librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-14 18:27:59	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 520 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-14 18:27:48	card stringlengths 11 1.01M
masakhane/byt5_en_zul_news	masakhane	2022-09-24T15:05:20Z	105	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "en", "zul", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:02:52Z	--- language: - en - zul license: afl-3.0 ---
masakhane/byt5_zul_en_news	masakhane	2022-09-24T15:05:19Z	107	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:03:09Z	--- language: - zul - en license: afl-3.0 ---
masakhane/mbart50_zul_en_news	masakhane	2022-09-24T15:05:19Z	105	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:04:09Z	--- language: - zul - en license: afl-3.0 ---
masakhane/mt5_zul_en_news	masakhane	2022-09-24T15:05:18Z	106	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:06:24Z	--- language: - zul - en license: afl-3.0 ---
masakhane/mbart50_en_zul_news	masakhane	2022-09-24T15:05:18Z	106	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "en", "zul", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:04:24Z	--- language: - en - zul license: afl-3.0 ---
masakhane/m2m100_418M_en_zul_rel_news	masakhane	2022-09-24T15:05:16Z	105	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "zul", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:09:23Z	--- language: - en - zul license: afl-3.0 ---
masakhane/m2m100_418M_zul_en_news	masakhane	2022-09-24T15:05:16Z	104	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:07:50Z	--- language: - zul - en license: afl-3.0 ---
masakhane/m2m100_418M_zul_en_rel_news_ft	masakhane	2022-09-24T15:05:15Z	105	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:13:41Z	--- language: - zul - en license: afl-3.0 ---
masakhane/m2m100_418M_en_zul_rel_ft	masakhane	2022-09-24T15:05:13Z	104	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "zul", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:15:36Z	--- language: - en - zul license: afl-3.0 ---
masakhane/m2m100_418M_zul_en_rel	masakhane	2022-09-24T15:05:12Z	105	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:18:45Z	--- language: - zul - en license: afl-3.0 ---
masakhane/m2m100_418M_en_zul_rel	masakhane	2022-09-24T15:05:12Z	105	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "zul", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:18:27Z	--- language: - en - zul license: afl-3.0 ---
masakhane/m2m100_418M_en_kin_rel	masakhane	2022-09-24T15:05:09Z	113	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "kin", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-08-25T22:07:12Z	--- language: - en - kin license: cc-by-nc-4.0 ---
masakhane/m2m100_418M_kin_en_rel	masakhane	2022-09-24T15:05:09Z	111	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "kin", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-08-25T22:06:42Z	--- language: - kin - en license: cc-by-nc-4.0 ---
masakhane/m2m100_418M_en_nya_rel	masakhane	2022-09-24T15:05:08Z	109	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "nya", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-08-25T22:07:46Z	--- language: - en - nya license: cc-by-nc-4.0 ---
masakhane/m2m100_418M_en_sna_rel	masakhane	2022-09-24T15:05:07Z	110	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "sna", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-08-25T22:08:45Z	--- language: - en - sna license: cc-by-nc-4.0 ---
gokuls/BERT-tiny-Massive-intent	gokuls	2022-09-24T14:26:13Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:massive", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-24T14:15:30Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - massive metrics: - accuracy model-index: - name: BERT-tiny-Massive-intent results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.8475159862272503 --- <!-- 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. --> # BERT-tiny-Massive-intent This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.6740 - Accuracy: 0.8475 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 3.6104 \| 1.0 \| 720 \| 3.0911 \| 0.3601 \| \| 2.8025 \| 2.0 \| 1440 \| 2.3800 \| 0.5165 \| \| 2.2292 \| 3.0 \| 2160 \| 1.9134 \| 0.5991 \| \| 1.818 \| 4.0 \| 2880 \| 1.5810 \| 0.6744 \| \| 1.5171 \| 5.0 \| 3600 \| 1.3522 \| 0.7108 \| \| 1.2876 \| 6.0 \| 4320 \| 1.1686 \| 0.7442 \| \| 1.1049 \| 7.0 \| 5040 \| 1.0355 \| 0.7683 \| \| 0.9623 \| 8.0 \| 5760 \| 0.9466 \| 0.7885 \| \| 0.8424 \| 9.0 \| 6480 \| 0.8718 \| 0.7875 \| \| 0.7473 \| 10.0 \| 7200 \| 0.8107 \| 0.8028 \| \| 0.6735 \| 11.0 \| 7920 \| 0.7710 \| 0.8180 \| \| 0.6085 \| 12.0 \| 8640 \| 0.7404 \| 0.8210 \| \| 0.5536 \| 13.0 \| 9360 \| 0.7180 \| 0.8229 \| \| 0.5026 \| 14.0 \| 10080 \| 0.6980 \| 0.8318 \| \| 0.4652 \| 15.0 \| 10800 \| 0.6970 \| 0.8337 \| \| 0.4234 \| 16.0 \| 11520 \| 0.6822 \| 0.8372 \| \| 0.3987 \| 17.0 \| 12240 \| 0.6691 \| 0.8436 \| \| 0.3707 \| 18.0 \| 12960 \| 0.6679 \| 0.8455 \| \| 0.3433 \| 19.0 \| 13680 \| 0.6740 \| 0.8475 \| \| 0.3206 \| 20.0 \| 14400 \| 0.6760 \| 0.8451 \| \| 0.308 \| 21.0 \| 15120 \| 0.6704 \| 0.8436 \| \| 0.2813 \| 22.0 \| 15840 \| 0.6701 \| 0.8416 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
gokuls/distilroberta-emotion-intent	gokuls	2022-09-24T13:36:17Z	105	1	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-24T13:26:33Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: distilroberta-emotion-intent results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9435 --- <!-- 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. --> # distilroberta-emotion-intent This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1496 - Accuracy: 0.9435 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4501 \| 1.0 \| 1000 \| 0.2432 \| 0.924 \| \| 0.1947 \| 2.0 \| 2000 \| 0.1646 \| 0.934 \| \| 0.1497 \| 3.0 \| 3000 \| 0.1382 \| 0.9405 \| \| 0.1316 \| 4.0 \| 4000 \| 0.1496 \| 0.9435 \| \| 0.1145 \| 5.0 \| 5000 \| 0.1684 \| 0.9385 \| \| 0.1 \| 6.0 \| 6000 \| 0.2342 \| 0.943 \| \| 0.0828 \| 7.0 \| 7000 \| 0.2807 \| 0.939 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
RebekkaB/rlt_2409_1450	RebekkaB	2022-09-24T13:22:34Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-24T12:52:36Z	--- tags: - generated_from_trainer metrics: - f1 model-index: - name: rlt_2409_1450 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. --> # rlt_2409_1450 This model is a fine-tuned version of [svalabs/gbert-large-zeroshot-nli](https://huggingface.co/svalabs/gbert-large-zeroshot-nli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0518 - F1: 0.9826 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 0.99 \| 36 \| 0.5165 \| 0.8542 \| \| No log \| 1.99 \| 72 \| 0.1459 \| 0.9599 \| \| No log \| 2.99 \| 108 \| 0.0733 \| 0.9882 \| \| No log \| 3.99 \| 144 \| 0.1385 \| 0.9502 \| \| No log \| 4.99 \| 180 \| 0.0948 \| 0.9806 \| \| No log \| 5.99 \| 216 \| 0.0699 \| 0.9822 \| \| No log \| 6.99 \| 252 \| 0.0582 \| 0.9859 \| \| No log \| 7.99 \| 288 \| 0.0340 \| 0.9933 \| \| No log \| 8.99 \| 324 \| 0.0475 \| 0.9826 \| \| No log \| 9.99 \| 360 \| 0.0518 \| 0.9826 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
SaurabhKaushik/distilbert-base-uncased-finetuned-ner	SaurabhKaushik	2022-09-24T12:38:00Z	105	0	transformers	[ "transformers", "pytorch", "distilbert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-09-24T11:26:55Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.9250386398763524 - name: Recall type: recall value: 0.9373531714956931 - name: F1 type: f1 value: 0.9311551925320887 - name: Accuracy type: accuracy value: 0.9839388692074285 --- <!-- 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. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0589 - Precision: 0.9250 - Recall: 0.9374 - F1: 0.9312 - Accuracy: 0.9839 ## 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 \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2343 \| 1.0 \| 878 \| 0.0674 \| 0.9177 \| 0.9233 \| 0.9205 \| 0.9818 \| \| 0.0525 \| 2.0 \| 1756 \| 0.0582 \| 0.9245 \| 0.9362 \| 0.9304 \| 0.9837 \| \| 0.0288 \| 3.0 \| 2634 \| 0.0589 \| 0.9250 \| 0.9374 \| 0.9312 \| 0.9839 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1 - Datasets 2.5.1 - Tokenizers 0.12.1
sd-concepts-library/dr-strange	sd-concepts-library	2022-09-24T12:11:20Z	0	28	null	[ "license:mit", "region:us" ]	null	2022-09-24T12:11:16Z	--- license: mit --- ### <dr-strange> on Stable Diffusion This is the `<dr-strange>` 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 a `style`: ![<dr-strange> 0](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/3.jpeg) ![<dr-strange> 1](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/1.jpeg) ![<dr-strange> 2](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/0.jpeg) ![<dr-strange> 3](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/2.jpeg)
RebekkaB/san_nli_2409_1325	RebekkaB	2022-09-24T11:50:33Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-24T11:27:27Z	--- tags: - generated_from_trainer metrics: - f1 model-index: - name: san_nli_2409_1325 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. --> # san_nli_2409_1325 This model is a fine-tuned version of [svalabs/gbert-large-zeroshot-nli](https://huggingface.co/svalabs/gbert-large-zeroshot-nli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3856 - F1: 0.9219 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 0.93 \| 10 \| 0.2410 \| 0.9219 \| \| No log \| 1.93 \| 20 \| 0.5240 \| 0.9149 \| \| No log \| 2.93 \| 30 \| 0.4756 \| 0.9219 \| \| No log \| 3.93 \| 40 \| 0.3856 \| 0.9219 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
huggingtweets/cz_binance	huggingtweets	2022-09-24T09:16:00Z	113	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-05T21:10:34Z	--- language: en thumbnail: http://www.huggingtweets.com/cz_binance/1664010956441/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('https://pbs.twimg.com/profile_images/1572269909513478146/dfyw817W_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">CZ 🔶 Binance</div> <div style="text-align: center; font-size: 14px;">@cz_binance</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 CZ 🔶 Binance. \| Data \| CZ 🔶 Binance \| \| --- \| --- \| \| Tweets downloaded \| 3246 \| \| Retweets \| 149 \| \| Short tweets \| 473 \| \| Tweets kept \| 2624 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/19171g9o/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 @cz_binance's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ngvvhd8) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ngvvhd8/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/cz_binance') 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)
sd-concepts-library/coop-himmelblau	sd-concepts-library	2022-09-24T09:06:36Z	0	6	null	[ "license:mit", "region:us" ]	null	2022-09-24T09:06:32Z	--- license: mit --- ### coop himmelblau on Stable Diffusion This is the `<coop himmelblau>` 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`: ![<coop himmelblau> 0](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/3.jpeg) ![<coop himmelblau> 1](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/1.jpeg) ![<coop himmelblau> 2](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/4.jpeg) ![<coop himmelblau> 3](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/5.jpeg) ![<coop himmelblau> 4](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/0.jpeg) ![<coop himmelblau> 5](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/2.jpeg)
aniketface/DialoGPT-product	aniketface	2022-09-24T09:05:12Z	121	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "convAI", "conversational", "facebook", "en", "dataset:blended_skill_talk", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-09-24T08:41:37Z	--- language: - en thumbnail: tags: - convAI - conversational - facebook license: apache-2.0 datasets: - blended_skill_talk metrics: - perplexity ---
huggingtweets/pentosh1	huggingtweets	2022-09-24T08:03:41Z	113	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-09-24T08:02:41Z	--- language: en thumbnail: http://www.huggingtweets.com/pentosh1/1664006616559/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('https://pbs.twimg.com/profile_images/1553520707472072708/5eseDj4F_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">Pentoshi 🐧</div> <div style="text-align: center; font-size: 14px;">@pentosh1</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 Pentoshi 🐧. \| Data \| Pentoshi 🐧 \| \| --- \| --- \| \| Tweets downloaded \| 3242 \| \| Retweets \| 24 \| \| Short tweets \| 573 \| \| Tweets kept \| 2645 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3kzanxqd/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 @pentosh1's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3e7vuikz) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3e7vuikz/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/pentosh1') 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)
huggingtweets/beranewsnetwork	huggingtweets	2022-09-24T07:04:15Z	113	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-09-24T07:01:56Z	--- language: en thumbnail: http://www.huggingtweets.com/beranewsnetwork/1664003049616/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('https://pbs.twimg.com/profile_images/1445950504102735872/bCnvrgeb_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">Bera News Network</div> <div style="text-align: center; font-size: 14px;">@beranewsnetwork</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 Bera News Network. \| Data \| Bera News Network \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 1 \| \| Short tweets \| 579 \| \| Tweets kept \| 2670 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/254oa32x/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 @beranewsnetwork's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1jqeuf1y) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1jqeuf1y/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/beranewsnetwork') 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)
huggingtweets/it_airmass	huggingtweets	2022-09-24T06:49:38Z	111	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-09-24T06:49:12Z	--- language: en thumbnail: http://www.huggingtweets.com/it_airmass/1664002173554/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('https://pbs.twimg.com/profile_images/1529248676647944193/-N1UKgKg_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">Airmass</div> <div style="text-align: center; font-size: 14px;">@it_airmass</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 Airmass. \| Data \| Airmass \| \| --- \| --- \| \| Tweets downloaded \| 3249 \| \| Retweets \| 126 \| \| Short tweets \| 370 \| \| Tweets kept \| 2753 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2f99nys0/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 @it_airmass's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/nvbqf9p2) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/nvbqf9p2/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/it_airmass') 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)
huggingtweets/marketsmeowmeow	huggingtweets	2022-09-24T06:43:25Z	111	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-09-24T06:42:56Z	--- language: en thumbnail: http://www.huggingtweets.com/marketsmeowmeow/1664001800470/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('https://pbs.twimg.com/profile_images/1570418907575377921/1mTVqZQZ_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">RB</div> <div style="text-align: center; font-size: 14px;">@marketsmeowmeow</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 RB. \| Data \| RB \| \| --- \| --- \| \| Tweets downloaded \| 3244 \| \| Retweets \| 14 \| \| Short tweets \| 700 \| \| Tweets kept \| 2530 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/a7yqyg23/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 @marketsmeowmeow's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ou0r1v87) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ou0r1v87/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/marketsmeowmeow') 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)
sd-concepts-library/museum-by-coop-himmelblau	sd-concepts-library	2022-09-24T06:39:31Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-24T06:39:25Z	--- license: mit --- ### museum by coop himmelblau on Stable Diffusion This is the `<coop himmelblau museum>` 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`: ![<coop himmelblau museum> 0](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/3.jpeg) ![<coop himmelblau museum> 1](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/1.jpeg) ![<coop himmelblau museum> 2](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/0.jpeg) ![<coop himmelblau museum> 3](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/2.jpeg)
sd-concepts-library/ransom	sd-concepts-library	2022-09-24T05:44:13Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-24T05:44:07Z	--- license: mit --- ### ransom on Stable Diffusion This is the `<ransom>` 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`: ![<ransom> 0](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/3.jpeg) ![<ransom> 1](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/1.jpeg) ![<ransom> 2](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/4.jpeg) ![<ransom> 3](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/6.jpeg) ![<ransom> 4](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/5.jpeg) ![<ransom> 5](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/0.jpeg) ![<ransom> 6](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/2.jpeg) ![<ransom> 7](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/7.jpeg)
ckiplab/bert-base-chinese-qa	ckiplab	2022-09-24T05:25:07Z	162	7	transformers	[ "transformers", "pytorch", "bert", "question-answering", "zh", "license:gpl-3.0", "endpoints_compatible", "region:us" ]	question-answering	2022-09-24T05:17:36Z	--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - question-answering - bert - zh license: gpl-3.0 --- # CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-base-chinese-qa') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
huggingtweets/tim_cook	huggingtweets	2022-09-24T01:11:00Z	112	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/tim_cook/1663981855625/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('https://pbs.twimg.com/profile_images/1535420431766671360/Pwq-1eJc_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">Tim Cook</div> <div style="text-align: center; font-size: 14px;">@tim_cook</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 Tim Cook. \| Data \| Tim Cook \| \| --- \| --- \| \| Tweets downloaded \| 1385 \| \| Retweets \| 20 \| \| Short tweets \| 13 \| \| Tweets kept \| 1352 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2d94dtsh/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 @tim_cook's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/19bm0x3l) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/19bm0x3l/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/tim_cook') 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)
farleyknight/arxiv-summarization-t5-base-2022-09-21	farleyknight	2022-09-24T00:31:57Z	180	3	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:ccdv/arxiv-summarization", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-21T20:31:09Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - ccdv/arxiv-summarization metrics: - rouge model-index: - name: arxiv-summarization-t5-base-2022-09-21 results: - task: name: Summarization type: summarization dataset: name: ccdv/arxiv-summarization type: ccdv/arxiv-summarization config: section split: train args: section metrics: - name: Rouge1 type: rouge value: 40.6781 --- <!-- 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. --> # arxiv-summarization-t5-base-2022-09-21 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the ccdv/arxiv-summarization dataset. It achieves the following results on the evaluation set: - Loss: 1.8650 - Rouge1: 40.6781 - Rouge2: 14.7167 - Rougel: 26.6375 - Rougelsum: 35.5959 - Gen Len: 117.1969 ## 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: 5e-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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 2.3291 \| 0.05 \| 10000 \| 2.1906 \| 18.6571 \| 7.1341 \| 14.8347 \| 16.9545 \| 19.0 \| \| 2.2454 \| 0.1 \| 20000 \| 2.1549 \| 18.5037 \| 7.1908 \| 14.7141 \| 16.8233 \| 18.9997 \| \| 2.2107 \| 0.15 \| 30000 \| 2.1013 \| 18.7638 \| 7.326 \| 14.9437 \| 17.072 \| 19.0 \| \| 2.1486 \| 0.2 \| 40000 \| 2.0845 \| 18.6879 \| 7.2441 \| 14.8835 \| 16.983 \| 19.0 \| \| 2.158 \| 0.25 \| 50000 \| 2.0699 \| 18.8314 \| 7.3712 \| 15.0166 \| 17.1215 \| 19.0 \| \| 2.1476 \| 0.3 \| 60000 \| 2.0424 \| 18.9783 \| 7.4138 \| 15.1121 \| 17.2778 \| 18.9981 \| \| 2.1164 \| 0.34 \| 70000 \| 2.0349 \| 18.9257 \| 7.4649 \| 15.0335 \| 17.1819 \| 19.0 \| \| 2.079 \| 0.39 \| 80000 \| 2.0208 \| 18.643 \| 7.4096 \| 14.8927 \| 16.9786 \| 18.9994 \| \| 2.101 \| 0.44 \| 90000 \| 2.0113 \| 19.3881 \| 7.7012 \| 15.3981 \| 17.6516 \| 19.0 \| \| 2.0576 \| 0.49 \| 100000 \| 2.0022 \| 18.9985 \| 7.542 \| 15.1157 \| 17.2972 \| 18.9992 \| \| 2.0983 \| 0.54 \| 110000 \| 1.9941 \| 18.7691 \| 7.4625 \| 15.0256 \| 17.1146 \| 19.0 \| \| 2.053 \| 0.59 \| 120000 \| 1.9855 \| 19.002 \| 7.5602 \| 15.1497 \| 17.2963 \| 19.0 \| \| 2.0434 \| 0.64 \| 130000 \| 1.9786 \| 19.2385 \| 7.6533 \| 15.3094 \| 17.5439 \| 18.9994 \| \| 2.0354 \| 0.69 \| 140000 \| 1.9746 \| 19.184 \| 7.7307 \| 15.2897 \| 17.491 \| 18.9992 \| \| 2.0347 \| 0.74 \| 150000 \| 1.9639 \| 19.2408 \| 7.693 \| 15.3357 \| 17.5297 \| 19.0 \| \| 2.0236 \| 0.79 \| 160000 \| 1.9590 \| 19.0781 \| 7.6256 \| 15.1932 \| 17.3486 \| 18.9998 \| \| 2.0187 \| 0.84 \| 170000 \| 1.9532 \| 19.0343 \| 7.6792 \| 15.1884 \| 17.3519 \| 19.0 \| \| 1.9939 \| 0.89 \| 180000 \| 1.9485 \| 18.8247 \| 7.5005 \| 15.0246 \| 17.1485 \| 18.9998 \| \| 1.9961 \| 0.94 \| 190000 \| 1.9504 \| 19.0695 \| 7.6559 \| 15.2139 \| 17.3814 \| 19.0 \| \| 2.0197 \| 0.99 \| 200000 \| 1.9399 \| 19.2821 \| 7.6685 \| 15.3029 \| 17.5374 \| 18.9988 \| \| 1.9457 \| 1.03 \| 210000 \| 1.9350 \| 19.053 \| 7.6502 \| 15.2123 \| 17.3793 \| 19.0 \| \| 1.9552 \| 1.08 \| 220000 \| 1.9317 \| 19.1878 \| 7.7235 \| 15.3272 \| 17.5252 \| 18.9998 \| \| 1.9772 \| 1.13 \| 230000 \| 1.9305 \| 19.0855 \| 7.6303 \| 15.1943 \| 17.3942 \| 18.9997 \| \| 1.9171 \| 1.18 \| 240000 \| 1.9291 \| 19.0711 \| 7.6437 \| 15.2175 \| 17.3893 \| 18.9995 \| \| 1.9393 \| 1.23 \| 250000 \| 1.9230 \| 19.276 \| 7.725 \| 15.3826 \| 17.586 \| 18.9995 \| \| 1.9295 \| 1.28 \| 260000 \| 1.9197 \| 19.2999 \| 7.7958 \| 15.3961 \| 17.6056 \| 18.9975 \| \| 1.9725 \| 1.33 \| 270000 \| 1.9173 \| 19.2958 \| 7.7121 \| 15.3659 \| 17.584 \| 19.0 \| \| 1.9668 \| 1.38 \| 280000 \| 1.9129 \| 19.089 \| 7.6846 \| 15.2395 \| 17.3879 \| 18.9998 \| \| 1.941 \| 1.43 \| 290000 \| 1.9132 \| 19.2127 \| 7.7336 \| 15.311 \| 17.4742 \| 18.9995 \| \| 1.9427 \| 1.48 \| 300000 \| 1.9108 \| 19.217 \| 7.7591 \| 15.334 \| 17.53 \| 18.9998 \| \| 1.9521 \| 1.53 \| 310000 \| 1.9041 \| 19.1285 \| 7.6736 \| 15.2625 \| 17.458 \| 19.0 \| \| 1.9352 \| 1.58 \| 320000 \| 1.9041 \| 19.1656 \| 7.723 \| 15.3035 \| 17.4818 \| 18.9991 \| \| 1.9342 \| 1.63 \| 330000 \| 1.9004 \| 19.2573 \| 7.7766 \| 15.3558 \| 17.5382 \| 19.0 \| \| 1.9631 \| 1.68 \| 340000 \| 1.8978 \| 19.236 \| 7.7584 \| 15.3408 \| 17.4993 \| 18.9998 \| \| 1.8987 \| 1.72 \| 350000 \| 1.8968 \| 19.1716 \| 7.7231 \| 15.2836 \| 17.4655 \| 18.9997 \| \| 1.9433 \| 1.77 \| 360000 \| 1.8924 \| 19.2644 \| 7.8294 \| 15.4018 \| 17.5808 \| 18.9998 \| \| 1.9159 \| 1.82 \| 370000 \| 1.8912 \| 19.1833 \| 7.8267 \| 15.3175 \| 17.4918 \| 18.9995 \| \| 1.9516 \| 1.87 \| 380000 \| 1.8856 \| 19.3077 \| 7.7432 \| 15.3723 \| 17.6115 \| 19.0 \| \| 1.9218 \| 1.92 \| 390000 \| 1.8880 \| 19.2668 \| 7.8231 \| 15.3834 \| 17.5701 \| 18.9994 \| \| 1.9159 \| 1.97 \| 400000 \| 1.8860 \| 19.2224 \| 7.7903 \| 15.3488 \| 17.4992 \| 18.9997 \| \| 1.8741 \| 2.02 \| 410000 \| 1.8854 \| 19.2572 \| 7.741 \| 15.3405 \| 17.5351 \| 19.0 \| \| 1.8668 \| 2.07 \| 420000 \| 1.8854 \| 19.3658 \| 7.8593 \| 15.4418 \| 17.656 \| 18.9995 \| \| 1.8638 \| 2.12 \| 430000 \| 1.8831 \| 19.305 \| 7.8218 \| 15.3843 \| 17.5861 \| 18.9997 \| \| 1.8334 \| 2.17 \| 440000 \| 1.8817 \| 19.3269 \| 7.8249 \| 15.4231 \| 17.5958 \| 18.9994 \| \| 1.8893 \| 2.22 \| 450000 \| 1.8803 \| 19.2949 \| 7.7885 \| 15.3947 \| 17.585 \| 18.9997 \| \| 1.8929 \| 2.27 \| 460000 \| 1.8783 \| 19.291 \| 7.8346 \| 15.428 \| 17.5797 \| 18.9997 \| \| 1.861 \| 2.32 \| 470000 \| 1.8766 \| 19.4284 \| 7.8832 \| 15.4746 \| 17.6946 \| 18.9997 \| \| 1.8719 \| 2.37 \| 480000 \| 1.8751 \| 19.1525 \| 7.7641 \| 15.3348 \| 17.47 \| 18.9998 \| \| 1.8889 \| 2.41 \| 490000 \| 1.8742 \| 19.1743 \| 7.768 \| 15.3292 \| 17.4665 \| 18.9998 \| \| 1.8834 \| 2.46 \| 500000 \| 1.8723 \| 19.3069 \| 7.7935 \| 15.3987 \| 17.5913 \| 18.9998 \| \| 1.8564 \| 2.51 \| 510000 \| 1.8695 \| 19.3217 \| 7.8292 \| 15.4063 \| 17.6081 \| 19.0 \| \| 1.8706 \| 2.56 \| 520000 \| 1.8697 \| 19.294 \| 7.8217 \| 15.3964 \| 17.581 \| 18.9998 \| \| 1.883 \| 2.61 \| 530000 \| 1.8703 \| 19.2784 \| 7.8634 \| 15.404 \| 17.5942 \| 18.9995 \| \| 1.8622 \| 2.66 \| 540000 \| 1.8677 \| 19.3165 \| 7.8378 \| 15.4259 \| 17.6064 \| 18.9988 \| \| 1.8781 \| 2.71 \| 550000 \| 1.8676 \| 19.3237 \| 7.7954 \| 15.3995 \| 17.6008 \| 19.0 \| \| 1.8793 \| 2.76 \| 560000 \| 1.8685 \| 19.2141 \| 7.7605 \| 15.3345 \| 17.5268 \| 18.9997 \| \| 1.8795 \| 2.81 \| 570000 \| 1.8675 \| 19.2694 \| 7.8082 \| 15.3996 \| 17.5831 \| 19.0 \| \| 1.8425 \| 2.86 \| 580000 \| 1.8659 \| 19.2886 \| 7.7987 \| 15.4005 \| 17.5859 \| 18.9997 \| \| 1.8605 \| 2.91 \| 590000 \| 1.8650 \| 19.2778 \| 7.7934 \| 15.3931 \| 17.5809 \| 18.9997 \| \| 1.8448 \| 2.96 \| 600000 \| 1.8655 \| 19.2884 \| 7.8087 \| 15.4025 \| 17.5856 \| 19.0 \| ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.0 - Datasets 2.5.1 - Tokenizers 0.13.0
ericntay/stbl_clinical_bert_ft_rs5	ericntay	2022-09-23T20:39:56Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-09-23T20:21:55Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: stbl_clinical_bert_ft_rs5 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. --> # stbl_clinical_bert_ft_rs5 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0936 - F1: 0.9268 ## 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2723 \| 1.0 \| 101 \| 0.0875 \| 0.8479 \| \| 0.066 \| 2.0 \| 202 \| 0.0688 \| 0.9002 \| \| 0.0328 \| 3.0 \| 303 \| 0.0668 \| 0.9070 \| \| 0.0179 \| 4.0 \| 404 \| 0.0689 \| 0.9129 \| \| 0.0098 \| 5.0 \| 505 \| 0.0790 \| 0.9147 \| \| 0.0069 \| 6.0 \| 606 \| 0.0805 \| 0.9205 \| \| 0.0033 \| 7.0 \| 707 \| 0.0835 \| 0.9268 \| \| 0.0022 \| 8.0 \| 808 \| 0.0904 \| 0.9262 \| \| 0.0021 \| 9.0 \| 909 \| 0.0882 \| 0.9263 \| \| 0.0015 \| 10.0 \| 1010 \| 0.0933 \| 0.9289 \| \| 0.0009 \| 11.0 \| 1111 \| 0.0921 \| 0.9311 \| \| 0.0009 \| 12.0 \| 1212 \| 0.0936 \| 0.9268 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
subtlegradient/distilbert-base-uncased-finetuned-cola	subtlegradient	2022-09-23T19:19:56Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-23T19:08:46Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue model-index: - name: distilbert-base-uncased-finetuned-cola 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. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5180 - eval_matthews_correlation: 0.4063 - eval_runtime: 0.8532 - eval_samples_per_second: 1222.419 - eval_steps_per_second: 77.353 - epoch: 1.0 - step: 535 ## 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: 5 ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu116 - Datasets 2.5.1 - Tokenizers 0.12.1
g30rv17ys/ddpm-geeve-drusen-1000-200ep	g30rv17ys	2022-09-23T19:12:36Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-09-23T15:39:11Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-geeve-drusen-1000-200ep ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/geevegeorge/ddpm-geeve-drusen-1000-200ep/tensorboard?#scalars)
g30rv17ys/ddpm-geeve-cnv-1000-200ep	g30rv17ys	2022-09-23T19:10:42Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-09-23T15:29:54Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-geeve-cnv-1000-200ep ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/geevegeorge/ddpm-geeve-cnv-1000-200ep/tensorboard?#scalars)
g30rv17ys/ddpm-geeve-dme-1000-200ep	g30rv17ys	2022-09-23T19:09:23Z	1	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-09-23T15:34:37Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-geeve-dme-1000-200ep ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/geevegeorge/ddpm-geeve-dme-1000-200ep/tensorboard?#scalars)
tszocinski/bart-base-squad-question-generation	tszocinski	2022-09-23T18:43:43Z	75	0	transformers	[ "transformers", "tf", "bart", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-22T19:36:46Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: tszocinski/bart-base-squad-question-generation results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # tszocinski/bart-base-squad-question-generation This model is a fine-tuned version of [tszocinski/bart-base-squad-question-generation](https://huggingface.co/tszocinski/bart-base-squad-question-generation) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 6.5656 - Validation Loss: 11.1958 - Epoch: 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: - optimizer: {'inner_optimizer': {'class_name': 'RMSprop', 'config': {'name': 'RMSprop', 'learning_rate': 0.001, 'decay': 0.0, 'rho': 0.9, 'momentum': 0.0, 'epsilon': 1e-07, 'centered': False}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 6.5656 \| 11.1958 \| 0 \| ### Framework versions - Transformers 4.22.1 - TensorFlow 2.8.2 - Datasets 2.5.1 - Tokenizers 0.12.1
tkuye/t5-ost	tkuye	2022-09-23T18:40:53Z	109	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-23T17:10:59Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-ost 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. --> # t5-ost This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0581 ## 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: 0.0005 - train_batch_size: 8 - 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.5695 \| 0.39 \| 500 \| 0.0591 \| \| 0.0606 \| 0.77 \| 1000 \| 0.0588 \| \| 0.0575 \| 1.16 \| 1500 \| 0.0588 \| \| 0.0551 \| 1.55 \| 2000 \| 0.0586 \| \| 0.0549 \| 1.93 \| 2500 \| 0.0581 \| \| 0.0487 \| 2.32 \| 3000 \| 0.0597 \| \| 0.0478 \| 2.71 \| 3500 \| 0.0594 \| \| 0.0463 \| 3.1 \| 4000 \| 0.0624 \| \| 0.0404 \| 3.48 \| 4500 \| 0.0625 \| \| 0.041 \| 3.87 \| 5000 \| 0.0617 \| \| 0.0366 \| 4.26 \| 5500 \| 0.0656 \| \| 0.0347 \| 4.64 \| 6000 \| 0.0658 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.0 - Datasets 2.5.1 - Tokenizers 0.12.1
mfreihaut/iab_classification-finetuned-mnli-finetuned-mnli	mfreihaut	2022-09-23T18:20:23Z	23	1	transformers	[ "transformers", "pytorch", "bart", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-21T18:05:28Z	--- tags: - generated_from_trainer model-index: - name: iab_classification-finetuned-mnli-finetuned-mnli 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. --> # iab_classification-finetuned-mnli-finetuned-mnli This model is a fine-tuned version of [mfreihaut/iab_classification-finetuned-mnli-finetuned-mnli](https://huggingface.co/mfreihaut/iab_classification-finetuned-mnli-finetuned-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8711 ## 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| No log \| 1.0 \| 250 \| 1.5956 \| \| 0.9361 \| 2.0 \| 500 \| 0.0409 \| \| 0.9361 \| 3.0 \| 750 \| 2.9853 \| \| 0.7634 \| 4.0 \| 1000 \| 0.1317 \| \| 0.7634 \| 5.0 \| 1250 \| 0.4056 \| \| 0.611 \| 6.0 \| 1500 \| 1.8038 \| \| 0.611 \| 7.0 \| 1750 \| 0.6305 \| \| 0.5627 \| 8.0 \| 2000 \| 0.6923 \| \| 0.5627 \| 9.0 \| 2250 \| 3.7410 \| \| 0.9863 \| 10.0 \| 2500 \| 2.1912 \| \| 0.9863 \| 11.0 \| 2750 \| 1.5405 \| \| 1.0197 \| 12.0 \| 3000 \| 1.9271 \| \| 1.0197 \| 13.0 \| 3250 \| 1.1741 \| \| 0.5186 \| 14.0 \| 3500 \| 1.1864 \| \| 0.5186 \| 15.0 \| 3750 \| 0.7945 \| \| 0.4042 \| 16.0 \| 4000 \| 1.0645 \| \| 0.4042 \| 17.0 \| 4250 \| 1.8826 \| \| 0.3637 \| 18.0 \| 4500 \| 0.3234 \| \| 0.3637 \| 19.0 \| 4750 \| 0.2641 \| \| 0.3464 \| 20.0 \| 5000 \| 0.8596 \| \| 0.3464 \| 21.0 \| 5250 \| 0.5601 \| \| 0.2449 \| 22.0 \| 5500 \| 0.4543 \| \| 0.2449 \| 23.0 \| 5750 \| 1.1986 \| \| 0.2595 \| 24.0 \| 6000 \| 0.3642 \| \| 0.2595 \| 25.0 \| 6250 \| 1.3606 \| \| 0.298 \| 26.0 \| 6500 \| 0.8154 \| \| 0.298 \| 27.0 \| 6750 \| 1.1105 \| \| 0.1815 \| 28.0 \| 7000 \| 0.7443 \| \| 0.1815 \| 29.0 \| 7250 \| 0.2616 \| \| 0.165 \| 30.0 \| 7500 \| 0.5318 \| \| 0.165 \| 31.0 \| 7750 \| 0.7608 \| \| 0.1435 \| 32.0 \| 8000 \| 0.9647 \| \| 0.1435 \| 33.0 \| 8250 \| 1.3749 \| \| 0.1516 \| 34.0 \| 8500 \| 0.7167 \| \| 0.1516 \| 35.0 \| 8750 \| 0.5426 \| \| 0.1359 \| 36.0 \| 9000 \| 0.7225 \| \| 0.1359 \| 37.0 \| 9250 \| 0.5453 \| \| 0.1266 \| 38.0 \| 9500 \| 0.4825 \| \| 0.1266 \| 39.0 \| 9750 \| 0.7271 \| \| 0.1153 \| 40.0 \| 10000 \| 0.9044 \| \| 0.1153 \| 41.0 \| 10250 \| 1.0363 \| \| 0.1175 \| 42.0 \| 10500 \| 0.7987 \| \| 0.1175 \| 43.0 \| 10750 \| 0.7596 \| \| 0.1089 \| 44.0 \| 11000 \| 0.8637 \| \| 0.1089 \| 45.0 \| 11250 \| 0.8327 \| \| 0.1092 \| 46.0 \| 11500 \| 0.7161 \| \| 0.1092 \| 47.0 \| 11750 \| 0.7768 \| \| 0.1068 \| 48.0 \| 12000 \| 0.9059 \| \| 0.1068 \| 49.0 \| 12250 \| 0.8829 \| \| 0.1045 \| 50.0 \| 12500 \| 0.8711 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.10.0 - Datasets 2.5.1 - Tokenizers 0.12.1
carbon225/transforchess-bart-base	carbon225	2022-09-23T18:13:23Z	110	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "license:cc0-1.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-07-31T18:02:04Z	--- license: cc0-1.0 widget: - text: " White pawn to d4. Black knight to f6." ---
sd-concepts-library/sintez-ico	sd-concepts-library	2022-09-23T18:13:17Z	0	1	null	[ "license:mit", "region:us" ]	null	2022-09-23T18:13:03Z	--- license: mit --- ### sintez-ico on Stable Diffusion This is the `<sintez-ico>` 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 a `style`: ![<sintez-ico> 0](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/7.jpeg) ![<sintez-ico> 1](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/2.jpeg) ![<sintez-ico> 2](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/6.jpeg) ![<sintez-ico> 3](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/4.jpeg) ![<sintez-ico> 4](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/5.jpeg) ![<sintez-ico> 5](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/0.jpeg) ![<sintez-ico> 6](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/1.jpeg) ![<sintez-ico> 7](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/3.jpeg)
Eulering/moonlight-night	Eulering	2022-09-23T14:47:20Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2022-09-23T14:47:20Z	--- license: bigscience-openrail-m ---
gokuls/bert-base-Massive-intent	gokuls	2022-09-23T14:26:09Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:massive", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-23T13:38:21Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - massive metrics: - accuracy model-index: - name: bert-base-Massive-intent results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.8858829316281358 --- <!-- 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. --> # bert-base-Massive-intent This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.6707 - Accuracy: 0.8859 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.6844 \| 1.0 \| 720 \| 0.7190 \| 0.8387 \| \| 0.4713 \| 2.0 \| 1440 \| 0.5449 \| 0.8726 \| \| 0.2459 \| 3.0 \| 2160 \| 0.5893 \| 0.8790 \| \| 0.1469 \| 4.0 \| 2880 \| 0.6631 \| 0.8795 \| \| 0.0874 \| 5.0 \| 3600 \| 0.6707 \| 0.8859 \| \| 0.0507 \| 6.0 \| 4320 \| 0.7189 \| 0.8844 \| \| 0.0344 \| 7.0 \| 5040 \| 0.7480 \| 0.8854 \| \| 0.0225 \| 8.0 \| 5760 \| 0.7956 \| 0.8844 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
bhumikak/resultsb	bhumikak	2022-09-23T14:21:23Z	105	0	transformers	[ "transformers", "pytorch", "pegasus", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-23T13:46:43Z	--- tags: - generated_from_trainer model-index: - name: resultsb 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. --> # resultsb This model is a fine-tuned version of [bhumikak/resultsa](https://huggingface.co/bhumikak/resultsa) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.8957 - Rouge2 Precision: 0.2127 - Rouge2 Recall: 0.2605 - Rouge2 Fmeasure: 0.2167 ## 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: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 50 - label_smoothing_factor: 0.1 ### Training results ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
Yousef-Cot/distilbert-base-uncased-finetuned-emotion	Yousef-Cot	2022-09-23T13:21:28Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-23T07:18:41Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9215 - name: F1 type: f1 value: 0.9218038766645168 --- <!-- 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. --> # 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: 0.2201 - Accuracy: 0.9215 - F1: 0.9218 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8242 \| 1.0 \| 250 \| 0.3311 \| 0.8965 \| 0.8931 \| \| 0.254 \| 2.0 \| 500 \| 0.2201 \| 0.9215 \| 0.9218 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2 - Datasets 2.4.0 - Tokenizers 0.11.6
combi2k2/MRC001	combi2k2	2022-09-23T13:01:39Z	0	0	null	[ "vi", "xlm-roberta", "dataset:UIT-ViQuAD2.0", "license:mit", "region:us" ]	null	2022-09-18T03:23:55Z	--- language: vi tags: - vi - xlm-roberta widget: - text: Toà nhà nào cao nhất Việt Nam? context: Landmark 81 là một toà nhà chọc trời trong tổ hợp dự án Vinhomes Tân Cảng, một dự án có tổng mức đầu tư 40.000 tỷ đồng, do Công ty Cổ phần Đầu tư xây dựng Tân Liên Phát thuộc Vingroup làm chủ đầu tư. Toà tháp cao 81 tầng, hiện tại là toà nhà cao nhất Việt Nam và là toà nhà cao nhất Đông Nam Á từ tháng 3 năm 2018. datasets: - UIT-ViQuAD2.0 license: mit metrics: - f1 - em --- # Machine Reading Comprehension Vietnamese [Colab Notebook](https://colab.research.google.com/drive/1JeyjSluVLIoZGzC_kOq6HXGUX-JMN3VP?usp=sharing) ## Overview - Language model: xlm-roberta-base - Language: Vietnamese - Downstream-task: Extractive QA - Dataset: [UIT-ViQuAD2.0](https://paperswithcode.com/dataset/uit-viquad) - Dataset Format: SQuAD 2.0 - Infrastructure: cuda Tesla P100-PCIE-16GB (Google Colab) ## Requirements The following modules are essential for running the trainer: - transformers - datasets - evaluate - numpy Run the following commands to install the required libraries: ``` >>> pip install datasets evaluate numpy >>> pip install git+https://github.com/huggingface/transformers ``` ## Hyperparameter ``` batch_size = 16 n_epochs = 10 base_LM_model = "xlm-roberta-base" max_seq_len = 256 learning_rate = 2e-5 weight_decay = 0.01 ``` ## Performance Evaluated on the UIT-ViQuAD2.0 dev set with the official eval script. ``` 'exact': 29.947276, 'f1': 43.627568, 'total': 2845, 'HasAns_exact': 43.827160, 'HasAns_f1': 63.847958, 'HasAns_total': 1944, 'NoAns_exact': 0.0, 'NoAns_f1': 0.0, 'NoAns_total': 901 ``` ## Usage ```python from transformers import { AutoModelForQuestionAnswering, AutoTokenizer, pipeline } model_checkpoint = "results/checkpoint-16000" question_answerer = pipeline("question-answering", model = model_checkpoint) # a) get predictions QA_input = { 'question': 'Hiến pháp Mali quy định thế nào đối với tôn giáo?', 'context': 'Ước tính có khoảng 90% dân số Mali theo đạo Hồi (phần lớn là hệ phái Sunni), khoảng 5% là theo Kitô giáo (khoảng hai phần ba theo Giáo hội Công giáo Rôma và một phần ba là theo Tin Lành) và 5% còn lại theo các tín ngưỡng vật linh truyền thống bản địa. Một số ít người Mali theo thuyết vô thần và thuyết bất khả tri, phần lớn họ thực hiện những nghi lễ tôn giáo cơ bản hằng ngày. Các phong tục Hồi giáo ở Mali có mức độ vừa phải, khoan dung, và đã thay đổi theo các điều kiện của địa phương; các mối quan hệ giữa người Hồi giáo và các cộng đồng tôn giáo nhỏ khác nói chung là thân thiện. Hiến pháp của Mali đã quy định một thể chế nhà nước thế tục và ủng hộ quyền tự do tôn giáo, và chính phủ Mali phải đảm bảo quyền này.' } res = question_answerer(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint) tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) ``` ## Author Duc Nguyen ## Citation ``` Kiet Van Nguyen, Son Quoc Tran, Luan Thanh Nguyen, Tin Van Huynh, Son T. Luu, Ngan Luu-Thuy Nguyen. "VLSP 2021 Shared Task: Vietnamese Machine Reading Comprehension." The 8th International Workshop on Vietnamese Language and Speech Processing (VLSP 2021) . ```
huggingtweets/cushbomb	huggingtweets	2022-09-23T12:40:19Z	113	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/cushbomb/1663936814713/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('https://pbs.twimg.com/profile_images/1560517790900969473/MPbfc6w2_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">matt christman</div> <div style="text-align: center; font-size: 14px;">@cushbomb</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 matt christman. \| Data \| matt christman \| \| --- \| --- \| \| Tweets downloaded \| 3230 \| \| Retweets \| 241 \| \| Short tweets \| 685 \| \| Tweets kept \| 2304 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39bxpmve/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 @cushbomb's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2gd8zqob) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2gd8zqob/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/cushbomb') 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)
rossedwa/bert-take-uncased-f1-epoch-8	rossedwa	2022-09-23T12:27:37Z	161	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-23T12:16:01Z	f1_score = 85 Kaggle Score: 0.71711
pulkitkumar13/dark-bert-finetuned-ner1	pulkitkumar13	2022-09-23T11:02:45Z	110	4	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-09-23T10:40:57Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: dark-bert-finetuned-ner1 results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.9337419247970846 - name: Recall type: recall value: 0.9486704813194211 - name: F1 type: f1 value: 0.9411470072627097 - name: Accuracy type: accuracy value: 0.9861364572908695 --- <!-- 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. --> # dark-bert-finetuned-ner1 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: 0.0833 - Precision: 0.9337 - Recall: 0.9487 - F1: 0.9411 - Accuracy: 0.9861 ## 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: 8 - eval_batch_size: 8 - 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 \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0358 \| 1.0 \| 1756 \| 0.0780 \| 0.9283 \| 0.9409 \| 0.9346 \| 0.9844 \| \| 0.0172 \| 2.0 \| 3512 \| 0.0708 \| 0.9375 \| 0.9488 \| 0.9431 \| 0.9860 \| \| 0.0056 \| 3.0 \| 5268 \| 0.0833 \| 0.9337 \| 0.9487 \| 0.9411 \| 0.9861 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.10.0 - Datasets 2.5.1 - Tokenizers 0.12.1
rinascimento/distilbert-base-uncased-finetuned-emotion	rinascimento	2022-09-23T09:52:40Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-23T06:15:09Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.924 - name: F1 type: f1 value: 0.9241401774459951 --- <!-- 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. --> # 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: 0.2167 - Accuracy: 0.924 - F1: 0.9241 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.815 \| 1.0 \| 250 \| 0.3051 \| 0.9045 \| 0.9022 \| \| 0.2496 \| 2.0 \| 500 \| 0.2167 \| 0.924 \| 0.9241 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
bryanleeharyanto/vtt-indonesia	bryanleeharyanto	2022-09-23T06:39:29Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-09-20T07:59:29Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: vtt-indonesia 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. --> # vtt-indonesia 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: 0.3472 - Wer: 0.3582 ## 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: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.7612 \| 3.23 \| 400 \| 0.6405 \| 0.6714 \| \| 0.4143 \| 6.45 \| 800 \| 0.3772 \| 0.4974 \| \| 0.2068 \| 9.68 \| 1200 \| 0.3877 \| 0.4442 \| \| 0.1436 \| 12.9 \| 1600 \| 0.3785 \| 0.4212 \| \| 0.1133 \| 16.13 \| 2000 \| 0.3944 \| 0.4144 \| \| 0.09 \| 19.35 \| 2400 \| 0.3695 \| 0.3925 \| \| 0.0705 \| 22.58 \| 2800 \| 0.3706 \| 0.3846 \| \| 0.057 \| 25.81 \| 3200 \| 0.3720 \| 0.3725 \| \| 0.048 \| 29.03 \| 3600 \| 0.3472 \| 0.3582 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
ryuno25/t5-base-finetuned-eli-5	ryuno25	2022-09-23T06:29:14Z	114	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:eli5", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-23T04:40:11Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - eli5 metrics: - rouge model-index: - name: t5-base-finetuned-eli-5 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: eli5 type: eli5 config: LFQA_reddit split: train_eli5 args: LFQA_reddit metrics: - name: Rouge1 type: rouge value: 13.4 --- <!-- 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. --> # t5-base-finetuned-eli-5 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the eli5 dataset. It achieves the following results on the evaluation set: - Loss: 3.4557 - Rouge1: 13.4 - Rouge2: 1.9415 - Rougel: 10.4671 - Rougelsum: 12.0693 - Gen Len: 18.9529 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 3.6754 \| 1.0 \| 8520 \| 3.4557 \| 13.4 \| 1.9415 \| 10.4671 \| 12.0693 \| 18.9529 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
SmilestheSad/hf_trainer	SmilestheSad	2022-09-23T04:39:53Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-23T03:36:00Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: hf_trainer 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. --> # hf_trainer This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0708 - F1: 0.9066 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.0344 \| 1.0 \| 565 \| 0.0661 \| 0.8811 \| \| 0.0354 \| 2.0 \| 1130 \| 0.0641 \| 0.8963 \| \| 0.0222 \| 3.0 \| 1695 \| 0.0690 \| 0.8994 \| \| 0.0145 \| 4.0 \| 2260 \| 0.0714 \| 0.9036 \| \| 0.011 \| 5.0 \| 2825 \| 0.0708 \| 0.9066 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
nvidia/tts_en_fastpitch	nvidia	2022-09-23T04:28:43Z	804	39	nemo	[ "nemo", "text-to-speech", "speech", "audio", "Transformer", "pytorch", "NeMo", "Riva", "en", "dataset:ljspeech", "arxiv:2006.06873", "arxiv:2108.10447", "license:cc-by-4.0", "region:us" ]	text-to-speech	2022-06-28T17:55:51Z	--- language: - en library_name: nemo datasets: - ljspeech thumbnail: null tags: - text-to-speech - speech - audio - Transformer - pytorch - NeMo - Riva license: cc-by-4.0 --- # NVIDIA FastPitch (en-US) <style> img { display: inline; } </style> \| [![Model architecture](https://img.shields.io/badge/Model_Arch-FastPitch--Transformer-lightgrey#model-badge)](#model-architecture) \| [![Model size](https://img.shields.io/badge/Params-45M-lightgrey#model-badge)](#model-architecture) \| [![Language](https://img.shields.io/badge/Language-en--US-lightgrey#model-badge)](#datasets) \| [![Riva Compatible](https://img.shields.io/badge/NVIDIA%20Riva-compatible-brightgreen#model-badge)](#deployment-with-nvidia-riva) \| FastPitch [1] is a fully-parallel transformer architecture with prosody control over pitch and individual phoneme duration. Additionally, it uses an unsupervised speech-text aligner [2]. See the [model architecture](#model-architecture) section for complete architecture details. It is also compatible with NVIDIA Riva for [production-grade server deployments](#deployment-with-nvidia-riva). ## Usage The model is available for use in the NeMo toolkit [3] and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed the latest PyTorch version. ``` pip install nemo_toolkit['all'] ``` ### Automatically instantiate the model Note: This model generates only spectrograms and a vocoder is needed to convert the spectrograms to waveforms. In this example HiFiGAN is used. ```python # Load FastPitch from nemo.collections.tts.models import FastPitchModel spec_generator = FastPitchModel.from_pretrained("nvidia/tts_en_fastpitch") # Load vocoder from nemo.collections.tts.models import HifiGanModel model = HifiGanModel.from_pretrained(model_name="nvidia/tts_hifigan") ``` ### Generate audio ```python import soundfile as sf parsed = spec_generator.parse("You can type your sentence here to get nemo to produce speech.") spectrogram = spec_generator.generate_spectrogram(tokens=parsed) audio = model.convert_spectrogram_to_audio(spec=spectrogram) ``` ### Save the generated audio file ```python # Save the audio to disk in a file called speech.wav sf.write("speech.wav", audio.to('cpu').detach().numpy()[0], 22050) ``` ### Input This model accepts batches of text. ### Output This model generates mel spectrograms. ## Model Architecture FastPitch is a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental frequency contours. The model predicts pitch contours during inference. By altering these predictions, the generated speech can be more expressive, better match the semantic of the utterance, and in the end more engaging to the listener. FastPitch is based on a fully-parallel Transformer architecture, with a much higher real-time factor than Tacotron2 for the mel-spectrogram synthesis of a typical utterance. It uses an unsupervised speech-text aligner. ## Training The NeMo toolkit [3] was used for training the models for 1000 epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/tts/fastpitch.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/tts/conf/fastpitch_align_v1.05.yaml). ### Datasets This model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent. ## Performance No performance information is available at this time. ## Limitations This checkpoint only works well with vocoders that were trained on 22050Hz data. Otherwise, the generated audio may be scratchy or choppy-sounding. ## Deployment with NVIDIA Riva For the best real-time accuracy, latency, and throughput, deploy the model with [NVIDIA Riva](https://developer.nvidia.com/riva), an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded. Additionally, Riva provides: * World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours * Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization * Streaming speech recognition, Kubernetes compatible scaling, and Enterprise-grade support Check out [Riva live demo](https://developer.nvidia.com/riva#demos). ## References - [1] [FastPitch: Parallel Text-to-speech with Pitch Prediction](https://arxiv.org/abs/2006.06873) - [2] [One TTS Alignment To Rule Them All](https://arxiv.org/abs/2108.10447) - [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
apapiu/diffusion_model_aesthetic_keras	apapiu	2022-09-23T03:56:11Z	0	1	null	[ "license:openrail", "region:us" ]	null	2022-09-21T19:14:31Z	--- license: openrail --- A sample from the [Laion 6.5+ ](https://laion.ai/blog/laion-aesthetics/) image + text dataset. You can see some samples [here](http://captions.christoph-schuhmann.de/2B-en-6.5.html). The samples are resized + center-cropped to 64x64x3 and the .npz file also contains CLIP embeddings. TODO: add img2dataset script. The data can be used to train a basic text-to-image model.
wenkai-li/new_classifer_epoch7	wenkai-li	2022-09-23T03:35:38Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-23T02:09:46Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: new_classifer_epoch7 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. --> # new_classifer_epoch7 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1305 - Accuracy: 0.9861 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.0526 \| 1.0 \| 4248 \| 0.0587 \| 0.9797 \| \| 0.0259 \| 2.0 \| 8496 \| 0.0502 \| 0.9855 \| \| 0.0121 \| 3.0 \| 12744 \| 0.1170 \| 0.9773 \| \| 0.0051 \| 4.0 \| 16992 \| 0.1379 \| 0.9811 \| \| 0.0026 \| 5.0 \| 21240 \| 0.1014 \| 0.9869 \| \| 0.0013 \| 6.0 \| 25488 \| 0.1312 \| 0.9859 \| \| 0.0002 \| 7.0 \| 29736 \| 0.1305 \| 0.9861 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
spacemanidol/t5-base-all-rewrite-correct-unchaged-no-prefix	spacemanidol	2022-09-23T02:59:17Z	5	1	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-19T19:44:11Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-base-all-rewrite-correct-unchaged-no-prefix 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. --> # t5-base-all-rewrite-correct-unchaged-no-prefix This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. ## 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: 5e-05 - train_batch_size: 256 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.21.2 - Pytorch 1.7.0+cu110 - Datasets 2.4.0 - Tokenizers 0.12.1
jamiehuang/t5-base-finetuned-xsum	jamiehuang	2022-09-23T02:11:02Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:eli5", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-22T15:06:03Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - eli5 model-index: - name: t5-base-finetuned-xsum 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. --> # t5-base-finetuned-xsum This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the eli5 dataset. ## 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: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
neelmehta00/t5-base-finetuned-eli5	neelmehta00	2022-09-22T23:16:27Z	101	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:eli5", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-22T15:04:24Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - eli5 metrics: - rouge model-index: - name: t5-base-finetuned-eli5 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: eli5 type: eli5 config: LFQA_reddit split: train_eli5 args: LFQA_reddit metrics: - name: Rouge1 type: rouge value: 14.5658 --- <!-- 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. --> # t5-base-finetuned-eli5 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the eli5 dataset. It achieves the following results on the evaluation set: - Loss: 3.1765 - Rouge1: 14.5658 - Rouge2: 2.2777 - Rougel: 11.2826 - Rougelsum: 13.1136 - Gen Len: 18.9938 ## 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 3.3398 \| 1.0 \| 17040 \| 3.1765 \| 14.5658 \| 2.2777 \| 11.2826 \| 13.1136 \| 18.9938 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
Kevin123/distilbert-base-uncased-finetuned-cola	Kevin123	2022-09-22T22:39:03Z	106	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-22T21:03:59Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5474713423103301 --- <!-- 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. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8663 - Matthews Correlation: 0.5475 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5248 \| 1.0 \| 535 \| 0.5171 \| 0.4210 \| \| 0.3418 \| 2.0 \| 1070 \| 0.4971 \| 0.5236 \| \| 0.2289 \| 3.0 \| 1605 \| 0.6874 \| 0.5023 \| \| 0.1722 \| 4.0 \| 2140 \| 0.7680 \| 0.5392 \| \| 0.118 \| 5.0 \| 2675 \| 0.8663 \| 0.5475 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.8.1+cu102 - Datasets 1.18.3 - Tokenizers 0.10.3
JJRohan/ppo-LunarLander-v2	JJRohan	2022-09-22T21:12:36Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-09-22T21:12:08Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 169.43 +/- 77.42 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
nlp-guild/bert-base-chinese-finetuned-intent_recognition-biomedical	nlp-guild	2022-09-22T20:06:57Z	136	4	transformers	[ "transformers", "pytorch", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-22T19:42:37Z	fine-tuned bert-base-chinese for intent recognition task on [dataset](https://huggingface.co/datasets/nlp-guild/intent-recognition-biomedical) # Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification from transformers import TextClassificationPipeline tokenizer = AutoTokenizer.from_pretrained("nlp-guild/bert-base-chinese-finetuned-intent_recognition-biomedical") model = AutoModelForSequenceClassification.from_pretrained("nlp-guild/bert-base-chinese-finetuned-intent_recognition-biomedical") nlp = TextClassificationPipeline(model = model, tokenizer = tokenizer) label_set = [ '定义', '病因', '预防', '临床表现(病症表现)', '相关病症', '治疗方法', '所属科室', '传染性', '治愈率', '禁忌', '化验/体检方案', '治疗时间', '其他' ] def readable_results(top_k:int, usr_query:str): raw = nlp(usr_query, top_k = top_k) def f(x): index = int(x['label'][6:]) x['label'] = label_set[index] for i in raw: f(i) return raw readable_results(3,'得了心脏病怎么办') ''' [{'label': '治疗方法', 'score': 0.9994503855705261}, {'label': '其他', 'score': 0.00018375989748165011}, {'label': '临床表现(病症表现)', 'score': 0.00010841596667887643}] ''' ```
SharpAI/benign-net-traffic-v2-t5-l12	SharpAI	2022-09-22T20:06:09Z	106	0	transformers	[ "transformers", "pytorch", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-21T00:02:21Z	--- tags: - generated_from_keras_callback model-index: - name: benign-net-traffic-v2-t5-l12 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # benign-net-traffic-v2-t5-l12 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: ## 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: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1
TingChenChang/hpvqa-lcqmc-ocnli-cnsd-multi-MiniLM-v2	TingChenChang	2022-09-22T19:23:21Z	6	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-09-22T19:23:08Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 12 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 1e-06 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 12, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
jayanta/twitter-roberta-base-sentiment-sentiment-memes-30epcohs	jayanta	2022-09-22T19:04:33Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-22T14:38:21Z	--- tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: twitter-roberta-base-sentiment-sentiment-memes-30epcohs 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. --> # twitter-roberta-base-sentiment-sentiment-memes-30epcohs This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3027 - Accuracy: 0.8517 - Precision: 0.8536 - Recall: 0.8517 - F1: 0.8523 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.2504 \| 1.0 \| 2147 \| 0.7129 \| 0.8087 \| 0.8112 \| 0.8087 \| 0.8036 \| \| 0.2449 \| 2.0 \| 4294 \| 0.7500 \| 0.8229 \| 0.8279 \| 0.8229 \| 0.8240 \| \| 0.2652 \| 3.0 \| 6441 \| 0.7460 \| 0.8181 \| 0.8185 \| 0.8181 \| 0.8149 \| \| 0.2585 \| 4.0 \| 8588 \| 0.7906 \| 0.8155 \| 0.8152 \| 0.8155 \| 0.8153 \| \| 0.2534 \| 5.0 \| 10735 \| 0.8178 \| 0.8061 \| 0.8180 \| 0.8061 \| 0.8080 \| \| 0.2498 \| 6.0 \| 12882 \| 0.8139 \| 0.8166 \| 0.8163 \| 0.8166 \| 0.8164 \| \| 0.2825 \| 7.0 \| 15029 \| 0.7494 \| 0.8155 \| 0.8210 \| 0.8155 \| 0.8168 \| \| 0.2459 \| 8.0 \| 17176 \| 0.8870 \| 0.8061 \| 0.8122 \| 0.8061 \| 0.8075 \| \| 0.2303 \| 9.0 \| 19323 \| 0.8699 \| 0.7987 \| 0.8060 \| 0.7987 \| 0.8003 \| \| 0.2425 \| 10.0 \| 21470 \| 0.8043 \| 0.8244 \| 0.8275 \| 0.8244 \| 0.8253 \| \| 0.2143 \| 11.0 \| 23617 \| 0.9163 \| 0.8208 \| 0.8251 \| 0.8208 \| 0.8219 \| \| 0.2054 \| 12.0 \| 25764 \| 0.8330 \| 0.8239 \| 0.8258 \| 0.8239 \| 0.8245 \| \| 0.208 \| 13.0 \| 27911 \| 1.0673 \| 0.8134 \| 0.8216 \| 0.8134 \| 0.8150 \| \| 0.1668 \| 14.0 \| 30058 \| 0.9071 \| 0.8270 \| 0.8276 \| 0.8270 \| 0.8273 \| \| 0.1571 \| 15.0 \| 32205 \| 0.9294 \| 0.8339 \| 0.8352 \| 0.8339 \| 0.8344 \| \| 0.1857 \| 16.0 \| 34352 \| 0.9909 \| 0.8354 \| 0.8350 \| 0.8354 \| 0.8352 \| \| 0.1476 \| 17.0 \| 36499 \| 0.9747 \| 0.8433 \| 0.8436 \| 0.8433 \| 0.8434 \| \| 0.1341 \| 18.0 \| 38646 \| 0.9372 \| 0.8422 \| 0.8415 \| 0.8422 \| 0.8415 \| \| 0.1181 \| 19.0 \| 40793 \| 1.0301 \| 0.8433 \| 0.8443 \| 0.8433 \| 0.8437 \| \| 0.1192 \| 20.0 \| 42940 \| 1.1332 \| 0.8407 \| 0.8415 \| 0.8407 \| 0.8410 \| \| 0.0983 \| 21.0 \| 45087 \| 1.2002 \| 0.8428 \| 0.8498 \| 0.8428 \| 0.8440 \| \| 0.0951 \| 22.0 \| 47234 \| 1.2141 \| 0.8475 \| 0.8504 \| 0.8475 \| 0.8483 \| \| 0.0784 \| 23.0 \| 49381 \| 1.1652 \| 0.8407 \| 0.8453 \| 0.8407 \| 0.8417 \| \| 0.0623 \| 24.0 \| 51528 \| 1.1730 \| 0.8417 \| 0.8443 \| 0.8417 \| 0.8425 \| \| 0.054 \| 25.0 \| 53675 \| 1.2900 \| 0.8454 \| 0.8496 \| 0.8454 \| 0.8464 \| \| 0.0584 \| 26.0 \| 55822 \| 1.2831 \| 0.8480 \| 0.8497 \| 0.8480 \| 0.8486 \| \| 0.0531 \| 27.0 \| 57969 \| 1.3043 \| 0.8506 \| 0.8524 \| 0.8506 \| 0.8512 \| \| 0.0522 \| 28.0 \| 60116 \| 1.2891 \| 0.8527 \| 0.8554 \| 0.8527 \| 0.8534 \| \| 0.037 \| 29.0 \| 62263 \| 1.3077 \| 0.8538 \| 0.8559 \| 0.8538 \| 0.8544 \| \| 0.038 \| 30.0 \| 64410 \| 1.3027 \| 0.8517 \| 0.8536 \| 0.8517 \| 0.8523 \| ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 1.15.2.dev0 - Tokenizers 0.10.1
cjj8168/stress_dreaddit	cjj8168	2022-09-22T16:46:33Z	159	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-22T16:44:29Z	--- license: mit tags: - generated_from_trainer model-index: - name: stress_dreaddit 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. --> # stress_dreaddit This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on an unknown dataset. ## 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: 0.005 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
facebook/spar-wiki-bm25-lexmodel-query-encoder	facebook	2022-09-22T16:44:45Z	111	2	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "arxiv:2110.06918", "endpoints_compatible", "region:us" ]	feature-extraction	2022-09-21T21:44:05Z	--- tags: - feature-extraction pipeline_tag: feature-extraction --- This model is the query encoder of the Wiki BM25 Lexical Model (Λ) from the SPAR paper: [Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?](https://arxiv.org/abs/2110.06918) <br> Xilun Chen, Kushal Lakhotia, Barlas Oğuz, Anchit Gupta, Patrick Lewis, Stan Peshterliev, Yashar Mehdad, Sonal Gupta and Wen-tau Yih <br> Meta AI The associated github repo is available here: https://github.com/facebookresearch/dpr-scale/tree/main/spar This model is a BERT-base sized dense retriever trained on Wikipedia articles to imitate the behavior of BM25. The following models are also available: Pretrained Model \| Corpus \| Teacher \| Architecture \| Query Encoder Path \| Context Encoder Path \|---\|---\|---\|---\|---\|--- Wiki BM25 Λ \| Wikipedia \| BM25 \| BERT-base \| facebook/spar-wiki-bm25-lexmodel-query-encoder \| facebook/spar-wiki-bm25-lexmodel-context-encoder PAQ BM25 Λ \| PAQ \| BM25 \| BERT-base \| facebook/spar-paq-bm25-lexmodel-query-encoder \| facebook/spar-paq-bm25-lexmodel-context-encoder MARCO BM25 Λ \| MS MARCO \| BM25 \| BERT-base \| facebook/spar-marco-bm25-lexmodel-query-encoder \| facebook/spar-marco-bm25-lexmodel-context-encoder MARCO UniCOIL Λ \| MS MARCO \| UniCOIL \| BERT-base \| facebook/spar-marco-unicoil-lexmodel-query-encoder \| facebook/spar-marco-unicoil-lexmodel-context-encoder # Using the Lexical Model (Λ) Alone This model should be used together with the associated context encoder, similar to the [DPR](https://huggingface.co/docs/transformers/v4.22.1/en/model_doc/dpr) model. ``` import torch from transformers import AutoTokenizer, AutoModel # The tokenizer is the same for the query and context encoder tokenizer = AutoTokenizer.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') query_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') context_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-context-encoder') query = "Where was Marie Curie born?" contexts = [ "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.", "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace." ] # Apply tokenizer query_input = tokenizer(query, return_tensors='pt') ctx_input = tokenizer(contexts, padding=True, truncation=True, return_tensors='pt') # Compute embeddings: take the last-layer hidden state of the [CLS] token query_emb = query_encoder(query_input).last_hidden_state[:, 0, :] ctx_emb = context_encoder(ctx_input).last_hidden_state[:, 0, :] # Compute similarity scores using dot product score1 = query_emb @ ctx_emb[0] # 341.3268 score2 = query_emb @ ctx_emb[1] # 340.1626 ``` # Using the Lexical Model (Λ) with a Base Dense Retriever as in SPAR As Λ learns lexical matching from a sparse teacher retriever, it can be used in combination with a standard dense retriever (e.g. [DPR](https://huggingface.co/docs/transformers/v4.22.1/en/model_doc/dpr#dpr), [Contriever](https://huggingface.co/facebook/contriever-msmarco)) to build a dense retriever that excels at both lexical and semantic matching. In the following example, we show how to build the SPAR-Wiki model for Open-Domain Question Answering by concatenating the embeddings of DPR and the Wiki BM25 Λ. ``` import torch from transformers import AutoTokenizer, AutoModel from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer from transformers import DPRContextEncoder, DPRContextEncoderTokenizer # DPR model dpr_ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-multiset-base") dpr_ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-multiset-base") dpr_query_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-multiset-base") dpr_query_encoder = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-multiset-base") # Wiki BM25 Λ model lexmodel_tokenizer = AutoTokenizer.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') lexmodel_query_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') lexmodel_context_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-context-encoder') query = "Where was Marie Curie born?" contexts = [ "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.", "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace." ] # Compute DPR embeddings dpr_query_input = dpr_query_tokenizer(query, return_tensors='pt')['input_ids'] dpr_query_emb = dpr_query_encoder(dpr_query_input).pooler_output dpr_ctx_input = dpr_ctx_tokenizer(contexts, padding=True, truncation=True, return_tensors='pt') dpr_ctx_emb = dpr_ctx_encoder(dpr_ctx_input).pooler_output # Compute Λ embeddings lexmodel_query_input = lexmodel_tokenizer(query, return_tensors='pt') lexmodel_query_emb = lexmodel_query_encoder(query_input).last_hidden_state[:, 0, :] lexmodel_ctx_input = lexmodel_tokenizer(contexts, padding=True, truncation=True, return_tensors='pt') lexmodel_ctx_emb = lexmodel_context_encoder(*ctx_input).last_hidden_state[:, 0, :] # Form SPAR embeddings via concatenation # The concatenation weight is only applied to query embeddings # Refer to the SPAR paper for details concat_weight = 0.7 spar_query_emb = torch.cat( [dpr_query_emb, concat_weight lexmodel_query_emb], dim=-1, ) spar_ctx_emb = torch.cat( [dpr_ctx_emb, lexmodel_ctx_emb], dim=-1, ) # Compute similarity scores score1 = spar_query_emb @ spar_ctx_emb[0] # 317.6931 score2 = spar_query_emb @ spar_ctx_emb[1] # 314.6144 ```
CoreyMorris/Reinforce-cartpole-v1	CoreyMorris	2022-09-22T16:21:40Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2022-09-22T16:20:39Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
Chemsseddine/distilbert-base-uncased-finetuned-cola	Chemsseddine	2022-09-22T15:31:00Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-07T17:23:46Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-cola 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. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0011 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 5 \| 2.1485 \| \| No log \| 2.0 \| 10 \| 2.0983 \| \| No log \| 3.0 \| 15 \| 2.0499 \| \| No log \| 4.0 \| 20 \| 2.0155 \| \| No log \| 5.0 \| 25 \| 2.0011 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
jayanta/twitter-roberta-base-sentiment-sentiment-memes	jayanta	2022-09-22T14:35:05Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-19T15:25:56Z	--- tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: twitter-roberta-base-sentiment-sentiment-memes 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. --> # twitter-roberta-base-sentiment-sentiment-memes This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9582 - Accuracy: 0.8187 - Precision: 0.8199 - Recall: 0.8187 - F1: 0.8191 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.4673 \| 1.0 \| 2147 \| 0.4373 \| 0.7647 \| 0.8180 \| 0.7647 \| 0.7657 \| \| 0.3987 \| 2.0 \| 4294 \| 0.5528 \| 0.7783 \| 0.8096 \| 0.7783 \| 0.7804 \| \| 0.3194 \| 3.0 \| 6441 \| 0.6432 \| 0.7752 \| 0.7767 \| 0.7752 \| 0.7680 \| \| 0.2855 \| 4.0 \| 8588 \| 0.6820 \| 0.7814 \| 0.8034 \| 0.7814 \| 0.7837 \| \| 0.2575 \| 5.0 \| 10735 \| 0.7427 \| 0.7720 \| 0.8070 \| 0.7720 \| 0.7741 \| \| 0.2154 \| 6.0 \| 12882 \| 0.8225 \| 0.7987 \| 0.8062 \| 0.7987 \| 0.8004 \| \| 0.2195 \| 7.0 \| 15029 \| 0.8361 \| 0.8071 \| 0.8086 \| 0.8071 \| 0.8077 \| \| 0.2322 \| 8.0 \| 17176 \| 0.8842 \| 0.8056 \| 0.8106 \| 0.8056 \| 0.8069 \| \| 0.2102 \| 9.0 \| 19323 \| 0.9188 \| 0.8129 \| 0.8144 \| 0.8129 \| 0.8135 \| \| 0.1893 \| 10.0 \| 21470 \| 0.9582 \| 0.8187 \| 0.8199 \| 0.8187 \| 0.8191 \| ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 1.15.2.dev0 - Tokenizers 0.10.1
huggingtweets/slime_machine	huggingtweets	2022-09-22T14:09:28Z	94	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/slime_machine/1663855763474/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('https://pbs.twimg.com/profile_images/1554733825220939777/lgFt_2e1_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">slime</div> <div style="text-align: center; font-size: 14px;">@slime_machine</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 slime. \| Data \| slime \| \| --- \| --- \| \| Tweets downloaded \| 3229 \| \| Retweets \| 441 \| \| Short tweets \| 589 \| \| Tweets kept \| 2199 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2s9inuxg/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 @slime_machine's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5xjy8nrj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5xjy8nrj/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/slime_machine') 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)
sd-concepts-library/pixel-mania	sd-concepts-library	2022-09-22T14:05:08Z	0	1	null	[ "license:mit", "region:us" ]	null	2022-09-22T05:26:54Z	--- license: mit --- ### pixel-mania on Stable Diffusion This is the `<pixel-mania>` 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).
m-lin20/satellite-instrument-roberta-NER	m-lin20	2022-09-22T13:33:22Z	108	1	transformers	[ "transformers", "pytorch", "roberta", "token-classification", "pt", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- language: "pt" widget: - text: "Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering the solar reflected wavelengths. The Thermal Infrared Sensor-2 (TIRS-2) improves upon Landsat 8 TIRS, addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror. In addition, Landsat 9 adds redundancy to TIRS-2, thus upgrading the instrument to a 5-year design life commensurate with other elements of the mission. Initial performance testing of OLI-2 and TIRS-2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality. " example_title: "example 1" - text: "Compared to its predecessor, Jason-3, the two AMR-C radiometer instruments have an external calibration system which enables higher radiometric stability accomplished by moving the secondary mirror between well-defined targets. Sentinel-6 allows continuing the study of the ocean circulation, climate change, and sea-level rise for at least another decade. Besides the external calibration for the AMR heritage radiometer (18.7, 23.8, and 34 GHz channels), the AMR-C contains a high-resolution microwave radiometer (HRMR) with radiometer channels at 90, 130, and 168 GHz. This subsystem allows for a factor of 5× higher spatial resolution at coastal transitions. This article presents a brief description of the instrument and the measured performance of the completed AMR-C-A and AMR-C-B instruments." example_title: "example 2" - text: "The Landsat 9 will continue the Landsat data record into its fifth decade with a near-copy build of Landsat 8 with launch scheduled for December 2020. The two instruments on Landsat 9 are Thermal Infrared Sensor-2 (TIRS-2) and Operational Land Imager-2 (OLI-2)." example_title: "example 3" inference: parameters: aggregation_strategy: "simple" --- # satellite-instrument-roberta-NER For details, please visit the [GitHub link](https://github.com/THU-EarthInformationScienceLab/Satellite-Instrument-NER). ## Citation Our [paper](https://www.tandfonline.com/doi/full/10.1080/17538947.2022.2107098) has been published in the International Journal of Digital Earth : ```bibtex @article{lin2022satellite, title={Satellite and instrument entity recognition using a pre-trained language model with distant supervision}, author={Lin, Ming and Jin, Meng and Liu, Yufu and Bai, Yuqi}, journal={International Journal of Digital Earth}, volume={15}, number={1}, pages={1290--1304}, year={2022}, publisher={Taylor \& Francis} } ```
m-lin20/satellite-instrument-bert-NER	m-lin20	2022-09-22T13:32:42Z	104	2	transformers	[ "transformers", "pytorch", "bert", "token-classification", "pt", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- language: "pt" widget: - text: "Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering the solar reflected wavelengths. The Thermal Infrared Sensor-2 (TIRS-2) improves upon Landsat 8 TIRS, addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror. In addition, Landsat 9 adds redundancy to TIRS-2, thus upgrading the instrument to a 5-year design life commensurate with other elements of the mission. Initial performance testing of OLI-2 and TIRS-2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality. " example_title: "example 1" - text: "Compared to its predecessor, Jason-3, the two AMR-C radiometer instruments have an external calibration system which enables higher radiometric stability accomplished by moving the secondary mirror between well-defined targets. Sentinel-6 allows continuing the study of the ocean circulation, climate change, and sea-level rise for at least another decade. Besides the external calibration for the AMR heritage radiometer (18.7, 23.8, and 34 GHz channels), the AMR-C contains a high-resolution microwave radiometer (HRMR) with radiometer channels at 90, 130, and 168 GHz. This subsystem allows for a factor of 5× higher spatial resolution at coastal transitions. This article presents a brief description of the instrument and the measured performance of the completed AMR-C-A and AMR-C-B instruments." example_title: "example 2" - text: "Landsat 9 will continue the Landsat data record into its fifth decade with a near-copy build of Landsat 8 with launch scheduled for December 2020. The two instruments on Landsat 9 are Thermal Infrared Sensor-2 (TIRS-2) and Operational Land Imager-2 (OLI-2)." example_title: "example 3" inference: parameters: aggregation_strategy: "first" --- # satellite-instrument-bert-NER For details, please visit the [GitHub link](https://github.com/THU-EarthInformationScienceLab/Satellite-Instrument-NER). ## Citation Our [paper](https://www.tandfonline.com/doi/full/10.1080/17538947.2022.2107098) has been published in the International Journal of Digital Earth : ```bibtex @article{lin2022satellite, title={Satellite and instrument entity recognition using a pre-trained language model with distant supervision}, author={Lin, Ming and Jin, Meng and Liu, Yufu and Bai, Yuqi}, journal={International Journal of Digital Earth}, volume={15}, number={1}, pages={1290--1304}, year={2022}, publisher={Taylor \& Francis} } ```
fxmarty/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic	fxmarty	2022-09-22T13:28:21Z	3	0	transformers	[ "transformers", "onnx", "distilbert", "text-classification", "dataset:sst2", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-22T13:19:36Z	--- license: apache-2.0 datasets: - sst2 - glue --- This model is a fork of https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english , quantized using dynamic Post-Training Quantization (PTQ) with ONNX Runtime and 🤗 Optimum library. It achieves 0.901 on the validation set. To load this model: ```python from optimum.onnxruntime import ORTModelForSequenceClassification model = ORTModelForSequenceClassification.from_pretrained("fxmarty/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic") ```
sd-concepts-library/bluebey-2	sd-concepts-library	2022-09-22T12:21:34Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-22T12:21:30Z	--- license: mit --- ### Bluebey-2 on Stable Diffusion This is the `<bluebey>` 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`: ![<bluebey> 0](https://huggingface.co/sd-concepts-library/bluebey-2/resolve/main/concept_images/0.jpeg) ![<bluebey> 1](https://huggingface.co/sd-concepts-library/bluebey-2/resolve/main/concept_images/2.jpeg) ![<bluebey> 2](https://huggingface.co/sd-concepts-library/bluebey-2/resolve/main/concept_images/1.jpeg)
muhtasham/bert-small-finetuned-finer	muhtasham	2022-09-22T11:50:51Z	161	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-08-25T20:36:59Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-small-finetuned-finer 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. --> # bert-small-finetuned-finer This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6137 ## 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: 128 - eval_batch_size: 8 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.8994 \| 1.0 \| 2433 \| 1.7597 \| \| 1.7226 \| 2.0 \| 4866 \| 1.6462 \| \| 1.6752 \| 3.0 \| 7299 \| 1.6137 \| ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
muhtasham/bert-small-finetuned-parsed20	muhtasham	2022-09-22T11:34:48Z	179	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-08-17T13:31:40Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-small-finetuned-parsed20 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. --> # bert-small-finetuned-parsed20 This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1193 ## 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: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 4 \| 3.0763 \| \| No log \| 2.0 \| 8 \| 2.8723 \| \| No log \| 3.0 \| 12 \| 3.5102 \| \| No log \| 4.0 \| 16 \| 2.8641 \| \| No log \| 5.0 \| 20 \| 2.7827 \| \| No log \| 6.0 \| 24 \| 2.8163 \| \| No log \| 7.0 \| 28 \| 3.2415 \| \| No log \| 8.0 \| 32 \| 3.0477 \| \| No log \| 9.0 \| 36 \| 3.5160 \| \| No log \| 10.0 \| 40 \| 3.1248 \| \| No log \| 11.0 \| 44 \| 3.2159 \| \| No log \| 12.0 \| 48 \| 3.2177 \| \| No log \| 13.0 \| 52 \| 2.9108 \| \| No log \| 14.0 \| 56 \| 3.3758 \| \| No log \| 15.0 \| 60 \| 3.1335 \| \| No log \| 16.0 \| 64 \| 2.9753 \| \| No log \| 17.0 \| 68 \| 2.9922 \| \| No log \| 18.0 \| 72 \| 3.2798 \| \| No log \| 19.0 \| 76 \| 2.7280 \| \| No log \| 20.0 \| 80 \| 3.1193 \| ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
muhtasham/bert-small-finetuned-parsed-longer50	muhtasham	2022-09-22T11:34:27Z	179	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-08-17T13:39:01Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-small-finetuned-finetuned-parsed-longer50 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. --> # bert-small-finetuned-finetuned-parsed-longer50 This model is a fine-tuned version of [muhtasham/bert-small-finetuned-parsed20](https://huggingface.co/muhtasham/bert-small-finetuned-parsed20) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9278 ## 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: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 4 \| 2.9807 \| \| No log \| 2.0 \| 8 \| 2.7267 \| \| No log \| 3.0 \| 12 \| 3.3484 \| \| No log \| 4.0 \| 16 \| 2.7573 \| \| No log \| 5.0 \| 20 \| 2.7063 \| \| No log \| 6.0 \| 24 \| 2.7353 \| \| No log \| 7.0 \| 28 \| 3.1290 \| \| No log \| 8.0 \| 32 \| 2.9371 \| \| No log \| 9.0 \| 36 \| 3.4265 \| \| No log \| 10.0 \| 40 \| 3.0537 \| \| No log \| 11.0 \| 44 \| 3.1382 \| \| No log \| 12.0 \| 48 \| 3.1454 \| \| No log \| 13.0 \| 52 \| 2.8379 \| \| No log \| 14.0 \| 56 \| 3.2760 \| \| No log \| 15.0 \| 60 \| 3.0504 \| \| No log \| 16.0 \| 64 \| 2.9001 \| \| No log \| 17.0 \| 68 \| 2.8892 \| \| No log \| 18.0 \| 72 \| 3.1837 \| \| No log \| 19.0 \| 76 \| 2.6404 \| \| No log \| 20.0 \| 80 \| 3.0600 \| \| No log \| 21.0 \| 84 \| 3.1432 \| \| No log \| 22.0 \| 88 \| 2.9608 \| \| No log \| 23.0 \| 92 \| 3.0513 \| \| No log \| 24.0 \| 96 \| 3.1038 \| \| No log \| 25.0 \| 100 \| 3.0975 \| \| No log \| 26.0 \| 104 \| 2.8977 \| \| No log \| 27.0 \| 108 \| 2.9416 \| \| No log \| 28.0 \| 112 \| 2.9015 \| \| No log \| 29.0 \| 116 \| 2.7947 \| \| No log \| 30.0 \| 120 \| 2.9278 \| ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
thisisHJLee/wav2vec2-large-xls-r-300m-korean-e	thisisHJLee	2022-09-22T10:38:09Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-09-22T08:52:18Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xls-r-300m-korean-e 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-korean-e This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6640 - Cer: 0.1518 ## 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: 0.0001 - train_batch_size: 8 - 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: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 5.3827 \| 3.25 \| 500 \| 3.5391 \| 1.0 \| \| 3.0633 \| 6.49 \| 1000 \| 2.9854 \| 0.8759 \| \| 1.2095 \| 9.74 \| 1500 \| 0.8384 \| 0.2103 \| \| 0.5499 \| 12.99 \| 2000 \| 0.6733 \| 0.1689 \| \| 0.3815 \| 16.23 \| 2500 \| 0.6778 \| 0.1591 \| \| 0.3111 \| 19.48 \| 3000 \| 0.6640 \| 0.1518 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.13.0
mahaveer/ppo-LunarLander-v2	mahaveer	2022-09-22T10:11:19Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-09-22T09:57:48Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 194.40 +/- 31.46 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
GItaf/gpt2-gpt2-TF-weight1-epoch10	GItaf	2022-09-22T09:36:24Z	113	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-09-21T08:05:36Z	--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-gpt2-TF-weight1-epoch10 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. --> # gpt2-gpt2-TF-weight1-epoch10 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. ## 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: 10 ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1
GItaf/roberta-base-roberta-base-TF-weight1-epoch10	GItaf	2022-09-22T09:35:57Z	49	0	transformers	[ "transformers", "pytorch", "roberta", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-09-22T09:34:27Z	--- license: mit tags: - generated_from_trainer model-index: - name: roberta-base-roberta-base-TF-weight1-epoch10 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-roberta-base-TF-weight1-epoch10 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. ## 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: 10 ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1
GItaf/roberta-base-roberta-base-TF-weight1-epoch5	GItaf	2022-09-22T09:32:53Z	47	0	transformers	[ "transformers", "pytorch", "roberta", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-09-22T09:31:40Z	--- license: mit tags: - generated_from_trainer model-index: - name: roberta-base-roberta-base-TF-weight1-epoch5 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-roberta-base-TF-weight1-epoch5 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. ## 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: 5 ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1
GItaf/roberta-base-roberta-base-TF-weight1-epoch15	GItaf	2022-09-22T09:23:00Z	6	0	transformers	[ "transformers", "pytorch", "roberta", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-09-21T15:32:11Z	--- tags: - generated_from_trainer model-index: - name: roberta-base-roberta-base-TF-weight1-epoch15 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-roberta-base-TF-weight1-epoch15 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.8322 - Cls loss: 0.6900 - Lm loss: 4.1423 - Cls Accuracy: 0.5401 - Cls F1: 0.3788 - Cls Precision: 0.2917 - Cls Recall: 0.5401 - Perplexity: 62.95 ## 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cls loss \| Lm loss \| Cls Accuracy \| Cls F1 \| Cls Precision \| Cls Recall \| Perplexity \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------:\|:------------:\|:------:\|:-------------:\|:----------:\|:----------:\| \| 5.3158 \| 1.0 \| 3470 \| 4.9858 \| 0.6910 \| 4.2949 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 73.32 \| \| 4.9772 \| 2.0 \| 6940 \| 4.8876 \| 0.6956 \| 4.1920 \| 0.4599 \| 0.2898 \| 0.2115 \| 0.4599 \| 66.15 \| \| 4.8404 \| 3.0 \| 10410 \| 4.8454 \| 0.6901 \| 4.1553 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 63.77 \| \| 4.7439 \| 4.0 \| 13880 \| 4.8177 \| 0.6904 \| 4.1274 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 62.02 \| \| 4.6667 \| 5.0 \| 17350 \| 4.8065 \| 0.6903 \| 4.1163 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 61.33 \| \| 4.6018 \| 6.0 \| 20820 \| 4.8081 \| 0.6963 \| 4.1119 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 61.06 \| \| 4.5447 \| 7.0 \| 24290 \| 4.8089 \| 0.6912 \| 4.1177 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 61.42 \| \| 4.4944 \| 8.0 \| 27760 \| 4.8128 \| 0.6900 \| 4.1228 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 61.73 \| \| 4.4505 \| 9.0 \| 31230 \| 4.8152 \| 0.6905 \| 4.1248 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 61.85 \| \| 4.4116 \| 10.0 \| 34700 \| 4.8129 \| 0.6908 \| 4.1221 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 61.69 \| \| 4.3787 \| 11.0 \| 38170 \| 4.8146 \| 0.6906 \| 4.1241 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 61.81 \| \| 4.3494 \| 12.0 \| 41640 \| 4.8229 \| 0.6900 \| 4.1329 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 62.36 \| \| 4.3253 \| 13.0 \| 45110 \| 4.8287 \| 0.6900 \| 4.1388 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 62.73 \| \| 4.3075 \| 14.0 \| 48580 \| 4.8247 \| 0.6900 \| 4.1347 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 62.47 \| \| 4.2936 \| 15.0 \| 52050 \| 4.8322 \| 0.6900 \| 4.1423 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 62.95 \| ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1
GItaf/gpt2-gpt2-TF-weight1-epoch15	GItaf	2022-09-22T09:21:30Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-09-21T15:31:41Z	--- tags: - generated_from_trainer model-index: - name: gpt2-gpt2-TF-weight1-epoch15 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. --> # gpt2-gpt2-TF-weight1-epoch15 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0647 - Cls loss: 2.1295 - Lm loss: 3.9339 - Cls Accuracy: 0.8375 - Cls F1: 0.8368 - Cls Precision: 0.8381 - Cls Recall: 0.8375 - Perplexity: 51.11 ## 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Cls loss \| Cls Accuracy \| Cls F1 \| Cls Precision \| Cls Recall \| Lm loss \| Perplexity \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:--------:\|:------------:\|:------:\|:-------------:\|:----------:\|:-------:\|:----------:\|:---------------:\| \| 4.8702 \| 1.0 \| 3470 \| 0.6951 \| 0.7752 \| 0.7670 \| 0.7978 \| 0.7752 \| 4.0201 \| 55.71 \| 4.7157 \| \| 4.5856 \| 2.0 \| 6940 \| 0.6797 \| 0.8352 \| 0.8333 \| 0.8406 \| 0.8352 \| 3.9868 \| 53.88 \| 4.6669 \| \| 4.4147 \| 3.0 \| 10410 \| 0.6899 \| 0.8375 \| 0.8368 \| 0.8384 \| 0.8375 \| 3.9716 \| 53.07 \| 4.6619 \| \| 4.2479 \| 4.0 \| 13880 \| 0.8678 \| 0.8403 \| 0.8396 \| 0.8413 \| 0.8403 \| 3.9622 \| 52.57 \| 4.8305 \| \| 4.1281 \| 5.0 \| 17350 \| 0.9747 \| 0.8340 \| 0.8334 \| 0.8346 \| 0.8340 \| 3.9596 \| 52.44 \| 4.9349 \| \| Training Loss \| Epoch \| Step \| Validation Loss \| Cls loss \| Lm loss \| Cls Accuracy \| Cls F1 \| Cls Precision \| Cls Recall \| Perplexity\| \|:-------------:\|:-----:\|:-----:\|:--------:\|:------------:\|:------:\|:-------------:\|:----------:\|:-------:\|:----------:\|:---------------:\| \| 4.195 \| 6.0 \| 20820 \| 4.9303 \| 0.9770 \| 3.9528 \| 0.8300 \| 0.8299 \| 0.8299 \| 0.8300 \| 52.08 \| \| 4.0645 \| 7.0 \| 24290 \| 5.0425 \| 1.0979 \| 3.9440 \| 0.8317 \| 0.8313 \| 0.8317 \| 0.8317 \| 51.62 \| \| 3.9637 \| 8.0 \| 27760 \| 5.3955 \| 1.4533 \| 3.9414 \| 0.8329 \| 0.8325 \| 0.8328 \| 0.8329 \| 51.49 \| \| 3.9094 \| 9.0 \| 31230 \| 5.6029 \| 1.6645 \| 3.9375 \| 0.8231 \| 0.8233 \| 0.8277 \| 0.8231 \| 51.29 \| \| 3.8661 \| 10.0 \| 34700 \| 5.8175 \| 1.8821 \| 3.9344 \| 0.8144 \| 0.8115 \| 0.8222 \| 0.8144 \| 51.13 \| \| 3.8357 \| 11.0 \| 38170 \| 5.6824 \| 1.7494 \| 3.9319 \| 0.8340 \| 0.8336 \| 0.8342 \| 0.8340 \| 51.01 \| \| 3.8019 \| 12.0 \| 41640 \| 5.8509 \| 1.9167 \| 3.9332 \| 0.8369 \| 0.8357 \| 0.8396 \| 0.8369 \| 51.07 \| \| 3.7815 \| 13.0 \| 45110 \| 5.9044 \| 1.9686 \| 3.9346 \| 0.8409 \| 0.8407 \| 0.8408 \| 0.8409 \| 51.14 \| \| 3.7662 \| 14.0 \| 48580 \| 6.0088 \| 2.0738 \| 3.9337 \| 0.8363 \| 0.8359 \| 0.8364 \| 0.8363 \| 51.10 \| \| 3.7524 \| 15.0 \| 52050 \| 6.0647 \| 2.1295 \| 3.9339 \| 0.8375 \| 0.8368 \| 0.8381 \| 0.8375 \| 51.11 \| ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1
chintagunta85/electramed-small-deid2014-ner-v5-classweights	chintagunta85	2022-09-22T09:08:27Z	102	0	transformers	[ "transformers", "pytorch", "tensorboard", "electra", "token-classification", "generated_from_trainer", "dataset:i2b22014", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-09-22T07:48:30Z	--- tags: - generated_from_trainer datasets: - i2b22014 metrics: - precision - recall - f1 - accuracy model-index: - name: electramed-small-deid2014-ner-v5-classweights results: - task: name: Token Classification type: token-classification dataset: name: i2b22014 type: i2b22014 config: i2b22014-deid split: train args: i2b22014-deid metrics: - name: Precision type: precision value: 0.8832236842105263 - name: Recall type: recall value: 0.6910561632502987 - name: F1 type: f1 value: 0.7754112732711052 - name: Accuracy type: accuracy value: 0.9883040491052534 --- <!-- 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. --> # electramed-small-deid2014-ner-v5-classweights This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the i2b22014 dataset. It achieves the following results on the evaluation set: - Loss: 0.0009 - Precision: 0.8832 - Recall: 0.6911 - F1: 0.7754 - Accuracy: 0.9883 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0001 \| 1.0 \| 1838 \| 0.0008 \| 0.7702 \| 0.3780 \| 0.5071 \| 0.9771 \| \| 0.0 \| 2.0 \| 3676 \| 0.0007 \| 0.8753 \| 0.5671 \| 0.6883 \| 0.9827 \| \| 0.0 \| 3.0 \| 5514 \| 0.0006 \| 0.8074 \| 0.4128 \| 0.5463 \| 0.9775 \| \| 0.0 \| 4.0 \| 7352 \| 0.0007 \| 0.8693 \| 0.6102 \| 0.7170 \| 0.9848 \| \| 0.0 \| 5.0 \| 9190 \| 0.0006 \| 0.8710 \| 0.6022 \| 0.7121 \| 0.9849 \| \| 0.0 \| 6.0 \| 11028 \| 0.0007 \| 0.8835 \| 0.6547 \| 0.7521 \| 0.9867 \| \| 0.0 \| 7.0 \| 12866 \| 0.0009 \| 0.8793 \| 0.6661 \| 0.7579 \| 0.9873 \| \| 0.0 \| 8.0 \| 14704 \| 0.0008 \| 0.8815 \| 0.6740 \| 0.7639 \| 0.9876 \| \| 0.0 \| 9.0 \| 16542 \| 0.0009 \| 0.8812 \| 0.6851 \| 0.7709 \| 0.9880 \| \| 0.0 \| 10.0 \| 18380 \| 0.0009 \| 0.8832 \| 0.6911 \| 0.7754 \| 0.9883 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
prakashkmr48/Prompt-image-inpainting	prakashkmr48	2022-09-22T08:58:57Z	0	0	null	[ "region:us" ]	null	2022-09-22T08:51:46Z	git lfs install git clone https://huggingface.co/prakashkmr48/Prompt-image-inpainting
Hoax0930/kyoto_marian	Hoax0930	2022-09-22T08:32:43Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "translation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-09-22T07:47:04Z	--- license: apache-2.0 tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: kyoto_marian 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. --> # kyoto_marian This model is a fine-tuned version of [Helsinki-NLP/opus-tatoeba-en-ja](https://huggingface.co/Helsinki-NLP/opus-tatoeba-en-ja) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1941 - Bleu: 13.4500 ## 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: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
sd-concepts-library/test2	sd-concepts-library	2022-09-22T06:29:49Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-22T06:29:45Z	--- license: mit --- ### TEST2 on Stable Diffusion This is the `<AIOCARD>` 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 a `style`: ![<AIOCARD> 0](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027D.jpg) ![<AIOCARD> 1](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027C.jpg) ![<AIOCARD> 2](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/00100282.jpg) ![<AIOCARD> 3](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027A.jpg) ![<AIOCARD> 4](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027B.jpg) ![<AIOCARD> 5](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/00100281.jpg) ![<AIOCARD> 6](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/00100280.jpg) ![<AIOCARD> 7](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027E.jpg) ![<AIOCARD> 8](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/00100279.jpg) ![<AIOCARD> 9](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027F.jpg)
airakoze/Lab04	airakoze	2022-09-22T05:34:23Z	0	0	null	[ "region:us" ]	null	2022-09-22T03:48:36Z	--- title: Housing price prediction in California colorFrom: gray colorTo: red sdk: gradio sdk_version: 3.0.4 app_file: app.py pinned: false ---
sd-concepts-library/bee	sd-concepts-library	2022-09-22T05:01:07Z	0	2	null	[ "license:mit", "region:us" ]	null	2022-09-22T05:00:56Z	--- license: mit --- ### BEE on Stable Diffusion This is the `<b-e-e>` 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`: ![<b-e-e> 0](https://huggingface.co/sd-concepts-library/bee/resolve/main/concept_images/0.jpeg) ![<b-e-e> 1](https://huggingface.co/sd-concepts-library/bee/resolve/main/concept_images/3.jpeg) ![<b-e-e> 2](https://huggingface.co/sd-concepts-library/bee/resolve/main/concept_images/2.jpeg) ![<b-e-e> 3](https://huggingface.co/sd-concepts-library/bee/resolve/main/concept_images/1.jpeg)
sd-concepts-library/yinit	sd-concepts-library	2022-09-22T04:58:38Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-22T04:58:24Z	--- license: mit --- ### yinit on Stable Diffusion This is the `yinit-dropcap` 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 a `style`: ![yinit-dropcap 0](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/4.jpeg) ![yinit-dropcap 1](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/12.jpeg) ![yinit-dropcap 2](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/8.jpeg) ![yinit-dropcap 3](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/0.jpeg) ![yinit-dropcap 4](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/6.jpeg) ![yinit-dropcap 5](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/3.jpeg) ![yinit-dropcap 6](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/20.jpeg) ![yinit-dropcap 7](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/11.jpeg) ![yinit-dropcap 8](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/19.jpeg) ![yinit-dropcap 9](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/24.jpeg) ![yinit-dropcap 10](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/17.jpeg) ![yinit-dropcap 11](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/10.jpeg) ![yinit-dropcap 12](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/7.jpeg) ![yinit-dropcap 13](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/13.jpeg) ![yinit-dropcap 14](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/16.jpeg) ![yinit-dropcap 15](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/2.jpeg) ![yinit-dropcap 16](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/25.jpeg) ![yinit-dropcap 17](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/22.jpeg) ![yinit-dropcap 18](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/9.jpeg) ![yinit-dropcap 19](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/15.jpeg) ![yinit-dropcap 20](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/21.jpeg) ![yinit-dropcap 21](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/1.jpeg) ![yinit-dropcap 22](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/14.jpeg) ![yinit-dropcap 23](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/5.jpeg) ![yinit-dropcap 24](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/18.jpeg) ![yinit-dropcap 25](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/23.jpeg)
sd-concepts-library/ibere-thenorio	sd-concepts-library	2022-09-22T04:52:22Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-22T04:52:08Z	--- license: mit --- ### Iberê Thenório on Stable Diffusion This is the `<ibere-thenorio>` 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`: ![<ibere-thenorio> 0](https://huggingface.co/sd-concepts-library/ibere-thenorio/resolve/main/concept_images/4.jpeg) ![<ibere-thenorio> 1](https://huggingface.co/sd-concepts-library/ibere-thenorio/resolve/main/concept_images/0.jpeg) ![<ibere-thenorio> 2](https://huggingface.co/sd-concepts-library/ibere-thenorio/resolve/main/concept_images/3.jpeg) ![<ibere-thenorio> 3](https://huggingface.co/sd-concepts-library/ibere-thenorio/resolve/main/concept_images/2.jpeg) ![<ibere-thenorio> 4](https://huggingface.co/sd-concepts-library/ibere-thenorio/resolve/main/concept_images/1.jpeg) ![<ibere-thenorio> 5](https://huggingface.co/sd-concepts-library/ibere-thenorio/resolve/main/concept_images/5.jpeg)
ashiqabdulkhader/GPT2-Poet	ashiqabdulkhader	2022-09-22T03:24:00Z	381	3	transformers	[ "transformers", "tf", "gpt2", "text-generation", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-09-22T02:45:20Z	--- license: bigscience-bloom-rail-1.0 widget : - text: "I used to have a lover" example_title: "I used to have a lover" - text : "The old cupola glinted above the clouds" example_title: "The old cupola" - text : "Behind the silo, the Mother Rabbit hunches" example_title : "Behind the silo" --- # GPT2-Poet ## Model description GPT2-Poet is a GPT-2 transformer model fine Tuned on a large corpus of English Poems dataset in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token i only uses the inputs from 1 to i but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. ## Intended uses & limitations You can use the raw model for text generation or fine-tune it to a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you. ## Usage You can use this model for English Poem generation: ```python >>> from transformers import TFGPT2LMHeadModel, GPT2Tokenizer >>> tokenizer = GPT2Tokenizer.from_pretrained("ashiqabdulkhader/GPT2-Poet") >>> model = TFGPT2LMHeadModel.from_pretrained("ashiqabdulkhader/GPT2-Poet") >>> text = "The quick brown fox" >>> input_ids = tokenizer.encode(text, return_tensors='tf') >>> sample_outputs = model.generate( input_ids, do_sample=True, max_length=100, top_k=0, top_p=0.9, temperature=1.0, num_return_sequences=3 ) >>> print("Output:", tokenizer.decode(sample_outputs[0], skip_special_tokens=True)) ```
bdotloh/distilbert-base-uncased-go-emotion-empathetic-dialogues-context-v2	bdotloh	2022-09-22T03:14:36Z	106	1	transformers	[ "transformers", "pytorch", "bert", "text-classification", "emotion-classification", "en", "dataset:go-emotions", "dataset:bdotloh/empathetic-dialogues-contexts", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-21T07:35:25Z	--- language: en tags: - emotion-classification datasets: - go-emotions - bdotloh/empathetic-dialogues-contexts --- # Model Description We performed transfer learning experiments on a distilbert-base-uncased model fine-tuned on the GoEmotions dataset for the purpose of classifying [(emotional) contexts in the Empathetic Dialogues dataset](https://huggingface.co/datasets/bdotloh/empathetic-dialogues-contexts). The fine-tuned distilbert-base-uncased can be found [here](https://huggingface.co/bhadresh-savani/bert-base-go-emotion). ### Limitations and bias GoEmotions: 1) Demographics of Reddit Users 2) Imbalanced class distribution 3) ... EmpatheticDialogues: 1) Unable to ascertain the degree of cultural specificity for the context that a respondent described when given an emotion label (i.e., p(description \| emotion, culture)) 2) ... ## Training data ## Training procedure ### Preprocessing ## Evaluation results
sd-concepts-library/char-con	sd-concepts-library	2022-09-22T02:54:22Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-22T02:54:17Z	--- license: mit --- ### char-con on Stable Diffusion This is the `<char-con>` 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`: ![<char-con> 0](https://huggingface.co/sd-concepts-library/char-con/resolve/main/concept_images/4.jpeg) ![<char-con> 1](https://huggingface.co/sd-concepts-library/char-con/resolve/main/concept_images/0.jpeg) ![<char-con> 2](https://huggingface.co/sd-concepts-library/char-con/resolve/main/concept_images/6.jpeg) ![<char-con> 3](https://huggingface.co/sd-concepts-library/char-con/resolve/main/concept_images/3.jpeg) ![<char-con> 4](https://huggingface.co/sd-concepts-library/char-con/resolve/main/concept_images/2.jpeg) ![<char-con> 5](https://huggingface.co/sd-concepts-library/char-con/resolve/main/concept_images/1.jpeg) ![<char-con> 6](https://huggingface.co/sd-concepts-library/char-con/resolve/main/concept_images/5.jpeg)
yuntian-deng/latex2im_ss	yuntian-deng	2022-09-22T02:20:24Z	1	0	diffusers	[ "diffusers", "en", "dataset:yuntian-deng/im2latex-100k", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-09-22T02:19:32Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: yuntian-deng/im2latex-100k metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # latex2im_ss ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `yuntian-deng/im2latex-100k` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: no ### Training results 📈 [TensorBoard logs](https://huggingface.co/yuntian-deng/latex2im_ss/tensorboard?#scalars)

masakhane/byt5_en_zul_news

masakhane

2022-09-24T15:05:20Z

105

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "en", "zul", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:02:52Z

--- language: - en - zul license: afl-3.0 ---

masakhane/byt5_zul_en_news

masakhane

2022-09-24T15:05:19Z

107

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:03:09Z

--- language: - zul - en license: afl-3.0 ---

masakhane/mbart50_zul_en_news

masakhane

2022-09-24T15:05:19Z

105

0

transformers

[ "transformers", "pytorch", "mbart", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:04:09Z

--- language: - zul - en license: afl-3.0 ---

masakhane/mt5_zul_en_news

masakhane

2022-09-24T15:05:18Z

106

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:06:24Z

--- language: - zul - en license: afl-3.0 ---

masakhane/mbart50_en_zul_news

masakhane

2022-09-24T15:05:18Z

106

0

transformers

[ "transformers", "pytorch", "mbart", "text2text-generation", "en", "zul", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:04:24Z

--- language: - en - zul license: afl-3.0 ---

masakhane/m2m100_418M_en_zul_rel_news

masakhane

2022-09-24T15:05:16Z

105

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "zul", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:09:23Z

--- language: - en - zul license: afl-3.0 ---

masakhane/m2m100_418M_zul_en_news

masakhane

2022-09-24T15:05:16Z

104

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:07:50Z

--- language: - zul - en license: afl-3.0 ---

masakhane/m2m100_418M_zul_en_rel_news_ft

masakhane

2022-09-24T15:05:15Z

105

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:13:41Z

--- language: - zul - en license: afl-3.0 ---

masakhane/m2m100_418M_en_zul_rel_ft

masakhane

2022-09-24T15:05:13Z

104

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "zul", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:15:36Z

--- language: - en - zul license: afl-3.0 ---

masakhane/m2m100_418M_zul_en_rel

masakhane

2022-09-24T15:05:12Z

105

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:18:45Z

--- language: - zul - en license: afl-3.0 ---

masakhane/m2m100_418M_en_zul_rel

masakhane

2022-09-24T15:05:12Z

105

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "zul", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:18:27Z

--- language: - en - zul license: afl-3.0 ---

masakhane/m2m100_418M_en_kin_rel

masakhane

2022-09-24T15:05:09Z

113

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "kin", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-08-25T22:07:12Z

--- language: - en - kin license: cc-by-nc-4.0 ---

masakhane/m2m100_418M_kin_en_rel

masakhane

2022-09-24T15:05:09Z

111

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "kin", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-08-25T22:06:42Z

--- language: - kin - en license: cc-by-nc-4.0 ---

masakhane/m2m100_418M_en_nya_rel

masakhane

2022-09-24T15:05:08Z

109

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "nya", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-08-25T22:07:46Z

--- language: - en - nya license: cc-by-nc-4.0 ---

masakhane/m2m100_418M_en_sna_rel

masakhane

2022-09-24T15:05:07Z

110

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "sna", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-08-25T22:08:45Z

--- language: - en - sna license: cc-by-nc-4.0 ---

gokuls/BERT-tiny-Massive-intent

gokuls

2022-09-24T14:26:13Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:massive", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-24T14:15:30Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - massive metrics: - accuracy model-index: - name: BERT-tiny-Massive-intent results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.8475159862272503 ---  # BERT-tiny-Massive-intent This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.6740 - Accuracy: 0.8475 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 3.6104 | 1.0 | 720 | 3.0911 | 0.3601 | | 2.8025 | 2.0 | 1440 | 2.3800 | 0.5165 | | 2.2292 | 3.0 | 2160 | 1.9134 | 0.5991 | | 1.818 | 4.0 | 2880 | 1.5810 | 0.6744 | | 1.5171 | 5.0 | 3600 | 1.3522 | 0.7108 | | 1.2876 | 6.0 | 4320 | 1.1686 | 0.7442 | | 1.1049 | 7.0 | 5040 | 1.0355 | 0.7683 | | 0.9623 | 8.0 | 5760 | 0.9466 | 0.7885 | | 0.8424 | 9.0 | 6480 | 0.8718 | 0.7875 | | 0.7473 | 10.0 | 7200 | 0.8107 | 0.8028 | | 0.6735 | 11.0 | 7920 | 0.7710 | 0.8180 | | 0.6085 | 12.0 | 8640 | 0.7404 | 0.8210 | | 0.5536 | 13.0 | 9360 | 0.7180 | 0.8229 | | 0.5026 | 14.0 | 10080 | 0.6980 | 0.8318 | | 0.4652 | 15.0 | 10800 | 0.6970 | 0.8337 | | 0.4234 | 16.0 | 11520 | 0.6822 | 0.8372 | | 0.3987 | 17.0 | 12240 | 0.6691 | 0.8436 | | 0.3707 | 18.0 | 12960 | 0.6679 | 0.8455 | | 0.3433 | 19.0 | 13680 | 0.6740 | 0.8475 | | 0.3206 | 20.0 | 14400 | 0.6760 | 0.8451 | | 0.308 | 21.0 | 15120 | 0.6704 | 0.8436 | | 0.2813 | 22.0 | 15840 | 0.6701 | 0.8416 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

gokuls/distilroberta-emotion-intent

gokuls

2022-09-24T13:36:17Z

105

1

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-24T13:26:33Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: distilroberta-emotion-intent results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9435 ---  # distilroberta-emotion-intent This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1496 - Accuracy: 0.9435 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4501 | 1.0 | 1000 | 0.2432 | 0.924 | | 0.1947 | 2.0 | 2000 | 0.1646 | 0.934 | | 0.1497 | 3.0 | 3000 | 0.1382 | 0.9405 | | 0.1316 | 4.0 | 4000 | 0.1496 | 0.9435 | | 0.1145 | 5.0 | 5000 | 0.1684 | 0.9385 | | 0.1 | 6.0 | 6000 | 0.2342 | 0.943 | | 0.0828 | 7.0 | 7000 | 0.2807 | 0.939 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

RebekkaB/rlt_2409_1450

RebekkaB

2022-09-24T13:22:34Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-24T12:52:36Z

--- tags: - generated_from_trainer metrics: - f1 model-index: - name: rlt_2409_1450 results: [] ---  # rlt_2409_1450 This model is a fine-tuned version of [svalabs/gbert-large-zeroshot-nli](https://huggingface.co/svalabs/gbert-large-zeroshot-nli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0518 - F1: 0.9826 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.99 | 36 | 0.5165 | 0.8542 | | No log | 1.99 | 72 | 0.1459 | 0.9599 | | No log | 2.99 | 108 | 0.0733 | 0.9882 | | No log | 3.99 | 144 | 0.1385 | 0.9502 | | No log | 4.99 | 180 | 0.0948 | 0.9806 | | No log | 5.99 | 216 | 0.0699 | 0.9822 | | No log | 6.99 | 252 | 0.0582 | 0.9859 | | No log | 7.99 | 288 | 0.0340 | 0.9933 | | No log | 8.99 | 324 | 0.0475 | 0.9826 | | No log | 9.99 | 360 | 0.0518 | 0.9826 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

SaurabhKaushik/distilbert-base-uncased-finetuned-ner

SaurabhKaushik

2022-09-24T12:38:00Z

105

0

transformers

[ "transformers", "pytorch", "distilbert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-09-24T11:26:55Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.9250386398763524 - name: Recall type: recall value: 0.9373531714956931 - name: F1 type: f1 value: 0.9311551925320887 - name: Accuracy type: accuracy value: 0.9839388692074285 ---  # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0589 - Precision: 0.9250 - Recall: 0.9374 - F1: 0.9312 - Accuracy: 0.9839 ## 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2343 | 1.0 | 878 | 0.0674 | 0.9177 | 0.9233 | 0.9205 | 0.9818 | | 0.0525 | 2.0 | 1756 | 0.0582 | 0.9245 | 0.9362 | 0.9304 | 0.9837 | | 0.0288 | 3.0 | 2634 | 0.0589 | 0.9250 | 0.9374 | 0.9312 | 0.9839 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1 - Datasets 2.5.1 - Tokenizers 0.12.1

sd-concepts-library/dr-strange

sd-concepts-library

2022-09-24T12:11:20Z

0

28

null

[ "license:mit", "region:us" ]

null

2022-09-24T12:11:16Z

--- license: mit --- ### <dr-strange> on Stable Diffusion This is the `<dr-strange>` 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 a `style`: ![<dr-strange> 0](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/3.jpeg) ![<dr-strange> 1](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/1.jpeg) ![<dr-strange> 2](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/0.jpeg) ![<dr-strange> 3](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/2.jpeg)

RebekkaB/san_nli_2409_1325

RebekkaB

2022-09-24T11:50:33Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-24T11:27:27Z

--- tags: - generated_from_trainer metrics: - f1 model-index: - name: san_nli_2409_1325 results: [] ---  # san_nli_2409_1325 This model is a fine-tuned version of [svalabs/gbert-large-zeroshot-nli](https://huggingface.co/svalabs/gbert-large-zeroshot-nli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3856 - F1: 0.9219 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.93 | 10 | 0.2410 | 0.9219 | | No log | 1.93 | 20 | 0.5240 | 0.9149 | | No log | 2.93 | 30 | 0.4756 | 0.9219 | | No log | 3.93 | 40 | 0.3856 | 0.9219 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

huggingtweets/cz_binance

huggingtweets

2022-09-24T09:16:00Z

113

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-05T21:10:34Z

--- language: en thumbnail: http://www.huggingtweets.com/cz_binance/1664010956441/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('https://pbs.twimg.com/profile_images/1572269909513478146/dfyw817W_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">CZ 🔶 Binance</div> <div style="text-align: center; font-size: 14px;">@cz_binance</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 CZ 🔶 Binance. | Data | CZ 🔶 Binance | | --- | --- | | Tweets downloaded | 3246 | | Retweets | 149 | | Short tweets | 473 | | Tweets kept | 2624 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/19171g9o/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 @cz_binance's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ngvvhd8) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ngvvhd8/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/cz_binance') 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)

sd-concepts-library/coop-himmelblau

sd-concepts-library

2022-09-24T09:06:36Z

0

6

null

[ "license:mit", "region:us" ]

null

2022-09-24T09:06:32Z

--- license: mit --- ### coop himmelblau on Stable Diffusion This is the `<coop himmelblau>` 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`: ![<coop himmelblau> 0](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/3.jpeg) ![<coop himmelblau> 1](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/1.jpeg) ![<coop himmelblau> 2](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/4.jpeg) ![<coop himmelblau> 3](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/5.jpeg) ![<coop himmelblau> 4](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/0.jpeg) ![<coop himmelblau> 5](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/2.jpeg)

aniketface/DialoGPT-product

aniketface

2022-09-24T09:05:12Z

121

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "convAI", "conversational", "facebook", "en", "dataset:blended_skill_talk", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-09-24T08:41:37Z

--- language: - en thumbnail: tags: - convAI - conversational - facebook license: apache-2.0 datasets: - blended_skill_talk metrics: - perplexity ---

huggingtweets/pentosh1

huggingtweets

2022-09-24T08:03:41Z

113

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-09-24T08:02:41Z

--- language: en thumbnail: http://www.huggingtweets.com/pentosh1/1664006616559/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('https://pbs.twimg.com/profile_images/1553520707472072708/5eseDj4F_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">Pentoshi 🐧</div> <div style="text-align: center; font-size: 14px;">@pentosh1</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 Pentoshi 🐧. | Data | Pentoshi 🐧 | | --- | --- | | Tweets downloaded | 3242 | | Retweets | 24 | | Short tweets | 573 | | Tweets kept | 2645 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3kzanxqd/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 @pentosh1's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3e7vuikz) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3e7vuikz/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/pentosh1') 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)

huggingtweets/beranewsnetwork

huggingtweets

2022-09-24T07:04:15Z

113

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-09-24T07:01:56Z

--- language: en thumbnail: http://www.huggingtweets.com/beranewsnetwork/1664003049616/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('https://pbs.twimg.com/profile_images/1445950504102735872/bCnvrgeb_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">Bera News Network</div> <div style="text-align: center; font-size: 14px;">@beranewsnetwork</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 Bera News Network. | Data | Bera News Network | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 1 | | Short tweets | 579 | | Tweets kept | 2670 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/254oa32x/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 @beranewsnetwork's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1jqeuf1y) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1jqeuf1y/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/beranewsnetwork') 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)

huggingtweets/it_airmass

huggingtweets

2022-09-24T06:49:38Z

111

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-09-24T06:49:12Z

--- language: en thumbnail: http://www.huggingtweets.com/it_airmass/1664002173554/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('https://pbs.twimg.com/profile_images/1529248676647944193/-N1UKgKg_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">Airmass</div> <div style="text-align: center; font-size: 14px;">@it_airmass</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 Airmass. | Data | Airmass | | --- | --- | | Tweets downloaded | 3249 | | Retweets | 126 | | Short tweets | 370 | | Tweets kept | 2753 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2f99nys0/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 @it_airmass's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/nvbqf9p2) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/nvbqf9p2/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/it_airmass') 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)

huggingtweets/marketsmeowmeow

huggingtweets

2022-09-24T06:43:25Z

111

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-09-24T06:42:56Z

--- language: en thumbnail: http://www.huggingtweets.com/marketsmeowmeow/1664001800470/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('https://pbs.twimg.com/profile_images/1570418907575377921/1mTVqZQZ_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">RB</div> <div style="text-align: center; font-size: 14px;">@marketsmeowmeow</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 RB. | Data | RB | | --- | --- | | Tweets downloaded | 3244 | | Retweets | 14 | | Short tweets | 700 | | Tweets kept | 2530 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/a7yqyg23/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 @marketsmeowmeow's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ou0r1v87) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ou0r1v87/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/marketsmeowmeow') 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)

sd-concepts-library/museum-by-coop-himmelblau

sd-concepts-library

2022-09-24T06:39:31Z

0

null

[ "license:mit", "region:us" ]

null

2022-09-24T06:39:25Z

--- license: mit --- ### museum by coop himmelblau on Stable Diffusion This is the `<coop himmelblau museum>` 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`: ![<coop himmelblau museum> 0](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/3.jpeg) ![<coop himmelblau museum> 1](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/1.jpeg) ![<coop himmelblau museum> 2](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/0.jpeg) ![<coop himmelblau museum> 3](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/2.jpeg)

sd-concepts-library/ransom

sd-concepts-library

2022-09-24T05:44:13Z

0

null

[ "license:mit", "region:us" ]

null

2022-09-24T05:44:07Z

--- license: mit --- ### ransom on Stable Diffusion This is the `<ransom>` 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`: ![<ransom> 0](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/3.jpeg) ![<ransom> 1](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/1.jpeg) ![<ransom> 2](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/4.jpeg) ![<ransom> 3](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/6.jpeg) ![<ransom> 4](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/5.jpeg) ![<ransom> 5](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/0.jpeg) ![<ransom> 6](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/2.jpeg) ![<ransom> 7](https://huggingface.co/sd-concepts-library/ransom/resolve/main/concept_images/7.jpeg)

ckiplab/bert-base-chinese-qa

ckiplab

2022-09-24T05:25:07Z

162

7

transformers

[ "transformers", "pytorch", "bert", "question-answering", "zh", "license:gpl-3.0", "endpoints_compatible", "region:us" ]

question-answering

2022-09-24T05:17:36Z

--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - question-answering - bert - zh license: gpl-3.0 --- # CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-base-chinese-qa') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。

huggingtweets/tim_cook

huggingtweets

2022-09-24T01:11:00Z

112

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/tim_cook/1663981855625/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('https://pbs.twimg.com/profile_images/1535420431766671360/Pwq-1eJc_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">Tim Cook</div> <div style="text-align: center; font-size: 14px;">@tim_cook</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 Tim Cook. | Data | Tim Cook | | --- | --- | | Tweets downloaded | 1385 | | Retweets | 20 | | Short tweets | 13 | | Tweets kept | 1352 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2d94dtsh/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 @tim_cook's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/19bm0x3l) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/19bm0x3l/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/tim_cook') 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)

farleyknight/arxiv-summarization-t5-base-2022-09-21

farleyknight

2022-09-24T00:31:57Z

180

3

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:ccdv/arxiv-summarization", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-09-21T20:31:09Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - ccdv/arxiv-summarization metrics: - rouge model-index: - name: arxiv-summarization-t5-base-2022-09-21 results: - task: name: Summarization type: summarization dataset: name: ccdv/arxiv-summarization type: ccdv/arxiv-summarization config: section split: train args: section metrics: - name: Rouge1 type: rouge value: 40.6781 ---  # arxiv-summarization-t5-base-2022-09-21 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the ccdv/arxiv-summarization dataset. It achieves the following results on the evaluation set: - Loss: 1.8650 - Rouge1: 40.6781 - Rouge2: 14.7167 - Rougel: 26.6375 - Rougelsum: 35.5959 - Gen Len: 117.1969 ## 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: 5e-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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.3291 | 0.05 | 10000 | 2.1906 | 18.6571 | 7.1341 | 14.8347 | 16.9545 | 19.0 | | 2.2454 | 0.1 | 20000 | 2.1549 | 18.5037 | 7.1908 | 14.7141 | 16.8233 | 18.9997 | | 2.2107 | 0.15 | 30000 | 2.1013 | 18.7638 | 7.326 | 14.9437 | 17.072 | 19.0 | | 2.1486 | 0.2 | 40000 | 2.0845 | 18.6879 | 7.2441 | 14.8835 | 16.983 | 19.0 | | 2.158 | 0.25 | 50000 | 2.0699 | 18.8314 | 7.3712 | 15.0166 | 17.1215 | 19.0 | | 2.1476 | 0.3 | 60000 | 2.0424 | 18.9783 | 7.4138 | 15.1121 | 17.2778 | 18.9981 | | 2.1164 | 0.34 | 70000 | 2.0349 | 18.9257 | 7.4649 | 15.0335 | 17.1819 | 19.0 | | 2.079 | 0.39 | 80000 | 2.0208 | 18.643 | 7.4096 | 14.8927 | 16.9786 | 18.9994 | | 2.101 | 0.44 | 90000 | 2.0113 | 19.3881 | 7.7012 | 15.3981 | 17.6516 | 19.0 | | 2.0576 | 0.49 | 100000 | 2.0022 | 18.9985 | 7.542 | 15.1157 | 17.2972 | 18.9992 | | 2.0983 | 0.54 | 110000 | 1.9941 | 18.7691 | 7.4625 | 15.0256 | 17.1146 | 19.0 | | 2.053 | 0.59 | 120000 | 1.9855 | 19.002 | 7.5602 | 15.1497 | 17.2963 | 19.0 | | 2.0434 | 0.64 | 130000 | 1.9786 | 19.2385 | 7.6533 | 15.3094 | 17.5439 | 18.9994 | | 2.0354 | 0.69 | 140000 | 1.9746 | 19.184 | 7.7307 | 15.2897 | 17.491 | 18.9992 | | 2.0347 | 0.74 | 150000 | 1.9639 | 19.2408 | 7.693 | 15.3357 | 17.5297 | 19.0 | | 2.0236 | 0.79 | 160000 | 1.9590 | 19.0781 | 7.6256 | 15.1932 | 17.3486 | 18.9998 | | 2.0187 | 0.84 | 170000 | 1.9532 | 19.0343 | 7.6792 | 15.1884 | 17.3519 | 19.0 | | 1.9939 | 0.89 | 180000 | 1.9485 | 18.8247 | 7.5005 | 15.0246 | 17.1485 | 18.9998 | | 1.9961 | 0.94 | 190000 | 1.9504 | 19.0695 | 7.6559 | 15.2139 | 17.3814 | 19.0 | | 2.0197 | 0.99 | 200000 | 1.9399 | 19.2821 | 7.6685 | 15.3029 | 17.5374 | 18.9988 | | 1.9457 | 1.03 | 210000 | 1.9350 | 19.053 | 7.6502 | 15.2123 | 17.3793 | 19.0 | | 1.9552 | 1.08 | 220000 | 1.9317 | 19.1878 | 7.7235 | 15.3272 | 17.5252 | 18.9998 | | 1.9772 | 1.13 | 230000 | 1.9305 | 19.0855 | 7.6303 | 15.1943 | 17.3942 | 18.9997 | | 1.9171 | 1.18 | 240000 | 1.9291 | 19.0711 | 7.6437 | 15.2175 | 17.3893 | 18.9995 | | 1.9393 | 1.23 | 250000 | 1.9230 | 19.276 | 7.725 | 15.3826 | 17.586 | 18.9995 | | 1.9295 | 1.28 | 260000 | 1.9197 | 19.2999 | 7.7958 | 15.3961 | 17.6056 | 18.9975 | | 1.9725 | 1.33 | 270000 | 1.9173 | 19.2958 | 7.7121 | 15.3659 | 17.584 | 19.0 | | 1.9668 | 1.38 | 280000 | 1.9129 | 19.089 | 7.6846 | 15.2395 | 17.3879 | 18.9998 | | 1.941 | 1.43 | 290000 | 1.9132 | 19.2127 | 7.7336 | 15.311 | 17.4742 | 18.9995 | | 1.9427 | 1.48 | 300000 | 1.9108 | 19.217 | 7.7591 | 15.334 | 17.53 | 18.9998 | | 1.9521 | 1.53 | 310000 | 1.9041 | 19.1285 | 7.6736 | 15.2625 | 17.458 | 19.0 | | 1.9352 | 1.58 | 320000 | 1.9041 | 19.1656 | 7.723 | 15.3035 | 17.4818 | 18.9991 | | 1.9342 | 1.63 | 330000 | 1.9004 | 19.2573 | 7.7766 | 15.3558 | 17.5382 | 19.0 | | 1.9631 | 1.68 | 340000 | 1.8978 | 19.236 | 7.7584 | 15.3408 | 17.4993 | 18.9998 | | 1.8987 | 1.72 | 350000 | 1.8968 | 19.1716 | 7.7231 | 15.2836 | 17.4655 | 18.9997 | | 1.9433 | 1.77 | 360000 | 1.8924 | 19.2644 | 7.8294 | 15.4018 | 17.5808 | 18.9998 | | 1.9159 | 1.82 | 370000 | 1.8912 | 19.1833 | 7.8267 | 15.3175 | 17.4918 | 18.9995 | | 1.9516 | 1.87 | 380000 | 1.8856 | 19.3077 | 7.7432 | 15.3723 | 17.6115 | 19.0 | | 1.9218 | 1.92 | 390000 | 1.8880 | 19.2668 | 7.8231 | 15.3834 | 17.5701 | 18.9994 | | 1.9159 | 1.97 | 400000 | 1.8860 | 19.2224 | 7.7903 | 15.3488 | 17.4992 | 18.9997 | | 1.8741 | 2.02 | 410000 | 1.8854 | 19.2572 | 7.741 | 15.3405 | 17.5351 | 19.0 | | 1.8668 | 2.07 | 420000 | 1.8854 | 19.3658 | 7.8593 | 15.4418 | 17.656 | 18.9995 | | 1.8638 | 2.12 | 430000 | 1.8831 | 19.305 | 7.8218 | 15.3843 | 17.5861 | 18.9997 | | 1.8334 | 2.17 | 440000 | 1.8817 | 19.3269 | 7.8249 | 15.4231 | 17.5958 | 18.9994 | | 1.8893 | 2.22 | 450000 | 1.8803 | 19.2949 | 7.7885 | 15.3947 | 17.585 | 18.9997 | | 1.8929 | 2.27 | 460000 | 1.8783 | 19.291 | 7.8346 | 15.428 | 17.5797 | 18.9997 | | 1.861 | 2.32 | 470000 | 1.8766 | 19.4284 | 7.8832 | 15.4746 | 17.6946 | 18.9997 | | 1.8719 | 2.37 | 480000 | 1.8751 | 19.1525 | 7.7641 | 15.3348 | 17.47 | 18.9998 | | 1.8889 | 2.41 | 490000 | 1.8742 | 19.1743 | 7.768 | 15.3292 | 17.4665 | 18.9998 | | 1.8834 | 2.46 | 500000 | 1.8723 | 19.3069 | 7.7935 | 15.3987 | 17.5913 | 18.9998 | | 1.8564 | 2.51 | 510000 | 1.8695 | 19.3217 | 7.8292 | 15.4063 | 17.6081 | 19.0 | | 1.8706 | 2.56 | 520000 | 1.8697 | 19.294 | 7.8217 | 15.3964 | 17.581 | 18.9998 | | 1.883 | 2.61 | 530000 | 1.8703 | 19.2784 | 7.8634 | 15.404 | 17.5942 | 18.9995 | | 1.8622 | 2.66 | 540000 | 1.8677 | 19.3165 | 7.8378 | 15.4259 | 17.6064 | 18.9988 | | 1.8781 | 2.71 | 550000 | 1.8676 | 19.3237 | 7.7954 | 15.3995 | 17.6008 | 19.0 | | 1.8793 | 2.76 | 560000 | 1.8685 | 19.2141 | 7.7605 | 15.3345 | 17.5268 | 18.9997 | | 1.8795 | 2.81 | 570000 | 1.8675 | 19.2694 | 7.8082 | 15.3996 | 17.5831 | 19.0 | | 1.8425 | 2.86 | 580000 | 1.8659 | 19.2886 | 7.7987 | 15.4005 | 17.5859 | 18.9997 | | 1.8605 | 2.91 | 590000 | 1.8650 | 19.2778 | 7.7934 | 15.3931 | 17.5809 | 18.9997 | | 1.8448 | 2.96 | 600000 | 1.8655 | 19.2884 | 7.8087 | 15.4025 | 17.5856 | 19.0 | ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.0 - Datasets 2.5.1 - Tokenizers 0.13.0

ericntay/stbl_clinical_bert_ft_rs5

ericntay

2022-09-23T20:39:56Z

104

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-09-23T20:21:55Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: stbl_clinical_bert_ft_rs5 results: [] ---  # stbl_clinical_bert_ft_rs5 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0936 - F1: 0.9268 ## 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2723 | 1.0 | 101 | 0.0875 | 0.8479 | | 0.066 | 2.0 | 202 | 0.0688 | 0.9002 | | 0.0328 | 3.0 | 303 | 0.0668 | 0.9070 | | 0.0179 | 4.0 | 404 | 0.0689 | 0.9129 | | 0.0098 | 5.0 | 505 | 0.0790 | 0.9147 | | 0.0069 | 6.0 | 606 | 0.0805 | 0.9205 | | 0.0033 | 7.0 | 707 | 0.0835 | 0.9268 | | 0.0022 | 8.0 | 808 | 0.0904 | 0.9262 | | 0.0021 | 9.0 | 909 | 0.0882 | 0.9263 | | 0.0015 | 10.0 | 1010 | 0.0933 | 0.9289 | | 0.0009 | 11.0 | 1111 | 0.0921 | 0.9311 | | 0.0009 | 12.0 | 1212 | 0.0936 | 0.9268 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

subtlegradient/distilbert-base-uncased-finetuned-cola

subtlegradient

2022-09-23T19:19:56Z

110

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-23T19:08:46Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue model-index: - name: distilbert-base-uncased-finetuned-cola results: [] ---  # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5180 - eval_matthews_correlation: 0.4063 - eval_runtime: 0.8532 - eval_samples_per_second: 1222.419 - eval_steps_per_second: 77.353 - epoch: 1.0 - step: 535 ## 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: 5 ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu116 - Datasets 2.5.1 - Tokenizers 0.12.1

g30rv17ys/ddpm-geeve-drusen-1000-200ep

g30rv17ys

2022-09-23T19:12:36Z

0

diffusers

[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

null

2022-09-23T15:39:11Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] ---  # ddpm-geeve-drusen-1000-200ep ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/geevegeorge/ddpm-geeve-drusen-1000-200ep/tensorboard?#scalars)

g30rv17ys/ddpm-geeve-cnv-1000-200ep

g30rv17ys

2022-09-23T19:10:42Z

0

diffusers

[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

null

2022-09-23T15:29:54Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] ---  # ddpm-geeve-cnv-1000-200ep ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/geevegeorge/ddpm-geeve-cnv-1000-200ep/tensorboard?#scalars)

g30rv17ys/ddpm-geeve-dme-1000-200ep

g30rv17ys

2022-09-23T19:09:23Z

1

0

diffusers

[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

null

2022-09-23T15:34:37Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] ---  # ddpm-geeve-dme-1000-200ep ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/geevegeorge/ddpm-geeve-dme-1000-200ep/tensorboard?#scalars)

tszocinski/bart-base-squad-question-generation

tszocinski

2022-09-23T18:43:43Z

75

0

transformers

[ "transformers", "tf", "bart", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-09-22T19:36:46Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: tszocinski/bart-base-squad-question-generation results: [] ---  # tszocinski/bart-base-squad-question-generation This model is a fine-tuned version of [tszocinski/bart-base-squad-question-generation](https://huggingface.co/tszocinski/bart-base-squad-question-generation) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 6.5656 - Validation Loss: 11.1958 - Epoch: 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: - optimizer: {'inner_optimizer': {'class_name': 'RMSprop', 'config': {'name': 'RMSprop', 'learning_rate': 0.001, 'decay': 0.0, 'rho': 0.9, 'momentum': 0.0, 'epsilon': 1e-07, 'centered': False}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 6.5656 | 11.1958 | 0 | ### Framework versions - Transformers 4.22.1 - TensorFlow 2.8.2 - Datasets 2.5.1 - Tokenizers 0.12.1

tkuye/t5-ost

tkuye

2022-09-23T18:40:53Z

109

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-09-23T17:10:59Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-ost results: [] ---  # t5-ost This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0581 ## 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: 0.0005 - train_batch_size: 8 - 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5695 | 0.39 | 500 | 0.0591 | | 0.0606 | 0.77 | 1000 | 0.0588 | | 0.0575 | 1.16 | 1500 | 0.0588 | | 0.0551 | 1.55 | 2000 | 0.0586 | | 0.0549 | 1.93 | 2500 | 0.0581 | | 0.0487 | 2.32 | 3000 | 0.0597 | | 0.0478 | 2.71 | 3500 | 0.0594 | | 0.0463 | 3.1 | 4000 | 0.0624 | | 0.0404 | 3.48 | 4500 | 0.0625 | | 0.041 | 3.87 | 5000 | 0.0617 | | 0.0366 | 4.26 | 5500 | 0.0656 | | 0.0347 | 4.64 | 6000 | 0.0658 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.0 - Datasets 2.5.1 - Tokenizers 0.12.1

mfreihaut/iab_classification-finetuned-mnli-finetuned-mnli

mfreihaut

2022-09-23T18:20:23Z

23

1

transformers

[ "transformers", "pytorch", "bart", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-21T18:05:28Z

--- tags: - generated_from_trainer model-index: - name: iab_classification-finetuned-mnli-finetuned-mnli results: [] ---  # iab_classification-finetuned-mnli-finetuned-mnli This model is a fine-tuned version of [mfreihaut/iab_classification-finetuned-mnli-finetuned-mnli](https://huggingface.co/mfreihaut/iab_classification-finetuned-mnli-finetuned-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8711 ## 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: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | No log | 1.0 | 250 | 1.5956 | | 0.9361 | 2.0 | 500 | 0.0409 | | 0.9361 | 3.0 | 750 | 2.9853 | | 0.7634 | 4.0 | 1000 | 0.1317 | | 0.7634 | 5.0 | 1250 | 0.4056 | | 0.611 | 6.0 | 1500 | 1.8038 | | 0.611 | 7.0 | 1750 | 0.6305 | | 0.5627 | 8.0 | 2000 | 0.6923 | | 0.5627 | 9.0 | 2250 | 3.7410 | | 0.9863 | 10.0 | 2500 | 2.1912 | | 0.9863 | 11.0 | 2750 | 1.5405 | | 1.0197 | 12.0 | 3000 | 1.9271 | | 1.0197 | 13.0 | 3250 | 1.1741 | | 0.5186 | 14.0 | 3500 | 1.1864 | | 0.5186 | 15.0 | 3750 | 0.7945 | | 0.4042 | 16.0 | 4000 | 1.0645 | | 0.4042 | 17.0 | 4250 | 1.8826 | | 0.3637 | 18.0 | 4500 | 0.3234 | | 0.3637 | 19.0 | 4750 | 0.2641 | | 0.3464 | 20.0 | 5000 | 0.8596 | | 0.3464 | 21.0 | 5250 | 0.5601 | | 0.2449 | 22.0 | 5500 | 0.4543 | | 0.2449 | 23.0 | 5750 | 1.1986 | | 0.2595 | 24.0 | 6000 | 0.3642 | | 0.2595 | 25.0 | 6250 | 1.3606 | | 0.298 | 26.0 | 6500 | 0.8154 | | 0.298 | 27.0 | 6750 | 1.1105 | | 0.1815 | 28.0 | 7000 | 0.7443 | | 0.1815 | 29.0 | 7250 | 0.2616 | | 0.165 | 30.0 | 7500 | 0.5318 | | 0.165 | 31.0 | 7750 | 0.7608 | | 0.1435 | 32.0 | 8000 | 0.9647 | | 0.1435 | 33.0 | 8250 | 1.3749 | | 0.1516 | 34.0 | 8500 | 0.7167 | | 0.1516 | 35.0 | 8750 | 0.5426 | | 0.1359 | 36.0 | 9000 | 0.7225 | | 0.1359 | 37.0 | 9250 | 0.5453 | | 0.1266 | 38.0 | 9500 | 0.4825 | | 0.1266 | 39.0 | 9750 | 0.7271 | | 0.1153 | 40.0 | 10000 | 0.9044 | | 0.1153 | 41.0 | 10250 | 1.0363 | | 0.1175 | 42.0 | 10500 | 0.7987 | | 0.1175 | 43.0 | 10750 | 0.7596 | | 0.1089 | 44.0 | 11000 | 0.8637 | | 0.1089 | 45.0 | 11250 | 0.8327 | | 0.1092 | 46.0 | 11500 | 0.7161 | | 0.1092 | 47.0 | 11750 | 0.7768 | | 0.1068 | 48.0 | 12000 | 0.9059 | | 0.1068 | 49.0 | 12250 | 0.8829 | | 0.1045 | 50.0 | 12500 | 0.8711 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.10.0 - Datasets 2.5.1 - Tokenizers 0.12.1

carbon225/transforchess-bart-base

carbon225

2022-09-23T18:13:23Z

110

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "license:cc0-1.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-07-31T18:02:04Z

--- license: cc0-1.0 widget: - text: " White pawn to d4. Black knight to f6." ---

sd-concepts-library/sintez-ico

sd-concepts-library

2022-09-23T18:13:17Z

0

1

null

[ "license:mit", "region:us" ]

null

2022-09-23T18:13:03Z

--- license: mit --- ### sintez-ico on Stable Diffusion This is the `<sintez-ico>` 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 a `style`: ![<sintez-ico> 0](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/7.jpeg) ![<sintez-ico> 1](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/2.jpeg) ![<sintez-ico> 2](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/6.jpeg) ![<sintez-ico> 3](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/4.jpeg) ![<sintez-ico> 4](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/5.jpeg) ![<sintez-ico> 5](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/0.jpeg) ![<sintez-ico> 6](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/1.jpeg) ![<sintez-ico> 7](https://huggingface.co/sd-concepts-library/sintez-ico/resolve/main/concept_images/3.jpeg)

Eulering/moonlight-night

Eulering

2022-09-23T14:47:20Z

0

null

[ "license:bigscience-openrail-m", "region:us" ]

null

2022-09-23T14:47:20Z

--- license: bigscience-openrail-m ---

gokuls/bert-base-Massive-intent

gokuls

2022-09-23T14:26:09Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:massive", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-23T13:38:21Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - massive metrics: - accuracy model-index: - name: bert-base-Massive-intent results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.8858829316281358 ---  # bert-base-Massive-intent This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.6707 - Accuracy: 0.8859 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6844 | 1.0 | 720 | 0.7190 | 0.8387 | | 0.4713 | 2.0 | 1440 | 0.5449 | 0.8726 | | 0.2459 | 3.0 | 2160 | 0.5893 | 0.8790 | | 0.1469 | 4.0 | 2880 | 0.6631 | 0.8795 | | 0.0874 | 5.0 | 3600 | 0.6707 | 0.8859 | | 0.0507 | 6.0 | 4320 | 0.7189 | 0.8844 | | 0.0344 | 7.0 | 5040 | 0.7480 | 0.8854 | | 0.0225 | 8.0 | 5760 | 0.7956 | 0.8844 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

bhumikak/resultsb

bhumikak

2022-09-23T14:21:23Z

105

0

transformers

[ "transformers", "pytorch", "pegasus", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-09-23T13:46:43Z

--- tags: - generated_from_trainer model-index: - name: resultsb results: [] ---  # resultsb This model is a fine-tuned version of [bhumikak/resultsa](https://huggingface.co/bhumikak/resultsa) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.8957 - Rouge2 Precision: 0.2127 - Rouge2 Recall: 0.2605 - Rouge2 Fmeasure: 0.2167 ## 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: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 50 - label_smoothing_factor: 0.1 ### Training results ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

Yousef-Cot/distilbert-base-uncased-finetuned-emotion

Yousef-Cot

2022-09-23T13:21:28Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-23T07:18:41Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9215 - name: F1 type: f1 value: 0.9218038766645168 ---  # 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: 0.2201 - Accuracy: 0.9215 - F1: 0.9218 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8242 | 1.0 | 250 | 0.3311 | 0.8965 | 0.8931 | | 0.254 | 2.0 | 500 | 0.2201 | 0.9215 | 0.9218 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2 - Datasets 2.4.0 - Tokenizers 0.11.6

combi2k2/MRC001

combi2k2

2022-09-23T13:01:39Z

0

null

[ "vi", "xlm-roberta", "dataset:UIT-ViQuAD2.0", "license:mit", "region:us" ]

null

2022-09-18T03:23:55Z

--- language: vi tags: - vi - xlm-roberta widget: - text: Toà nhà nào cao nhất Việt Nam? context: Landmark 81 là một toà nhà chọc trời trong tổ hợp dự án Vinhomes Tân Cảng, một dự án có tổng mức đầu tư 40.000 tỷ đồng, do Công ty Cổ phần Đầu tư xây dựng Tân Liên Phát thuộc Vingroup làm chủ đầu tư. Toà tháp cao 81 tầng, hiện tại là toà nhà cao nhất Việt Nam và là toà nhà cao nhất Đông Nam Á từ tháng 3 năm 2018. datasets: - UIT-ViQuAD2.0 license: mit metrics: - f1 - em --- # Machine Reading Comprehension Vietnamese **[Colab Notebook](https://colab.research.google.com/drive/1JeyjSluVLIoZGzC_kOq6HXGUX-JMN3VP?usp=sharing)** ## Overview - Language model: xlm-roberta-base - Language: Vietnamese - Downstream-task: Extractive QA - Dataset: [UIT-ViQuAD2.0](https://paperswithcode.com/dataset/uit-viquad) - Dataset Format: SQuAD 2.0 - Infrastructure: cuda Tesla P100-PCIE-16GB (Google Colab) ## Requirements The following modules are essential for running the trainer: - **transformers** - **datasets** - **evaluate** - **numpy** Run the following commands to install the required libraries: ``` >>> pip install datasets evaluate numpy >>> pip install git+https://github.com/huggingface/transformers ``` ## Hyperparameter ``` batch_size = 16 n_epochs = 10 base_LM_model = "xlm-roberta-base" max_seq_len = 256 learning_rate = 2e-5 weight_decay = 0.01 ``` ## Performance Evaluated on the UIT-ViQuAD2.0 dev set with the official eval script. ``` 'exact': 29.947276, 'f1': 43.627568, 'total': 2845, 'HasAns_exact': 43.827160, 'HasAns_f1': 63.847958, 'HasAns_total': 1944, 'NoAns_exact': 0.0, 'NoAns_f1': 0.0, 'NoAns_total': 901 ``` ## Usage ```python from transformers import { AutoModelForQuestionAnswering, AutoTokenizer, pipeline } model_checkpoint = "results/checkpoint-16000" question_answerer = pipeline("question-answering", model = model_checkpoint) # a) get predictions QA_input = { 'question': 'Hiến pháp Mali quy định thế nào đối với tôn giáo?', 'context': 'Ước tính có khoảng 90% dân số Mali theo đạo Hồi (phần lớn là hệ phái Sunni), khoảng 5% là theo Kitô giáo (khoảng hai phần ba theo Giáo hội Công giáo Rôma và một phần ba là theo Tin Lành) và 5% còn lại theo các tín ngưỡng vật linh truyền thống bản địa. Một số ít người Mali theo thuyết vô thần và thuyết bất khả tri, phần lớn họ thực hiện những nghi lễ tôn giáo cơ bản hằng ngày. Các phong tục Hồi giáo ở Mali có mức độ vừa phải, khoan dung, và đã thay đổi theo các điều kiện của địa phương; các mối quan hệ giữa người Hồi giáo và các cộng đồng tôn giáo nhỏ khác nói chung là thân thiện. Hiến pháp của Mali đã quy định một thể chế nhà nước thế tục và ủng hộ quyền tự do tôn giáo, và chính phủ Mali phải đảm bảo quyền này.' } res = question_answerer(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint) tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) ``` ## Author Duc Nguyen ## Citation ``` Kiet Van Nguyen, Son Quoc Tran, Luan Thanh Nguyen, Tin Van Huynh, Son T. Luu, Ngan Luu-Thuy Nguyen. "VLSP 2021 Shared Task: Vietnamese Machine Reading Comprehension." The 8th International Workshop on Vietnamese Language and Speech Processing (VLSP 2021) . ```

huggingtweets/cushbomb

huggingtweets

2022-09-23T12:40:19Z

113

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/cushbomb/1663936814713/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('https://pbs.twimg.com/profile_images/1560517790900969473/MPbfc6w2_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">matt christman</div> <div style="text-align: center; font-size: 14px;">@cushbomb</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 matt christman. | Data | matt christman | | --- | --- | | Tweets downloaded | 3230 | | Retweets | 241 | | Short tweets | 685 | | Tweets kept | 2304 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39bxpmve/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 @cushbomb's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2gd8zqob) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2gd8zqob/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/cushbomb') 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)

rossedwa/bert-take-uncased-f1-epoch-8

rossedwa

2022-09-23T12:27:37Z

161

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-23T12:16:01Z

f1_score = 85 Kaggle Score: 0.71711

pulkitkumar13/dark-bert-finetuned-ner1

pulkitkumar13

2022-09-23T11:02:45Z

110

4

transformers

[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-09-23T10:40:57Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: dark-bert-finetuned-ner1 results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.9337419247970846 - name: Recall type: recall value: 0.9486704813194211 - name: F1 type: f1 value: 0.9411470072627097 - name: Accuracy type: accuracy value: 0.9861364572908695 ---  # dark-bert-finetuned-ner1 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: 0.0833 - Precision: 0.9337 - Recall: 0.9487 - F1: 0.9411 - Accuracy: 0.9861 ## 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: 8 - eval_batch_size: 8 - 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0358 | 1.0 | 1756 | 0.0780 | 0.9283 | 0.9409 | 0.9346 | 0.9844 | | 0.0172 | 2.0 | 3512 | 0.0708 | 0.9375 | 0.9488 | 0.9431 | 0.9860 | | 0.0056 | 3.0 | 5268 | 0.0833 | 0.9337 | 0.9487 | 0.9411 | 0.9861 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.10.0 - Datasets 2.5.1 - Tokenizers 0.12.1

rinascimento/distilbert-base-uncased-finetuned-emotion

rinascimento

2022-09-23T09:52:40Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-23T06:15:09Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.924 - name: F1 type: f1 value: 0.9241401774459951 ---  # 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: 0.2167 - Accuracy: 0.924 - F1: 0.9241 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.815 | 1.0 | 250 | 0.3051 | 0.9045 | 0.9022 | | 0.2496 | 2.0 | 500 | 0.2167 | 0.924 | 0.9241 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

bryanleeharyanto/vtt-indonesia

bryanleeharyanto

2022-09-23T06:39:29Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-09-20T07:59:29Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: vtt-indonesia results: [] ---  # vtt-indonesia 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: 0.3472 - Wer: 0.3582 ## 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: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.7612 | 3.23 | 400 | 0.6405 | 0.6714 | | 0.4143 | 6.45 | 800 | 0.3772 | 0.4974 | | 0.2068 | 9.68 | 1200 | 0.3877 | 0.4442 | | 0.1436 | 12.9 | 1600 | 0.3785 | 0.4212 | | 0.1133 | 16.13 | 2000 | 0.3944 | 0.4144 | | 0.09 | 19.35 | 2400 | 0.3695 | 0.3925 | | 0.0705 | 22.58 | 2800 | 0.3706 | 0.3846 | | 0.057 | 25.81 | 3200 | 0.3720 | 0.3725 | | 0.048 | 29.03 | 3600 | 0.3472 | 0.3582 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

ryuno25/t5-base-finetuned-eli-5

ryuno25

2022-09-23T06:29:14Z

114

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:eli5", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-09-23T04:40:11Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - eli5 metrics: - rouge model-index: - name: t5-base-finetuned-eli-5 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: eli5 type: eli5 config: LFQA_reddit split: train_eli5 args: LFQA_reddit metrics: - name: Rouge1 type: rouge value: 13.4 ---  # t5-base-finetuned-eli-5 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the eli5 dataset. It achieves the following results on the evaluation set: - Loss: 3.4557 - Rouge1: 13.4 - Rouge2: 1.9415 - Rougel: 10.4671 - Rougelsum: 12.0693 - Gen Len: 18.9529 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:| | 3.6754 | 1.0 | 8520 | 3.4557 | 13.4 | 1.9415 | 10.4671 | 12.0693 | 18.9529 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

SmilestheSad/hf_trainer

SmilestheSad

2022-09-23T04:39:53Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-23T03:36:00Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: hf_trainer results: [] ---  # hf_trainer This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0708 - F1: 0.9066 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0344 | 1.0 | 565 | 0.0661 | 0.8811 | | 0.0354 | 2.0 | 1130 | 0.0641 | 0.8963 | | 0.0222 | 3.0 | 1695 | 0.0690 | 0.8994 | | 0.0145 | 4.0 | 2260 | 0.0714 | 0.9036 | | 0.011 | 5.0 | 2825 | 0.0708 | 0.9066 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

nvidia/tts_en_fastpitch

nvidia

2022-09-23T04:28:43Z

804

39

nemo

[ "nemo", "text-to-speech", "speech", "audio", "Transformer", "pytorch", "NeMo", "Riva", "en", "dataset:ljspeech", "arxiv:2006.06873", "arxiv:2108.10447", "license:cc-by-4.0", "region:us" ]

text-to-speech

2022-06-28T17:55:51Z

--- language: - en library_name: nemo datasets: - ljspeech thumbnail: null tags: - text-to-speech - speech - audio - Transformer - pytorch - NeMo - Riva license: cc-by-4.0 --- # NVIDIA FastPitch (en-US) <style> img { display: inline; } </style> | [![Model architecture](https://img.shields.io/badge/Model_Arch-FastPitch--Transformer-lightgrey#model-badge)](#model-architecture) | [![Model size](https://img.shields.io/badge/Params-45M-lightgrey#model-badge)](#model-architecture) | [![Language](https://img.shields.io/badge/Language-en--US-lightgrey#model-badge)](#datasets) | [![Riva Compatible](https://img.shields.io/badge/NVIDIA%20Riva-compatible-brightgreen#model-badge)](#deployment-with-nvidia-riva) | FastPitch [1] is a fully-parallel transformer architecture with prosody control over pitch and individual phoneme duration. Additionally, it uses an unsupervised speech-text aligner [2]. See the [model architecture](#model-architecture) section for complete architecture details. It is also compatible with NVIDIA Riva for [production-grade server deployments](#deployment-with-nvidia-riva). ## Usage The model is available for use in the NeMo toolkit [3] and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed the latest PyTorch version. ``` pip install nemo_toolkit['all'] ``` ### Automatically instantiate the model Note: This model generates only spectrograms and a vocoder is needed to convert the spectrograms to waveforms. In this example HiFiGAN is used. ```python # Load FastPitch from nemo.collections.tts.models import FastPitchModel spec_generator = FastPitchModel.from_pretrained("nvidia/tts_en_fastpitch") # Load vocoder from nemo.collections.tts.models import HifiGanModel model = HifiGanModel.from_pretrained(model_name="nvidia/tts_hifigan") ``` ### Generate audio ```python import soundfile as sf parsed = spec_generator.parse("You can type your sentence here to get nemo to produce speech.") spectrogram = spec_generator.generate_spectrogram(tokens=parsed) audio = model.convert_spectrogram_to_audio(spec=spectrogram) ``` ### Save the generated audio file ```python # Save the audio to disk in a file called speech.wav sf.write("speech.wav", audio.to('cpu').detach().numpy()[0], 22050) ``` ### Input This model accepts batches of text. ### Output This model generates mel spectrograms. ## Model Architecture FastPitch is a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental frequency contours. The model predicts pitch contours during inference. By altering these predictions, the generated speech can be more expressive, better match the semantic of the utterance, and in the end more engaging to the listener. FastPitch is based on a fully-parallel Transformer architecture, with a much higher real-time factor than Tacotron2 for the mel-spectrogram synthesis of a typical utterance. It uses an unsupervised speech-text aligner. ## Training The NeMo toolkit [3] was used for training the models for 1000 epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/tts/fastpitch.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/tts/conf/fastpitch_align_v1.05.yaml). ### Datasets This model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent. ## Performance No performance information is available at this time. ## Limitations This checkpoint only works well with vocoders that were trained on 22050Hz data. Otherwise, the generated audio may be scratchy or choppy-sounding. ## Deployment with NVIDIA Riva For the best real-time accuracy, latency, and throughput, deploy the model with [NVIDIA Riva](https://developer.nvidia.com/riva), an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded. Additionally, Riva provides: * World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours * Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization * Streaming speech recognition, Kubernetes compatible scaling, and Enterprise-grade support Check out [Riva live demo](https://developer.nvidia.com/riva#demos). ## References - [1] [FastPitch: Parallel Text-to-speech with Pitch Prediction](https://arxiv.org/abs/2006.06873) - [2] [One TTS Alignment To Rule Them All](https://arxiv.org/abs/2108.10447) - [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)

apapiu/diffusion_model_aesthetic_keras

apapiu

2022-09-23T03:56:11Z

0

1

null

[ "license:openrail", "region:us" ]

null

2022-09-21T19:14:31Z

--- license: openrail --- A sample from the [Laion 6.5+ ](https://laion.ai/blog/laion-aesthetics/) image + text dataset. You can see some samples [here](http://captions.christoph-schuhmann.de/2B-en-6.5.html). The samples are resized + center-cropped to 64x64x3 and the .npz file also contains CLIP embeddings. TODO: add img2dataset script. The data can be used to train a basic text-to-image model.

wenkai-li/new_classifer_epoch7

wenkai-li

2022-09-23T03:35:38Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-23T02:09:46Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: new_classifer_epoch7 results: [] ---  # new_classifer_epoch7 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1305 - Accuracy: 0.9861 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.0526 | 1.0 | 4248 | 0.0587 | 0.9797 | | 0.0259 | 2.0 | 8496 | 0.0502 | 0.9855 | | 0.0121 | 3.0 | 12744 | 0.1170 | 0.9773 | | 0.0051 | 4.0 | 16992 | 0.1379 | 0.9811 | | 0.0026 | 5.0 | 21240 | 0.1014 | 0.9869 | | 0.0013 | 6.0 | 25488 | 0.1312 | 0.9859 | | 0.0002 | 7.0 | 29736 | 0.1305 | 0.9861 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

spacemanidol/t5-base-all-rewrite-correct-unchaged-no-prefix

spacemanidol

2022-09-23T02:59:17Z

5

1

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-09-19T19:44:11Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-base-all-rewrite-correct-unchaged-no-prefix results: [] ---  # t5-base-all-rewrite-correct-unchaged-no-prefix This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. ## 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: 5e-05 - train_batch_size: 256 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.21.2 - Pytorch 1.7.0+cu110 - Datasets 2.4.0 - Tokenizers 0.12.1

jamiehuang/t5-base-finetuned-xsum

jamiehuang

2022-09-23T02:11:02Z

110

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:eli5", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-09-22T15:06:03Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - eli5 model-index: - name: t5-base-finetuned-xsum results: [] ---  # t5-base-finetuned-xsum This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the eli5 dataset. ## 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: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

neelmehta00/t5-base-finetuned-eli5

neelmehta00

2022-09-22T23:16:27Z

101

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:eli5", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-09-22T15:04:24Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - eli5 metrics: - rouge model-index: - name: t5-base-finetuned-eli5 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: eli5 type: eli5 config: LFQA_reddit split: train_eli5 args: LFQA_reddit metrics: - name: Rouge1 type: rouge value: 14.5658 ---  # t5-base-finetuned-eli5 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the eli5 dataset. It achieves the following results on the evaluation set: - Loss: 3.1765 - Rouge1: 14.5658 - Rouge2: 2.2777 - Rougel: 11.2826 - Rougelsum: 13.1136 - Gen Len: 18.9938 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 3.3398 | 1.0 | 17040 | 3.1765 | 14.5658 | 2.2777 | 11.2826 | 13.1136 | 18.9938 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

Kevin123/distilbert-base-uncased-finetuned-cola

Kevin123

2022-09-22T22:39:03Z

106

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-22T21:03:59Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5474713423103301 ---  # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8663 - Matthews Correlation: 0.5475 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5248 | 1.0 | 535 | 0.5171 | 0.4210 | | 0.3418 | 2.0 | 1070 | 0.4971 | 0.5236 | | 0.2289 | 3.0 | 1605 | 0.6874 | 0.5023 | | 0.1722 | 4.0 | 2140 | 0.7680 | 0.5392 | | 0.118 | 5.0 | 2675 | 0.8663 | 0.5475 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.8.1+cu102 - Datasets 1.18.3 - Tokenizers 0.10.3

JJRohan/ppo-LunarLander-v2

JJRohan

2022-09-22T21:12:36Z

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-09-22T21:12:08Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 169.43 +/- 77.42 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

nlp-guild/bert-base-chinese-finetuned-intent_recognition-biomedical

nlp-guild

2022-09-22T20:06:57Z

136

4

transformers

[ "transformers", "pytorch", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-22T19:42:37Z

fine-tuned bert-base-chinese for intent recognition task on [dataset](https://huggingface.co/datasets/nlp-guild/intent-recognition-biomedical) # Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification from transformers import TextClassificationPipeline tokenizer = AutoTokenizer.from_pretrained("nlp-guild/bert-base-chinese-finetuned-intent_recognition-biomedical") model = AutoModelForSequenceClassification.from_pretrained("nlp-guild/bert-base-chinese-finetuned-intent_recognition-biomedical") nlp = TextClassificationPipeline(model = model, tokenizer = tokenizer) label_set = [ '定义', '病因', '预防', '临床表现(病症表现)', '相关病症', '治疗方法', '所属科室', '传染性', '治愈率', '禁忌', '化验/体检方案', '治疗时间', '其他' ] def readable_results(top_k:int, usr_query:str): raw = nlp(usr_query, top_k = top_k) def f(x): index = int(x['label'][6:]) x['label'] = label_set[index] for i in raw: f(i) return raw readable_results(3,'得了心脏病怎么办') ''' [{'label': '治疗方法', 'score': 0.9994503855705261}, {'label': '其他', 'score': 0.00018375989748165011}, {'label': '临床表现(病症表现)', 'score': 0.00010841596667887643}] ''' ```

SharpAI/benign-net-traffic-v2-t5-l12

SharpAI

2022-09-22T20:06:09Z

106

0

transformers

[ "transformers", "pytorch", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-09-21T00:02:21Z

--- tags: - generated_from_keras_callback model-index: - name: benign-net-traffic-v2-t5-l12 results: [] ---  # benign-net-traffic-v2-t5-l12 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: ## 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: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1

TingChenChang/hpvqa-lcqmc-ocnli-cnsd-multi-MiniLM-v2

TingChenChang

2022-09-22T19:23:21Z

6

0

sentence-transformers

[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-09-22T19:23:08Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 12 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 1e-06 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 12, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

jayanta/twitter-roberta-base-sentiment-sentiment-memes-30epcohs

jayanta

2022-09-22T19:04:33Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-22T14:38:21Z

--- tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: twitter-roberta-base-sentiment-sentiment-memes-30epcohs results: [] ---  # twitter-roberta-base-sentiment-sentiment-memes-30epcohs This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3027 - Accuracy: 0.8517 - Precision: 0.8536 - Recall: 0.8517 - F1: 0.8523 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.2504 | 1.0 | 2147 | 0.7129 | 0.8087 | 0.8112 | 0.8087 | 0.8036 | | 0.2449 | 2.0 | 4294 | 0.7500 | 0.8229 | 0.8279 | 0.8229 | 0.8240 | | 0.2652 | 3.0 | 6441 | 0.7460 | 0.8181 | 0.8185 | 0.8181 | 0.8149 | | 0.2585 | 4.0 | 8588 | 0.7906 | 0.8155 | 0.8152 | 0.8155 | 0.8153 | | 0.2534 | 5.0 | 10735 | 0.8178 | 0.8061 | 0.8180 | 0.8061 | 0.8080 | | 0.2498 | 6.0 | 12882 | 0.8139 | 0.8166 | 0.8163 | 0.8166 | 0.8164 | | 0.2825 | 7.0 | 15029 | 0.7494 | 0.8155 | 0.8210 | 0.8155 | 0.8168 | | 0.2459 | 8.0 | 17176 | 0.8870 | 0.8061 | 0.8122 | 0.8061 | 0.8075 | | 0.2303 | 9.0 | 19323 | 0.8699 | 0.7987 | 0.8060 | 0.7987 | 0.8003 | | 0.2425 | 10.0 | 21470 | 0.8043 | 0.8244 | 0.8275 | 0.8244 | 0.8253 | | 0.2143 | 11.0 | 23617 | 0.9163 | 0.8208 | 0.8251 | 0.8208 | 0.8219 | | 0.2054 | 12.0 | 25764 | 0.8330 | 0.8239 | 0.8258 | 0.8239 | 0.8245 | | 0.208 | 13.0 | 27911 | 1.0673 | 0.8134 | 0.8216 | 0.8134 | 0.8150 | | 0.1668 | 14.0 | 30058 | 0.9071 | 0.8270 | 0.8276 | 0.8270 | 0.8273 | | 0.1571 | 15.0 | 32205 | 0.9294 | 0.8339 | 0.8352 | 0.8339 | 0.8344 | | 0.1857 | 16.0 | 34352 | 0.9909 | 0.8354 | 0.8350 | 0.8354 | 0.8352 | | 0.1476 | 17.0 | 36499 | 0.9747 | 0.8433 | 0.8436 | 0.8433 | 0.8434 | | 0.1341 | 18.0 | 38646 | 0.9372 | 0.8422 | 0.8415 | 0.8422 | 0.8415 | | 0.1181 | 19.0 | 40793 | 1.0301 | 0.8433 | 0.8443 | 0.8433 | 0.8437 | | 0.1192 | 20.0 | 42940 | 1.1332 | 0.8407 | 0.8415 | 0.8407 | 0.8410 | | 0.0983 | 21.0 | 45087 | 1.2002 | 0.8428 | 0.8498 | 0.8428 | 0.8440 | | 0.0951 | 22.0 | 47234 | 1.2141 | 0.8475 | 0.8504 | 0.8475 | 0.8483 | | 0.0784 | 23.0 | 49381 | 1.1652 | 0.8407 | 0.8453 | 0.8407 | 0.8417 | | 0.0623 | 24.0 | 51528 | 1.1730 | 0.8417 | 0.8443 | 0.8417 | 0.8425 | | 0.054 | 25.0 | 53675 | 1.2900 | 0.8454 | 0.8496 | 0.8454 | 0.8464 | | 0.0584 | 26.0 | 55822 | 1.2831 | 0.8480 | 0.8497 | 0.8480 | 0.8486 | | 0.0531 | 27.0 | 57969 | 1.3043 | 0.8506 | 0.8524 | 0.8506 | 0.8512 | | 0.0522 | 28.0 | 60116 | 1.2891 | 0.8527 | 0.8554 | 0.8527 | 0.8534 | | 0.037 | 29.0 | 62263 | 1.3077 | 0.8538 | 0.8559 | 0.8538 | 0.8544 | | 0.038 | 30.0 | 64410 | 1.3027 | 0.8517 | 0.8536 | 0.8517 | 0.8523 | ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 1.15.2.dev0 - Tokenizers 0.10.1

cjj8168/stress_dreaddit

cjj8168

2022-09-22T16:46:33Z

159

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-22T16:44:29Z

--- license: mit tags: - generated_from_trainer model-index: - name: stress_dreaddit results: [] ---  # stress_dreaddit This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on an unknown dataset. ## 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: 0.005 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

facebook/spar-wiki-bm25-lexmodel-query-encoder

facebook

2022-09-22T16:44:45Z

111

2

transformers

[ "transformers", "pytorch", "bert", "feature-extraction", "arxiv:2110.06918", "endpoints_compatible", "region:us" ]

feature-extraction

2022-09-21T21:44:05Z

--- tags: - feature-extraction pipeline_tag: feature-extraction --- This model is the query encoder of the Wiki BM25 Lexical Model (Λ) from the SPAR paper: [Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?](https://arxiv.org/abs/2110.06918) <br> Xilun Chen, Kushal Lakhotia, Barlas Oğuz, Anchit Gupta, Patrick Lewis, Stan Peshterliev, Yashar Mehdad, Sonal Gupta and Wen-tau Yih <br> **Meta AI** The associated github repo is available here: https://github.com/facebookresearch/dpr-scale/tree/main/spar This model is a BERT-base sized dense retriever trained on Wikipedia articles to imitate the behavior of BM25. The following models are also available: Pretrained Model | Corpus | Teacher | Architecture | Query Encoder Path | Context Encoder Path |---|---|---|---|---|--- Wiki BM25 Λ | Wikipedia | BM25 | BERT-base | facebook/spar-wiki-bm25-lexmodel-query-encoder | facebook/spar-wiki-bm25-lexmodel-context-encoder PAQ BM25 Λ | PAQ | BM25 | BERT-base | facebook/spar-paq-bm25-lexmodel-query-encoder | facebook/spar-paq-bm25-lexmodel-context-encoder MARCO BM25 Λ | MS MARCO | BM25 | BERT-base | facebook/spar-marco-bm25-lexmodel-query-encoder | facebook/spar-marco-bm25-lexmodel-context-encoder MARCO UniCOIL Λ | MS MARCO | UniCOIL | BERT-base | facebook/spar-marco-unicoil-lexmodel-query-encoder | facebook/spar-marco-unicoil-lexmodel-context-encoder # Using the Lexical Model (Λ) Alone This model should be used together with the associated context encoder, similar to the [DPR](https://huggingface.co/docs/transformers/v4.22.1/en/model_doc/dpr) model. ``` import torch from transformers import AutoTokenizer, AutoModel # The tokenizer is the same for the query and context encoder tokenizer = AutoTokenizer.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') query_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') context_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-context-encoder') query = "Where was Marie Curie born?" contexts = [ "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.", "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace." ] # Apply tokenizer query_input = tokenizer(query, return_tensors='pt') ctx_input = tokenizer(contexts, padding=True, truncation=True, return_tensors='pt') # Compute embeddings: take the last-layer hidden state of the [CLS] token query_emb = query_encoder(**query_input).last_hidden_state[:, 0, :] ctx_emb = context_encoder(**ctx_input).last_hidden_state[:, 0, :] # Compute similarity scores using dot product score1 = query_emb @ ctx_emb[0] # 341.3268 score2 = query_emb @ ctx_emb[1] # 340.1626 ``` # Using the Lexical Model (Λ) with a Base Dense Retriever as in SPAR As Λ learns lexical matching from a sparse teacher retriever, it can be used in combination with a standard dense retriever (e.g. [DPR](https://huggingface.co/docs/transformers/v4.22.1/en/model_doc/dpr#dpr), [Contriever](https://huggingface.co/facebook/contriever-msmarco)) to build a dense retriever that excels at both lexical and semantic matching. In the following example, we show how to build the SPAR-Wiki model for Open-Domain Question Answering by concatenating the embeddings of DPR and the Wiki BM25 Λ. ``` import torch from transformers import AutoTokenizer, AutoModel from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer from transformers import DPRContextEncoder, DPRContextEncoderTokenizer # DPR model dpr_ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-multiset-base") dpr_ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-multiset-base") dpr_query_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-multiset-base") dpr_query_encoder = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-multiset-base") # Wiki BM25 Λ model lexmodel_tokenizer = AutoTokenizer.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') lexmodel_query_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') lexmodel_context_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-context-encoder') query = "Where was Marie Curie born?" contexts = [ "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.", "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace." ] # Compute DPR embeddings dpr_query_input = dpr_query_tokenizer(query, return_tensors='pt')['input_ids'] dpr_query_emb = dpr_query_encoder(dpr_query_input).pooler_output dpr_ctx_input = dpr_ctx_tokenizer(contexts, padding=True, truncation=True, return_tensors='pt') dpr_ctx_emb = dpr_ctx_encoder(**dpr_ctx_input).pooler_output # Compute Λ embeddings lexmodel_query_input = lexmodel_tokenizer(query, return_tensors='pt') lexmodel_query_emb = lexmodel_query_encoder(**query_input).last_hidden_state[:, 0, :] lexmodel_ctx_input = lexmodel_tokenizer(contexts, padding=True, truncation=True, return_tensors='pt') lexmodel_ctx_emb = lexmodel_context_encoder(**ctx_input).last_hidden_state[:, 0, :] # Form SPAR embeddings via concatenation # The concatenation weight is only applied to query embeddings # Refer to the SPAR paper for details concat_weight = 0.7 spar_query_emb = torch.cat( [dpr_query_emb, concat_weight * lexmodel_query_emb], dim=-1, ) spar_ctx_emb = torch.cat( [dpr_ctx_emb, lexmodel_ctx_emb], dim=-1, ) # Compute similarity scores score1 = spar_query_emb @ spar_ctx_emb[0] # 317.6931 score2 = spar_query_emb @ spar_ctx_emb[1] # 314.6144 ```

CoreyMorris/Reinforce-cartpole-v1

CoreyMorris

2022-09-22T16:21:40Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2022-09-22T16:20:39Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5

Chemsseddine/distilbert-base-uncased-finetuned-cola

Chemsseddine

2022-09-22T15:31:00Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-07T17:23:46Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-cola results: [] ---  # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0011 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 5 | 2.1485 | | No log | 2.0 | 10 | 2.0983 | | No log | 3.0 | 15 | 2.0499 | | No log | 4.0 | 20 | 2.0155 | | No log | 5.0 | 25 | 2.0011 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

jayanta/twitter-roberta-base-sentiment-sentiment-memes

jayanta

2022-09-22T14:35:05Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-19T15:25:56Z

--- tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: twitter-roberta-base-sentiment-sentiment-memes results: [] ---  # twitter-roberta-base-sentiment-sentiment-memes This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9582 - Accuracy: 0.8187 - Precision: 0.8199 - Recall: 0.8187 - F1: 0.8191 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.4673 | 1.0 | 2147 | 0.4373 | 0.7647 | 0.8180 | 0.7647 | 0.7657 | | 0.3987 | 2.0 | 4294 | 0.5528 | 0.7783 | 0.8096 | 0.7783 | 0.7804 | | 0.3194 | 3.0 | 6441 | 0.6432 | 0.7752 | 0.7767 | 0.7752 | 0.7680 | | 0.2855 | 4.0 | 8588 | 0.6820 | 0.7814 | 0.8034 | 0.7814 | 0.7837 | | 0.2575 | 5.0 | 10735 | 0.7427 | 0.7720 | 0.8070 | 0.7720 | 0.7741 | | 0.2154 | 6.0 | 12882 | 0.8225 | 0.7987 | 0.8062 | 0.7987 | 0.8004 | | 0.2195 | 7.0 | 15029 | 0.8361 | 0.8071 | 0.8086 | 0.8071 | 0.8077 | | 0.2322 | 8.0 | 17176 | 0.8842 | 0.8056 | 0.8106 | 0.8056 | 0.8069 | | 0.2102 | 9.0 | 19323 | 0.9188 | 0.8129 | 0.8144 | 0.8129 | 0.8135 | | 0.1893 | 10.0 | 21470 | 0.9582 | 0.8187 | 0.8199 | 0.8187 | 0.8191 | ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 1.15.2.dev0 - Tokenizers 0.10.1

huggingtweets/slime_machine

huggingtweets

2022-09-22T14:09:28Z

94

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/slime_machine/1663855763474/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('https://pbs.twimg.com/profile_images/1554733825220939777/lgFt_2e1_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">slime</div> <div style="text-align: center; font-size: 14px;">@slime_machine</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 slime. | Data | slime | | --- | --- | | Tweets downloaded | 3229 | | Retweets | 441 | | Short tweets | 589 | | Tweets kept | 2199 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2s9inuxg/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 @slime_machine's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5xjy8nrj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5xjy8nrj/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/slime_machine') 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)

sd-concepts-library/pixel-mania

sd-concepts-library

2022-09-22T14:05:08Z

0

1

null

[ "license:mit", "region:us" ]

null

2022-09-22T05:26:54Z

--- license: mit --- ### pixel-mania on Stable Diffusion This is the `<pixel-mania>` 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).

m-lin20/satellite-instrument-roberta-NER

m-lin20

2022-09-22T13:33:22Z

108

1

transformers

[ "transformers", "pytorch", "roberta", "token-classification", "pt", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-03-02T23:29:05Z

--- language: "pt" widget: - text: "Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering the solar reflected wavelengths. The Thermal Infrared Sensor-2 (TIRS-2) improves upon Landsat 8 TIRS, addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror. In addition, Landsat 9 adds redundancy to TIRS-2, thus upgrading the instrument to a 5-year design life commensurate with other elements of the mission. Initial performance testing of OLI-2 and TIRS-2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality. " example_title: "example 1" - text: "Compared to its predecessor, Jason-3, the two AMR-C radiometer instruments have an external calibration system which enables higher radiometric stability accomplished by moving the secondary mirror between well-defined targets. Sentinel-6 allows continuing the study of the ocean circulation, climate change, and sea-level rise for at least another decade. Besides the external calibration for the AMR heritage radiometer (18.7, 23.8, and 34 GHz channels), the AMR-C contains a high-resolution microwave radiometer (HRMR) with radiometer channels at 90, 130, and 168 GHz. This subsystem allows for a factor of 5× higher spatial resolution at coastal transitions. This article presents a brief description of the instrument and the measured performance of the completed AMR-C-A and AMR-C-B instruments." example_title: "example 2" - text: "The Landsat 9 will continue the Landsat data record into its fifth decade with a near-copy build of Landsat 8 with launch scheduled for December 2020. The two instruments on Landsat 9 are Thermal Infrared Sensor-2 (TIRS-2) and Operational Land Imager-2 (OLI-2)." example_title: "example 3" inference: parameters: aggregation_strategy: "simple" --- # satellite-instrument-roberta-NER For details, please visit the [GitHub link](https://github.com/THU-EarthInformationScienceLab/Satellite-Instrument-NER). ## Citation Our [paper](https://www.tandfonline.com/doi/full/10.1080/17538947.2022.2107098) has been published in the International Journal of Digital Earth : ```bibtex @article{lin2022satellite, title={Satellite and instrument entity recognition using a pre-trained language model with distant supervision}, author={Lin, Ming and Jin, Meng and Liu, Yufu and Bai, Yuqi}, journal={International Journal of Digital Earth}, volume={15}, number={1}, pages={1290--1304}, year={2022}, publisher={Taylor \& Francis} } ```

m-lin20/satellite-instrument-bert-NER

m-lin20

2022-09-22T13:32:42Z

104

2

transformers

[ "transformers", "pytorch", "bert", "token-classification", "pt", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-03-02T23:29:05Z

--- language: "pt" widget: - text: "Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering the solar reflected wavelengths. The Thermal Infrared Sensor-2 (TIRS-2) improves upon Landsat 8 TIRS, addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror. In addition, Landsat 9 adds redundancy to TIRS-2, thus upgrading the instrument to a 5-year design life commensurate with other elements of the mission. Initial performance testing of OLI-2 and TIRS-2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality. " example_title: "example 1" - text: "Compared to its predecessor, Jason-3, the two AMR-C radiometer instruments have an external calibration system which enables higher radiometric stability accomplished by moving the secondary mirror between well-defined targets. Sentinel-6 allows continuing the study of the ocean circulation, climate change, and sea-level rise for at least another decade. Besides the external calibration for the AMR heritage radiometer (18.7, 23.8, and 34 GHz channels), the AMR-C contains a high-resolution microwave radiometer (HRMR) with radiometer channels at 90, 130, and 168 GHz. This subsystem allows for a factor of 5× higher spatial resolution at coastal transitions. This article presents a brief description of the instrument and the measured performance of the completed AMR-C-A and AMR-C-B instruments." example_title: "example 2" - text: "Landsat 9 will continue the Landsat data record into its fifth decade with a near-copy build of Landsat 8 with launch scheduled for December 2020. The two instruments on Landsat 9 are Thermal Infrared Sensor-2 (TIRS-2) and Operational Land Imager-2 (OLI-2)." example_title: "example 3" inference: parameters: aggregation_strategy: "first" --- # satellite-instrument-bert-NER For details, please visit the [GitHub link](https://github.com/THU-EarthInformationScienceLab/Satellite-Instrument-NER). ## Citation Our [paper](https://www.tandfonline.com/doi/full/10.1080/17538947.2022.2107098) has been published in the International Journal of Digital Earth : ```bibtex @article{lin2022satellite, title={Satellite and instrument entity recognition using a pre-trained language model with distant supervision}, author={Lin, Ming and Jin, Meng and Liu, Yufu and Bai, Yuqi}, journal={International Journal of Digital Earth}, volume={15}, number={1}, pages={1290--1304}, year={2022}, publisher={Taylor \& Francis} } ```

fxmarty/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic

fxmarty

2022-09-22T13:28:21Z

3

0

transformers

[ "transformers", "onnx", "distilbert", "text-classification", "dataset:sst2", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-22T13:19:36Z

--- license: apache-2.0 datasets: - sst2 - glue --- This model is a fork of https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english , quantized using dynamic Post-Training Quantization (PTQ) with ONNX Runtime and 🤗 Optimum library. It achieves 0.901 on the validation set. To load this model: ```python from optimum.onnxruntime import ORTModelForSequenceClassification model = ORTModelForSequenceClassification.from_pretrained("fxmarty/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic") ```

sd-concepts-library/bluebey-2

sd-concepts-library

2022-09-22T12:21:34Z

0

null

[ "license:mit", "region:us" ]

null

2022-09-22T12:21:30Z

--- license: mit --- ### Bluebey-2 on Stable Diffusion This is the `<bluebey>` 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`: ![<bluebey> 0](https://huggingface.co/sd-concepts-library/bluebey-2/resolve/main/concept_images/0.jpeg) ![<bluebey> 1](https://huggingface.co/sd-concepts-library/bluebey-2/resolve/main/concept_images/2.jpeg) ![<bluebey> 2](https://huggingface.co/sd-concepts-library/bluebey-2/resolve/main/concept_images/1.jpeg)

muhtasham/bert-small-finetuned-finer

muhtasham

2022-09-22T11:50:51Z

161

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-08-25T20:36:59Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-small-finetuned-finer results: [] ---  # bert-small-finetuned-finer This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6137 ## 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: 128 - eval_batch_size: 8 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 1.8994 | 1.0 | 2433 | 1.7597 | | 1.7226 | 2.0 | 4866 | 1.6462 | | 1.6752 | 3.0 | 7299 | 1.6137 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

muhtasham/bert-small-finetuned-parsed20

muhtasham

2022-09-22T11:34:48Z

179

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-08-17T13:31:40Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-small-finetuned-parsed20 results: [] ---  # bert-small-finetuned-parsed20 This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1193 ## 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: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 4 | 3.0763 | | No log | 2.0 | 8 | 2.8723 | | No log | 3.0 | 12 | 3.5102 | | No log | 4.0 | 16 | 2.8641 | | No log | 5.0 | 20 | 2.7827 | | No log | 6.0 | 24 | 2.8163 | | No log | 7.0 | 28 | 3.2415 | | No log | 8.0 | 32 | 3.0477 | | No log | 9.0 | 36 | 3.5160 | | No log | 10.0 | 40 | 3.1248 | | No log | 11.0 | 44 | 3.2159 | | No log | 12.0 | 48 | 3.2177 | | No log | 13.0 | 52 | 2.9108 | | No log | 14.0 | 56 | 3.3758 | | No log | 15.0 | 60 | 3.1335 | | No log | 16.0 | 64 | 2.9753 | | No log | 17.0 | 68 | 2.9922 | | No log | 18.0 | 72 | 3.2798 | | No log | 19.0 | 76 | 2.7280 | | No log | 20.0 | 80 | 3.1193 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

muhtasham/bert-small-finetuned-parsed-longer50

muhtasham

2022-09-22T11:34:27Z

179

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-08-17T13:39:01Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-small-finetuned-finetuned-parsed-longer50 results: [] ---  # bert-small-finetuned-finetuned-parsed-longer50 This model is a fine-tuned version of [muhtasham/bert-small-finetuned-parsed20](https://huggingface.co/muhtasham/bert-small-finetuned-parsed20) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9278 ## 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: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 4 | 2.9807 | | No log | 2.0 | 8 | 2.7267 | | No log | 3.0 | 12 | 3.3484 | | No log | 4.0 | 16 | 2.7573 | | No log | 5.0 | 20 | 2.7063 | | No log | 6.0 | 24 | 2.7353 | | No log | 7.0 | 28 | 3.1290 | | No log | 8.0 | 32 | 2.9371 | | No log | 9.0 | 36 | 3.4265 | | No log | 10.0 | 40 | 3.0537 | | No log | 11.0 | 44 | 3.1382 | | No log | 12.0 | 48 | 3.1454 | | No log | 13.0 | 52 | 2.8379 | | No log | 14.0 | 56 | 3.2760 | | No log | 15.0 | 60 | 3.0504 | | No log | 16.0 | 64 | 2.9001 | | No log | 17.0 | 68 | 2.8892 | | No log | 18.0 | 72 | 3.1837 | | No log | 19.0 | 76 | 2.6404 | | No log | 20.0 | 80 | 3.0600 | | No log | 21.0 | 84 | 3.1432 | | No log | 22.0 | 88 | 2.9608 | | No log | 23.0 | 92 | 3.0513 | | No log | 24.0 | 96 | 3.1038 | | No log | 25.0 | 100 | 3.0975 | | No log | 26.0 | 104 | 2.8977 | | No log | 27.0 | 108 | 2.9416 | | No log | 28.0 | 112 | 2.9015 | | No log | 29.0 | 116 | 2.7947 | | No log | 30.0 | 120 | 2.9278 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

thisisHJLee/wav2vec2-large-xls-r-300m-korean-e

thisisHJLee

2022-09-22T10:38:09Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-09-22T08:52:18Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xls-r-300m-korean-e results: [] ---  # wav2vec2-large-xls-r-300m-korean-e This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6640 - Cer: 0.1518 ## 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: 0.0001 - train_batch_size: 8 - 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: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.3827 | 3.25 | 500 | 3.5391 | 1.0 | | 3.0633 | 6.49 | 1000 | 2.9854 | 0.8759 | | 1.2095 | 9.74 | 1500 | 0.8384 | 0.2103 | | 0.5499 | 12.99 | 2000 | 0.6733 | 0.1689 | | 0.3815 | 16.23 | 2500 | 0.6778 | 0.1591 | | 0.3111 | 19.48 | 3000 | 0.6640 | 0.1518 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.13.0

mahaveer/ppo-LunarLander-v2

mahaveer

2022-09-22T10:11:19Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-09-22T09:57:48Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 194.40 +/- 31.46 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

GItaf/gpt2-gpt2-TF-weight1-epoch10

GItaf

2022-09-22T09:36:24Z

113

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-09-21T08:05:36Z

--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-gpt2-TF-weight1-epoch10 results: [] ---  # gpt2-gpt2-TF-weight1-epoch10 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. ## 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: 10 ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

GItaf/roberta-base-roberta-base-TF-weight1-epoch10

GItaf

2022-09-22T09:35:57Z

49

0

transformers

[ "transformers", "pytorch", "roberta", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-09-22T09:34:27Z

--- license: mit tags: - generated_from_trainer model-index: - name: roberta-base-roberta-base-TF-weight1-epoch10 results: [] ---  # roberta-base-roberta-base-TF-weight1-epoch10 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. ## 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: 10 ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

GItaf/roberta-base-roberta-base-TF-weight1-epoch5

GItaf

2022-09-22T09:32:53Z

47

0

transformers

[ "transformers", "pytorch", "roberta", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-09-22T09:31:40Z

--- license: mit tags: - generated_from_trainer model-index: - name: roberta-base-roberta-base-TF-weight1-epoch5 results: [] ---  # roberta-base-roberta-base-TF-weight1-epoch5 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. ## 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: 5 ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

GItaf/roberta-base-roberta-base-TF-weight1-epoch15

GItaf

2022-09-22T09:23:00Z

6

0

transformers

[ "transformers", "pytorch", "roberta", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-09-21T15:32:11Z

--- tags: - generated_from_trainer model-index: - name: roberta-base-roberta-base-TF-weight1-epoch15 results: [] ---  # roberta-base-roberta-base-TF-weight1-epoch15 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.8322 - Cls loss: 0.6900 - Lm loss: 4.1423 - Cls Accuracy: 0.5401 - Cls F1: 0.3788 - Cls Precision: 0.2917 - Cls Recall: 0.5401 - Perplexity: 62.95 ## 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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Cls loss | Lm loss | Cls Accuracy | Cls F1 | Cls Precision | Cls Recall | Perplexity | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------:|:------------:|:------:|:-------------:|:----------:|:----------:| | 5.3158 | 1.0 | 3470 | 4.9858 | 0.6910 | 4.2949 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 73.32 | | 4.9772 | 2.0 | 6940 | 4.8876 | 0.6956 | 4.1920 | 0.4599 | 0.2898 | 0.2115 | 0.4599 | 66.15 | | 4.8404 | 3.0 | 10410 | 4.8454 | 0.6901 | 4.1553 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 63.77 | | 4.7439 | 4.0 | 13880 | 4.8177 | 0.6904 | 4.1274 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 62.02 | | 4.6667 | 5.0 | 17350 | 4.8065 | 0.6903 | 4.1163 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 61.33 | | 4.6018 | 6.0 | 20820 | 4.8081 | 0.6963 | 4.1119 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 61.06 | | 4.5447 | 7.0 | 24290 | 4.8089 | 0.6912 | 4.1177 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 61.42 | | 4.4944 | 8.0 | 27760 | 4.8128 | 0.6900 | 4.1228 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 61.73 | | 4.4505 | 9.0 | 31230 | 4.8152 | 0.6905 | 4.1248 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 61.85 | | 4.4116 | 10.0 | 34700 | 4.8129 | 0.6908 | 4.1221 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 61.69 | | 4.3787 | 11.0 | 38170 | 4.8146 | 0.6906 | 4.1241 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 61.81 | | 4.3494 | 12.0 | 41640 | 4.8229 | 0.6900 | 4.1329 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 62.36 | | 4.3253 | 13.0 | 45110 | 4.8287 | 0.6900 | 4.1388 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 62.73 | | 4.3075 | 14.0 | 48580 | 4.8247 | 0.6900 | 4.1347 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 62.47 | | 4.2936 | 15.0 | 52050 | 4.8322 | 0.6900 | 4.1423 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 62.95 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

GItaf/gpt2-gpt2-TF-weight1-epoch15

GItaf

2022-09-22T09:21:30Z

4

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-09-21T15:31:41Z

--- tags: - generated_from_trainer model-index: - name: gpt2-gpt2-TF-weight1-epoch15 results: [] ---  # gpt2-gpt2-TF-weight1-epoch15 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0647 - Cls loss: 2.1295 - Lm loss: 3.9339 - Cls Accuracy: 0.8375 - Cls F1: 0.8368 - Cls Precision: 0.8381 - Cls Recall: 0.8375 - Perplexity: 51.11 ## 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: 15 ### Training results | Training Loss | Epoch | Step | Cls loss | Cls Accuracy | Cls F1 | Cls Precision | Cls Recall | Lm loss | Perplexity | Validation Loss | |:-------------:|:-----:|:-----:|:--------:|:------------:|:------:|:-------------:|:----------:|:-------:|:----------:|:---------------:| | 4.8702 | 1.0 | 3470 | 0.6951 | 0.7752 | 0.7670 | 0.7978 | 0.7752 | 4.0201 | 55.71 | 4.7157 | | 4.5856 | 2.0 | 6940 | 0.6797 | 0.8352 | 0.8333 | 0.8406 | 0.8352 | 3.9868 | 53.88 | 4.6669 | | 4.4147 | 3.0 | 10410 | 0.6899 | 0.8375 | 0.8368 | 0.8384 | 0.8375 | 3.9716 | 53.07 | 4.6619 | | 4.2479 | 4.0 | 13880 | 0.8678 | 0.8403 | 0.8396 | 0.8413 | 0.8403 | 3.9622 | 52.57 | 4.8305 | | 4.1281 | 5.0 | 17350 | 0.9747 | 0.8340 | 0.8334 | 0.8346 | 0.8340 | 3.9596 | 52.44 | 4.9349 | | Training Loss | Epoch | Step | Validation Loss | Cls loss | Lm loss | Cls Accuracy | Cls F1 | Cls Precision | Cls Recall | Perplexity| |:-------------:|:-----:|:-----:|:--------:|:------------:|:------:|:-------------:|:----------:|:-------:|:----------:|:---------------:| | 4.195 | 6.0 | 20820 | 4.9303 | 0.9770 | 3.9528 | 0.8300 | 0.8299 | 0.8299 | 0.8300 | 52.08 | | 4.0645 | 7.0 | 24290 | 5.0425 | 1.0979 | 3.9440 | 0.8317 | 0.8313 | 0.8317 | 0.8317 | 51.62 | | 3.9637 | 8.0 | 27760 | 5.3955 | 1.4533 | 3.9414 | 0.8329 | 0.8325 | 0.8328 | 0.8329 | 51.49 | | 3.9094 | 9.0 | 31230 | 5.6029 | 1.6645 | 3.9375 | 0.8231 | 0.8233 | 0.8277 | 0.8231 | 51.29 | | 3.8661 | 10.0 | 34700 | 5.8175 | 1.8821 | 3.9344 | 0.8144 | 0.8115 | 0.8222 | 0.8144 | 51.13 | | 3.8357 | 11.0 | 38170 | 5.6824 | 1.7494 | 3.9319 | 0.8340 | 0.8336 | 0.8342 | 0.8340 | 51.01 | | 3.8019 | 12.0 | 41640 | 5.8509 | 1.9167 | 3.9332 | 0.8369 | 0.8357 | 0.8396 | 0.8369 | 51.07 | | 3.7815 | 13.0 | 45110 | 5.9044 | 1.9686 | 3.9346 | 0.8409 | 0.8407 | 0.8408 | 0.8409 | 51.14 | | 3.7662 | 14.0 | 48580 | 6.0088 | 2.0738 | 3.9337 | 0.8363 | 0.8359 | 0.8364 | 0.8363 | 51.10 | | 3.7524 | 15.0 | 52050 | 6.0647 | 2.1295 | 3.9339 | 0.8375 | 0.8368 | 0.8381 | 0.8375 | 51.11 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

chintagunta85/electramed-small-deid2014-ner-v5-classweights

chintagunta85

2022-09-22T09:08:27Z

102

0

transformers

[ "transformers", "pytorch", "tensorboard", "electra", "token-classification", "generated_from_trainer", "dataset:i2b22014", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-09-22T07:48:30Z

--- tags: - generated_from_trainer datasets: - i2b22014 metrics: - precision - recall - f1 - accuracy model-index: - name: electramed-small-deid2014-ner-v5-classweights results: - task: name: Token Classification type: token-classification dataset: name: i2b22014 type: i2b22014 config: i2b22014-deid split: train args: i2b22014-deid metrics: - name: Precision type: precision value: 0.8832236842105263 - name: Recall type: recall value: 0.6910561632502987 - name: F1 type: f1 value: 0.7754112732711052 - name: Accuracy type: accuracy value: 0.9883040491052534 ---  # electramed-small-deid2014-ner-v5-classweights This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the i2b22014 dataset. It achieves the following results on the evaluation set: - Loss: 0.0009 - Precision: 0.8832 - Recall: 0.6911 - F1: 0.7754 - Accuracy: 0.9883 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0001 | 1.0 | 1838 | 0.0008 | 0.7702 | 0.3780 | 0.5071 | 0.9771 | | 0.0 | 2.0 | 3676 | 0.0007 | 0.8753 | 0.5671 | 0.6883 | 0.9827 | | 0.0 | 3.0 | 5514 | 0.0006 | 0.8074 | 0.4128 | 0.5463 | 0.9775 | | 0.0 | 4.0 | 7352 | 0.0007 | 0.8693 | 0.6102 | 0.7170 | 0.9848 | | 0.0 | 5.0 | 9190 | 0.0006 | 0.8710 | 0.6022 | 0.7121 | 0.9849 | | 0.0 | 6.0 | 11028 | 0.0007 | 0.8835 | 0.6547 | 0.7521 | 0.9867 | | 0.0 | 7.0 | 12866 | 0.0009 | 0.8793 | 0.6661 | 0.7579 | 0.9873 | | 0.0 | 8.0 | 14704 | 0.0008 | 0.8815 | 0.6740 | 0.7639 | 0.9876 | | 0.0 | 9.0 | 16542 | 0.0009 | 0.8812 | 0.6851 | 0.7709 | 0.9880 | | 0.0 | 10.0 | 18380 | 0.0009 | 0.8832 | 0.6911 | 0.7754 | 0.9883 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

prakashkmr48/Prompt-image-inpainting

prakashkmr48

2022-09-22T08:58:57Z

0

null

[ "region:us" ]

null

2022-09-22T08:51:46Z

git lfs install git clone https://huggingface.co/prakashkmr48/Prompt-image-inpainting

Hoax0930/kyoto_marian

Hoax0930

2022-09-22T08:32:43Z

104

0

transformers

[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "translation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2022-09-22T07:47:04Z

--- license: apache-2.0 tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: kyoto_marian results: [] ---  # kyoto_marian This model is a fine-tuned version of [Helsinki-NLP/opus-tatoeba-en-ja](https://huggingface.co/Helsinki-NLP/opus-tatoeba-en-ja) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1941 - Bleu: 13.4500 ## 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: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

sd-concepts-library/test2

sd-concepts-library

2022-09-22T06:29:49Z

0

null

[ "license:mit", "region:us" ]

null

2022-09-22T06:29:45Z

--- license: mit --- ### TEST2 on Stable Diffusion This is the `<AIOCARD>` 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 a `style`: ![<AIOCARD> 0](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027D.jpg) ![<AIOCARD> 1](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027C.jpg) ![<AIOCARD> 2](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/00100282.jpg) ![<AIOCARD> 3](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027A.jpg) ![<AIOCARD> 4](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027B.jpg) ![<AIOCARD> 5](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/00100281.jpg) ![<AIOCARD> 6](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/00100280.jpg) ![<AIOCARD> 7](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027E.jpg) ![<AIOCARD> 8](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/00100279.jpg) ![<AIOCARD> 9](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027F.jpg)

airakoze/Lab04

airakoze

2022-09-22T05:34:23Z

0

null

[ "region:us" ]

null

2022-09-22T03:48:36Z

--- title: Housing price prediction in California colorFrom: gray colorTo: red sdk: gradio sdk_version: 3.0.4 app_file: app.py pinned: false ---

sd-concepts-library/bee

sd-concepts-library

2022-09-22T05:01:07Z

0

2

null

[ "license:mit", "region:us" ]

null

2022-09-22T05:00:56Z

--- license: mit --- ### BEE on Stable Diffusion This is the `<b-e-e>` 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`: ![<b-e-e> 0](https://huggingface.co/sd-concepts-library/bee/resolve/main/concept_images/0.jpeg) ![<b-e-e> 1](https://huggingface.co/sd-concepts-library/bee/resolve/main/concept_images/3.jpeg) ![<b-e-e> 2](https://huggingface.co/sd-concepts-library/bee/resolve/main/concept_images/2.jpeg) ![<b-e-e> 3](https://huggingface.co/sd-concepts-library/bee/resolve/main/concept_images/1.jpeg)

sd-concepts-library/yinit

sd-concepts-library

2022-09-22T04:58:38Z

0

null

[ "license:mit", "region:us" ]

null

2022-09-22T04:58:24Z

--- license: mit --- ### yinit on Stable Diffusion This is the `yinit-dropcap` 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 a `style`: ![yinit-dropcap 0](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/4.jpeg) ![yinit-dropcap 1](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/12.jpeg) ![yinit-dropcap 2](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/8.jpeg) ![yinit-dropcap 3](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/0.jpeg) ![yinit-dropcap 4](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/6.jpeg) ![yinit-dropcap 5](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/3.jpeg) ![yinit-dropcap 6](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/20.jpeg) ![yinit-dropcap 7](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/11.jpeg) ![yinit-dropcap 8](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/19.jpeg) ![yinit-dropcap 9](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/24.jpeg) ![yinit-dropcap 10](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/17.jpeg) ![yinit-dropcap 11](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/10.jpeg) ![yinit-dropcap 12](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/7.jpeg) ![yinit-dropcap 13](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/13.jpeg) ![yinit-dropcap 14](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/16.jpeg) ![yinit-dropcap 15](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/2.jpeg) ![yinit-dropcap 16](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/25.jpeg) ![yinit-dropcap 17](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/22.jpeg) ![yinit-dropcap 18](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/9.jpeg) ![yinit-dropcap 19](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/15.jpeg) ![yinit-dropcap 20](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/21.jpeg) ![yinit-dropcap 21](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/1.jpeg) ![yinit-dropcap 22](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/14.jpeg) ![yinit-dropcap 23](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/5.jpeg) ![yinit-dropcap 24](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/18.jpeg) ![yinit-dropcap 25](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/23.jpeg)

sd-concepts-library/ibere-thenorio

sd-concepts-library

2022-09-22T04:52:22Z

0

null

[ "license:mit", "region:us" ]

null

2022-09-22T04:52:08Z

--- license: mit --- ### Iberê Thenório on Stable Diffusion This is the `<ibere-thenorio>` 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`: ![<ibere-thenorio> 0](https://huggingface.co/sd-concepts-library/ibere-thenorio/resolve/main/concept_images/4.jpeg) ![<ibere-thenorio> 1](https://huggingface.co/sd-concepts-library/ibere-thenorio/resolve/main/concept_images/0.jpeg) ![<ibere-thenorio> 2](https://huggingface.co/sd-concepts-library/ibere-thenorio/resolve/main/concept_images/3.jpeg) ![<ibere-thenorio> 3](https://huggingface.co/sd-concepts-library/ibere-thenorio/resolve/main/concept_images/2.jpeg) ![<ibere-thenorio> 4](https://huggingface.co/sd-concepts-library/ibere-thenorio/resolve/main/concept_images/1.jpeg) ![<ibere-thenorio> 5](https://huggingface.co/sd-concepts-library/ibere-thenorio/resolve/main/concept_images/5.jpeg)

ashiqabdulkhader/GPT2-Poet

ashiqabdulkhader

2022-09-22T03:24:00Z

381

3

transformers

[ "transformers", "tf", "gpt2", "text-generation", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-09-22T02:45:20Z

--- license: bigscience-bloom-rail-1.0 widget : - text: "I used to have a lover" example_title: "I used to have a lover" - text : "The old cupola glinted above the clouds" example_title: "The old cupola" - text : "Behind the silo, the Mother Rabbit hunches" example_title : "Behind the silo" --- # GPT2-Poet ## Model description GPT2-Poet is a GPT-2 transformer model fine Tuned on a large corpus of English Poems dataset in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token i only uses the inputs from 1 to i but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. ## Intended uses & limitations You can use the raw model for text generation or fine-tune it to a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you. ## Usage You can use this model for English Poem generation: ```python >>> from transformers import TFGPT2LMHeadModel, GPT2Tokenizer >>> tokenizer = GPT2Tokenizer.from_pretrained("ashiqabdulkhader/GPT2-Poet") >>> model = TFGPT2LMHeadModel.from_pretrained("ashiqabdulkhader/GPT2-Poet") >>> text = "The quick brown fox" >>> input_ids = tokenizer.encode(text, return_tensors='tf') >>> sample_outputs = model.generate( input_ids, do_sample=True, max_length=100, top_k=0, top_p=0.9, temperature=1.0, num_return_sequences=3 ) >>> print("Output:", tokenizer.decode(sample_outputs[0], skip_special_tokens=True)) ```

bdotloh/distilbert-base-uncased-go-emotion-empathetic-dialogues-context-v2

bdotloh

2022-09-22T03:14:36Z

106

1

transformers

[ "transformers", "pytorch", "bert", "text-classification", "emotion-classification", "en", "dataset:go-emotions", "dataset:bdotloh/empathetic-dialogues-contexts", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-21T07:35:25Z

--- language: en tags: - emotion-classification datasets: - go-emotions - bdotloh/empathetic-dialogues-contexts --- # Model Description We performed transfer learning experiments on a distilbert-base-uncased model fine-tuned on the GoEmotions dataset for the purpose of classifying [(emotional) contexts in the Empathetic Dialogues dataset](https://huggingface.co/datasets/bdotloh/empathetic-dialogues-contexts). The fine-tuned distilbert-base-uncased can be found [here](https://huggingface.co/bhadresh-savani/bert-base-go-emotion). ### Limitations and bias GoEmotions: 1) Demographics of Reddit Users 2) Imbalanced class distribution 3) ... EmpatheticDialogues: 1) Unable to ascertain the degree of cultural specificity for the context that a respondent described when given an emotion label (i.e., p(description | emotion, *culture*)) 2) ... ## Training data ## Training procedure ### Preprocessing ## Evaluation results

sd-concepts-library/char-con

sd-concepts-library

2022-09-22T02:54:22Z

0

null

[ "license:mit", "region:us" ]

null

2022-09-22T02:54:17Z

--- license: mit --- ### char-con on Stable Diffusion This is the `<char-con>` 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`: ![<char-con> 0](https://huggingface.co/sd-concepts-library/char-con/resolve/main/concept_images/4.jpeg) ![<char-con> 1](https://huggingface.co/sd-concepts-library/char-con/resolve/main/concept_images/0.jpeg) ![<char-con> 2](https://huggingface.co/sd-concepts-library/char-con/resolve/main/concept_images/6.jpeg) ![<char-con> 3](https://huggingface.co/sd-concepts-library/char-con/resolve/main/concept_images/3.jpeg) ![<char-con> 4](https://huggingface.co/sd-concepts-library/char-con/resolve/main/concept_images/2.jpeg) ![<char-con> 5](https://huggingface.co/sd-concepts-library/char-con/resolve/main/concept_images/1.jpeg) ![<char-con> 6](https://huggingface.co/sd-concepts-library/char-con/resolve/main/concept_images/5.jpeg)

yuntian-deng/latex2im_ss

yuntian-deng

2022-09-22T02:20:24Z

1

0

diffusers

[ "diffusers", "en", "dataset:yuntian-deng/im2latex-100k", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

null

2022-09-22T02:19:32Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: yuntian-deng/im2latex-100k metrics: [] ---  # latex2im_ss ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `yuntian-deng/im2latex-100k` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: no ### Training results 📈 [TensorBoard logs](https://huggingface.co/yuntian-deng/latex2im_ss/tensorboard?#scalars)