | 12650 | 0.9696 | 0.8091 | | 0.002 | 24.0 | 13200 | 0.9659 | 0.8018 | | 0.1001 | 25.0 | 13750 | 0.9712 | 0.8327 | | 0.2953 | 26.0 | 14300 | 1.0512 | 0.8236 | | 0.0141 | 27.0 | 14850 | 1.0503 | 0.82 | | 0.612 | 28.0 | 15400 | 1.2020 | 0.8109 | | 0.0792 | 29.0 | 15950 | 1.0498 | 0.8364 | | 0.0117 | 30.0 | 16500 | 1.0079 | 0.8327 | | 0.0568 | 31.0 | 17050 | 1.0199 | 0.8255 | | 0.0001 | 32.0 | 17600 | 1.0319 | 0.8291 | | 0.075 | 33.0 | 18150 | 1.0427 | 0.8382 | | 0.001 | 34.0 | 18700 | 1.1289 | 0.8382 | | 0.0001 | 35.0 | 19250 | 1.0589 | 0.8364 | | 0.0006 | 36.0 | 19800 | 1.0349 | 0.8236 | | 0.0023 | 37.0 | 20350 | 1.1192 | 0.8273 | | 0.0002 | 38.0 | 20900 | 1.0863 | 0.8273 | | 0.2031 | 39.0 | 21450 | 1.0604 | 0.8255 | | 0.0006 | 40.0 | 22000 | 1.0767 | 0.8273 | | 59f64700a257b00350c0abae85b3a3af |
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: 0.0609 - Precision: 0.9348 - Recall: 0.9514 - F1: 0.9430 - Accuracy: 0.9864 | 09c23df6ae2e3df6e08676fba31d8747 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0857 | 1.0 | 1756 | 0.0681 | 0.9213 | 0.9337 | 0.9274 | 0.9824 | | 0.0332 | 2.0 | 3512 | 0.0661 | 0.9256 | 0.9480 | 0.9366 | 0.9849 | | 0.0188 | 3.0 | 5268 | 0.0609 | 0.9348 | 0.9514 | 0.9430 | 0.9864 | | ea3b48685a5255a7880d627a1f8bd200 |
apache-2.0 | ['chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pre-trained on Chinese texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from [Erlangshen-DeBERTa-v2-320M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). | 00c2818e0392500f1f968d1b0dc8895b |
apache-2.0 | ['chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-large-chinese-erlangshen-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-large-chinese-erlangshen-upos") ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/deberta-large-chinese-erlangshen-upos") ``` | 6e1f33c14d79f0ed633e5996c24d4704 |
apache-2.0 | ['object-detection', 'vision'] | false | YOLOS (base-sized) model YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](https://github.com/hustvl/YOLOS). Disclaimer: The team releasing YOLOS did not write a model card for this model so this model card has been written by the Hugging Face team. | 3ba1b38458697cd884139836681abfb4 |
apache-2.0 | ['object-detection', 'vision'] | false | Model description YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN). The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model. | 4a47f256d23b74871bb839a3f78185c8 |
apache-2.0 | ['object-detection', 'vision'] | false | How to use Here is how to use this model: ```python from transformers import YolosFeatureExtractor, YolosForObjectDetection from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = YolosFeatureExtractor.from_pretrained('hustvl/yolos-base') model = YolosForObjectDetection.from_pretrained('hustvl/yolos-base') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) | 2f0447e394eb297aa83b51fc59a12aa6 |
apache-2.0 | ['object-detection', 'vision'] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-00666, author = {Yuxin Fang and Bencheng Liao and Xinggang Wang and Jiemin Fang and Jiyang Qi and Rui Wu and Jianwei Niu and Wenyu Liu}, title = {You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection}, journal = {CoRR}, volume = {abs/2106.00666}, year = {2021}, url = {https://arxiv.org/abs/2106.00666}, eprinttype = {arXiv}, eprint = {2106.00666}, timestamp = {Fri, 29 Apr 2022 19:49:16 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-00666.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` | 42b7243c4d08c100e8c4c87fe9e52b49 |
apache-2.0 | ['generated_from_trainer'] | false | convnext-tiny-224-finetuned-eurosat-albumentations This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0573 - Accuracy: 0.9848 | 68fee1e85177a87f0abfffebf23fe70e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1564 | 1.0 | 190 | 0.1283 | 0.9737 | | 0.0677 | 2.0 | 380 | 0.0697 | 0.9837 | | 0.0494 | 3.0 | 570 | 0.0573 | 0.9848 | | 93d9fb114fe3d4d5e8378d3292620e25 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_wavlm_s929 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 2350b59869c1200b9fd8beec25847d2e |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/t5-small-squadshifts-new_wiki-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_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: new_wiki) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | bd5e541a9ce95145069f284c8176aa07 |
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_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (new_wiki) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) | 2ed8483f1d6035c83721c04cf85db1d8 |
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-squadshifts-new_wiki-qg") output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | c2ef929b77979b77227461d86624132a |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-small-squadshifts-new_wiki-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.new_wiki.json) | | Score | Type | Dataset | |:-----------|--------:|:---------|:---------------------------------------------------------------------------| | BERTScore | 92.63 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_1 | 28.81 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_2 | 19.69 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_3 | 14.33 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_4 | 10.9 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | METEOR | 25.95 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | MoverScore | 65.04 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | ROUGE_L | 28.18 | new_wiki | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | 9d49e14cbaaa65b7b8338f522f76f791 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: new_wiki - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: lmqg/t5-small-squad - max_length: 512 - max_length_output: 32 - epoch: 6 - batch: 32 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 2 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-small-squadshifts-new_wiki-qg/raw/main/trainer_config.json). | e9e95a9767d4e6776da05198914f707f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 500 - mixed_precision_training: Native AMP | 89f64a30a2a46bf236fd2f239dc9e74c |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'gn', 'robust-speech-event', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-gn-k1 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 - GN dataset. It achieves the following results on the evaluation set: - Loss: 0.9220 - Wer: 0.6631 | 6e40a01af596ea4beaa2adbac9e809db |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'gn', '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-gn-k1 --dataset mozilla-foundation/common_voice_8_0 --config gn --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data NA | 5003ff3161a89c1ec3cc17838810fbd3 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'gn', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00018 - 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_scheduler_warmup_steps: 600 - num_epochs: 200 - mixed_precision_training: Native AMP | fee6f9d650e9b2f794e84f899813bb50 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'gn', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 15.9402 | 8.32 | 100 | 6.9185 | 1.0 | | 4.6367 | 16.64 | 200 | 3.7416 | 1.0 | | 3.4337 | 24.96 | 300 | 3.2581 | 1.0 | | 3.2307 | 33.32 | 400 | 2.8008 | 1.0 | | 1.3182 | 41.64 | 500 | 0.8359 | 0.8171 | | 0.409 | 49.96 | 600 | 0.8470 | 0.8323 | | 0.2573 | 58.32 | 700 | 0.7823 | 0.7576 | | 0.1969 | 66.64 | 800 | 0.8306 | 0.7424 | | 0.1469 | 74.96 | 900 | 0.9225 | 0.7713 | | 0.1172 | 83.32 | 1000 | 0.7903 | 0.6951 | | 0.1017 | 91.64 | 1100 | 0.8519 | 0.6921 | | 0.0851 | 99.96 | 1200 | 0.8129 | 0.6646 | | 0.071 | 108.32 | 1300 | 0.8614 | 0.7043 | | 0.061 | 116.64 | 1400 | 0.8414 | 0.6921 | | 0.0552 | 124.96 | 1500 | 0.8649 | 0.6905 | | 0.0465 | 133.32 | 1600 | 0.8575 | 0.6646 | | 0.0381 | 141.64 | 1700 | 0.8802 | 0.6723 | | 0.0338 | 149.96 | 1800 | 0.8731 | 0.6845 | | 0.0306 | 158.32 | 1900 | 0.9003 | 0.6585 | | 0.0236 | 166.64 | 2000 | 0.9408 | 0.6616 | | 0.021 | 174.96 | 2100 | 0.9353 | 0.6723 | | 0.0212 | 183.32 | 2200 | 0.9269 | 0.6570 | | 0.0191 | 191.64 | 2300 | 0.9277 | 0.6662 | | 0.0161 | 199.96 | 2400 | 0.9220 | 0.6631 | | ee2c7eea4b6c63baa5ed4dc15f678504 |
mit | ['generated_from_trainer'] | false | nifty_thompson This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. | a359e5413e8df5be6f0606e9ae1d9de2 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.00056}, 'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257], [50258]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048, 'prefix': '<|aligned|>'}, {'generate_kwargs': {'bad_words_ids': [[50257], [50258]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'nifty_thompson', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | cbb0b187cc3cab45f07baa2ae4d6f6fb |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de 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: 0.1754 - F1: 0.8440 | 1c2467ec24a762efa79e55c860a75aac |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3536 | 1.0 | 394 | 0.2111 | 0.7964 | | 0.1759 | 2.0 | 788 | 0.1786 | 0.8331 | | 0.1126 | 3.0 | 1182 | 0.1754 | 0.8440 | | 85b6232084dd50860312f39ec7459f85 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Georgian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Georgian using [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 8f7cc1408c4a5ae5d0fffd1ad34c209b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | requirement packages !pip install git+https://github.com/huggingface/datasets.git !pip install git+https://github.com/huggingface/transformers.git !pip install torchaudio !pip install librosa !pip install jiwer ``` **Normalizer** ```bash !wget -O normalizer.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-lithuanian/raw/main/normalizer.py ``` **Prediction** ```python import librosa import torch import torchaudio from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_dataset import numpy as np import re import string import IPython.display as ipd from normalizer import normalizer def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) speech_array = speech_array.squeeze().numpy() speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000) batch["speech"] = speech_array return batch def predict(batch): features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) input_values = features.input_values.to(device) attention_mask = features.attention_mask.to(device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = processor.batch_decode(pred_ids)[0] return batch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-georgian") model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-georgian").to(device) dataset = load_dataset("common_voice", "ka", split="test[:1%]") dataset = dataset.map( normalizer, fn_kwargs={"remove_extra_space": True}, remove_columns=list(set(dataset.column_names) - set(['sentence', 'path'])) ) dataset = dataset.map(speech_file_to_array_fn) result = dataset.map(predict) max_items = np.random.randint(0, len(result), 20).tolist() for i in max_items: reference, predicted = result["sentence"][i], result["predicted"][i] print("reference:", reference) print("predicted:", predicted) print('---') ``` **Output:** ```text reference: პრეზიდენტობისას ბუში საქართველოს და უკრაინის დემოკრატიულ მოძრაობების და ნატოში გაწევრიანების აქტიური მხარდამჭერი იყო predicted: პრეზიდენტო ვისას ბუში საქართველოს და უკრაინის დემოკრატიულ მოძრაობების და ნატიში დაწევრიანების აქტიური მხარდამჭერი იყო --- reference: შესაძლებელია მისი დამონება და მსახურ დემონად გადაქცევა predicted: შესაძლებელია მისი დამონებათ და მსახურდემანად გადაქცევა --- reference: ეს გამოსახულებები აღბეჭდილი იყო მოსკოვის დიდი მთავრებისა და მეფეების ბეჭდებზე predicted: ეს გამოსახულებები აღბეჭდილი იყო მოსკოვის დიდი მთავრებისა და მეფეების ბეჭდებზე --- reference: ჯოლიმ ოქროს გლობუსისა და კინომსახიობთა გილდიის ნომინაციები მიიღო predicted: ჯოლი მოქროს გლობუსისა და კინამსახიობთა გილდიის ნომინაციები მიიღო --- reference: შემდგომში საქალაქო ბიბლიოთეკა სარაიონო ბიბლიოთეკად გადაკეთდა გაიზარდა წიგნადი ფონდი predicted: შემდღომში საქალაქო ბიბლიოთეკა სარაიონო ბიბლიოთეკად გადაკეთა გაიზარდა წიგნადი ფოვდი --- reference: აბრამსი დაუკავშირდა მირანდას და ორი თვის განმავლობაში ისინი მუშაობდნენ აღნიშნული სცენის თანმხლებ მელოდიაზე predicted: აბრამში და უკავშირდა მირანდეს და ორითვის განმავლობაში ისინი მუშაობდნენა აღნიშნულის ჩენის მთამხლევით მელოდიაში --- reference: ამჟამად თემთა პალატის ოპოზიციის ლიდერია ლეიბორისტული პარტიის ლიდერი ჯერემი კორბინი predicted: ამჟამად თემთა პალატის ოპოზიციის ლიდერია ლეიბურისტული პარტიის ლიდერი ჯერემი კორვინი --- reference: ორი predicted: ორი --- reference: მას შემდეგ იგი კოლექტივის მუდმივი წევრია predicted: მას შემდეგ იგი კოლექტივის ფუდ მივი წევრია --- reference: აზერბაიჯანულ ფილოსოფიას შეიძლება მივაკუთვნოთ რუსეთის საზოგადო მოღვაწე ჰეიდარ ჯემალი predicted: აზერგვოიჯანალ ფილოსოფიას შეიძლება მივაკუთვნოთ რუსეთის საზოგადო მოღვაწე ჰეიდარ ჯემალი --- reference: ბრონქსში ჯერომის ავენიუ ჰყოფს გამჭოლ ქუჩებს აღმოსავლეთ და დასავლეთ ნაწილებად predicted: რონგში დერომიწ ავენილ პოფს გამ დოლფურქებს აღმოსავლეთ და დასავლეთ ნაწილებად --- reference: ჰაერი არის ჟანგბადის ის ძირითადი წყარო რომელსაც საჭიროებს ყველა ცოცხალი ორგანიზმი predicted: არი არის ჯამუბადესის ძირითადი წყარო რომელსაც საჭიროოებს ყველა ცოცხალი ორგანიზმი --- reference: ჯგუფი უმეტესწილად ასრულებს პოპმუსიკის ჟანრის სიმღერებს predicted: ჯგუფიუმეტესწევად ასრულებს პოპნუსიკის ჟანრის სიმრერებს --- reference: ბაბილინა მუდმივად ცდილობდა შესაძლებლობების ფარგლებში მიეღო ცოდნა და ახალი ინფორმაცია predicted: ბაბილინა მუდმივა ცდილობდა შესაძლებლობების ფარგლებში მიიღო ცოტნა და ახალი ინფორმაცია --- reference: მრევლის რწმენით რომელი ჯგუფიც გაიმარჯვებდა მთელი წლის მანძილზე სიუხვე და ბარაქა არ მოაკლდებოდა predicted: მრევრის რწმენით რომელიჯგუფის გაიმარჯვებდა მთელიჭლის მანძილზა სიუყვეტაბარაქა არ მოაკლდებოდა --- reference: ნინო ჩხეიძეს განსაკუთრებული ღვაწლი მიუძღვის ქუთაისისა და რუსთაველის თეატრების შემოქმედებით ცხოვრებაში predicted: მინო ჩხეიძეს განსაკუთრებული ღოვაწლი მიოცხვის ქუთაისისა და რუსთაველის თეატრების შემოქმედებით ცხოვრებაში --- reference: იგი სამი დიალექტისგან შედგება predicted: იგი სამი დიალეთის გან შედგება --- reference: ფორმით სირაქლემებს წააგვანან predicted: ომიცი რაქლემებს ააგვანამ --- reference: დანი დაიბადა კოლუმბუსში ოჰაიოში predicted: დონი დაიბაოდა კოლუმბუსში ოხვაიოში --- reference: მშენებლობისათვის გამოიყო ადგილი ყოფილი აეროპორტის რაიონში predicted: შენებლობისათვის გამოიყო ადგილი ყოფილი აეროპორტის რაიონში --- ``` | bda8d6b3422e4abb1440a587a8837b51 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Georgian test data of Common Voice. ```python import librosa import torch import torchaudio from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_dataset, load_metric import numpy as np import re import string from normalizer import normalizer def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) speech_array = speech_array.squeeze().numpy() speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000) batch["speech"] = speech_array return batch def predict(batch): features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) input_values = features.input_values.to(device) attention_mask = features.attention_mask.to(device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = processor.batch_decode(pred_ids)[0] return batch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-georgian") model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-georgian").to(device) dataset = load_dataset("common_voice", "ka", split="test") dataset = dataset.map( normalizer, fn_kwargs={"remove_extra_space": True}, remove_columns=list(set(dataset.column_names) - set(['sentence', 'path'])) ) dataset = dataset.map(speech_file_to_array_fn) result = dataset.map(predict) wer = load_metric("wer") print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"]))) ``` **Test Result**: - WER: 43.86% | a7ef3b9f69bb1cd071091a7ddb2e2751 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training & Report The Common Voice `train`, `validation` datasets were used for training. You can see the training states [here](https://wandb.ai/m3hrdadfi/wav2vec2_large_xlsr_ka/reports/Fine-Tuning-for-Wav2Vec2-Large-XLSR-53-Georgian--Vmlldzo1OTQyMzk?accessToken=ytf7jseje66a3byuheh68o6a7215thjviscv5k2ewl5hgq9yqr50yxbko0bnf1d3) The script used for training can be found [here](https://colab.research.google.com/github/m3hrdadfi/notebooks/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Georgian_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb) | c5335514bd7640ad5ecf43f9e5c1213c |
apache-2.0 | ['lexical normalization'] | false | Fine-tuned ByT5-small for MultiLexNorm (Indonesian-English version)  This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://noisy-text.github.io/2021/multi-lexnorm.html), which evaluates lexical-normalization systems on 12 social media datasets in 11 languages. Our system is based on [ByT5](https://arxiv.org/abs/2105.13626), which we first pre-train on synthetic data and then fine-tune on authentic normalization data. It achieves the best performance by a wide margin in intrinsic evaluation, and also the best performance in extrinsic evaluation through dependency parsing. In addition to these fine-tuned models, we also release the source files on [GitHub](https://github.com/ufal/multilexnorm2021) and an interactive demo on [Google Colab](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing). | f3bedd1e1ad9a0990603aa298b6bba77 |
mit | ['audio', 'music', 'generation', 'tensorflow'] | false | Model provided by: DarkDude31 Fine-tuned (from misc) halvany\_oszi\_rozsa model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation. Introduced in [this paper](https://arxiv.org/abs/2208.08706). | 9e7bf36aba9c655d5f179fe17d41cb03 |
mit | ['audio', 'music', 'generation', 'tensorflow'] | false | How to use You can generate music from this fine-tuned (from misc) halvany_oszi_rozsa model using the notebook available [here](https://colab.research.google.com/drive/1HJWliBXPi-Xlx3gY8cjFI5-xaZgrTD7r). Only the `gen_ema.h5` file is needed to generate music. Place it in your `checkpoints` folder. | 96897d7711c2f4db1eec7f3a867803fa |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout e08a89e0a43db7fc12bec835c62a000ad10bd417 pip install -e . cd egs2/mini_librispeech/diar1 ./run.sh --skip_data_prep false --skip_train true --download_model jkang/espnet2_mini_librispeech_diar ``` <!-- Generated by scripts/utils/show_diar_result.sh --> | cb1bd2164fcf1eeb3b579b09c0f97cea |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | Environments - date: `Tue Feb 8 16:41:16 KST 2022` - python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` - espnet version: `espnet 0.10.6a1` - pytorch version: `pytorch 1.10.1` - Git hash: `e08a89e0a43db7fc12bec835c62a000ad10bd417` - Commit date: `Sun Feb 6 18:54:20 2022 -0500` | 3fcc01b0b56afc14c9693ed894bd4d5a |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | DER dev_clean_2_ns2_beta2_500 |threshold_median_collar|DER| |---|---| |result_th0.3_med11_collar0.0|31.39| |result_th0.3_med1_collar0.0|31.78| |result_th0.4_med11_collar0.0|29.99| |result_th0.4_med1_collar0.0|30.61| |result_th0.5_med11_collar0.0|29.28| |result_th0.5_med1_collar0.0|30.19| |result_th0.6_med11_collar0.0|29.50| |result_th0.6_med1_collar0.0|30.66| |result_th0.7_med11_collar0.0|30.90| |result_th0.7_med1_collar0.0|32.38| | 861f4d08f79651573109764d8932e5c3 |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | DIAR config <details><summary>expand</summary> ``` config: conf/train_diar.yaml print_config: false log_level: INFO dry_run: false iterator_type: chunk output_dir: exp/diar_train_diar_raw ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: 3 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 3 nbest_averaging_interval: 0 grad_clip: 5 grad_clip_type: 2.0 grad_noise: false accum_grad: 2 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 16 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/diar_stats_8k/train/speech_shape - exp/diar_stats_8k/train/spk_labels_shape valid_shape_file: - exp/diar_stats_8k/valid/speech_shape - exp/diar_stats_8k/valid/spk_labels_shape batch_type: folded valid_batch_type: null fold_length: - 80000 - 800 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 200000 chunk_shift_ratio: 0.5 num_cache_chunks: 64 train_data_path_and_name_and_type: - - dump/raw/simu/data/train_clean_5_ns2_beta2_500/wav.scp - speech - sound - - dump/raw/simu/data/train_clean_5_ns2_beta2_500/espnet_rttm - spk_labels - rttm valid_data_path_and_name_and_type: - - dump/raw/simu/data/dev_clean_2_ns2_beta2_500/wav.scp - speech - sound - - dump/raw/simu/data/dev_clean_2_ns2_beta2_500/espnet_rttm - spk_labels - rttm allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.01 scheduler: noamlr scheduler_conf: warmup_steps: 1000 num_spk: 2 init: xavier_uniform input_size: null model_conf: attractor_weight: 1.0 use_preprocessor: true frontend: default frontend_conf: fs: 8k hop_length: 128 specaug: null specaug_conf: {} normalize: global_mvn normalize_conf: stats_file: exp/diar_stats_8k/train/feats_stats.npz encoder: transformer encoder_conf: input_layer: linear num_blocks: 2 linear_units: 512 dropout_rate: 0.1 output_size: 256 attention_heads: 4 attention_dropout_rate: 0.0 decoder: linear decoder_conf: {} label_aggregator: label_aggregator label_aggregator_conf: {} attractor: null attractor_conf: {} required: - output_dir version: 0.10.6a1 distributed: false ``` </details> | 133cb462854b331a85cce4a39eeaf385 |
apache-2.0 | ['Quality Estimation', 'microtransquest'] | false | Using Pre-trained Models ```python from transquest.algo.word_level.microtransquest.run_model import MicroTransQuestModel import torch model = MicroTransQuestModel("xlmroberta", "TransQuest/microtransquest-en_zh-wiki", labels=["OK", "BAD"], use_cuda=torch.cuda.is_available()) source_tags, target_tags = model.predict([["if not , you may not be protected against the diseases . ", "ja tā nav , Jūs varat nepasargāt no slimībām . "]]) ``` | 706fcf188f9dc6f93636ddc4d1d91137 |
apache-2.0 | ['translation'] | false | opus-mt-fi-sv * source languages: fi * target languages: sv * OPUS readme: [fi-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-sv/README.md) * dataset: opus+bt * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus+bt-2020-04-11.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-sv/opus+bt-2020-04-11.zip) * test set translations: [opus+bt-2020-04-11.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-sv/opus+bt-2020-04-11.test.txt) * test set scores: [opus+bt-2020-04-11.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-sv/opus+bt-2020-04-11.eval.txt) | 56e6f5115290f52a017546b9956122dc |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_cola This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6837 - Matthews Correlation: 0.1055 | 3e7654094aef78c44d730bd87de1c66b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:-----:|:---------------:|:--------------------:| | 0.6247 | 1.0 | 1669 | 0.6837 | 0.1055 | | 0.5458 | 2.0 | 3338 | 0.7216 | 0.1168 | | 0.5041 | 3.0 | 5007 | 0.7127 | 0.1296 | | 0.4445 | 4.0 | 6676 | 0.7718 | 0.1436 | | 0.3961 | 5.0 | 8345 | 0.8417 | 0.1284 | | 0.3603 | 6.0 | 10014 | 0.7805 | 0.1240 | | 6d3fcc97cd6c56be4c2511ecec6be7ad |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Model Dreambooth concept any-ely-wd-Noah_Titan-4200 được train bởi hr16 bằng [Shinja Zero SoTA DreamBooth_Stable_Diffusion](https://colab.research.google.com/drive/1G7qx6M_S1PDDlsWIMdbZXwdZik6sUlEh) notebook <br> Test concept bằng [Shinja Zero no Notebook](https://colab.research.google.com/drive/1Hp1ZIjPbsZKlCtomJVmt2oX7733W44b0) <br> Hoặc test bằng `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Ảnh mẫu của concept: WIP | f863fd1a857de3afa69ec09fea201631 |
apache-2.0 | ['splinter', 'SplinterModel'] | false | Splinter large model, (with pretrained QASS-layer weights) Splinter-large is the pretrained model discussed in the paper [Few-Shot Question Answering by Pretraining Span Selection](https://aclanthology.org/2021.acl-long.239/) (at ACL 2021). Its original repository can be found [here](https://github.com/oriram/splinter). The model is case-sensitive. Note (1): This model **does** contain the pretrained weights for the QASS layer (see paper for details). For the model **without** those weights, see [tau/splinter-large](https://huggingface.co/tau/splinter-large). Note (2): Splinter-large was trained after the paper was released, so the results are not reported. However, this model outperforms the base model by large margins. For example, on SQuAD, the model is able to reach 80% F1 given only 128 examples, whereas the base model obtains only ~73%). See the results for Splinter-large in the Appendix of [this paper](https://arxiv.org/pdf/2108.05857.pdf). | df02b7b1dd81c41c26f18e6fdf96379e |
apache-2.0 | ['splinter', 'SplinterModel'] | false | BibTeX entry and citation info ```bibtex @inproceedings{ram-etal-2021-shot, title = "Few-Shot Question Answering by Pretraining Span Selection", author = "Ram, Ori and Kirstain, Yuval and Berant, Jonathan and Globerson, Amir and Levy, Omer", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.239", doi = "10.18653/v1/2021.acl-long.239", pages = "3066--3079", } ``` | 6bb5674ba5ffa790a2e65ea764910cc3 |
mit | ['generated_from_trainer'] | false | hasoc19-microsoft-mdeberta-v3-base-HatredStatement-new This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6039 - Accuracy: 0.7329 - Precision: 0.7324 - Recall: 0.7329 - F1: 0.7316 | 4ca61a29dd124fe8234fb78d881e8502 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.5276 | 0.7253 | 0.7258 | 0.7253 | 0.7225 | | 0.5406 | 2.0 | 592 | 0.5513 | 0.7319 | 0.7348 | 0.7319 | 0.7278 | | 0.5406 | 3.0 | 888 | 0.5466 | 0.7357 | 0.7458 | 0.7357 | 0.7283 | | 0.4372 | 4.0 | 1184 | 0.5531 | 0.7452 | 0.7502 | 0.7452 | 0.7406 | | 0.4372 | 5.0 | 1480 | 0.5927 | 0.7367 | 0.7364 | 0.7367 | 0.7352 | | 0.3868 | 6.0 | 1776 | 0.6039 | 0.7329 | 0.7324 | 0.7329 | 0.7316 | | b84b9d7b8be92c668f5179311237b65a |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/mbart-large-cc25-koquad-qg` This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 552ad034351775ca072fd8476488fd30 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.", list_answer="남부군") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-koquad-qg") output = pipe("1990년 영화 《 <hl> 남부군 <hl> 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.") ``` | 6e48bd3096438a8d995ab37c0e5e8725 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-koquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 83.89 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | Bleu_1 | 26.92 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | Bleu_2 | 19.57 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | Bleu_3 | 14.52 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | Bleu_4 | 10.92 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | METEOR | 30.23 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | MoverScore | 82.95 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | ROUGE_L | 27.76 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | - ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-koquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_koquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 88.18 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | QAAlignedF1Score (MoverScore) | 85.53 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | QAAlignedPrecision (BERTScore) | 88.22 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | QAAlignedPrecision (MoverScore) | 85.62 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | QAAlignedRecall (BERTScore) | 88.15 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | QAAlignedRecall (MoverScore) | 85.46 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | - ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mbart-large-cc25-koquad-ae`](https://huggingface.co/lmqg/mbart-large-cc25-koquad-ae). [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-koquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_koquad.default.lmqg_mbart-large-cc25-koquad-ae.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 80.64 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | QAAlignedF1Score (MoverScore) | 82.74 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | QAAlignedPrecision (BERTScore) | 77.67 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | QAAlignedPrecision (MoverScore) | 78.99 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | QAAlignedRecall (BERTScore) | 83.95 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | QAAlignedRecall (MoverScore) | 87.04 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | a91e642ce44f3a4f1454d528f7328562 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_koquad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/mbart-large-cc25 - max_length: 512 - max_length_output: 32 - epoch: 6 - batch: 4 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 16 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-koquad-qg/raw/main/trainer_config.json). | 84e194d5dc7cac81b9d22ee0e8f4ccb5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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 - num_epochs: 1.0 | b9f7081ab2705a96703f4181ae521832 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-gc-indep This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1014 - Accuracy: 0.983 - F1: 0.9746 | a20ae0832c56185769b9621815acd011 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2611 | 1.0 | 32 | 0.1014 | 0.983 | 0.9746 | | c52b65daed0d11cfde40fb0b6c549d3a |
apache-2.0 | ['generated_from_trainer'] | false | whisper-small-toi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1668 - Wer: 63.5938 | ea6d5fbc184584561297523f610632aa |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP | 650d920af925721dec97ad6d952c6caa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.568 | 1.47 | 500 | 2.1883 | 72.0402 | | 0.2614 | 2.95 | 1000 | 2.1071 | 67.1034 | | 0.0811 | 4.42 | 1500 | 2.3456 | 67.5012 | | 0.0383 | 5.9 | 2000 | 2.4961 | 67.9691 | | 0.021 | 7.37 | 2500 | 2.6259 | 68.8348 | | 0.0077 | 8.85 | 3000 | 2.6423 | 66.6823 | | 0.0046 | 10.32 | 3500 | 2.8497 | 65.9336 | | 0.0005 | 11.8 | 4000 | 2.8305 | 64.6467 | | 0.0014 | 13.27 | 4500 | 2.9174 | 66.0739 | | 0.0003 | 14.75 | 5000 | 2.9358 | 63.2663 | | 0.0002 | 16.22 | 5500 | 2.9820 | 63.8278 | | 0.0002 | 17.7 | 6000 | 3.0369 | 64.7403 | | 0.0001 | 19.17 | 6500 | 3.0641 | 63.3832 | | 0.0005 | 20.65 | 7000 | 3.0512 | 63.1493 | | 0.0001 | 22.12 | 7500 | 3.0924 | 63.5002 | | 0.0001 | 23.6 | 8000 | 3.1215 | 65.0679 | | 0.0001 | 25.07 | 8500 | 3.1336 | 64.6233 | | 0.0001 | 26.55 | 9000 | 3.1513 | 63.7108 | | 0.0001 | 28.02 | 9500 | 3.1620 | 63.5938 | | 0.0001 | 29.5 | 10000 | 3.1668 | 63.5938 | | c76f08822790621e83ba0180df065963 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_300k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 300k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model | 8dadbd074490d048fa930c13ee5d56c7 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_300k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_300k') model = TFBertModel.from_pretrained("google/multiberts-seed_2-step_300k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_300k') model = BertModel.from_pretrained("google/multiberts-seed_2-step_300k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 323c70d725d068bab900f7cd6e3c1613 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-multilingual-cased-finetuned-viquad This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9815 | d9f4223e35b78afc0b7b0c9ee52acf49 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 65 | 2.5534 | | No log | 2.0 | 130 | 2.1165 | | No log | 3.0 | 195 | 1.9815 | | 94a402a9e3a5b168fa901f2a6bed534d |
apache-2.0 | ['translation'] | false | ine-eng * source group: Indo-European languages * target group: English * OPUS readme: [ine-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ine-eng/README.md) * model: transformer * source language(s): afr aln ang_Latn arg asm ast awa bel bel_Latn ben bho bos_Latn bre bul bul_Latn cat ces cor cos csb_Latn cym dan deu dsb egl ell enm_Latn ext fao fra frm_Latn frr fry gcf_Latn gla gle glg glv gom gos got_Goth grc_Grek gsw guj hat hif_Latn hin hrv hsb hye ind isl ita jdt_Cyrl ksh kur_Arab kur_Latn lad lad_Latn lat_Latn lav lij lit lld_Latn lmo ltg ltz mai mar max_Latn mfe min mkd mwl nds nld nno nob nob_Hebr non_Latn npi oci ori orv_Cyrl oss pan_Guru pap pdc pes pes_Latn pes_Thaa pms pnb pol por prg_Latn pus roh rom ron rue rus san_Deva scn sco sgs sin slv snd_Arab spa sqi srp_Cyrl srp_Latn stq swe swg tgk_Cyrl tly_Latn tmw_Latn ukr urd vec wln yid zlm_Latn zsm_Latn zza * target language(s): eng * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ine-eng/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ine-eng/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ine-eng/opus2m-2020-08-01.eval.txt) | 97b6312db9e409b7ee0819880fa3d16a |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2014-hineng.hin.eng | 11.2 | 0.375 | | newsdev2016-enro-roneng.ron.eng | 35.5 | 0.614 | | newsdev2017-enlv-laveng.lav.eng | 25.1 | 0.542 | | newsdev2019-engu-gujeng.guj.eng | 16.0 | 0.420 | | newsdev2019-enlt-liteng.lit.eng | 24.0 | 0.522 | | newsdiscussdev2015-enfr-fraeng.fra.eng | 30.1 | 0.550 | | newsdiscusstest2015-enfr-fraeng.fra.eng | 33.4 | 0.572 | | newssyscomb2009-ceseng.ces.eng | 24.0 | 0.520 | | newssyscomb2009-deueng.deu.eng | 25.7 | 0.526 | | newssyscomb2009-fraeng.fra.eng | 27.9 | 0.550 | | newssyscomb2009-itaeng.ita.eng | 31.4 | 0.574 | | newssyscomb2009-spaeng.spa.eng | 28.3 | 0.555 | | news-test2008-deueng.deu.eng | 24.0 | 0.515 | | news-test2008-fraeng.fra.eng | 24.5 | 0.524 | | news-test2008-spaeng.spa.eng | 25.5 | 0.533 | | newstest2009-ceseng.ces.eng | 23.3 | 0.516 | | newstest2009-deueng.deu.eng | 23.2 | 0.512 | | newstest2009-fraeng.fra.eng | 27.3 | 0.545 | | newstest2009-itaeng.ita.eng | 30.3 | 0.567 | | newstest2009-spaeng.spa.eng | 27.9 | 0.549 | | newstest2010-ceseng.ces.eng | 23.8 | 0.523 | | newstest2010-deueng.deu.eng | 26.2 | 0.545 | | newstest2010-fraeng.fra.eng | 28.6 | 0.562 | | newstest2010-spaeng.spa.eng | 31.4 | 0.581 | | newstest2011-ceseng.ces.eng | 24.2 | 0.521 | | newstest2011-deueng.deu.eng | 23.9 | 0.522 | | newstest2011-fraeng.fra.eng | 29.5 | 0.570 | | newstest2011-spaeng.spa.eng | 30.3 | 0.570 | | newstest2012-ceseng.ces.eng | 23.5 | 0.516 | | newstest2012-deueng.deu.eng | 24.9 | 0.529 | | newstest2012-fraeng.fra.eng | 30.0 | 0.568 | | newstest2012-ruseng.rus.eng | 29.9 | 0.565 | | newstest2012-spaeng.spa.eng | 33.3 | 0.593 | | newstest2013-ceseng.ces.eng | 25.6 | 0.531 | | newstest2013-deueng.deu.eng | 27.7 | 0.545 | | newstest2013-fraeng.fra.eng | 30.0 | 0.561 | | newstest2013-ruseng.rus.eng | 24.4 | 0.514 | | newstest2013-spaeng.spa.eng | 30.8 | 0.577 | | newstest2014-csen-ceseng.ces.eng | 27.7 | 0.558 | | newstest2014-deen-deueng.deu.eng | 27.7 | 0.545 | | newstest2014-fren-fraeng.fra.eng | 32.2 | 0.592 | | newstest2014-hien-hineng.hin.eng | 16.7 | 0.450 | | newstest2014-ruen-ruseng.rus.eng | 27.2 | 0.552 | | newstest2015-encs-ceseng.ces.eng | 25.4 | 0.518 | | newstest2015-ende-deueng.deu.eng | 28.8 | 0.552 | | newstest2015-enru-ruseng.rus.eng | 25.6 | 0.527 | | newstest2016-encs-ceseng.ces.eng | 27.0 | 0.540 | | newstest2016-ende-deueng.deu.eng | 33.5 | 0.592 | | newstest2016-enro-roneng.ron.eng | 32.8 | 0.591 | | newstest2016-enru-ruseng.rus.eng | 24.8 | 0.523 | | newstest2017-encs-ceseng.ces.eng | 23.7 | 0.510 | | newstest2017-ende-deueng.deu.eng | 29.3 | 0.556 | | newstest2017-enlv-laveng.lav.eng | 18.9 | 0.486 | | newstest2017-enru-ruseng.rus.eng | 28.0 | 0.546 | | newstest2018-encs-ceseng.ces.eng | 24.9 | 0.521 | | newstest2018-ende-deueng.deu.eng | 36.0 | 0.604 | | newstest2018-enru-ruseng.rus.eng | 23.8 | 0.517 | | newstest2019-deen-deueng.deu.eng | 31.5 | 0.570 | | newstest2019-guen-gujeng.guj.eng | 12.1 | 0.377 | | newstest2019-lten-liteng.lit.eng | 26.6 | 0.555 | | newstest2019-ruen-ruseng.rus.eng | 27.5 | 0.541 | | Tatoeba-test.afr-eng.afr.eng | 59.0 | 0.724 | | Tatoeba-test.ang-eng.ang.eng | 9.9 | 0.254 | | Tatoeba-test.arg-eng.arg.eng | 41.6 | 0.487 | | Tatoeba-test.asm-eng.asm.eng | 22.8 | 0.392 | | Tatoeba-test.ast-eng.ast.eng | 36.1 | 0.521 | | Tatoeba-test.awa-eng.awa.eng | 11.6 | 0.280 | | Tatoeba-test.bel-eng.bel.eng | 42.2 | 0.597 | | Tatoeba-test.ben-eng.ben.eng | 45.8 | 0.598 | | Tatoeba-test.bho-eng.bho.eng | 34.4 | 0.518 | | Tatoeba-test.bre-eng.bre.eng | 24.4 | 0.405 | | Tatoeba-test.bul-eng.bul.eng | 50.8 | 0.660 | | Tatoeba-test.cat-eng.cat.eng | 51.2 | 0.677 | | Tatoeba-test.ces-eng.ces.eng | 47.6 | 0.641 | | Tatoeba-test.cor-eng.cor.eng | 5.4 | 0.214 | | Tatoeba-test.cos-eng.cos.eng | 61.0 | 0.675 | | Tatoeba-test.csb-eng.csb.eng | 22.5 | 0.394 | | Tatoeba-test.cym-eng.cym.eng | 34.7 | 0.522 | | Tatoeba-test.dan-eng.dan.eng | 56.2 | 0.708 | | Tatoeba-test.deu-eng.deu.eng | 44.9 | 0.625 | | Tatoeba-test.dsb-eng.dsb.eng | 21.0 | 0.383 | | Tatoeba-test.egl-eng.egl.eng | 6.9 | 0.221 | | Tatoeba-test.ell-eng.ell.eng | 62.1 | 0.741 | | Tatoeba-test.enm-eng.enm.eng | 22.6 | 0.466 | | Tatoeba-test.ext-eng.ext.eng | 33.2 | 0.496 | | Tatoeba-test.fao-eng.fao.eng | 28.1 | 0.460 | | Tatoeba-test.fas-eng.fas.eng | 9.6 | 0.306 | | Tatoeba-test.fra-eng.fra.eng | 50.3 | 0.661 | | Tatoeba-test.frm-eng.frm.eng | 30.0 | 0.457 | | Tatoeba-test.frr-eng.frr.eng | 15.2 | 0.301 | | Tatoeba-test.fry-eng.fry.eng | 34.4 | 0.525 | | Tatoeba-test.gcf-eng.gcf.eng | 18.4 | 0.317 | | Tatoeba-test.gla-eng.gla.eng | 24.1 | 0.400 | | Tatoeba-test.gle-eng.gle.eng | 52.2 | 0.671 | | Tatoeba-test.glg-eng.glg.eng | 50.5 | 0.669 | | Tatoeba-test.glv-eng.glv.eng | 5.7 | 0.189 | | Tatoeba-test.gos-eng.gos.eng | 19.2 | 0.378 | | Tatoeba-test.got-eng.got.eng | 0.1 | 0.022 | | Tatoeba-test.grc-eng.grc.eng | 0.9 | 0.095 | | Tatoeba-test.gsw-eng.gsw.eng | 23.9 | 0.390 | | Tatoeba-test.guj-eng.guj.eng | 28.0 | 0.428 | | Tatoeba-test.hat-eng.hat.eng | 44.2 | 0.567 | | Tatoeba-test.hbs-eng.hbs.eng | 51.6 | 0.666 | | Tatoeba-test.hif-eng.hif.eng | 22.3 | 0.451 | | Tatoeba-test.hin-eng.hin.eng | 41.7 | 0.585 | | Tatoeba-test.hsb-eng.hsb.eng | 46.4 | 0.590 | | Tatoeba-test.hye-eng.hye.eng | 40.4 | 0.564 | | Tatoeba-test.isl-eng.isl.eng | 43.8 | 0.605 | | Tatoeba-test.ita-eng.ita.eng | 60.7 | 0.735 | | Tatoeba-test.jdt-eng.jdt.eng | 5.5 | 0.091 | | Tatoeba-test.kok-eng.kok.eng | 7.8 | 0.205 | | Tatoeba-test.ksh-eng.ksh.eng | 15.8 | 0.284 | | Tatoeba-test.kur-eng.kur.eng | 11.6 | 0.232 | | Tatoeba-test.lad-eng.lad.eng | 30.7 | 0.484 | | Tatoeba-test.lah-eng.lah.eng | 11.0 | 0.286 | | Tatoeba-test.lat-eng.lat.eng | 24.4 | 0.432 | | Tatoeba-test.lav-eng.lav.eng | 47.2 | 0.646 | | Tatoeba-test.lij-eng.lij.eng | 9.0 | 0.287 | | Tatoeba-test.lit-eng.lit.eng | 51.7 | 0.670 | | Tatoeba-test.lld-eng.lld.eng | 22.4 | 0.369 | | Tatoeba-test.lmo-eng.lmo.eng | 26.1 | 0.381 | | Tatoeba-test.ltz-eng.ltz.eng | 39.8 | 0.536 | | Tatoeba-test.mai-eng.mai.eng | 72.3 | 0.758 | | Tatoeba-test.mar-eng.mar.eng | 32.0 | 0.554 | | Tatoeba-test.mfe-eng.mfe.eng | 63.1 | 0.822 | | Tatoeba-test.mkd-eng.mkd.eng | 49.5 | 0.638 | | Tatoeba-test.msa-eng.msa.eng | 38.6 | 0.566 | | Tatoeba-test.multi.eng | 45.6 | 0.615 | | Tatoeba-test.mwl-eng.mwl.eng | 40.4 | 0.767 | | Tatoeba-test.nds-eng.nds.eng | 35.5 | 0.538 | | Tatoeba-test.nep-eng.nep.eng | 4.9 | 0.209 | | Tatoeba-test.nld-eng.nld.eng | 54.2 | 0.694 | | Tatoeba-test.non-eng.non.eng | 39.3 | 0.573 | | Tatoeba-test.nor-eng.nor.eng | 50.9 | 0.663 | | Tatoeba-test.oci-eng.oci.eng | 19.6 | 0.386 | | Tatoeba-test.ori-eng.ori.eng | 16.2 | 0.364 | | Tatoeba-test.orv-eng.orv.eng | 13.6 | 0.288 | | Tatoeba-test.oss-eng.oss.eng | 9.4 | 0.301 | | Tatoeba-test.pan-eng.pan.eng | 17.1 | 0.389 | | Tatoeba-test.pap-eng.pap.eng | 57.0 | 0.680 | | Tatoeba-test.pdc-eng.pdc.eng | 41.6 | 0.526 | | Tatoeba-test.pms-eng.pms.eng | 13.7 | 0.333 | | Tatoeba-test.pol-eng.pol.eng | 46.5 | 0.632 | | Tatoeba-test.por-eng.por.eng | 56.4 | 0.710 | | Tatoeba-test.prg-eng.prg.eng | 2.3 | 0.193 | | Tatoeba-test.pus-eng.pus.eng | 3.2 | 0.194 | | Tatoeba-test.roh-eng.roh.eng | 17.5 | 0.420 | | Tatoeba-test.rom-eng.rom.eng | 5.0 | 0.237 | | Tatoeba-test.ron-eng.ron.eng | 51.4 | 0.670 | | Tatoeba-test.rue-eng.rue.eng | 26.0 | 0.447 | | Tatoeba-test.rus-eng.rus.eng | 47.8 | 0.634 | | Tatoeba-test.san-eng.san.eng | 4.0 | 0.195 | | Tatoeba-test.scn-eng.scn.eng | 45.1 | 0.440 | | Tatoeba-test.sco-eng.sco.eng | 41.9 | 0.582 | | Tatoeba-test.sgs-eng.sgs.eng | 38.7 | 0.498 | | Tatoeba-test.sin-eng.sin.eng | 29.7 | 0.499 | | Tatoeba-test.slv-eng.slv.eng | 38.2 | 0.564 | | Tatoeba-test.snd-eng.snd.eng | 12.7 | 0.342 | | Tatoeba-test.spa-eng.spa.eng | 53.2 | 0.687 | | Tatoeba-test.sqi-eng.sqi.eng | 51.9 | 0.679 | | Tatoeba-test.stq-eng.stq.eng | 9.0 | 0.391 | | Tatoeba-test.swe-eng.swe.eng | 57.4 | 0.705 | | Tatoeba-test.swg-eng.swg.eng | 18.0 | 0.338 | | Tatoeba-test.tgk-eng.tgk.eng | 24.3 | 0.413 | | Tatoeba-test.tly-eng.tly.eng | 1.1 | 0.094 | | Tatoeba-test.ukr-eng.ukr.eng | 48.0 | 0.639 | | Tatoeba-test.urd-eng.urd.eng | 27.2 | 0.471 | | Tatoeba-test.vec-eng.vec.eng | 28.0 | 0.398 | | Tatoeba-test.wln-eng.wln.eng | 17.5 | 0.320 | | Tatoeba-test.yid-eng.yid.eng | 26.9 | 0.457 | | Tatoeba-test.zza-eng.zza.eng | 1.7 | 0.131 | | f0bb0bf7ec3170f8a76f6554cc93bdaf |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ine-eng - source_languages: ine - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ine-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ca', 'es', 'os', 'ro', 'fy', 'cy', 'sc', 'is', 'yi', 'lb', 'an', 'sq', 'fr', 'ht', 'rm', 'ps', 'af', 'uk', 'sl', 'lt', 'bg', 'be', 'gd', 'si', 'en', 'br', 'mk', 'or', 'mr', 'ru', 'fo', 'co', 'oc', 'pl', 'gl', 'nb', 'bn', 'id', 'hy', 'da', 'gv', 'nl', 'pt', 'hi', 'as', 'kw', 'ga', 'sv', 'gu', 'wa', 'lv', 'el', 'it', 'hr', 'ur', 'nn', 'de', 'cs', 'ine'] - src_constituents: {'cat', 'spa', 'pap', 'mwl', 'lij', 'bos_Latn', 'lad_Latn', 'lat_Latn', 'pcd', 'oss', 'ron', 'fry', 'cym', 'awa', 'swg', 'zsm_Latn', 'srd', 'gcf_Latn', 'isl', 'yid', 'bho', 'ltz', 'kur_Latn', 'arg', 'pes_Thaa', 'sqi', 'csb_Latn', 'fra', 'hat', 'non_Latn', 'sco', 'pnb', 'roh', 'bul_Latn', 'pus', 'afr', 'ukr', 'slv', 'lit', 'tmw_Latn', 'hsb', 'tly_Latn', 'bul', 'bel', 'got_Goth', 'lat_Grek', 'ext', 'gla', 'mai', 'sin', 'hif_Latn', 'eng', 'bre', 'nob_Hebr', 'prg_Latn', 'ang_Latn', 'aln', 'mkd', 'ori', 'mar', 'afr_Arab', 'san_Deva', 'gos', 'rus', 'fao', 'orv_Cyrl', 'bel_Latn', 'cos', 'zza', 'grc_Grek', 'oci', 'mfe', 'gom', 'bjn', 'sgs', 'tgk_Cyrl', 'hye_Latn', 'pdc', 'srp_Cyrl', 'pol', 'ast', 'glg', 'pms', 'nob', 'ben', 'min', 'srp_Latn', 'zlm_Latn', 'ind', 'rom', 'hye', 'scn', 'enm_Latn', 'lmo', 'npi', 'pes', 'dan', 'rus_Latn', 'jdt_Cyrl', 'gsw', 'glv', 'nld', 'snd_Arab', 'kur_Arab', 'por', 'hin', 'dsb', 'asm', 'lad', 'frm_Latn', 'ksh', 'pan_Guru', 'cor', 'gle', 'swe', 'guj', 'wln', 'lav', 'ell', 'frr', 'rue', 'ita', 'hrv', 'urd', 'stq', 'nno', 'deu', 'lld_Latn', 'ces', 'egl', 'vec', 'max_Latn', 'pes_Latn', 'ltg', 'nds'} - tgt_constituents: {'eng'} - src_multilingual: True - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ine-eng/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ine-eng/opus2m-2020-08-01.test.txt - src_alpha3: ine - tgt_alpha3: eng - short_pair: ine-en - chrF2_score: 0.615 - bleu: 45.6 - brevity_penalty: 0.997 - ref_len: 71872.0 - src_name: Indo-European languages - tgt_name: English - train_date: 2020-08-01 - src_alpha2: ine - tgt_alpha2: en - prefer_old: False - long_pair: ine-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 4a9a4959e7a6f3d7da3cf0dd1231cdc5 |
apache-2.0 | ['chinese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | Model Description This is a BERT model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from [chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). | befab40ea1fa7fa443b5972ec49931a6 |
apache-2.0 | ['chinese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/chinese-bert-wwm-ext-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/chinese-bert-wwm-ext-upos") ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/chinese-bert-wwm-ext-upos") ``` | 6147d85507761b32b01b23b5e6fb815f |
apache-2.0 | ['conversational', 'dialogue', 'response generation'] | false | Model Card for 🧑🏻🚀COSMO 🧑🏻🚀COSMO is a conversation agent with greater generalizability on both in- and out-of-domain chitchat datasets (e.g., DailyDialog, BlendedSkillTalk). It is trained on two datasets: SODA and ProsocialDialog. COSMO is especially aiming to model natural human conversations. It can accept situation descriptions as well as instructions on what role it should play in the situation. | 67262498d8c2f8dbe4639f6374576e63 |
apache-2.0 | ['conversational', 'dialogue', 'response generation'] | false | Model Description - **Repository:** [Code](https://github.com/skywalker023/sodaverse) - **Paper:** [SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization](https://arxiv.org/abs/2212.10465) - **Point of Contact:** [Hyunwoo Kim](mailto:hyunwook@allenai.org) | 71c622481961f777073d6a0606348f95 |
apache-2.0 | ['conversational', 'dialogue', 'response generation'] | false | Model Training 🧑🏻🚀COSMO is trained on our two recent datasets: 🥤[SODA](https://huggingface.co/datasets/allenai/soda) and [Prosocial Dialog](https://huggingface.co/datasets/allenai/prosocial-dialog). The backbone model of COSMO is the [lm-adapted T5](https://huggingface.co/google/t5-xl-lm-adapt). | d9763db571b16cc0b64152e8cffd4b86 |
apache-2.0 | ['conversational', 'dialogue', 'response generation'] | false | How to use > 💡 <b>Note:</b> The HuggingFace inference API for Cosmo is not working correctly, we gently guide you to [our repository](https://hyunw.kim/sodaverse) to try out the demo code! > 🚨 <b>Disclaimer:</b> We would like to emphasize that COSMO is trained on SODA and ProsocialDialog mainly for academic/research purposes. We discourage using COSMO in real-world applications or services as is. Model outputs should not be used for advice for humans, and could be potentially offensive, problematic, or harmful. The model’s output does not necessarily reflect the views and opinions of the authors and their associated affiliations. Below is a simple code snippet to get Cosmo running :) ```python import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM device = torch.device("cuda" if torch.cuda.is_available() else "cpu") tokenizer = AutoTokenizer.from_pretrained("allenai/cosmo-xl") model = AutoModelForSeq2SeqLM.from_pretrained("allenai/cosmo-xl").to(device) def set_input(situation_narrative, role_instruction, conversation_history): input_text = " <turn> ".join(conversation_history) if role_instruction != "": input_text = "{} <sep> {}".format(role_instruction, input_text) if situation_narrative != "": input_text = "{} <sep> {}".format(situation_narrative, input_text) return input_text def generate(situation_narrative, role_instruction, conversation_history): """ situation_narrative: the description of situation/context with the characters included (e.g., "David goes to an amusement park") role_instruction: the perspective/speaker instruction (e.g., "Imagine you are David and speak to his friend Sarah"). conversation_history: the previous utterances in the conversation in a list """ input_text = set_input(situation_narrative, role_instruction, conversation_history) inputs = tokenizer([input_text], return_tensors="pt").to(device) outputs = model.generate(inputs["input_ids"], max_new_tokens=128, temperature=1.0, top_p=.95, do_sample=True) response = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False) return response situation = "Cosmo had a really fun time participating in the EMNLP conference at Abu Dhabi." instruction = "You are Cosmo and you are talking to a friend." | eba1c7fcb9342fd8ed0533aee029c37f |
apache-2.0 | ['conversational', 'dialogue', 'response generation'] | false | Further Details, Social Impacts, Bias, and Limitations Please refer to our [paper](https://arxiv.org/abs/2212.10465). Cosmo is mostly trained on social chitchat. Therefore, we do not encourage having knowledge-intensive conversations (e.g., science, medical issues, law). Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. 2021](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. 2021](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. | 611ff0017ea946da36a611309a851dde |
apache-2.0 | ['conversational', 'dialogue', 'response generation'] | false | Citation Please cite our work if you find the resources in this repository useful: ``` @article{kim2022soda, title={SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization}, author={Hyunwoo Kim and Jack Hessel and Liwei Jiang and Peter West and Ximing Lu and Youngjae Yu and Pei Zhou and Ronan Le Bras and Malihe Alikhani and Gunhee Kim and Maarten Sap and Yejin Choi}, journal={ArXiv}, year={2022}, volume={abs/2212.10465} } ``` | 234c847c2054f1c54beff580569fe862 |
apache-2.0 | ['generated_from_trainer'] | false | bert-emotion This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.2559 - Precision: 0.7221 - Recall: 0.7242 - Fscore: 0.7223 | e7f3f216e5184d6f1c0e19e33a76f9bc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 0.8588 | 1.0 | 815 | 0.8342 | 0.7807 | 0.6117 | 0.6364 | | 0.5394 | 2.0 | 1630 | 0.9126 | 0.7363 | 0.6923 | 0.7096 | | 0.2805 | 3.0 | 2445 | 1.2559 | 0.7221 | 0.7242 | 0.7223 | | 7ba00996c86e346c865ff563eb2d42fa |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT', 'RobBERTje'] | false | The models | Model | Description | Parameters | Training size | Huggingface id | |--------------|-------------|------------------|-------------------|------------------------------------------------------------------------------------| | Non-shuffled | Trained on the non-shuffled variant of the oscar corpus, without any operations to preserve this order during training and distillation. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-non-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-non-shuffled) | | Shuffled | Trained on the publicly available and shuffled OSCAR corpus. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-shuffled) | | Merged (p=0.5) | Same as the non-shuffled variant, but sequential sentences of the same document are merged with a probability of 50%. | 74 M | 1 GB | this model | | BORT | A smaller version with 8 attention heads instead of 12 and 4 layers instead of 6 (and 12 for RobBERT). | 46 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-bort](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-bort) | | 24c9191fd72f8fb8750d010cb8824ac8 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Chinese-zh-CN-aishell1 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chinese using the [AISHELL-1](https://github.com/kaldi-asr/kaldi/tree/master/egs/aishell) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | b44f4cac1f7513abb169f405a0c775f7 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import librosa from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor device = "cuda:0" if torch.cuda.is_available() else "cpu" processor = Wav2Vec2Processor.from_pretrained( 'qinyue/wav2vec2-large-xlsr-53-chinese-zn-cn-aishell1') model = Wav2Vec2ForCTC.from_pretrained( 'qinyue/wav2vec2-large-xlsr-53-chinese-zn-cn-aishell1').to(device) filepath = 'test.wav' audio, sr = librosa.load(filepath, sr=16000, mono=True) inputs = processor(audio, sample_rate=16000, return_tensors="pt").to(device) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) pred_str = processor.decode(predicted_ids[0]) print(pred_str) ``` | 4bd49f2e3f9976c0334dde46261d986f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation ```python wer_metric = load_metric("wer") def compute_metrics(pred): pred_logits = pred.predictions pred_ids = np.argmax(pred_logits, axis=-1) pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id pred_str = processor.batch_decode(pred_ids, spaces_between_special_tokens=True) label_str = processor.batch_decode(pred.label_ids, group_tokens=False, spaces_between_special_tokens=True) wer = wer_metric.compute(predictions=pred_str, references=label_str) return {"wer": wer} ``` | 70ffa01b9a302f15fd0b17737914a18b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Results | Reference | Prediction | | ------------- | ------------- | | 据 伟 业 我 爱 我 家 市 场 研 究 院 测 算 | 据 北 业 我 爱 我 家 市 场 研 究 院 测 算 | | 七 月 北 京 公 积 金 贷 款 成 交 量 提 升 了 百 分 之 五 | 七 月 北 京 公 积 金 贷 款 成 交 量 提 升 了 百 分 之 五 | | 培 育 门 类 丰 富 层 次 齐 用 的 综 合 利 用 产 业 | 培 育 门 类 丰 富 层 资 集 业 的 综 合 利 用 产 业 | | 我 们 迎 来 了 赶 超 发 达 国 家 的 难 得 机 遇 | 我 们 迎 来 了 赶 超 发 达 国 家 的 单 得 机 遇 | | 坚 持 基 本 草 原 保 护 制 度 | 坚 持 基 本 草 员 保 护 制 度 | | 强 化 水 生 生 态 修 复 和 建 设 | 强 化 水 生 生 态 修 复 和 建 设 | | 温 州 两 男 子 为 争 女 人 驾 奔 驰 宝 马 街 头 四 次 对 撞 | 温 州 两 男 子 为 争 女 人 架 奔 驰 宝 马 接 头 四 次 对 重 | | 她 表 示 应 该 是 吃 吃 饭 看 电 影 之 类 的 | 他 表 示 一 的 是 吃 吃 饭 看 电 影 之 理 | | 加 强 畜 禽 遗 传 资 源 和 农 业 野 生 植 物 资 源 保 护 | 加 强 续 紧 遗 传 资 源 和 农 业 野 生 职 物 资 源 保 护 | | 两 人 都 是 依 赖 电 话 沟 通 | 两 人 都 是 依 赖 电 话 沟 通 | **Test Result**: In the table below I report the Word Error Rate (WER) of the model on the AISHELL-1 test dataset. | Model | WER | WER-with-LM | | ------------- | ------------- | ------------- | | qinyue/wav2vec2-large-xlsr-53-chinese-zn-cn-aishell1 | **7.04%** | **3.96%** | | 88e1ea1068abea26f9964e6816990184 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3213 - Accuracy: 0.8667 - F1: 0.8684 | 2a8fce11ce73f1381a5be163edb2aeca |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4721 | 796644f7451862ba7f5955e72d5eae1e |
apache-2.0 | [] | false | _Copyright 2023 Anugrah Akbar Praramadhan. All rights reserved._ _Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at_ _[http://www.apache.org/licenses/LICENSE-2.0)](http://www.apache.org/licenses/LICENSE-2.0)_ _Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License._ | 0357d1b368f13cac18e34f3cb6fee574 |
apache-2.0 | [] | false | Model Description A GPT-2 *(Generative Pretrained Transformer-2)* model is a transformer based architecture for Causal Language Modeling, meaning it's required a left token/word as an input prompt for generating the right/next token, developed by Open AI *{Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}*. See the paper here: [https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) | cca0c8a93d08292f12b6b6ff0150c28e |
apache-2.0 | [] | false | Limitation Since GPT-2 is an unsupervised model and trained using an unlabelled of text sequences without any explicit supervision, the clarity and output of this model often comes with randomness. To overcome this issue we have to create a specific seed for determined output. Supported language for this model is only English *(get from GPT-2 pretrained model)* and Indonesian *(fine tune using Indonesian Wikipedia Dataset)*. | 7b1abe26bffefd6e291d1022eded5147 |
apache-2.0 | [] | false | How To Use Direct use of using Pytorch: ```python >>> from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, set_seed >>> model_name = 'anugrahap/gpt2-indo-textgen' >>> tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left') >>> model = AutoModelForCausalLM.from_pretrained(model_name, pad_token_id=tokenizer.eos_token_id) >>> generator = pipeline('text-generation', model=model, tokenizer=tokenizer) >>> | 03bf36fc158a724a8d450be08a945b5a |
apache-2.0 | [] | false | Learn more | [GPT-2 Pretrained Model Medium-345M Parameters](https://github.com/openai/gpt-2/blob/master/download_model.py)<br> | [Indo4B Wikipedia CoNLL-U Dataset - 433MB by IndoNLP](https://drive.google.com/file/d/1ZoKd31yr3soveU0O38XEIFUBKx-D66t5/view?usp=sharing)<br> | [References for CoNLL-U format](https://universaldependencies.org/format.html)<br> | [Project Repository](https://huggingface.co/spaces/anugrahap/gpt2-indo-text-gen/tree/main) | 867c1f3ce67ee9b3ccf5b7907c5e2893 |
apache-2.0 | ['generated_from_keras_callback'] | false | tfranklin/bert-a-saurus 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: 0.0003 - Validation Loss: 0.0004 - Epoch: 2 | 9028785b06e1bc3acc7083e1438f665d |
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': 1202, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | f4f2d6e50bb1b8d3f3e4d70009790292 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.2424 | 0.0004 | 0 | | 0.0004 | 0.0004 | 1 | | 0.0003 | 0.0004 | 2 | | c1c1f872b94907d2f17335adb35c8d5c |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | {INSTANCE_NAME} Dreambooth model trained by zuruyu 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 started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars). Sample pictures of this concept: | 0ecf0b07d61481727d89bb952e0616ab |
apache-2.0 | [] | false | This is a pretrained [MT5](https://github.com/google-research/multilingual-t5) large model (**973M** parameters). Training was performed with the span corruption task on a clean 80GB Romanian text corpus for 4M total steps with these [scripts](https://github.com/dumitrescustefan/t5x_models), starting from the 1M public mt5x-large checkpoint. The model was trained with an encoder and decoder sequence length of 512, and has the same mt5x vocabulary as the 1M multilingual checkpoint. **!! IMPORTANT !!** This model was pretrained on the span corruption MLM task, meaning this model is **not usable** in any downstream task **without finetuning** first! | 284ef9f449447402f0fbece34c3a2fea |
apache-2.0 | [] | false | How to load an mt5x model ```python from transformers import MT5Model, T5Tokenizer model = MT5Model.from_pretrained('dumitrescustefan/mt5-large-romanian') tokenizer = T5Tokenizer.from_pretrained('dumitrescustefan/mt5-large-romanian') input_text = "Acesta este un test." target_text = "Acesta este" inputs = tokenizer(input_text, return_tensors="pt") labels = tokenizer(text_target=target_text, return_tensors="pt") outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"]) hidden_states = outputs.last_hidden_state print(hidden_states.shape) | 8b0807bdd8a211e8eaeec46ee91970f6 |
apache-2.0 | [] | false | this will print [1, 4, 1024] ``` Remember to always sanitize your text! Replace ``ş`` and ``ţ`` cedilla-letters to comma-letters with : ```python text = text.replace("ţ", "ț").replace("ş", "ș").replace("Ţ", "Ț").replace("Ş", "Ș") ``` because the model was **not** trained on cedilla ``ş`` and ``ţ``s. If you don't, you will have decreased performance due to ``<UNK>``s and increased number of tokens per word. | 71fcdf84ad1f14cda9d95dd155e5ee6f |
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