license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_data_aug_stsb This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.8342 - Pearson: 0.1765 - Spearmanr: 0.1800 - Combined Score: 0.1782 | 08935f70a27ba13798eb3e612044d5dd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------:|:--------------:| | 1.0254 | 1.0 | 2518 | 2.8776 | 0.1575 | 0.1742 | 0.1659 | | 0.5854 | 2.0 | 5036 | 3.1464 | 0.1591 | 0.1679 | 0.1635 | | 0.4255 | 3.0 | 7554 | 2.8342 | 0.1765 | 0.1800 | 0.1782 | | 0.2765 | 4.0 | 10072 | 2.8524 | 0.1815 | 0.1838 | 0.1827 | | 0.1862 | 5.0 | 12590 | 2.9184 | 0.1736 | 0.1768 | 0.1752 | | 0.1339 | 6.0 | 15108 | 2.9817 | 0.1688 | 0.1728 | 0.1708 | | 0.1029 | 7.0 | 17626 | 2.9702 | 0.1618 | 0.1643 | 0.1631 | | 0.0806 | 8.0 | 20144 | 3.0033 | 0.1588 | 0.1624 | 0.1606 | | b89fded8d9bdb7c4d94390db31d303bd |
bsd-3-clause | [] | false | Model description CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in [this repository](https://github.com/salesforce/CodeGen), under 3 pre-training data variants (`NL`, `Multi`, `Mono`) and 4 model size variants (`350M`, `2B`, `6B`, `16B`). The checkpoint included in this repository is denoted as **CodeGen-Multi 6B** in the paper, where "Multi" means the model is initialized with *CodeGen-NL 6B* and further pre-trained on a dataset of multiple programming languages, and "6B" refers to the number of trainable parameters. | 768df5c3f3b15b6e1347e3b93a3e8c13 |
bsd-3-clause | [] | false | Training data This checkpoint (CodeGen-Multi 6B) was firstly initialized with *CodeGen-NL 6B*, and then pre-trained on [BigQuery](https://console.cloud.google.com/marketplace/details/github/github-repos), a large-scale dataset of multiple programming languages from GitHub repositories. The data consists of 119.2B tokens and includes C, C++, Go, Java, JavaScript, and Python. | f0cfc50e3ed42342fc09135365420f00 |
bsd-3-clause | [] | false | How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-6B-multi") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-6B-multi") text = "def hello_world():" input_ids = tokenizer(text, return_tensors="pt").input_ids generated_ids = model.generate(input_ids, max_length=128) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) ``` | 894e7fd5c7092a1d4dcbce0a57af6a03 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1561 - Accuracy: 0.9285 | 351da6f5ba1ec8753ab998be6045f53f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 250 | 0.1635 | 0.9295 | | 0.111 | 2.0 | 500 | 0.1515 | 0.936 | | 0.111 | 3.0 | 750 | 0.1561 | 0.9285 | | 0be4f1e560c03f667a8f057bb4b2ddfc |
afl-3.0 | [] | false | afro-xlmr-small AfroXLMR-small was created by [first reducing the vocabulary token size](https://aclanthology.org/2020.sustainlp-1.16/) of XLM-R-base from 250K to 70k, followed by MLM adaptation on 17 African languages (Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Naija, Kinyarwanda, Kirundi, Shona, Somali, Sesotho, Swahili, isiXhosa, Yoruba, and isiZulu) covering the major African language families and 3 high resource languages (Arabic, French, and English). | 9f805ca95c3727cbe5a25903392bc6a9 |
afl-3.0 | [] | false | Eval results on MasakhaNER (F-score) language| XLM-R-miniLM| XLM-R-base |XLM-R-large| afro-xlmr-base | afro-xlmr-small | afro-xlmr-mini -|-|-|-|-|-|- amh |69.5|70.6|76.2|76.1|70.1|69.7 hau |74.5|89.5|90.5|91.2|91.4|87.7 ibo |81.9|84.8|84.1|87.4|86.6|83.5 kin |68.6|73.3|73.8|78.0|77.5|74.1 lug |64.7|79.7|81.6|82.9|83.2|77.4 luo |11.7|74.9|73.6|75.1|75.4|17.5 pcm |83.2|87.3|89.0|89.6|89.0|85.5 swa |86.3|87.4|89.4|88.6|88.7|86.0 wol |51.7|63.9|67.9|67.4|65.9|59.0 yor |72.0|78.3|78.9|82.1|81.3|75.1 | 2037b6031a6a5d4c0670dd9fb05f1993 |
afl-3.0 | [] | false | BibTeX entry and citation info ``` @inproceedings{alabi-etal-2022-adapting, title = "Adapting Pre-trained Language Models to {A}frican Languages via Multilingual Adaptive Fine-Tuning", author = "Alabi, Jesujoba O. and Adelani, David Ifeoluwa and Mosbach, Marius and Klakow, Dietrich", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2022.coling-1.382", pages = "4336--4349", abstract = "Multilingual pre-trained language models (PLMs) have demonstrated impressive performance on several downstream tasks for both high-resourced and low-resourced languages. However, there is still a large performance drop for languages unseen during pre-training, especially African languages. One of the most effective approaches to adapt to a new language is language adaptive fine-tuning (LAFT) {---} fine-tuning a multilingual PLM on monolingual texts of a language using the pre-training objective. However, adapting to target language individually takes large disk space and limits the cross-lingual transfer abilities of the resulting models because they have been specialized for a single language. In this paper, we perform multilingual adaptive fine-tuning on 17 most-resourced African languages and three other high-resource languages widely spoken on the African continent to encourage cross-lingual transfer learning. To further specialize the multilingual PLM, we removed vocabulary tokens from the embedding layer that corresponds to non-African writing scripts before MAFT, thus reducing the model size by around 50{\%}. Our evaluation on two multilingual PLMs (AfriBERTa and XLM-R) and three NLP tasks (NER, news topic classification, and sentiment classification) shows that our approach is competitive to applying LAFT on individual languages while requiring significantly less disk space. Additionally, we show that our adapted PLM also improves the zero-shot cross-lingual transfer abilities of parameter efficient fine-tuning methods.", } ``` | f418ed9c109c1a5ef977d3c9bdecbcde |
apache-2.0 | ['generated_from_keras_callback'] | false | bert-base-cased-finetuned-log-parser-winlogbeat_nowhitespace_large This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: | 4b2f2dee39f19509b8af11da0d4c7d3e |
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': 15321, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 15321, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-06, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | 15acaf76d05569dda7015faf4eb775b1 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_xlsr-53_s870 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition on English using the train split of [Common Voice 7.0](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. | da41435f34a8dea915ef52197c01d213 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4568 - Wer: 0.3422 | 77334a71c5f1c0156ecf3286b7b62957 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3896 | 4.0 | 500 | 1.1573 | 0.8886 | | 0.5667 | 8.0 | 1000 | 0.4841 | 0.4470 | | 0.2126 | 12.0 | 1500 | 0.4201 | 0.3852 | | 0.1235 | 16.0 | 2000 | 0.4381 | 0.3623 | | 0.0909 | 20.0 | 2500 | 0.4784 | 0.3748 | | 0.0611 | 24.0 | 3000 | 0.4390 | 0.3577 | | 0.0454 | 28.0 | 3500 | 0.4568 | 0.3422 | | 439fc53444edc630516429678e78003c |
other | [] | false | https://huggingface.co/dixipi9178/MyCoolModel/resolve/main/corneos7thHeavenMix_v2.safetensors https://huggingface.co/dixipi9178/MyCoolModel/resolve/main/novelai%20f111%20sd1.4%20add%20difference%201.0.ckpt https://huggingface.co/dixipi9178/MyCoolModel/resolve/main/Anything-V3.0-pruned-fp16.ckpt !gdown https://huggingface.co/dixipi9178/MyCoolModel/resolve/main/novelai%20f111%20sd1.4%20add%20difference%201.0.ckpt -O /content/stable-diffusion-webui/models/Stable-diffusion/nai_f111.ckpt | cf851e8167729142c23aa3852c7cb480 |
mit | ['generated_from_keras_callback'] | false | LeoFelix/bert-finetuned-squad This model is a fine-tuned version of [pierreguillou/bert-base-cased-squad-v1.1-portuguese](https://huggingface.co/pierreguillou/bert-base-cased-squad-v1.1-portuguese) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0193 - Epoch: 2 | b8c43b2159dd40f46fdd73ae584fea36 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 852, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 | 9d2105bdaef9db8ceb1b1ae035733f28 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-kinyarwanda This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3917 - Wer: 0.3246 | 17fb7735b017433e856a41406a832927 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 8 - mixed_precision_training: Native AMP | d571ee571e10ba58d985bfca7473cc09 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 9.0634 | 0.12 | 400 | 3.0554 | 1.0 | | 2.8009 | 0.24 | 800 | 1.5927 | 0.9554 | | 0.9022 | 0.36 | 1200 | 0.7328 | 0.6445 | | 0.6213 | 0.48 | 1600 | 0.6138 | 0.5510 | | 0.5299 | 0.6 | 2000 | 0.6072 | 0.5223 | | 0.4999 | 0.72 | 2400 | 0.5449 | 0.4969 | | 0.4731 | 0.84 | 2800 | 0.5261 | 0.4828 | | 0.458 | 0.96 | 3200 | 0.5058 | 0.4607 | | 0.4158 | 1.09 | 3600 | 0.4892 | 0.4463 | | 0.4037 | 1.21 | 4000 | 0.4759 | 0.4429 | | 0.4021 | 1.33 | 4400 | 0.4615 | 0.4330 | | 0.3934 | 1.45 | 4800 | 0.4593 | 0.4315 | | 0.3808 | 1.57 | 5200 | 0.4736 | 0.4344 | | 0.3838 | 1.69 | 5600 | 0.4569 | 0.4249 | | 0.3726 | 1.81 | 6000 | 0.4473 | 0.4140 | | 0.3623 | 1.93 | 6400 | 0.4403 | 0.4097 | | 0.3517 | 2.05 | 6800 | 0.4389 | 0.4061 | | 0.333 | 2.17 | 7200 | 0.4383 | 0.4104 | | 0.3354 | 2.29 | 7600 | 0.4360 | 0.3955 | | 0.3257 | 2.41 | 8000 | 0.4226 | 0.3942 | | 0.3275 | 2.53 | 8400 | 0.4206 | 0.4040 | | 0.3262 | 2.65 | 8800 | 0.4172 | 0.3875 | | 0.3206 | 2.77 | 9200 | 0.4209 | 0.3877 | | 0.323 | 2.89 | 9600 | 0.4177 | 0.3825 | | 0.3099 | 3.01 | 10000 | 0.4101 | 0.3691 | | 0.3008 | 3.14 | 10400 | 0.4055 | 0.3709 | | 0.2918 | 3.26 | 10800 | 0.4085 | 0.3800 | | 0.292 | 3.38 | 11200 | 0.4089 | 0.3713 | | 0.292 | 3.5 | 11600 | 0.4092 | 0.3730 | | 0.2785 | 3.62 | 12000 | 0.4151 | 0.3687 | | 0.2941 | 3.74 | 12400 | 0.4004 | 0.3639 | | 0.2838 | 3.86 | 12800 | 0.4108 | 0.3703 | | 0.2854 | 3.98 | 13200 | 0.3911 | 0.3596 | | 0.2683 | 4.1 | 13600 | 0.3944 | 0.3575 | | 0.2647 | 4.22 | 14000 | 0.3836 | 0.3538 | | 0.2704 | 4.34 | 14400 | 0.4006 | 0.3540 | | 0.2664 | 4.46 | 14800 | 0.3974 | 0.3553 | | 0.2662 | 4.58 | 15200 | 0.3890 | 0.3470 | | 0.2615 | 4.7 | 15600 | 0.3856 | 0.3507 | | 0.2553 | 4.82 | 16000 | 0.3814 | 0.3497 | | 0.2587 | 4.94 | 16400 | 0.3837 | 0.3440 | | 0.2522 | 5.06 | 16800 | 0.3834 | 0.3486 | | 0.2451 | 5.19 | 17200 | 0.3897 | 0.3414 | | 0.2423 | 5.31 | 17600 | 0.3864 | 0.3481 | | 0.2434 | 5.43 | 18000 | 0.3808 | 0.3416 | | 0.2525 | 5.55 | 18400 | 0.3795 | 0.3408 | | 0.2427 | 5.67 | 18800 | 0.3841 | 0.3411 | | 0.2411 | 5.79 | 19200 | 0.3804 | 0.3366 | | 0.2404 | 5.91 | 19600 | 0.3800 | 0.3328 | | 0.2372 | 6.03 | 20000 | 0.3749 | 0.3335 | | 0.2244 | 6.15 | 20400 | 0.3820 | 0.3327 | | 0.2381 | 6.27 | 20800 | 0.3789 | 0.3325 | | 0.2294 | 6.39 | 21200 | 0.3867 | 0.3298 | | 0.2378 | 6.51 | 21600 | 0.3843 | 0.3281 | | 0.2312 | 6.63 | 22000 | 0.3813 | 0.3277 | | 0.2411 | 6.75 | 22400 | 0.3780 | 0.3268 | | 0.2315 | 6.87 | 22800 | 0.3790 | 0.3280 | | 0.241 | 6.99 | 23200 | 0.3776 | 0.3281 | | 0.2313 | 7.11 | 23600 | 0.3929 | 0.3283 | | 0.2423 | 7.24 | 24000 | 0.3905 | 0.3280 | | 0.2337 | 7.36 | 24400 | 0.3979 | 0.3249 | | 0.2368 | 7.48 | 24800 | 0.3980 | 0.3257 | | 0.2409 | 7.6 | 25200 | 0.3937 | 0.3229 | | 0.2416 | 7.72 | 25600 | 0.3867 | 0.3237 | | 0.2364 | 7.84 | 26000 | 0.3912 | 0.3253 | | 0.234 | 7.96 | 26400 | 0.3917 | 0.3246 | | 0d2bf19e47706f9c76c66700f3b4c5f5 |
mit | [] | false | Model description This SLED model is based on the BART model, which is described in its [model card](https://huggingface.co/facebook/bart-base). BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). When used as a BART-SLED model, it can be applied on long text tasks. This model was finetuned on the [GovReport](https://arxiv.org/abs/2104.02112) | ad927b026c1eeb720200e0001201c2b8 |
mit | [] | false | BibTeX entry and citation info Please cite both the SLED [paper](https://arxiv.org/abs/2208.00748.pdf) and the BART [paper](https://arxiv.org/abs/1910.13461) by Lewis et al as well as GovReport by Huang et al ```bibtex @inproceedings{Ivgi2022EfficientLU, title={Efficient Long-Text Understanding with Short-Text Models}, author={Maor Ivgi and Uri Shaham and Jonathan Berant}, year={2022} } ``` ```bibtex @article{DBLP:journals/corr/abs-1910-13461, author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov and Luke Zettlemoyer}, title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension}, journal = {CoRR}, volume = {abs/1910.13461}, year = {2019}, url = {http://arxiv.org/abs/1910.13461}, eprinttype = {arXiv}, eprint = {1910.13461}, timestamp = {Thu, 31 Oct 2019 14:02:26 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ```bibtex @inproceedings{huang2021govreport, title = "Efficient Attentions for Long Document Summarization", author = "Huang, Luyang and Cao, Shuyang and Parulian, Nikolaus and Ji, Heng and Wang, Lu", booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.naacl-main.112", doi = "10.18653/v1/2021.naacl-main.112", pages = "1419--1436" } ``` | 855cef2d434896ca6f02521f2096b532 |
other | ['generated_from_trainer'] | false | opt-125m-finetuned-wikitext2 This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3409 | 8cd13772cb220576c103c94bb8092952 |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.4123 | 1.0 | 2370 | 3.3621 | | 3.2096 | 2.0 | 4740 | 3.3452 | | 3.0822 | 3.0 | 7110 | 3.3409 | | bd7a3bbbe31e9e251fcce638bcc86a85 |
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: 0.0594 - Precision: 0.9331 - Recall: 0.9529 - F1: 0.9429 - Accuracy: 0.9872 | 399b1ed0f7bac7247ee19966162b86e4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0872 | 1.0 | 1756 | 0.0631 | 0.9128 | 0.9359 | 0.9242 | 0.9827 | | 0.0338 | 2.0 | 3512 | 0.0578 | 0.9322 | 0.9510 | 0.9415 | 0.9867 | | 0.0174 | 3.0 | 5268 | 0.0594 | 0.9331 | 0.9529 | 0.9429 | 0.9872 | | 1d0ce2eca033c77f651bc39ff675caa6 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-squad This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1433 | 998ad2ec60b0cc2c1e9d748ba4b3eb22 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.4107 | 1.0 | 3693 | 2.2321 | | 2.1359 | 2.0 | 7386 | 2.1499 | | 1.9214 | 3.0 | 11079 | 2.1433 | | 5cbc527912643e373c2dabd740323bac |
apache-2.0 | ['generated_from_keras_callback'] | false | pedramyamini/distilbert-base-multilingual-cased-finetuned-mobile-banks-cafebazaar2022-09-12-08-14-58 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4986 - Validation Loss: 0.7589 - Epoch: 7 | 806aaa4196b4fb210e03abbc286e23db |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 21392, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | b9220bd777dafdff3835d924595e0151 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.7544 | 0.7034 | 0 | | 0.6815 | 0.6905 | 1 | | 0.6463 | 0.6960 | 2 | | 0.6135 | 0.6896 | 3 | | 0.5764 | 0.7041 | 4 | | 0.5447 | 0.7340 | 5 | | 0.5170 | 0.7562 | 6 | | 0.4986 | 0.7589 | 7 | | 9bde80bfa9288cfbfa0b827b754745bd |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab1 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7170 - Wer: 0.4784 | fb3c6a4e23ae0719ebd6f5839a950f31 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 60 - mixed_precision_training: Native AMP | a123a6abe3046dfe1951568eb9bbb208 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.1915 | 13.89 | 500 | 3.1318 | 1.0 | | 1.4993 | 27.78 | 1000 | 0.6736 | 0.5485 | | 0.3416 | 41.67 | 1500 | 0.7111 | 0.5092 | | 0.1937 | 55.56 | 2000 | 0.7170 | 0.4784 | | 720821d241ff5f94e5a1c8dd8ffa003a |
openrail | ['generated_from_trainer', 'bash', 'shell', 'code', 'codegen'] | false | SantaCoder 🎅 fine-tuned on bash/shell 🐚 scripts This model is a fine-tuned version of [BigCode/SantaCoder](https://huggingface.co/bigcode/santacoder) on The Stack [bash/shell scripts](https://huggingface.co/datasets/bigcode/the-stack-dedup). It achieves the following results on the evaluation set: - Loss: 1.2272 | fc3722d0563197744234a246d580822c |
openrail | ['generated_from_trainer', 'bash', 'shell', 'code', 'codegen'] | false | Model description The [SantaCoder](https://huggingface.co/bigcode/santacoder) models are a series of 1.1B parameter models trained on the Python, Java, and JavaScript subset of [The Stack (v1.1)](https://huggingface.co/datasets/bigcode/the-stack) (which excluded opt-out requests). The main model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), was trained using near-deduplication and comment-to-code ratio as filtering criteria and using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255). In addition, there are several models that were trained on datasets with different filter parameters and with architecture and objective variations. | 278ad3c4ec5917172028cbff809d747d |
openrail | ['generated_from_trainer', 'bash', 'shell', 'code', 'codegen'] | false | Intended uses & limitations The model has been trained on source code in Python, Java, and JavaScript and fine-tuned on bash/shell scripts. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. | 6afda4dca092ca933a59c8a7783c2afc |
openrail | ['generated_from_trainer', 'bash', 'shell', 'code', 'codegen'] | false | Training and evaluation data The Stack contains over 6TB of permissively-licensed source code files covering 358 programming languages. The dataset was created as part of the [BigCode Project](https://www.bigcode-project.org/), an open scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs). The Stack serves as a pre-training dataset for Code LLMs, i.e., code-generating AI systems which enable the synthesis of programs from natural language descriptions as well as other from code snippets. **This is the near-deduplicated version with 3TB data.** | 3fda5b8428996a762aadf8f028349dd0 |
openrail | ['generated_from_trainer', 'bash', 'shell', 'code', 'codegen'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 10000 | 0a77cdb0e189c2f7270e5e85b2875040 |
openrail | ['generated_from_trainer', 'bash', 'shell', 'code', 'codegen'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.6101 | 0.05 | 500 | 1.5078 | | 1.6156 | 0.1 | 1000 | 1.4687 | | 1.4916 | 0.15 | 1500 | 1.4728 | | 1.4027 | 0.2 | 2000 | 1.4237 | | 1.499 | 0.25 | 2500 | 1.4067 | | 1.4378 | 0.3 | 3000 | 1.3838 | | 1.3698 | 0.35 | 3500 | 1.3767 | | 1.3021 | 0.4 | 4000 | 1.3562 | | 4.0521 | 0.45 | 4500 | 1.3433 | | 0.9722 | 0.5 | 5000 | 1.3461 | | 1.3836 | 0.55 | 5500 | 1.2955 | | 1.3727 | 0.6 | 6000 | 1.2809 | | 1.3332 | 0.65 | 6500 | 1.2665 | | 1.2232 | 0.7 | 7000 | 1.2573 | | 1.2373 | 0.75 | 7500 | 1.2463 | | 1.3759 | 0.8 | 8000 | 1.2391 | | 1.3021 | 0.85 | 8500 | 1.2325 | | 1.369 | 0.9 | 9000 | 1.2292 | | 1.4911 | 0.95 | 9500 | 1.2275 | | 1.1677 | 1.0 | 10000 | 1.2272 | | 4065a361e20b8e0eaf00b71e9db8b3c4 |
openrail | ['generated_from_trainer', 'bash', 'shell', 'code', 'codegen'] | false | Citation ``` @misc {manuel_romero_2023, author = { {Manuel Romero} }, title = { santacoder-finetuned-the-stack-bash-shell (Revision d3e56a7) }, year = 2023, url = { https://huggingface.co/mrm8488/santacoder-finetuned-the-stack-bash-shell }, doi = { 10.57967/hf/0320 }, publisher = { Hugging Face } } ``` | 1e4efe8ab7d71b1c11e4e62680ad620c |
apache-2.0 | ['automatic-speech-recognition', 'timit_asr', 'generated_from_trainer'] | false | sew-d-small-100k-timit This model is a fine-tuned version of [asapp/sew-d-small-100k](https://huggingface.co/asapp/sew-d-small-100k) on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set: - Loss: 1.7541 - Wer: 0.8061 | 8db6730df1bc2358268230065a9f1a70 |
apache-2.0 | ['automatic-speech-recognition', 'timit_asr', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.2068 | 0.69 | 100 | 4.0802 | 1.0 | | 2.9805 | 1.38 | 200 | 2.9792 | 1.0 | | 2.9781 | 2.07 | 300 | 2.9408 | 1.0 | | 2.9655 | 2.76 | 400 | 2.9143 | 1.0 | | 2.8953 | 3.45 | 500 | 2.8775 | 1.0 | | 2.7718 | 4.14 | 600 | 2.7787 | 1.0 | | 2.6711 | 4.83 | 700 | 2.6401 | 0.9786 | | 2.6403 | 5.52 | 800 | 2.5435 | 1.0392 | | 2.4052 | 6.21 | 900 | 2.4580 | 1.0706 | | 2.1708 | 6.9 | 1000 | 2.2800 | 1.0090 | | 2.2555 | 7.59 | 1100 | 2.1493 | 0.9579 | | 2.3673 | 8.28 | 1200 | 2.0709 | 0.9051 | | 2.091 | 8.97 | 1300 | 2.0258 | 0.8926 | | 1.8433 | 9.66 | 1400 | 1.9645 | 0.8243 | | 1.6824 | 10.34 | 1500 | 1.9211 | 0.8707 | | 2.2282 | 11.03 | 1600 | 1.8914 | 0.8695 | | 1.9027 | 11.72 | 1700 | 1.8718 | 0.8343 | | 1.6303 | 12.41 | 1800 | 1.8646 | 0.8232 | | 1.648 | 13.1 | 1900 | 1.8297 | 0.8177 | | 2.0429 | 13.79 | 2000 | 1.8127 | 0.8642 | | 1.8833 | 14.48 | 2100 | 1.8005 | 0.8307 | | 1.5996 | 15.17 | 2200 | 1.7926 | 0.8467 | | 1.4876 | 15.86 | 2300 | 1.7795 | 0.8341 | | 1.8925 | 16.55 | 2400 | 1.7716 | 0.8199 | | 1.814 | 17.24 | 2500 | 1.7846 | 0.8086 | | 1.536 | 17.93 | 2600 | 1.7655 | 0.8019 | | 1.4476 | 18.62 | 2700 | 1.7599 | 0.8070 | | 1.7629 | 19.31 | 2800 | 1.7589 | 0.8119 | | 1.7646 | 20.0 | 2900 | 1.7541 | 0.8061 | | 0dc5e66eb95fee4ef75e80a8d8caab14 |
mit | ['text', 'Twitter'] | false | distilbert-depression-mixed This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) trained on CLPsych 2015 and a scraped dataset, and evaluated on a scraped dataset from Twitter to detect potential users in Twitter for depression. It achieves the following results on the evaluation set: - Evaluation Loss: 0.71 - Accuracy: 0.63 - F1: 0.59 - Precision: 0.66 - Recall: 0.53 - AUC: 0.63 | 469806c6134d73bd3a51f65a351a9749 |
mit | ['text', 'Twitter'] | false | How to use You can use this model directly with a pipeline for sentiment analysis: ```python >>> from transformers import DistilBertTokenizerFast, AutoTokenizer >>> tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased') >>> from transformers import DistilBertForSequenceClassification >>> model = DistilBertForSequenceClassification.from_pretrained(r"distilbert-depression-mixed") >>> from transformers import pipeline >>> classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) >>> tokenizer_kwargs = {'padding':True,'truncation':True,'max_length':512} >>> result=classifier('pain peko',**tokenizer_kwargs) | 1e44d1a5805745c26918674cfbf3c180 |
mit | ['text', 'Twitter'] | false | Should note that the string passed as the input can be a corpus of tweets concatenated together into one document. [{'label': 'LABEL_1', 'score': 0.5048992037773132}] ``` Otherwise, download the files and specify within the pipeline the path to the folder that contains the config.json, pytorch_model.bin, and training_args.bin | 98428a64be1f73160c8abbc528a66283 |
mit | ['text', 'Twitter'] | false | Training results | Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall | AUC | |:-----:|:-------------:|:---------------:|:--------:|:--------:|:---------:|:--------:|:--------:| | 1.0 | 0.68 | 0.66 | 0.61 | 0.54 | 0.60 | 0.50 | 0.60 | | 2.0 | 0.65 | 0.65 | 0.63 | 0.49 | 0.70 | 0.37 | 0.62 | | 3.0 | 0.53 | 0.63 | 0.66 | 0.58 | 0.69 | 0.50 | 0.65 | | 4.0 | 0.39 | 0.66 | 0.67 | 0.61 | 0.69 | 0.54 | 0.67 | | 5.0 | 0.27 | 0.72 | 0.65 | 0.61 | 0.63 | 0.60 | 0.64 | | fb5b069f9369ce136b5af646e0667d85 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Romansh-Sursilvan Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Romansh Sursilvan using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | 7f286761f95f9449a3e0e5e5e1a10a45 |
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 torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "rm-sursilv", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") resampler = torchaudio.transforms.Resample(48_000, 16_000) | 538885ea723d5024b4015b38897c1d6a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "rm-sursilv", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") model.to("cuda") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\…\\«\\»\\–]' resampler = torchaudio.transforms.Resample(48_000, 16_000) | fd87971f003daf4a8f492c92703abd79 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 25.16 % | 581983e7cc9a4660de815bdf14120354 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train` and `validation` datasets were used for training. The code can be found [here](https://colab.research.google.com/drive/1dpZr_GzRowCciUbzM3GnW04TNKnB7vrP?usp=sharing). | 7ee348bf7ba8a3fd7b373fbeed823231 |
apache-2.0 | [] | false | Example Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-small-smiles2caption", model_max_length=512) model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-small-smiles2caption') input_text = 'C1=CC2=C(C(=C1)[O-])NC(=CC2=O)C(=O)O' input_ids = tokenizer(input_text, return_tensors="pt").input_ids outputs = model.generate(input_ids, num_beams=5, max_length=512) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` | 89262a11feb0c6589c60dfaef1c84b92 |
apache-2.0 | ['generated_from_keras_callback'] | false | piyusharma/bert-base-uncased-finetuned-lex This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2112 - Epoch: 0 | f5202b366271b732d94d8974c3b919a7 |
mit | ['generated_from_keras_callback'] | false | recklessrecursion/Heresy-clustered This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1793 - Train End Logits Accuracy: 0.9618 - Train Start Logits Accuracy: 0.9549 - Validation Loss: 0.7725 - Validation End Logits Accuracy: 0.6667 - Validation Start Logits Accuracy: 0.3333 - Epoch: 0 | c3c95f275383f41743c329dec59da0ee |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.1793 | 0.9618 | 0.9549 | 0.7725 | 0.6667 | 0.3333 | 0 | | bbb1c18e2390cb0c1bed3fa01122c127 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_qnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6648 - Accuracy: 0.6066 | 1167a8b701cd7289eed8daf892962389 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6886 | 1.0 | 410 | 0.6648 | 0.6066 | | 0.6569 | 2.0 | 820 | 0.6677 | 0.5999 | | 0.6419 | 3.0 | 1230 | 0.6672 | 0.5914 | | 0.6293 | 4.0 | 1640 | 0.6677 | 0.5977 | | 0.6118 | 5.0 | 2050 | 0.6691 | 0.6002 | | 0.5857 | 6.0 | 2460 | 0.6854 | 0.6077 | | 4606c53e7560bfd512ebbf619876bd55 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | DreamBooth model for the Berlinberger concept trained by veereshd on the veereshd/Dreambooth_food_dataset dataset. This is a Stable Diffusion model fine-tuned on the Berlinberger concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of Berlinberger berger** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! | 11f8b40d9b26cb942c6b13f718e2dc7d |
mit | ['generated_from_trainer'] | false | confident_knuth This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. | a7e3d40751e0563d2e0f89754026e27b |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25177], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'value_head_config': {'is_detached': False}}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 0.5, 'beta': 0.1, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'confident_knuth', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | a76379a4c2c3a4d65a9625a50b7e1f21 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2240 - Accuracy: 0.9265 - F1: 0.9265 | a11f0cb718e9eea19b4eb47ec36205c5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8488 | 1.0 | 250 | 0.3268 | 0.9055 | 0.9031 | | 0.2532 | 2.0 | 500 | 0.2240 | 0.9265 | 0.9265 | | 405ffe4406a9c7dea348d7ffdc87f8a9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2161 - Accuracy: 0.9225 - F1: 0.9226 | 59e3ffe218782dd17190a24a8865eced |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8009 | 1.0 | 250 | 0.3027 | 0.9045 | 0.9015 | | 0.2402 | 2.0 | 500 | 0.2161 | 0.9225 | 0.9226 | | 15baacad642e01bfe4f2baeef46c293a |
apache-2.0 | ['generated_from_trainer'] | false | openai/whisper-base This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1929 - Wer: 4.3549 | 20308a522dabf04df57b87217e142ae0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0326 | 10.0 | 500 | 0.1670 | 5.0398 | | 0.0019 | 20.0 | 1000 | 0.1728 | 4.5113 | | 0.0008 | 30.01 | 1500 | 0.1820 | 4.4071 | | 0.0005 | 40.01 | 2000 | 0.1847 | 4.3773 | | 0.0004 | 51.0 | 2500 | 0.1886 | 4.3549 | | 0.0003 | 61.0 | 3000 | 0.1910 | 4.3475 | | 0.0003 | 71.01 | 3500 | 0.1925 | 4.3549 | | 0.0002 | 81.01 | 4000 | 0.1929 | 4.3549 | | 9bb1ffff5c98b4d71d086bac19089106 |
apache-2.0 | ['automatic-speech-recognition', 'de', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Wav2Vec2-Large-XLSR-53-German-GPT2 This is an encoder-decoder model for automatic speech recognition trained on on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - DE dataset. The encoder was initialized from [jonatasgrosman/wav2vec2-large-xlsr-53-german](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-german) and the decoder from [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2). It was trained using a two step process: * fine-tuning only the cross-attention weights and the decoder using the pre-computed outputs of the Wav2Vec-Modell * relatively fast training * also works on small GPU (eg. 8 GB) * but may take a lot of disk space * should already yield decent results * fine-tuning the model end-to-end * much slower * needs a bigger GPU There is also one trick, which seemed to improve performance significantly: adding position embeddings to the encoder outputs and initializing them with the pre-trained position embeddings of the GPT2 model (See `eval.py`). The training notebooks are still early drafts. Also results can probably improved a lot by using for example a learning rate schedule. | 923cfae3aa6a594b76443080ad35b18a |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53k-russian This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.2660 - Wer: 0.2052 | b3189c181a0d276cffa3107c796b9e7e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 96 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 192 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP | 3f5f2b2cfdedef652e8cf5bbc329e092 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.2873 | 1.09 | 400 | 0.8580 | 0.8982 | | 0.4728 | 2.19 | 800 | 0.3182 | 0.3892 | | 0.1639 | 9.83 | 1200 | 0.2374 | 0.2646 | | 0.1014 | 13.11 | 1600 | 0.2470 | 0.2467 | | 0.0754 | 16.39 | 2000 | 0.2516 | 0.2337 | | 0.0616 | 19.67 | 2400 | 0.2559 | 0.2237 | | 0.0505 | 22.95 | 2800 | 0.2557 | 0.2155 | | 0.0437 | 26.23 | 3200 | 0.2711 | 0.2099 | | 0.0377 | 29.51 | 3600 | 0.2660 | 0.2052 | | 08a581ce0c00b291717ce63498aafa6e |
apache-2.0 | ['translation'] | false | opus-mt-fi-ZH * source languages: fi * target languages: cmn,cn,yue,ze_zh,zh_cn,zh_CN,zh_HK,zh_tw,zh_TW,zh_yue,zhs,zht,zh * OPUS readme: [fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh/opus-2020-01-16.eval.txt) | 530b037808df4b799844d7ba9aba5076 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-0'] | false | MultiBERTs Seed 0 Checkpoint 1000k (uncased) Seed 0 intermediate checkpoint 1000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-0](https://hf.co/multberts-seed-0). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). | 9f08e31f7074d3e6c13355fd42c74d32 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-0'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-0-1000k') model = BertModel.from_pretrained("multiberts-seed-0-1000k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | bda382aff8269cb2d947a733aee681dc |
mit | [] | false | Uncertainty types label | type | description | example ---| ---| ---| --- E | Epistemic | The proposition is possible, but its truth-value cannot be decided at the moment. | She **may** be already asleep. I | Investigation | The proposition is in the process of having its truth-value determined. | She **examined** the role of NF-kappaB in protein activation. D | Doxatic | The proposition expresses beliefs and hypotheses, which may be known as true or false by others. | She **believes** that the Earth is flat. N | Condition | The proposition is true or false based on the truth-value of another proposition. | **If** she gets the job, she will move to Utrecht. C | *certain* | *n/a* | *n/a* | e7fbf98d2e8c9f9836b4689456470e6d |
mit | [] | false | Intended uses and limitations - The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. | 1778b7856a3402a04c5f115458456b42 |
mit | [] | false | How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.ner import NERModel model = NERModel( 'bert', 'jeniakim/hedgehog', use_cuda=False, labels=["C", "D", "E", "I", "N"], ) example = "As much as I definitely enjoy solitude, I wouldn't mind perhaps spending little time with you (Björk)" predictions, raw_outputs = model.predict([example]) ``` The predictions look like this: ``` [[{'As': 'C'}, {'much': 'C'}, {'as': 'C'}, {'I': 'C'}, {'definitely': 'C'}, {'enjoy': 'C'}, {'solitude,': 'C'}, {'I': 'C'}, {"wouldn't": 'C'}, {'mind': 'C'}, {'perhaps': 'E'}, {'spending': 'C'}, {'little': 'C'}, {'time': 'C'}, {'with': 'C'}, {'you': 'C'}, {'(Björk)': 'C'}]] ``` In other words, the token 'perhaps' is recognized as an **epistemic uncertainty cue** and all the other tokens are not uncertainty cues. | ae8f1af950fa750dcc20ad8025939e1f |
mit | [] | false | Training Data HEDGEhog is trained and evaluated on the [Szeged Uncertainty Corpus](https://rgai.inf.u-szeged.hu/node/160) (Szarvas et al. 2012<sup>1</sup>). The original sentence-level XML version of this dataset is available [here](https://rgai.inf.u-szeged.hu/node/160). The token-level version that was used for the training can be downloaded from [here](https://1drv.ms/u/s!AvPkt_QxBozXk7BiazucDqZkVxLo6g?e=IisuM6) in a form of pickled pandas DataFrame's. You can download either the split sets (```train.pkl``` 137MB, ```test.pkl``` 17MB, ```dev.pkl``` 17MB) or the full dataset (```szeged_fixed.pkl``` 172MB). Each row in the df contains a token, its features (these are not relevant for HEDGEhog; they were used to train the baseline CRF model, see [here](https://github.com/vanboefer/uncertainty_crf)), its sentence ID, and its label. | b006a414ac09655590f6b68cb0b9a27c |
mit | [] | false | Evaluation Results class | precision | recall | F1-score | support ---|---|---|---|--- Epistemic | 0.90 | 0.85 | 0.88 | 624 Doxatic | 0.88 | 0.92 | 0.90 | 142 Investigation | 0.83 | 0.86 | 0.84 | 111 Condition | 0.85 | 0.87 | 0.86 | 86 Certain | 1.00 | 1.00 | 1.00 | 104,751 **macro average** | **0.89** | **0.90** | **0.89** | 105,714 | eeee2e668cdbc6103d315185ab147132 |
mit | [] | false | References <sup>1</sup> Szarvas, G., Vincze, V., Farkas, R., Móra, G., & Gurevych, I. (2012). Cross-genre and cross-domain detection of semantic uncertainty. *Computational Linguistics, 38*(2), 335-367. | 3dca0ed6ce453006129868704f7bcc5f |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'nl', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset. This model is also available with a language model which improves these results. This model can be found at https://huggingface.co/RuudVelo/wav2vec2-large-xls-r-1b-nl-lm. The Common Voice 8 Dutch test Wer is 9.73 of that model. It achieves the following results on the evaluation set: - Loss: 0.1479 - Wer: 0.1156 | f81381209d4de1ecbb43f7291ea1e601 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'nl', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.2223 | 0.52 | 500 | 0.3866 | 0.3425 | | 1.0748 | 1.03 | 1000 | 0.2574 | 0.2169 | | 1.0416 | 1.55 | 1500 | 0.2177 | 0.1946 | | 0.9951 | 2.06 | 2000 | 0.2008 | 0.1760 | | 0.975 | 2.58 | 2500 | 0.1961 | 0.1751 | | 0.9461 | 3.1 | 3000 | 0.1989 | 0.1782 | | 0.9381 | 3.61 | 3500 | 0.1928 | 0.1699 | | 0.934 | 4.13 | 4000 | 0.1923 | 0.1633 | | 0.9322 | 4.64 | 4500 | 0.1871 | 0.1634 | | 0.9012 | 5.16 | 5000 | 0.1890 | 0.1702 | | 0.9045 | 5.68 | 5500 | 0.1882 | 0.1740 | | 0.8826 | 6.19 | 6000 | 0.1856 | 0.1575 | | 0.8848 | 6.71 | 6500 | 0.1861 | 0.1617 | | 0.8723 | 7.22 | 7000 | 0.1927 | 0.1646 | | 0.8725 | 7.74 | 7500 | 0.1798 | 0.1531 | | 0.8573 | 8.26 | 8000 | 0.1781 | 0.1587 | | 0.8633 | 8.77 | 8500 | 0.1852 | 0.1628 | | 0.8603 | 9.29 | 9000 | 0.1833 | 0.1601 | | 0.8421 | 9.8 | 9500 | 0.1788 | 0.1543 | | 0.8404 | 10.32 | 10000 | 0.1844 | 0.1556 | | 0.8342 | 10.84 | 10500 | 0.1770 | 0.1538 | | 0.8161 | 11.35 | 11000 | 0.1821 | 0.1567 | | 0.8371 | 11.87 | 11500 | 0.1909 | 0.1629 | | 0.8083 | 12.38 | 12000 | 0.1778 | 0.1498 | | 0.806 | 12.9 | 12500 | 0.1802 | 0.1547 | | 0.8013 | 13.42 | 13000 | 0.1859 | 0.1584 | | 0.7913 | 13.93 | 13500 | 0.1875 | 0.1517 | | 0.8063 | 14.45 | 14000 | 0.1799 | 0.1571 | | 0.7991 | 14.96 | 14500 | 0.1792 | 0.1538 | | 0.7843 | 15.48 | 15000 | 0.1753 | 0.1464 | | 0.7905 | 16.0 | 15500 | 0.1784 | 0.1508 | | 0.7808 | 16.51 | 16000 | 0.1771 | 0.1485 | | 0.7743 | 17.03 | 16500 | 0.1795 | 0.1491 | | 0.7833 | 17.54 | 17000 | 0.1722 | 0.1484 | | 0.7763 | 18.06 | 17500 | 0.1767 | 0.1518 | | 0.7698 | 18.58 | 18000 | 0.1720 | 0.1460 | | 0.7571 | 19.09 | 18500 | 0.1735 | 0.1478 | | 0.7673 | 19.61 | 19000 | 0.1817 | 0.1511 | | 0.7415 | 20.12 | 19500 | 0.1763 | 0.1481 | | 0.751 | 20.64 | 20000 | 0.1742 | 0.1484 | | 0.7563 | 21.16 | 20500 | 0.1810 | 0.1611 | | 0.7423 | 21.67 | 21000 | 0.1817 | 0.1557 | | 0.7242 | 22.19 | 21500 | 0.1690 | 0.1446 | | 0.7251 | 22.7 | 22000 | 0.1684 | 0.1446 | | 0.7302 | 23.22 | 22500 | 0.1735 | 0.1430 | | 0.733 | 23.74 | 23000 | 0.1720 | 0.1454 | | 0.7128 | 24.25 | 23500 | 0.1668 | 0.1383 | | 0.7184 | 24.77 | 24000 | 0.1635 | 0.1377 | | 0.7015 | 25.28 | 24500 | 0.1646 | 0.1389 | | 0.7198 | 25.8 | 25000 | 0.1775 | 0.1462 | | 0.7178 | 26.32 | 25500 | 0.1705 | 0.1419 | | 0.7199 | 26.83 | 26000 | 0.1649 | 0.1416 | | 0.6981 | 27.35 | 26500 | 0.1724 | 0.1418 | | 0.6886 | 27.86 | 27000 | 0.1633 | 0.1382 | | 0.6922 | 28.38 | 27500 | 0.1698 | 0.1420 | | 0.6833 | 28.9 | 28000 | 0.1611 | 0.1351 | | 0.6798 | 29.41 | 28500 | 0.1639 | 0.1365 | | 0.6711 | 29.93 | 29000 | 0.1668 | 0.1358 | | 0.6762 | 30.44 | 29500 | 0.1682 | 0.1355 | | 0.6594 | 30.96 | 30000 | 0.1629 | 0.1345 | | 0.6664 | 31.48 | 30500 | 0.1625 | 0.1321 | | 0.6838 | 31.99 | 31000 | 0.1597 | 0.1372 | | 0.6603 | 32.51 | 31500 | 0.1583 | 0.1302 | | 0.6468 | 33.02 | 32000 | 0.1595 | 0.1322 | | 0.6464 | 33.54 | 32500 | 0.1609 | 0.1315 | | 0.6623 | 34.06 | 33000 | 0.1622 | 0.1366 | | 0.6414 | 34.57 | 33500 | 0.1587 | 0.1330 | | 0.6242 | 35.09 | 34000 | 0.1614 | 0.1337 | | 0.632 | 35.6 | 34500 | 0.1568 | 0.1272 | | 0.6346 | 36.12 | 35000 | 0.1583 | 0.1274 | | 0.6143 | 36.64 | 35500 | 0.1576 | 0.1264 | | 0.6208 | 37.15 | 36000 | 0.1621 | 0.1263 | | 0.6185 | 37.67 | 36500 | 0.1623 | 0.1270 | | 0.6128 | 38.18 | 37000 | 0.1604 | 0.1268 | | 0.6151 | 38.7 | 37500 | 0.1593 | 0.1246 | | 0.6082 | 39.22 | 38000 | 0.1532 | 0.1238 | | 0.6 | 39.73 | 38500 | 0.1524 | 0.1224 | | 0.6032 | 40.25 | 39000 | 0.1521 | 0.1212 | | 0.6016 | 40.76 | 39500 | 0.1551 | 0.1215 | | 0.6009 | 41.28 | 40000 | 0.1523 | 0.1215 | | 0.5875 | 41.8 | 40500 | 0.1541 | 0.1216 | | 0.608 | 42.31 | 41000 | 0.1536 | 0.1209 | | 0.5876 | 42.83 | 41500 | 0.1567 | 0.1211 | | 0.5714 | 43.34 | 42000 | 0.1532 | 0.1217 | | 0.5756 | 43.86 | 42500 | 0.1516 | 0.1196 | | 0.5719 | 44.38 | 43000 | 0.1491 | 0.1191 | | 0.5829 | 44.89 | 43500 | 0.1497 | 0.1193 | | 0.5664 | 45.41 | 44000 | 0.1487 | 0.1173 | | 0.5707 | 45.92 | 44500 | 0.1470 | 0.1164 | | 0.5696 | 46.44 | 45000 | 0.1479 | 0.1161 | | 0.5767 | 46.96 | 45500 | 0.1492 | 0.1175 | | 0.5573 | 47.47 | 46000 | 0.1471 | 0.1165 | | 0.5625 | 47.99 | 46500 | 0.1484 | 0.1168 | | 0.5671 | 48.5 | 47000 | 0.1474 | 0.1162 | | 0.5484 | 49.02 | 47500 | 0.1479 | 0.1158 | | 0.555 | 49.54 | 48000 | 0.1477 | 0.1157 | | f136de17698ad43949e6c279f8c933a5 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_accent_us-10_england-0_s253 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 that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | b3ba3f574e1925e365d96bd430884962 |
mit | ['summarization', 'translation', 'question-answering'] | false | How to use For more details, do check out [our Github repo](https://github.com/vietai/mtet). [Finetunning examples can be found here](https://github.com/vietai/ViT5/tree/main/finetunning_huggingface). ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("VietAI/envit5-base") model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/envit5-base") model.cuda() | 1eabf0987934b9abead11b566c06d0a8 |
mit | ['summarization', 'translation', 'question-answering'] | false | need prefix for en: and vi: sentences inputs = [ "vi: VietAI là tổ chức phi lợi nhuận với sứ mệnh ươm mầm tài năng về trí tuệ nhân tạo và xây dựng một cộng đồng các chuyên gia trong lĩnh vực trí tuệ nhân tạo đẳng cấp quốc tế tại Việt Nam.", "vi: Theo báo cáo mới nhất của Linkedin về danh sách việc làm triển vọng với mức lương hấp dẫn năm 2020, các chức danh công việc liên quan đến AI như Chuyên gia AI (Artificial Intelligence Specialist), Kỹ sư ML (Machine Learning Engineer) đều xếp thứ hạng cao.", "en: Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.", "en: We're on a journey to advance and democratize artificial intelligence through open source and open science." ] outputs = model.generate(tokenizer(inputs, return_tensors="pt", padding=True).input_ids.to('cuda'), max_length=512) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) ``` | 3bb59fe6ef8e7f35be85143b369a17bc |
mit | ['summarization', 'translation', 'question-answering'] | false | Citation ``` @misc{mtet, doi = {10.48550/ARXIV.2210.05610}, url = {https://arxiv.org/abs/2210.05610}, author = {Ngo, Chinh and Trinh, Trieu H. and Phan, Long and Tran, Hieu and Dang, Tai and Nguyen, Hieu and Nguyen, Minh and Luong, Minh-Thang}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {MTet: Multi-domain Translation for English and Vietnamese}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` | 260dd7a09368a8c272e58133418bec81 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'furry', 'anything-v3.0'] | false |  FurryDiffusion is a model made to generate furry art, this model is very much in beta still and will keep improoving! To use this please make sure to include `furry` in your prompt and to make a specific breed add the breed name only. Example Prompts: ``` Positive: highres, furry, fox, orange fur, blue eyes Negative: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, blurry ``` Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can 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) **NOTE**: Its better to run it in Google Colab since you can use google's powerful gpu's for free. Go ahead try it now! | 64ca91160f64ff363bd9bcc53c77b3c4 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-pubmed1o3 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the scientific_papers dataset. It achieves the following results on the evaluation set: - Loss: 1.9359 - Rouge1: 36.7566 - Rouge2: 14.813 - Rougel: 22.4693 - Rougelsum: 33.4325 - Gen Len: 138.7332 | 1a32e7d379d2a1347b977869585820bb |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:| | 2.028 | 1.0 | 19988 | 1.9359 | 36.7566 | 14.813 | 22.4693 | 33.4325 | 138.7332 | | 644d2fc9631ef2fd1b9541c98f5b96c8 |
apache-2.0 | ['generated_from_trainer'] | false | NLP2122_FranciosoDonato 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: 0.8885 | 5c8c922ec5ff36038ebf965e0aaaa5b8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 13 | 8.1476 | | No log | 2.0 | 26 | 7.4435 | | No log | 3.0 | 39 | 7.2082 | | 542a007d5640c51921f36a2d68a9227e |
apache-2.0 | ['automatic-speech-recognition', 'ja'] | false | exp_w2v2t_ja_vp-100k_s219 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ja)](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. | 4e3915ff19ab919bdb3bea3f3f33afb4 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-ta 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: 0.2183 - F1: 0.8145 | cbec4adeb10e46abd6ac371a13556fc4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5477 | 1.0 | 209 | 0.2732 | 0.7305 | | 0.2506 | 2.0 | 418 | 0.2425 | 0.7626 | | 0.168 | 3.0 | 627 | 0.2183 | 0.8145 | | d8be82d155328d493b27c2f11bb69fa5 |
apache-2.0 | ['text-classification', 'fact-or-opinion', 'transformers'] | false | By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC) This is an XLM-Roberta-base model with a binary classification head. Given a sentence, it can classify it either as a fact or an opinion based on its content. You can use this model in any of the XLM-R supported languages for the same task, taking advantage of its 0-shot learning capabilities. However, the model was trained only using English and Greek sentences. Legend of HuggingFace API labels: * Label 0: Opinion/Subjective sentence * Label 1: Fact/Objective sentence | 64a9928d35d5fb2f11d03a4dcbc70f37 |
apache-2.0 | ['text-classification', 'fact-or-opinion', 'transformers'] | false | Dataset training info The original dataset (available here: https://github.com/1024er/cbert_aug/tree/crayon/datasets/subj) contained aprox. 9000 annotated sentences (classified as subjective or objective). It was translated to Greek using Google Translate. The Greek version was then concatenated with the original English one to create the mixed EN-EL dataset. The model was trained for 5 epochs, using batch size = 8. Detailed metrics and hyperparameters available on the "Metrics" tab. | 07b393957af8532b8d58e3e28749ca6d |
mit | [] | false | schloss mosigkau on Stable Diffusion This is the `<ralph>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:      | 25e0294765b791af5f7a99ae25df1b75 |
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: 0.0637 - Precision: 0.9336 - Recall: 0.9488 - F1: 0.9412 - Accuracy: 0.9854 | 01cf7555e9f1101e505561a32b32ac6e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0897 | 1.0 | 1756 | 0.0648 | 0.9152 | 0.9408 | 0.9278 | 0.9837 | | 0.0384 | 2.0 | 3512 | 0.0601 | 0.9277 | 0.9507 | 0.9391 | 0.9859 | | 0.0201 | 3.0 | 5268 | 0.0637 | 0.9336 | 0.9488 | 0.9412 | 0.9854 | | 70969295f60de8195f8fb99417c50a74 |
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