Datasets:
davanstrien
/
pytorchmodelmetadata

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masakhane/afrimt5_en_swa_news
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2022-05-10T13:50:27.000Z
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masakhane/afrimbart_swa_en_news
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2022-05-10T13:50:29.000Z
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masakhane/afrimbart_en_swa_news
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masakhane/afribyt5_swa_en_news
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masakhane/afribyt5_en_swa_news
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masakhane/mt5_swa_en_news
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masakhane/mbart50_en_swa_news
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masakhane/m2m100_418M_en_swa_news
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masakhane/m2m100_418M_swa_en_news
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masakhane/m2m100_418M_swa_en_rel_news
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masakhane/m2m100_418M_en_swa_rel_news_ft
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masakhane/m2m100_418M_en_swa_rel_ft
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masakhane/m2m100_418M_swa_en_rel_ft
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masakhane/m2m100_418M_swa_en_rel
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masakhane/m2m100_418M_en_swa_rel
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2022-05-10T14:40:06.000Z
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huggingtweets/_avichalp_
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2022-05-10T11:56:46.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
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--- language: en thumbnail: http://www.huggingtweets.com/_avichalp_/1652183801632/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1472922431396331520/eqT17_QF_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">avi</div> <div style="text-align: center; font-size: 14px;">@_avichalp_</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from avi. | Data | avi | | --- | --- | | Tweets downloaded | 2625 | | Retweets | 259 | | Short tweets | 596 | | Tweets kept | 1770 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2wg7ysai/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_avichalp_'s tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ae6t1qq) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ae6t1qq/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/_avichalp_') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
masakhane/afrimt5_en_yor_news
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2022-05-10T12:41:12.000Z
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masakhane/m2m100_418M_yor_en_rel_ft
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37,440
--- license: afl-3.0 ---
masakhane/m2m100_418M_en_yor_rel
2b3310280a8d722bca9d5a5a2ffc759309302e12
2022-05-10T13:38:28.000Z
[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/m2m100_418M_en_yor_rel
0
null
transformers
37,441
--- license: afl-3.0 ---
masakhane/afrimt5_en_tsn_news
df3c9a2a1ecd3d8a7efcd461b4305fb4ca4df275
2022-05-10T14:50:57.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/afrimt5_en_tsn_news
0
null
transformers
37,442
--- license: afl-3.0 ---
masakhane/afrimt5_tsn_en_news
df91a8c50e119865f27d279c58fba9f7697e745f
2022-05-10T14:51:00.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/afrimt5_tsn_en_news
0
null
transformers
37,443
--- license: afl-3.0 ---
masakhane/afrimbart_tsn_en_news
a9484b162ca56515904d4e0f25f5e2c52ea79cf3
2022-05-10T14:51:02.000Z
[ "pytorch", "mbart", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/afrimbart_tsn_en_news
0
null
transformers
37,444
--- license: afl-3.0 ---
masakhane/afrimbart_en_tsn_news
303765b01229dcd37996426839d0d70f7ea9b7d5
2022-05-10T14:51:05.000Z
[ "pytorch", "mbart", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/afrimbart_en_tsn_news
0
null
transformers
37,445
--- license: afl-3.0 ---
masakhane/afribyt5_tsn_en_news
a822051c38e74058da932cc1c84c424fad05ff40
2022-05-10T16:15:00.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/afribyt5_tsn_en_news
0
null
transformers
37,446
--- license: afl-3.0 ---
masakhane/afribyt5_en_tsn_news
c0c6561545344dbaed286dc7e67f5eb3d8dffbd9
2022-05-10T16:15:07.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/afribyt5_en_tsn_news
0
null
transformers
37,447
--- license: afl-3.0 ---
masakhane/byt5_en_tsn_news
9185455f4df1a5cfda75abb75d90c36c7c0289a6
2022-05-10T16:15:05.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/byt5_en_tsn_news
0
null
transformers
37,448
--- license: afl-3.0 ---
masakhane/byt5_tsn_en_news
748bf3e329e485fe642d4ac2f91d5b1bda841b77
2022-05-10T16:15:03.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/byt5_tsn_en_news
0
null
transformers
37,449
--- license: afl-3.0 ---
masakhane/mt5_tsn_en_news
da8cb90d429cd7e0c0611d0c1172ebe9942b1a68
2022-05-10T16:25:23.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/mt5_tsn_en_news
0
null
transformers
37,450
--- license: afl-3.0 ---
masakhane/mt5_en_tsn_news
4fa67ef4ee8ac2baecb7af933bfd59e26d037bba
2022-05-10T16:25:21.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/mt5_en_tsn_news
0
null
transformers
37,451
--- license: afl-3.0 ---
masakhane/mbart50_en_tsn_news
7f1f82bd3f4d4fd7fec0e69d7a975ac371cc22b9
2022-05-10T16:25:19.000Z
[ "pytorch", "mbart", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/mbart50_en_tsn_news
0
null
transformers
37,452
--- license: afl-3.0 ---
masakhane/mbart50_tsn_en_news
fdd9a61592051d3549daf8d0c39e69e6a24f82cd
2022-05-10T16:25:25.000Z
[ "pytorch", "mbart", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/mbart50_tsn_en_news
0
null
transformers
37,453
--- license: afl-3.0 ---
huxxx657/roberta-base-finetuned-scrambled-squad-5
44fcc845adbaa9a6c81a69640adbefcaa1dae057
2022-05-10T16:43:15.000Z
[ "pytorch", "tensorboard", "roberta", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
question-answering
false
huxxx657
null
huxxx657/roberta-base-finetuned-scrambled-squad-5
0
null
transformers
37,454
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-base-finetuned-scrambled-squad-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-finetuned-scrambled-squad-5 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.7078 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.7695 | 1.0 | 5532 | 1.7078 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
masakhane/m2m100_418M_en_tsn_news
ee0dba7751223c74280cb4cca13049bba1c4ec1b
2022-05-10T16:32:31.000Z
[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/m2m100_418M_en_tsn_news
0
null
transformers
37,455
--- license: afl-3.0 ---
masakhane/m2m100_418M_tsn_en_news
d920bad02448c53ea43959de2dca3971873f71c4
2022-05-10T16:32:34.000Z
[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/m2m100_418M_tsn_en_news
0
null
transformers
37,456
--- license: afl-3.0 ---
masakhane/m2m100_418M_tsn_en_rel_news
f9ae74a2263e17c88ebc4cde25902ea139d85622
2022-05-10T16:32:41.000Z
[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/m2m100_418M_tsn_en_rel_news
0
null
transformers
37,457
--- license: afl-3.0 ---
masakhane/m2m100_418M_en_tsn_rel_news
d2ad774e541b0bde8d486b9e3ba65b621fc902fc
2022-05-10T16:32:38.000Z
[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/m2m100_418M_en_tsn_rel_news
0
null
transformers
37,458
--- license: afl-3.0 ---
masakhane/m2m100_418M_en_tsn_rel_ft
fc60cc683e029ad0589781598c8cad3e11c70a14
2022-05-10T16:43:42.000Z
[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/m2m100_418M_en_tsn_rel_ft
0
null
transformers
37,459
--- license: afl-3.0 ---
masakhane/m2m100_418M_tsn_en_rel_ft
454da39ce31c457851916fa2ad32c10077bb6191
2022-05-10T16:43:45.000Z
[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/m2m100_418M_tsn_en_rel_ft
0
null
transformers
37,460
--- license: afl-3.0 ---
masakhane/m2m100_418M_en_tsn_rel_news_ft
fc18956d2f543afe10b103a9903ab986b9a3d018
2022-05-10T16:43:37.000Z
[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/m2m100_418M_en_tsn_rel_news_ft
0
null
transformers
37,461
--- license: afl-3.0 ---
masakhane/m2m100_418M_tsn_en_rel_news_ft
9d8d32051cde29765d73c50b9cf9d45a340a7a2d
2022-05-10T16:43:40.000Z
[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/m2m100_418M_tsn_en_rel_news_ft
0
null
transformers
37,462
--- license: afl-3.0 ---
masakhane/m2m100_418M_tsn_en_rel
42bc944d3d076214f367258c3b8a7d259e2db351
2022-05-10T16:50:25.000Z
[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/m2m100_418M_tsn_en_rel
0
null
transformers
37,463
--- license: afl-3.0 ---
masakhane/m2m100_418M_en_tsn_rel
f761abcdc74e00f0cf9a02c7a50c102b8274fbd6
2022-05-10T16:50:27.000Z
[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/m2m100_418M_en_tsn_rel
0
null
transformers
37,464
--- license: afl-3.0 ---
husnu/wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_6
894be4059e821f91b73853a7346692699c20b337
2022-05-11T01:56:24.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
husnu
null
husnu/wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_6
0
null
transformers
37,465
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_6 This model is a fine-tuned version of [husnu/wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_5](https://huggingface.co/husnu/wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_5) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3646 - Wer: 0.3478 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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: 500 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1024 | 0.51 | 400 | 0.4030 | 0.4171 | | 0.1533 | 1.02 | 800 | 0.4733 | 0.4570 | | 0.1584 | 1.53 | 1200 | 0.4150 | 0.4371 | | 0.1538 | 2.04 | 1600 | 0.4104 | 0.4390 | | 0.1395 | 2.55 | 2000 | 0.3891 | 0.4133 | | 0.1415 | 3.07 | 2400 | 0.3877 | 0.4015 | | 0.1261 | 3.58 | 2800 | 0.3685 | 0.3899 | | 0.1149 | 4.09 | 3200 | 0.3791 | 0.3881 | | 0.1003 | 4.6 | 3600 | 0.3642 | 0.3626 | | 0.0934 | 5.11 | 4000 | 0.3755 | 0.3516 | | 0.0805 | 5.62 | 4400 | 0.3646 | 0.3478 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 2.1.0 - Tokenizers 0.10.3
theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed-v3-e16
7704dd633264ecff26869252d0695ca4e02de462
2022-05-11T14:04:06.000Z
[ "pytorch", "tensorboard", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
theojolliffe
null
theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed-v3-e16
0
null
transformers
37,466
--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: distilbart-cnn-arxiv-pubmed-pubmed-v3-e16 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbart-cnn-arxiv-pubmed-pubmed-v3-e16 This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed](https://huggingface.co/theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8306 - Rouge1: 56.4519 - Rouge2: 41.6818 - Rougel: 44.7833 - Rougelsum: 54.6359 - Gen Len: 141.9815 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 1.1157 | 50.9487 | 31.3005 | 34.0145 | 48.6057 | 141.8519 | | 1.3569 | 2.0 | 796 | 0.9688 | 53.0653 | 34.1855 | 37.0759 | 50.5942 | 141.2963 | | 0.8704 | 3.0 | 1194 | 0.9053 | 53.9684 | 36.0388 | 38.6674 | 51.9604 | 142.0 | | 0.6287 | 4.0 | 1592 | 0.8515 | 54.2379 | 36.4915 | 39.1393 | 51.6991 | 141.4074 | | 0.6287 | 5.0 | 1990 | 0.8274 | 53.6806 | 34.8373 | 37.7369 | 51.239 | 141.6481 | | 0.465 | 6.0 | 2388 | 0.8486 | 55.2534 | 39.1757 | 41.6366 | 53.2989 | 141.9259 | | 0.3432 | 7.0 | 2786 | 0.8116 | 54.539 | 37.6314 | 40.5531 | 52.1997 | 141.3889 | | 0.2577 | 8.0 | 3184 | 0.7976 | 54.8212 | 36.8347 | 40.6768 | 52.7785 | 142.0 | | 0.204 | 9.0 | 3582 | 0.8010 | 53.9302 | 37.3523 | 40.135 | 52.139 | 141.7778 | | 0.204 | 10.0 | 3980 | 0.8168 | 54.3151 | 38.0665 | 42.4112 | 52.4682 | 142.0 | | 0.1663 | 11.0 | 4378 | 0.8171 | 54.7027 | 38.3117 | 42.0196 | 52.8821 | 142.0 | | 0.135 | 12.0 | 4776 | 0.8202 | 54.1035 | 37.9154 | 40.7676 | 52.2509 | 142.0 | | 0.1102 | 13.0 | 5174 | 0.8204 | 56.223 | 41.0947 | 44.0131 | 54.3353 | 142.0 | | 0.0928 | 14.0 | 5572 | 0.8280 | 56.1637 | 41.0408 | 44.2931 | 54.5488 | 142.0 | | 0.0928 | 15.0 | 5970 | 0.8273 | 56.2608 | 41.3855 | 44.4432 | 54.5778 | 142.0 | | 0.0847 | 16.0 | 6368 | 0.8306 | 56.4519 | 41.6818 | 44.7833 | 54.6359 | 141.9815 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.0 - Tokenizers 0.12.1
huxxx657/roberta-base-finetuned-scrambled-squad-15
24ffd28c0a60a8201f41739591c90f9a92b1e9c5
2022-05-10T21:13:58.000Z
[ "pytorch", "tensorboard", "roberta", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
question-answering
false
huxxx657
null
huxxx657/roberta-base-finetuned-scrambled-squad-15
0
null
transformers
37,467
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-base-finetuned-scrambled-squad-15 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-finetuned-scrambled-squad-15 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.8722 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.8944 | 1.0 | 5590 | 1.8722 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
subhasisj/zh-finetuned-squad-qa-minilmv2-32
5410b4aa997a95d1c193323f4d4d645f351943d1
2022-05-10T21:58:09.000Z
[ "pytorch", "tensorboard", "bert", "question-answering", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
subhasisj
null
subhasisj/zh-finetuned-squad-qa-minilmv2-32
0
null
transformers
37,468
--- tags: - generated_from_trainer model-index: - name: zh-finetuned-squad-qa-minilmv2-32 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # zh-finetuned-squad-qa-minilmv2-32 This model is a fine-tuned version of [subhasisj/zh-TAPT-MLM-MiniLM](https://huggingface.co/subhasisj/zh-TAPT-MLM-MiniLM) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4311 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 338 | 2.3706 | | 3.1254 | 2.0 | 676 | 1.7422 | | 1.6449 | 3.0 | 1014 | 1.5323 | | 1.6449 | 4.0 | 1352 | 1.4375 | | 1.3122 | 5.0 | 1690 | 1.4311 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.0 - Tokenizers 0.12.1
huxxx657/roberta-base-finetuned-scrambled-squad-5-new
b40a9f360dddc2147ffd4fcf068ef649c4a464d3
2022-05-10T22:48:00.000Z
[ "pytorch", "tensorboard", "roberta", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
question-answering
false
huxxx657
null
huxxx657/roberta-base-finetuned-scrambled-squad-5-new
0
null
transformers
37,469
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-base-finetuned-scrambled-squad-5-new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-finetuned-scrambled-squad-5-new This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 0.9098 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.941 | 1.0 | 5536 | 0.9098 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.0 - Tokenizers 0.12.1
theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed-v3-e8
20c3e323d1e1c4136e9f007d595b911396f24f88
2022-05-11T12:17:20.000Z
[ "pytorch", "tensorboard", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
theojolliffe
null
theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed-v3-e8
0
null
transformers
37,470
--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: distilbart-cnn-arxiv-pubmed-pubmed-v3-e8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbart-cnn-arxiv-pubmed-pubmed-v3-e8 This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed](https://huggingface.co/theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8422 - Rouge1: 54.9328 - Rouge2: 36.7154 - Rougel: 39.5674 - Rougelsum: 52.4889 - Gen Len: 142.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 1.1158 | 50.9754 | 30.9416 | 33.9908 | 48.4925 | 142.0 | | 1.3585 | 2.0 | 796 | 0.9733 | 52.7954 | 33.8196 | 36.7836 | 50.4929 | 141.9259 | | 0.8785 | 3.0 | 1194 | 0.9142 | 53.5548 | 35.3954 | 37.4787 | 51.1024 | 142.0 | | 0.6485 | 4.0 | 1592 | 0.8666 | 52.6449 | 34.0018 | 37.5391 | 50.428 | 141.4074 | | 0.6485 | 5.0 | 1990 | 0.8458 | 53.8913 | 35.4481 | 38.1552 | 51.3737 | 141.8889 | | 0.4993 | 6.0 | 2388 | 0.8571 | 54.7333 | 36.8173 | 40.228 | 52.5574 | 141.9444 | | 0.3957 | 7.0 | 2786 | 0.8455 | 54.9826 | 37.9674 | 40.5786 | 52.5968 | 141.9815 | | 0.328 | 8.0 | 3184 | 0.8422 | 54.9328 | 36.7154 | 39.5674 | 52.4889 | 142.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.0 - Tokenizers 0.12.1
huxxx657/roberta-base-finetuned-scrambled-squad-10-new
fd7f0c5d95889a1dbdfa2480414ec2d86896c7b4
2022-05-11T00:56:16.000Z
[ "pytorch", "tensorboard", "roberta", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
question-answering
false
huxxx657
null
huxxx657/roberta-base-finetuned-scrambled-squad-10-new
0
null
transformers
37,471
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-base-finetuned-scrambled-squad-10-new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-finetuned-scrambled-squad-10-new This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 0.9721 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9984 | 1.0 | 5536 | 0.9721 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.0 - Tokenizers 0.12.1
mcurmei/flat_N_max
db7f186a638054b3ed0b2108c701e373b32b5aed
2022-05-11T03:33:16.000Z
[ "pytorch", "tensorboard", "distilbert", "question-answering", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
mcurmei
null
mcurmei/flat_N_max
0
null
transformers
37,472
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: flat_N_max results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flat_N_max 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: 1.8536 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2462 | 1.0 | 2213 | 1.7958 | | 0.9293 | 2.0 | 4426 | 1.8093 | | 0.7249 | 3.0 | 6639 | 1.8536 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.0 - Tokenizers 0.12.1
mcurmei/single_label_N_max
8e835a9b0c57580bb351420aa9d1955595e5523e
2022-05-11T04:49:29.000Z
[ "pytorch", "tensorboard", "distilbert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
mcurmei
null
mcurmei/single_label_N_max
0
null
transformers
37,473
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: single_label_N_max results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # single_label_N_max This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.9326 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.9746 | 1.0 | 674 | 2.0265 | | 1.6756 | 2.0 | 1348 | 1.9134 | | 1.1333 | 3.0 | 2022 | 1.9326 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.0 - Tokenizers 0.12.1
mcurmei/unique_N_max
8f16ca5a07d01a9e70bcf2725a7e3cc5d67d5766
2022-05-11T06:19:57.000Z
[ "pytorch", "tensorboard", "distilbert", "question-answering", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
mcurmei
null
mcurmei/unique_N_max
0
null
transformers
37,474
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: unique_N_max results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # unique_N_max 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: 1.7409 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0901 | 1.0 | 1162 | 1.8326 | | 1.5479 | 2.0 | 2324 | 1.7201 | | 1.2903 | 3.0 | 3486 | 1.7409 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.0 - Tokenizers 0.12.1
kathywu/DialoGPT-small-kathy
12aadc9ada0900ae05069852d9550366d77cd5be
2022-05-12T05:14:40.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
kathywu
null
kathywu/DialoGPT-small-kathy
0
null
transformers
37,475
--- tags: - conversational ---
huggingtweets/elonmusk-kimkardashian
6f24ba7c0f73e3573992ff66a39d97bfeff817b3
2022-05-11T07:03:54.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/elonmusk-kimkardashian
0
null
transformers
37,476
--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1521957986335297536/itVSA7l0_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1446623190252343301/qIJAwo9I_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Kim Kardashian</div> <div style="text-align: center; font-size: 14px;">@elonmusk-kimkardashian</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Kim Kardashian. | Data | Elon Musk | Kim Kardashian | | --- | --- | --- | | Tweets downloaded | 222 | 3241 | | Retweets | 16 | 715 | | Short tweets | 47 | 667 | | Tweets kept | 159 | 1859 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/17bd0o7t/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elonmusk-kimkardashian's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2g9hft2n) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2g9hft2n/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/elonmusk-kimkardashian') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv
806c5e145bbbf3ab70e9570ca85fe9b1d8fd5c44
2022-05-11T13:55:48.000Z
[ "pytorch", "tensorboard", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
text2text-generation
false
theojolliffe
null
theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv
0
null
transformers
37,477
--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8065 - Rouge1: 54.5916 - Rouge2: 36.7817 - Rougel: 40.4708 - Rougelsum: 52.5754 - Gen Len: 142.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 1.2945 | 1.0 | 795 | 0.9555 | 51.91 | 32.0926 | 33.6727 | 49.5306 | 142.0 | | 0.7153 | 2.0 | 1590 | 0.8317 | 52.4708 | 34.1035 | 35.2968 | 50.2966 | 141.963 | | 0.5398 | 3.0 | 2385 | 0.8133 | 52.4603 | 33.497 | 36.4227 | 50.2513 | 141.8704 | | 0.3568 | 4.0 | 3180 | 0.8091 | 52.3993 | 34.2424 | 37.7819 | 50.2069 | 142.0 | | 0.2842 | 5.0 | 3975 | 0.8065 | 54.5916 | 36.7817 | 40.4708 | 52.5754 | 142.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.0 - Tokenizers 0.12.1
masakhane/afrimt5_en_twi_news
45eccd6d401769818c7f7983ca794935d4a7e2f4
2022-05-12T11:55:30.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/afrimt5_en_twi_news
0
null
transformers
37,478
--- license: afl-3.0 ---
masakhane/afrimt5_twi_en_news
872285c9175e4e6d470a96348cc70e850955ed61
2022-05-12T11:55:47.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/afrimt5_twi_en_news
0
null
transformers
37,479
--- license: afl-3.0 ---
masakhane/afrimt5_zul_en_news
39ea55cc4095b85a226a81c763018324ed7cae72
2022-05-12T12:51:45.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/afrimt5_zul_en_news
0
null
transformers
37,480
--- license: afl-3.0 ---
masakhane/afrimbart_en_zul_news
0b4389b70b17eb0e10b633c0106074a3a2c45d3b
2022-05-12T12:51:49.000Z
[ "pytorch", "mbart", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/afrimbart_en_zul_news
0
null
transformers
37,481
--- license: afl-3.0 ---
masakhane/afribyt5_twi_en_news
5bc4ed934d97c928439f0c73b1e8ac2620ef4a65
2022-05-12T12:07:35.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/afribyt5_twi_en_news
0
null
transformers
37,482
--- license: afl-3.0 ---
masakhane/afribyt5_en_twi_news
c2074a74a1ef1916b3d0dce432fcd456bc864569
2022-05-12T12:07:32.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/afribyt5_en_twi_news
0
null
transformers
37,483
--- license: afl-3.0 ---
masakhane/afribyt5_zul_en_news
af94d5014988f24f5ed30b0b72fea3d85e5e8e05
2022-05-12T12:59:08.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/afribyt5_zul_en_news
0
null
transformers
37,484
--- license: afl-3.0 ---
masakhane/byt5_en_twi_news
5f1fc04f243bf27bc9944b8d21510883a066e07e
2022-05-12T12:07:37.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/byt5_en_twi_news
0
null
transformers
37,485
--- license: afl-3.0 ---
masakhane/byt5_en_zul_news
1740c75e08da71a06e8aae6c53a3b46fc4a950e6
2022-05-12T12:59:10.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/byt5_en_zul_news
0
null
transformers
37,486
--- license: afl-3.0 ---
masakhane/mt5_twi_en_news
21041431db90a40fa4efd3c7ac61cdf7303d0e5a
2022-05-12T12:16:04.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/mt5_twi_en_news
0
null
transformers
37,487
--- license: afl-3.0 ---
masakhane/mt5_en_twi_news
9404feb8904f00262b5a73e6b9036e2f2cb91b21
2022-05-12T12:16:07.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/mt5_en_twi_news
0
null
transformers
37,488
--- license: afl-3.0 ---
masakhane/mt5_zul_en_news
e8980f16f1be056679c1cea885042b1554bc2016
2022-05-12T13:06:22.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/mt5_zul_en_news
0
null
transformers
37,489
--- license: afl-3.0 ---
masakhane/mt5_en_zul_news
c0a588b3d1a386eed70946e792fe509fc81d091d
2022-05-12T13:06:25.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/mt5_en_zul_news
0
null
transformers
37,490
--- license: afl-3.0 ---
masakhane/m2m100_418M_twi_en_news
7eeb65dfce7dc881bd03f9a63c89cb97a277cd84
2022-05-12T12:27:55.000Z
[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
masakhane
null
masakhane/m2m100_418M_twi_en_news
0
null
transformers
37,491
--- license: afl-3.0 ---
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