davanstrien/model_cards_with_readmes · Datasets at Hugging Face

oskarandrsson/mt-uk-sv-finetuned

oskarandrsson

marian

11

19

transformers

0

translation

true

false

apache-2.0

['uk', 'sv']

null

1

0

['generated_from_trainer', 'translation']

true

1,171

false

# mt-uk-sv-finetuned This model is a fine-tuned version of [Helsinki-NLP/opus-mt-uk-sv](https://huggingface.co/Helsinki-NLP/opus-mt-uk-sv) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 1.4210 - eval_bleu: 40.6634 - eval_runtime: 966.5303 - eval_samples_per_second: 18.744 - eval_steps_per_second: 4.687 - epoch: 6.0 - step: 40764 ## 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-06 - train_batch_size: 24 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.1

163e9e496198d44a5e1edf8af147c856

sayakpaul/glpn-nyu-finetuned-diode-221116-110652

sayakpaul

glpn

7

1

transformers

0

depth-estimation

true

false

apache-2.0

null

0

['vision', 'depth-estimation', 'generated_from_trainer']

true

2,737

false

# glpn-nyu-finetuned-diode-221116-110652 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.4018 - Mae: 0.3272 - Rmse: 0.4546 - Abs Rel: 0.3934 - Log Mae: 0.1380 - Log Rmse: 0.1907 - Delta1: 0.4598 - Delta2: 0.7659 - Delta3: 0.9082 ## 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: 1e-05 - train_batch_size: 24 - eval_batch_size: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 1.3984 | 1.0 | 72 | 1.1606 | 3.2154 | 3.2710 | 4.6927 | 0.6627 | 0.7082 | 0.0 | 0.0053 | 0.0893 | | 0.8305 | 2.0 | 144 | 0.5445 | 0.6035 | 0.8404 | 0.8013 | 0.2102 | 0.2726 | 0.2747 | 0.5358 | 0.7609 | | 0.4601 | 3.0 | 216 | 0.4484 | 0.4041 | 0.5376 | 0.5417 | 0.1617 | 0.2188 | 0.3771 | 0.6932 | 0.8692 | | 0.4211 | 4.0 | 288 | 0.4251 | 0.3634 | 0.4914 | 0.4800 | 0.1499 | 0.2069 | 0.4136 | 0.7270 | 0.8931 | | 0.4162 | 5.0 | 360 | 0.4170 | 0.3537 | 0.4833 | 0.4483 | 0.1455 | 0.2005 | 0.4303 | 0.7444 | 0.8992 | | 0.3776 | 6.0 | 432 | 0.4115 | 0.3491 | 0.4692 | 0.4558 | 0.1449 | 0.1999 | 0.4281 | 0.7471 | 0.9018 | | 0.3729 | 7.0 | 504 | 0.4058 | 0.3337 | 0.4590 | 0.4135 | 0.1396 | 0.1935 | 0.4517 | 0.7652 | 0.9072 | | 0.3235 | 8.0 | 576 | 0.4035 | 0.3304 | 0.4602 | 0.4043 | 0.1383 | 0.1929 | 0.4613 | 0.7679 | 0.9073 | | 0.3382 | 9.0 | 648 | 0.3990 | 0.3254 | 0.4546 | 0.3937 | 0.1365 | 0.1900 | 0.4671 | 0.7717 | 0.9102 | | 0.3265 | 10.0 | 720 | 0.4018 | 0.3272 | 0.4546 | 0.3934 | 0.1380 | 0.1907 | 0.4598 | 0.7659 | 0.9082 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu116 - Tokenizers 0.13.2

89a55d0c3aab1c17acfe84c0f8818285

gokuls/distilbert_add_GLUE_Experiment_logit_kd_pretrain_qnli

gokuls

distilbert

17

3

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,859

false

# distilbert_add_GLUE_Experiment_logit_kd_pretrain_qnli This model is a fine-tuned version of [gokuls/distilbert_add_pre-training-complete](https://huggingface.co/gokuls/distilbert_add_pre-training-complete) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3579 - Accuracy: 0.6522 ## 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: 256 - seed: 10 - distributed_type: multi-GPU - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4059 | 1.0 | 410 | 0.4016 | 0.5585 | | 0.3907 | 2.0 | 820 | 0.3735 | 0.6094 | | 0.3715 | 3.0 | 1230 | 0.3602 | 0.6480 | | 0.352 | 4.0 | 1640 | 0.3579 | 0.6522 | | 0.3314 | 5.0 | 2050 | 0.3626 | 0.6670 | | 0.309 | 6.0 | 2460 | 0.3650 | 0.6776 | | 0.2865 | 7.0 | 2870 | 0.3799 | 0.6776 | | 0.2679 | 8.0 | 3280 | 0.3817 | 0.6903 | | 0.2525 | 9.0 | 3690 | 0.3942 | 0.6822 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

622a8c8599de7e9698fb0935712226d7

Zhaohui/finetuning-misinfo-model-700-Zhaohui-1_misinfo

Zhaohui

distilbert

13

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,063

false

# finetuning-misinfo-model-700-Zhaohui-1_misinfo This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5343 - Accuracy: 0.8571 - F1: 0.8571 ## 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: 20 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

2d0305e6347a7c0796af99e8a3c4e4f6

vasista22/whisper-kannada-tiny

vasista22

whisper

12

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['kn']

null

0

['whisper-event']

true

1,317

false

# Whisper Kannada Tiny This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Kannada data available from multiple publicly available ASR corpuses. It has been fine-tuned as a part of the Whisper fine-tuning sprint. ## Training and evaluation data Training Data: MILE ASR Corpus, ULCA ASR Corpus, Shrutilipi ASR Corpus, Google/Fleurs Train+Dev set. Evaluation Data: Google/Fleurs Test set, MILE Test set, OpenSLR. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 88 - eval_batch_size: 88 - seed: 22 - optimizer: adamw_bnb_8bit - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - training_steps: 15008 (terminated upon convergence. Initially set to 51570 steps) - mixed_precision_training: True ## Acknowledgement This work was done at Speech Lab, IITM. The compute resources for this work were funded by "Bhashini: National Language translation Mission" project of the Ministry of Electronics and Information Technology (MeitY), Government of India.

8427c3083f00cb9306407779b3a688cb

Helsinki-NLP/opus-mt-fse-fi

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-fse-fi * source languages: fse * target languages: fi * OPUS readme: [fse-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fse-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/fse-fi/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fse-fi/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fse-fi/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.fse.fi | 90.2 | 0.943 |

660644bdfeefc4e0c3ac5bc28bf8a9cb

tosin/dialogpt_mwoz_idioms

tosin

gpt2

9

3

transformers

0

conversational

true

false

cc-by-4.0

['en']

['multi_woz_v22 and PIE-English idioms corpus']

null

0

['conversational', 'transformers']

false

true

2,300

false

## DialoGPT_MWOZ_Idioms This is a fine-tuned model of DialoGPT (medium)-MultiWOZ on the PIE-English idioms corpus. It is intended to be used as an idiom-aware conversational system. The dataset it's trained on is limited in scope, as it covers only 10 classes of idioms ( metaphor, simile, euphemism, parallelism, personification, oxymoron, paradox, hyperbole, irony and literal). The perplexity achieved on the test and validation sets are 160.9 and 160.46, respectively. More information about the original pre-trained model can be found [here](https://huggingface.co/tosin/dialogpt_mwoz) * Generation example from an interactive environment: |Role | Response | |---------|------------| |User | remind her to take it off the hook. | |Bot | the bookie took it on the nod | |User | A lengthy break is in order ; so make sure you keep your nose clean | |Bot | they are under a cloud because of fears about US President's wife Hillary Clinton | |User | Some families breed like rabbits and send us all their male heirs | |Bot | he was out for the count | Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT) ### How to use Now we are ready to try out how the model works as a chatting partner! ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch tokenizer = AutoTokenizer.from_pretrained("tosin/dialogpt_mwoz_idioms") model = AutoModelForCausalLM.from_pretrained("tosin/dialogpt_mwoz_idioms") # Let's chat for 5 lines for step in range(5): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id) # pretty print last ouput tokens from bot print("DialoGPT_MWOZ_Bot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))

a6bf7cbd1df03ea0c772d94bb707b830

jonatasgrosman/exp_w2v2t_sv-se_vp-it_s817

jonatasgrosman

wav2vec2

10

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['sv-SE']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'sv-SE']

false

true

475

false

# exp_w2v2t_sv-se_vp-it_s817 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

062c36861549d27668a59ac9ff7ca776

henryjiang/distilbert-base-uncased-finetuned-emotion

henryjiang

distilbert

12

1

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,343

false

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2223 - Accuracy: 0.927 - F1: 0.9271 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8412 | 1.0 | 250 | 0.3215 | 0.904 | 0.9010 | | 0.2535 | 2.0 | 500 | 0.2223 | 0.927 | 0.9271 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

b22436f7da3fbd5e41a5f218e45a449a

josetapia/hygpt2-cml

josetapia

gpt2

29

2

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

978

false

# hygpt2-cml 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: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 15 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2 - Datasets 1.18.4 - Tokenizers 0.11.6

596740ff975b17d062dcf9f4cb64fa6c

Helsinki-NLP/opus-mt-sem-sem

Helsinki-NLP

marian

11

7

transformers

0

translation

true

false

apache-2.0

['mt', 'ar', 'he', 'ti', 'am', 'sem']

null

1

0

['translation']

false

true

2,578

false

### sem-sem * source group: Semitic languages * target group: Semitic languages * OPUS readme: [sem-sem](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/sem-sem/README.md) * model: transformer * source language(s): apc ara arq arz heb mlt * target language(s): apc ara arq arz heb mlt * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2020-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/sem-sem/opus-2020-07-27.zip) * test set translations: [opus-2020-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/sem-sem/opus-2020-07-27.test.txt) * test set scores: [opus-2020-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/sem-sem/opus-2020-07-27.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.ara-ara.ara.ara | 4.2 | 0.200 | | Tatoeba-test.ara-heb.ara.heb | 34.0 | 0.542 | | Tatoeba-test.ara-mlt.ara.mlt | 16.6 | 0.513 | | Tatoeba-test.heb-ara.heb.ara | 18.8 | 0.477 | | Tatoeba-test.mlt-ara.mlt.ara | 20.7 | 0.388 | | Tatoeba-test.multi.multi | 27.1 | 0.507 | ### System Info: - hf_name: sem-sem - source_languages: sem - target_languages: sem - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/sem-sem/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['mt', 'ar', 'he', 'ti', 'am', 'sem'] - src_constituents: {'apc', 'mlt', 'arz', 'ara', 'heb', 'tir', 'arq', 'afb', 'amh', 'acm', 'ary'} - tgt_constituents: {'apc', 'mlt', 'arz', 'ara', 'heb', 'tir', 'arq', 'afb', 'amh', 'acm', 'ary'} - src_multilingual: True - tgt_multilingual: True - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/sem-sem/opus-2020-07-27.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/sem-sem/opus-2020-07-27.test.txt - src_alpha3: sem - tgt_alpha3: sem - short_pair: sem-sem - chrF2_score: 0.507 - bleu: 27.1 - brevity_penalty: 0.972 - ref_len: 13472.0 - src_name: Semitic languages - tgt_name: Semitic languages - train_date: 2020-07-27 - src_alpha2: sem - tgt_alpha2: sem - prefer_old: False - long_pair: sem-sem - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

c9a37e0ab4fa857c319bbeed3508aee3

annahaz/roberta-large-mnli-misogyny-sexism-4tweets-3e-05-0.05

annahaz

roberta

11

1

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,899

false

# roberta-large-mnli-misogyny-sexism-4tweets-3e-05-0.05 This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co/roberta-large-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9069 - Accuracy: 0.6914 - F1: 0.7061 - Precision: 0.6293 - Recall: 0.8043 - Mae: 0.3086 - Tn: 320 - Fp: 218 - Fn: 90 - Tp: 370 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:---:|:---:|:---:|:---:| | 0.5248 | 1.0 | 1346 | 0.7245 | 0.6513 | 0.6234 | 0.6207 | 0.6261 | 0.3487 | 362 | 176 | 172 | 288 | | 0.4553 | 2.0 | 2692 | 0.6894 | 0.6693 | 0.7043 | 0.5991 | 0.8543 | 0.3307 | 275 | 263 | 67 | 393 | | 0.3753 | 3.0 | 4038 | 0.6966 | 0.7234 | 0.7326 | 0.6608 | 0.8217 | 0.2766 | 344 | 194 | 82 | 378 | | 0.2986 | 4.0 | 5384 | 0.9069 | 0.6914 | 0.7061 | 0.6293 | 0.8043 | 0.3086 | 320 | 218 | 90 | 370 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

4c2fd564125fbd37a8e6448c11ec6372

google/t5-efficient-small-nl36

google

t5

12

32

transformers

3

text2text-generation

true

apache-2.0

['en']

['c4']

null

0

['deep-narrow']

false

true

6,257

false

# T5-Efficient-SMALL-NL36 (Deep-Narrow version) T5-Efficient-SMALL-NL36 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. ## Details model architecture This model checkpoint - **t5-efficient-small-nl36** - is of model type **Small** with the following variations: - **nl** is **36** It has **280.87** million parameters and thus requires *ca.* **1123.47 MB** of memory in full precision (*fp32*) or **561.74 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | #Params| | ----| ---- | ---- | ---- | ---- | ---- | ----| | Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M| | Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M| | Small | 6/6 | 2048 | 512 | 32 | 8 | 60M| | Base | 12/12 | 3072 | 768 | 64 | 12 | 220M| | Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M| | Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B| | XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B| whereas the following abbreviations are used: | Abbreviation | Definition | | ----| ---- | | nl | Number of transformer blocks (depth) | | dm | Dimension of embedding vector (output vector of transformers block) | | kv | Dimension of key/value projection matrix | | nh | Number of attention heads | | ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) | | el | Number of transformer blocks in the encoder (encoder depth) | | dl | Number of transformer blocks in the decoder (decoder depth) | | sh | Signifies that attention heads are shared | | skv | Signifies that key-values projection matrices are tied | If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*. ## Pre-Training The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using the span-based masked language modeling (MLM) objective. ## Fine-Tuning **Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage. The checkpoint was pretrained in English and is therefore only useful for English NLP tasks. You can follow on of the following examples on how to fine-tune the model: *PyTorch*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) - [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *Tensorflow*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *JAX/Flax*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. ## Downstream Performance TODO: Add table if available ## Computational Complexity TODO: Add table if available ## More information We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint. As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv* model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future.

3fc50de70a2ca161c3393e012cadca62

tkubotake/xlm-roberta-base-finetuned-panx-en

tkubotake

xlm-roberta

9

8

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,375

false

# xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [tkubotake/xlm-roberta-base-finetuned-panx-de](https://huggingface.co/tkubotake/xlm-roberta-base-finetuned-panx-de) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.5430 - F1: 0.7580 ## 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: 24 - eval_batch_size: 24 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1318 | 1.0 | 50 | 0.4145 | 0.7557 | | 0.0589 | 2.0 | 100 | 0.5016 | 0.7524 | | 0.0314 | 3.0 | 150 | 0.5430 | 0.7580 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

5255d1d2d19580e965c3d35d1e080141

geevegeorge/customdbmodelv7

geevegeorge

null

8

3

diffusers

0

null

false

apache-2.0

['en']

['geevegeorge/customdbv7']

null

0

[]

false

true

1,210

false

# customdbmodelv7 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `geevegeorge/customdbv7` 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: 2 - eval_batch_size: 2 - gradient_accumulation_steps: 8 - optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08 - lr_scheduler: cosine - lr_warmup_steps: 500 - ema_inv_gamma: 1.0 - ema_inv_gamma: 0.75 - ema_inv_gamma: 0.9999 - mixed_precision: no ### Training results 📈 [TensorBoard logs](https://huggingface.co/geevegeorge/customdbmodelv7/tensorboard?#scalars)

b458654ef066d0441e09770b9ed641bf

ishaankul67/2008_Sichuan_earthquake-clustered

ishaankul67

distilbert

8

0

transformers

0

question-answering

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,880

false

# ishaankul67/2008_Sichuan_earthquake-clustered This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4882 - Train End Logits Accuracy: 0.8924 - Train Start Logits Accuracy: 0.7882 - Validation Loss: 0.2788 - Validation End Logits Accuracy: 0.8947 - Validation Start Logits Accuracy: 0.8947 - 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: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.4882 | 0.8924 | 0.7882 | 0.2788 | 0.8947 | 0.8947 | 0 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

a6174c4800ee0a8bcbc021f3fa50cb71

hrdipto/wav2vec2-base-timit-demo-colab

hrdipto

wav2vec2

12

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,641

false

# wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4241 - Wer: 0.3381 ## 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: 32 - 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: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.7749 | 4.0 | 500 | 2.0639 | 1.0018 | | 0.9252 | 8.0 | 1000 | 0.4853 | 0.4821 | | 0.3076 | 12.0 | 1500 | 0.4507 | 0.4044 | | 0.1732 | 16.0 | 2000 | 0.4315 | 0.3688 | | 0.1269 | 20.0 | 2500 | 0.4481 | 0.3559 | | 0.1087 | 24.0 | 3000 | 0.4354 | 0.3464 | | 0.0832 | 28.0 | 3500 | 0.4241 | 0.3381 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

b6f85e1e1b2faad70c5a106927e1d6b9

microsoft/conditional-detr-resnet-50

microsoft

conditional_detr

5

1,007

transformers

1

object-detection

true

false

apache-2.0

null

['coco']

null

1

0

1

0

['object-detection', 'vision']

false

true

4,237

false

# Conditional DETR model with ResNet-50 backbone Conditional DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Meng et al. and first released in [this repository](https://github.com/Atten4Vis/ConditionalDETR). ## Model description The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7× faster for the backbones R50 and R101 and 10× faster for stronger backbones DC5-R50 and DC5-R101. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/conditional_detr_curve.jpg) ## Intended uses & limitations You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=microsoft/conditional-detr) to look for all available Conditional DETR models. ### How to use Here is how to use this model: ```python from transformers import AutoImageProcessor, ConditionalDetrForObjectDetection import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50") model = ConditionalDetrForObjectDetection.from_pretrained("microsoft/conditional-detr-resnet-50") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) # convert outputs (bounding boxes and class logits) to COCO API # let's only keep detections with score > 0.7 target_sizes = torch.tensor([image.size[::-1]]) results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0] for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.tolist()] print( f"Detected {model.config.id2label[label.item()]} with confidence " f"{round(score.item(), 3)} at location {box}" ) ``` This should output: ``` Detected remote with confidence 0.833 at location [38.31, 72.1, 177.63, 118.45] Detected cat with confidence 0.831 at location [9.2, 51.38, 321.13, 469.0] Detected cat with confidence 0.804 at location [340.3, 16.85, 642.93, 370.95] ``` Currently, both the feature extractor and model support PyTorch. ## Training data The Conditional DETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. ### BibTeX entry and citation info ```bibtex @inproceedings{MengCFZLYS021, author = {Depu Meng and Xiaokang Chen and Zejia Fan and Gang Zeng and Houqiang Li and Yuhui Yuan and Lei Sun and Jingdong Wang}, title = {Conditional {DETR} for Fast Training Convergence}, booktitle = {2021 {IEEE/CVF} International Conference on Computer Vision, {ICCV} 2021, Montreal, QC, Canada, October 10-17, 2021}, } ```

45f3301dbac25d6bb39c97dd7c33cd14

wyu1/FiD-NQ

wyu1

t5

6

0

transformers

1

null

true

false

cc-by-4.0

null

0

[]

false

true

747

false

# FiD model trained on NQ -- This is the model checkpoint of FiD [2], based on the T5 large (with 770M parameters) and trained on the natural question (NQ) dataset [1]. -- Hyperparameters: 8 x 40GB A100 GPUs; batch size 8; AdamW; LR 3e-5; 50000 steps References: [1] Natural Questions: A Benchmark for Question Answering Research. TACL 2019. [2] Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering. EACL 2021. ## Model performance We evaluate it on the NQ dataset, the EM score is 51.3 (0.1 lower than original performance reported in the paper). <a href="https://huggingface.co/exbert/?model=bert-base-uncased"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

b3b8e6e356edf3d566328022e047557b

OpenMatch/t5-ance

OpenMatch

t5

7

2

transformers

0

feature-extraction

true

false

mit

null

0

[]

false

true

597

false

# T5-ANCE T5-ANCE generally follows the training procedure described in [this page](https://openmatch.readthedocs.io/en/latest/dr-msmarco-passage.html), but uses a much larger batch size. Dataset used for training: - MS MARCO Passage Evaluation result: |Dataset|Metric|Result| |---|---|---| |MS MARCO Passage (dev) | MRR@10 | 0.3570| Important hyper-parameters: |Name|Value| |---|---| |Global batch size|256| |Learning rate|5e-6| |Maximum length of query|32| |Maximum length of document|128| |Template for query|`<text>`| |Template for document|`Title: <title> Text: <text>`| ### Paper \-

38561e01fe2690d63f701d179786a2cd

shreeshaaithal/DialoGPT-small-Michael-Scott

shreeshaaithal

gpt2

9

4

transformers

0

conversational

true

false

mit

null

0

['conversational']

false

true

1,643

false

# DialoGPT Trained on WhatsApp chats This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on WhatsApp chats or you can train this model on [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script). feel free to ask me questions on discord server [discord server](https://discord.gg/Gqhje8Z7DX) Chat with the model: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("harrydonni/DialoGPT-small-Michael-Scott") model = AutoModelWithLMHead.from_pretrained("harrydonni/DialoGPT-small-Michael-Scott") # Let's chat for 4 lines for step in range(4): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # print(new_user_input_ids) # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate( bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=3, do_sample=True, top_k=100, top_p=0.7, temperature=0.8 ) # pretty print last ouput tokens from bot print("Michael: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ``` this is done by shreesha thank you......

1fc68a1eb8946e0738b753a1206c67da

it5/mt5-small-repubblica-to-ilgiornale

it5

mt5

11

4

transformers

0

text2text-generation

true

apache-2.0

['it']

['gsarti/change_it']

{'emissions': '17g', 'source': 'Google Cloud Platform Carbon Footprint', 'training_type': 'fine-tuning', 'geographical_location': 'Eemshaven, Netherlands, Europe', 'hardware_used': '1 TPU v3-8 VM'}

0

['italian', 'sequence-to-sequence', 'newspaper', 'ilgiornale', 'repubblica', 'style-transfer']

true

3,264

false

# mT5 Small for News Headline Style Transfer (Repubblica to Il Giornale) 🗞️➡️🗞️ 🇮🇹 This repository contains the checkpoint for the [mT5 Small](https://huggingface.co/google/mt5-small) model fine-tuned on news headline style transfer in the Repubblica to Il Giornale direction on the Italian CHANGE-IT dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io). A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach. ## Using the model The model is trained to generate an headline in the style of Il Giornale from the full body of an article written in the style of Repubblica. Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines r2g = pipeline("text2text-generation", model='it5/mt5-small-repubblica-to-ilgiornale') r2g("Arriva dal Partito nazionalista basco (Pnv) la conferma che i cinque deputati che siedono in parlamento voteranno la sfiducia al governo guidato da Mariano Rajoy. Pochi voti, ma significativi quelli della formazione politica di Aitor Esteban, che interverrà nel pomeriggio. Pur con dimensioni molto ridotte, il partito basco si è trovato a fare da ago della bilancia in aula. E il sostegno alla mozione presentata dai Socialisti potrebbe significare per il primo ministro non trovare quei 176 voti che gli servono per continuare a governare. \" Perché dovrei dimettermi io che per il momento ho la fiducia della Camera e quella che mi è stato data alle urne \", ha detto oggi Rajoy nel suo intervento in aula, mentre procedeva la discussione sulla mozione di sfiducia. Il voto dei baschi ora cambia le carte in tavola e fa crescere ulteriormente la pressione sul premier perché rassegni le sue dimissioni. La sfiducia al premier, o un'eventuale scelta di dimettersi, porterebbe alle estreme conseguenze lo scandalo per corruzione che ha investito il Partito popolare. Ma per ora sembra pensare a tutt'altro. \"Non ha intenzione di dimettersi - ha detto il segretario generale del Partito popolare , María Dolores de Cospedal - Non gioverebbe all'interesse generale o agli interessi del Pp\".") >>> [{"generated_text": "il nazionalista rajoy: 'voteremo la sfiducia'"}] ``` or loaded using autoclasses: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("it5/mt5-small-repubblica-to-ilgiornale") model = AutoModelForSeq2SeqLM.from_pretrained("it5/mt5-small-repubblica-to-ilgiornale") ``` If you use this model in your research, please cite our work as: ```bibtex @article{sarti-nissim-2022-it5, title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation}, author={Sarti, Gabriele and Nissim, Malvina}, journal={ArXiv preprint 2203.03759}, url={https://arxiv.org/abs/2203.03759}, year={2022}, month={mar} } ```

8ebe2663939cc869a7c7a1838b2cff9e

Lvxue/distilled-mt5-small-010099

Lvxue

mt5

14

2

transformers

0

text2text-generation

true

false

apache-2.0

['en', 'ro']

['wmt16']

null

1

0

['generated_from_trainer']

true

1,037

false

# distilled-mt5-small-010099 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.9787 - Bleu: 5.9209 - Gen Len: 50.1856 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

4d6ead1af09698b62bee7ec0279bbbdd

aXhyra/presentation_hate_1234567

aXhyra

distilbert

10

5

transformers

0

text-classification

true

false

apache-2.0

null

['tweet_eval']

null

0

['generated_from_trainer']

true

1,403

false

# presentation_hate_1234567 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.8438 - F1: 0.7680 ## 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: 5.436235805743952e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 1234567 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6027 | 1.0 | 282 | 0.5186 | 0.7209 | | 0.3537 | 2.0 | 564 | 0.4989 | 0.7619 | | 0.0969 | 3.0 | 846 | 0.6405 | 0.7697 | | 0.0514 | 4.0 | 1128 | 0.8438 | 0.7680 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3

6474939a362aaf6d43fd44d86802e47b

sd-concepts-library/cortana

sd-concepts-library

null

12

0

null

0

null

false

mit

null

0

[]

false

true

1,306

false

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

cda8285610d93c8d879c929285372443

gchhablani/wav2vec2-large-xlsr-eo

gchhablani

wav2vec2

10

8

transformers

0

automatic-speech-recognition

true

false

true

apache-2.0

['eo']

['common_voice']

null

0

['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']

true

4,414

false

# Wav2Vec2-Large-XLSR-53-Esperanto Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Esperanto using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "eo", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained('gchhablani/wav2vec2-large-xlsr-eo') model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-eo') resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re import jiwer def chunked_wer(targets, predictions, chunk_size=None): if chunk_size is None: return jiwer.wer(targets, predictions) start = 0 end = chunk_size H, S, D, I = 0, 0, 0, 0 while start < len(targets): chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end]) H = H + chunk_metrics["hits"] S = S + chunk_metrics["substitutions"] D = D + chunk_metrics["deletions"] I = I + chunk_metrics["insertions"] start += chunk_size end += chunk_size return float(S + D + I) / float(H + S + D) test_dataset = load_dataset("common_voice", "eo", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained('gchhablani/wav2vec2-large-xlsr-eo') model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-eo') model.to("cuda") chars_to_ignore_regex = """[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“\\\\\\\\%\\\\\\\\‘\\\\\\\\”\\\\\\\\�\\\\\\\\„\\\\\\\\«\\\\\\\\(\\\\\\\\»\\\\\\\\)\\\\\\\\’\\\\\\\\']""" resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace('—',' ').replace('–',' ') speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * chunked_wer(predictions=result["pred_strings"], targets=result["sentence"],chunk_size=5000))) ``` **Test Result**: 10.13 % ## Training The Common Voice `train` and `validation` datasets were used for training. The code can be found [here](https://github.com/gchhablani/wav2vec2-week/blob/main/fine-tune-xlsr-wav2vec2-on-esperanto-asr-with-transformers-final.ipynb).

e8503a345651d80c47d7e06114314d20

Helsinki-NLP/opus-mt-de-hil

Helsinki-NLP

marian

10

8

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-de-hil * source languages: de * target languages: hil * OPUS readme: [de-hil](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-hil/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-hil/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-hil/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-hil/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.de.hil | 33.9 | 0.563 |

5073fce856762ba8521956a93424dd0b

espnet/YushiUeda_mini_librispeech_diar_train_diar_raw_valid.acc.best

espnet

null

24

2

espnet

0

null

false

cc-by-4.0

['noinfo']

['mini_librispeech']

null

0

['espnet', 'audio', 'diarization']

false

true

5,382

false

## ESPnet2 DIAR model ### `espnet/YushiUeda_mini_librispeech_diar_train_diar_raw_valid.acc.best` This model was trained by YushiUeda using mini_librispeech recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout 650472b45a67612eaac09c7fbd61dc25f8ff2405 pip install -e . cd egs2/mini_librispeech/diar1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/YushiUeda_mini_librispeech_diar_train_diar_raw_valid.acc.best ```  # RESULTS ## Environments - date: `Tue Jan 4 16:43:34 EST 2022` - python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]` - espnet version: `espnet 0.10.5a1` - pytorch version: `pytorch 1.9.0+cu102` - Git hash: `0b2a6786b6f627f47defaee22911b3c2dc04af2a` - Commit date: `Thu Dec 23 12:22:49 2021 -0500` ## diar_train_diar_raw ### DER dev_clean_2_ns2_beta2_500 |threshold_median_collar|DER| |---|---| |result_th0.3_med11_collar0.0|32.28| |result_th0.3_med1_collar0.0|32.64| |result_th0.4_med11_collar0.0|30.43| |result_th0.4_med1_collar0.0|31.15| |result_th0.5_med11_collar0.0|29.45| |result_th0.5_med1_collar0.0|30.53| |result_th0.6_med11_collar0.0|29.52| |result_th0.6_med1_collar0.0|30.95| |result_th0.7_med11_collar0.0|30.92| |result_th0.7_med1_collar0.0|32.69| ## DIAR config <details><summary>expand</summary> ``` config: conf/train_diar.yaml print_config: false log_level: INFO dry_run: false iterator_type: chunk output_dir: exp/diar_train_diar_raw ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 33757 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: 3 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 3 nbest_averaging_interval: 0 grad_clip: 5 grad_clip_type: 2.0 grad_noise: false accum_grad: 2 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 16 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/diar_stats_8k/train/speech_shape - exp/diar_stats_8k/train/spk_labels_shape valid_shape_file: - exp/diar_stats_8k/valid/speech_shape - exp/diar_stats_8k/valid/spk_labels_shape batch_type: folded valid_batch_type: null fold_length: - 80000 - 800 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 200000 chunk_shift_ratio: 0.5 num_cache_chunks: 64 train_data_path_and_name_and_type: - - dump/raw/simu/data/train_clean_5_ns2_beta2_500/wav.scp - speech - sound - - dump/raw/simu/data/train_clean_5_ns2_beta2_500/espnet_rttm - spk_labels - rttm valid_data_path_and_name_and_type: - - dump/raw/simu/data/dev_clean_2_ns2_beta2_500/wav.scp - speech - sound - - dump/raw/simu/data/dev_clean_2_ns2_beta2_500/espnet_rttm - spk_labels - rttm allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.01 scheduler: noamlr scheduler_conf: warmup_steps: 1000 num_spk: 2 init: xavier_uniform input_size: null model_conf: attractor_weight: 1.0 use_preprocessor: true frontend: default frontend_conf: fs: 8k hop_length: 128 specaug: null specaug_conf: {} normalize: global_mvn normalize_conf: stats_file: exp/diar_stats_8k/train/feats_stats.npz encoder: transformer encoder_conf: input_layer: linear num_blocks: 2 linear_units: 512 dropout_rate: 0.1 output_size: 256 attention_heads: 4 attention_dropout_rate: 0.0 decoder: linear decoder_conf: {} label_aggregator: label_aggregator label_aggregator_conf: {} attractor: null attractor_conf: {} required: - output_dir version: 0.10.5a1 distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

1b8fcad247fa5591f9e0586e668e8d44

k4black/edos-2023-baseline-bert-base-uncased-label_vector

k4black

bert

10

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,706

false

# edos-2023-baseline-bert-base-uncased-label_vector This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1312 - F1: 0.4311 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.1453 | 0.59 | 100 | 1.9401 | 0.1077 | | 1.8818 | 1.18 | 200 | 1.7312 | 0.1350 | | 1.7149 | 1.78 | 300 | 1.5556 | 0.2047 | | 1.5769 | 2.37 | 400 | 1.4030 | 0.2815 | | 1.4909 | 2.96 | 500 | 1.3020 | 0.3217 | | 1.3472 | 3.55 | 600 | 1.2238 | 0.3872 | | 1.2856 | 4.14 | 700 | 1.1584 | 0.4162 | | 1.2455 | 4.73 | 800 | 1.1312 | 0.4311 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

28620131ac8aa21dac8ca7369237143d

Evgeneus/distilbert-base-uncased-finetuned-ner

Evgeneus

distilbert

15

4

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

1

0

['generated_from_trainer']

true

1,372

false

# distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0845 - Precision: 0.8754 - Recall: 0.9058 - F1: 0.8904 - Accuracy: 0.9763 ## 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2529 | 1.0 | 878 | 0.0845 | 0.8754 | 0.9058 | 0.8904 | 0.9763 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

a43473c2c3ba5110311d451e6536cf46

yhavinga/ul2-small-dutch

yhavinga

t5

18

37

transformers

0

text2text-generation

true

false

true

apache-2.0

['nl']

['yhavinga/mc4_nl_cleaned', 'yhavinga/nedd_wiki_news']

null

0

['dutch', 't5', 't5x', 'ul2', 'seq2seq']

false

true

10,354

false

# ul2-small-dutch for Dutch Pretrained T5 model on Dutch using a UL2 (Mixture-of-Denoisers) objective. The T5 model was introduced in [this paper](https://arxiv.org/abs/1910.10683) and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer). The UL2 objective was introduced in [this paper](https://arxiv.org/abs/2205.05131) and first released at [this page](https://github.com/google-research/google-research/tree/master/ul2). **Note:** The Hugging Face inference widget is deactivated because this model needs a text-to-text fine-tuning on a specific downstream task to be useful in practice. ## Model description T5 is an encoder-decoder model and treats all NLP problems in a text-to-text format. `ul2-small-dutch` T5 is a transformers model pretrained on a very large corpus of Dutch data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and outputs from those texts. This model used the [T5 v1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) improvements compared to the original T5 model during the pretraining: - GEGLU activation in the feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202) - Dropout was turned off during pre-training. Dropout should be re-enabled during fine-tuning - Pre-trained on self-supervised objective only without mixing in the downstream tasks - No parameter sharing between embedding and classifier layer ### UL2 pretraining objective This model was pretrained with the UL2's Mixture-of-Denoisers (MoD) objective, that combines diverse pre-training paradigms together. UL2 frames different objective functions for training language models as denoising tasks, where the model has to recover missing sub-sequences of a given input. During pre-training it uses a novel mixture-of-denoisers that samples from a varied set of such objectives, each with different configurations. UL2 is trained using a mixture of three denoising tasks: 1. R-denoising (or regular span corruption), which emulates the standard T5 span corruption objective; 2. X-denoising (or extreme span corruption); and 3. S-denoising (or sequential PrefixLM). During pre-training, we sample from the available denoising tasks based on user-specified ratios. UL2 introduces a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training denoising task. During the pre-training, a paradigm token is inserted to the input (`[NLU]` for R-denoising, `[NLG]` for X-denoising, or `[S2S]` for S-denoising) indicating the denoising task at hand. Then, during fine-tuning the same input token should be inserted to get the best performance for different downstream fine-tuning tasks. ## Intended uses & limitations This model was only pretrained in a self-supervised way excluding any supervised training. Therefore, this model has to be fine-tuned before it is usable on a downstream task, like text classification, unlike the Google's original T5 model. **Note:** You most likely need to fine-tune these T5/UL2 models without mixed precision so fine-tune them with full fp32 precision. Fine-tuning with Flax in bf16 - `model.to_bf16()` - is possible if you set the mask correctly to exclude layernorm and embedding layers. Also note that the T5x pre-training and fine-tuning configs set `z_loss` to 1e-4, which is used to keep the loss scale from underflowing. You can also find more fine-tuning tips from [here](https://discuss.huggingface.co/t/t5-finetuning-tips), for example. **Note**: For fine-tuning, most likely you can get better results if you insert a prefix token of `[NLU]`, `[NLG]`, or `[S2S]` to your input texts. For general language understanding fine-tuning tasks, you could use the `[NLU]` token. For GPT-style causal language generation, you could use the `[S2S]` token. The token `[NLG]` of the X-denoising pretrain task is somewhat mix between the language understanding and causal language generation so the token `[NLG]` could maybe be used for language generation fine-tuning too. ### How to use Here is how to use this model in PyTorch: ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("yhavinga/ul2-small-dutch", use_fast=False) model = T5ForConditionalGeneration.from_pretrained("yhavinga/ul2-small-dutch") ``` and in Flax: ```python from transformers import T5Tokenizer, FlaxT5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("yhavinga/ul2-small-dutch", use_fast=False) model = FlaxT5ForConditionalGeneration.from_pretrained("yhavinga/ul2-small-dutch") ``` ### Limitations and bias The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model. ## Training data The `ul2-small-dutch` T5 model was pre-trained simultaneously on a combination of several datasets, including the full version of the "mc4_nl_cleaned" dataset, which is a cleaned version of Common Crawl's web crawl corpus, Dutch books, the Dutch subset of Wikipedia (2022-03-20), and a subset of "mc4_nl_cleaned" containing only texts from Dutch and Belgian newspapers. This last dataset is oversampled to bias the model towards descriptions of events in the Netherlands and Belgium. ## Training procedure ### Preprocessing The ul2-small-dutch T5 model uses a SentencePiece unigram tokenizer with a vocabulary of 32,000 tokens. The tokenizer includes the special tokens `<pad>`, `</s>`, `<unk>`, known from the original T5 paper, `[NLU]`, `[NLG]` and `[S2S]` for the MoD pre-training, and `<n>` for newline. During pre-training with the UL2 objective, input and output sequences consist of 512 consecutive tokens. The tokenizer does not lowercase texts and is therefore case-sensitive; it distinguises between `dutch` and `Dutch`. Additionally, 100+28 extra tokens were added for pre-training tasks, resulting in a total of 32,128 tokens. ### Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 957300 steps with a batch size of 128 (in total 62 B tokens). The optimizer used was AdaFactor with learning rate warmup for 10K steps with a constant learning rate of 1e-2, and then an inverse square root decay (exponential decay) of the learning rate after. The model was trained with Google's Jax/Flax based [t5x framework](https://github.com/google-research/t5x) with help from [Stephenn Fernandes](https://huggingface.co/StephennFernandes) to get started writing task definitions that wrap HF datasets. The UL2 training objective code used with the [t5x framework](https://github.com/google-research/t5x) was copied and slightly modified from the [UL2 paper](https://arxiv.org/pdf/2205.05131.pdf) appendix chapter 9.2 by the authors of the Finnish ul2 models. Used UL2 objective code is available in the repository [Finnish-NLP/ul2-base-nl36-finnish](https://huggingface.co/Finnish-NLP/ul2-base-nl36-finnish) in the files `ul2_objective.py` and `tasks.py`. UL2's mixture-of-denoisers configuration was otherwise equal to the UL2 paper but for the rate of mixing denoisers, 20% for S-denoising was used (suggested at the paper chapter 4.5) and the rest was divided equally between the R-denoising and X-denoising (i.e. 40% for both). ### Model list Models in this series: | | ul2-base-dutch | ul2-base-nl36-dutch | ul2-large-dutch | ul2-small-dutch | |:---------------------|:---------------------|:----------------------|:---------------------|:---------------------| | model_type | t5 | t5 | t5 | t5 | | _pipeline_tag | text2text-generation | text2text-generation | text2text-generation | text2text-generation | | d_model | 768 | 768 | 1024 | 512 | | d_ff | 2048 | 3072 | 2816 | 1024 | | num_heads | 12 | 12 | 16 | 6 | | d_kv | 64 | 64 | 64 | 64 | | num_layers | 12 | 36 | 24 | 8 | | num_decoder_layers | 12 | 36 | 24 | 8 | | feed_forward_proj | gated-gelu | gated-gelu | gated-gelu | gated-gelu | | dense_act_fn | gelu_new | gelu_new | gelu_new | gelu_new | | vocab_size | 32128 | 32128 | 32128 | 32128 | | tie_word_embeddings | 0 | 0 | 0 | 0 | | torch_dtype | float32 | float32 | float32 | float32 | | _gin_batch_size | 128 | 64 | 64 | 128 | | _gin_z_loss | 0.0001 | 0.0001 | 0.0001 | 0.0001 | | _gin_t5_config_dtype | 'bfloat16' | 'bfloat16' | 'bfloat16' | 'bfloat16' | ## Evaluation results See the evaluation section in the interactive [Pre-training Dutch T5 Models](https://huggingface.co/spaces/yhavinga/pre-training-dutch-t5-models) blog. ## Acknowledgements This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/). Thanks to the [Finnish-NLP](https://huggingface.co/Finnish-NLP) authors for releasing their code for the UL2 objective and associated task definitions. Thanks to [Stephenn Fernandes](https://huggingface.co/StephennFernandes) for helping me get started with the t5x framework. Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)

c200abb83db6bf59e12f2439ee1de499

tomekkorbak/lucid_varahamihira

tomekkorbak

gpt2

23

0

transformers

0

null

true

false

mit

['en']

['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000']

null

0

['generated_from_trainer']

true

7,561

false

# lucid_varahamihira This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## 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: 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 - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}], 'scorer_config': {}}, 'kl_gpt3_callback': {'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'lucid_varahamihira', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/2nk79xf2

bfbd1c0dc039022965523d55b3167087

minhtoan/t5-finetune-bbc-news

minhtoan

t5

8

1

transformers

0

summarization

true

false

mit

['en']

['x_sum']

null

0

['summarization']

false

true

3,095

false

# Text Summarization of News Articles State-of-the-art lightweights pretrained Transformer-based encoder-decoder model for text summarization. Model trained on dataset BBC News (The Extreme Summarization XSum dataset) with input length = 512, output length = 150 ## How to use ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("minhtoan/t5-finetune-bbc-news") model = AutoModelForSeq2SeqLM.from_pretrained("minhtoan/t5-finetune-bbc-news") model.cuda() src = "summarize: The full cost of damage in Newton Stewart, one of the areas worst affected, is still being assessed.Repair work is ongoing in Hawick and many roads in Peeblesshire remain badly affected by standing water.Trains on the west coast mainline face disruption due to damage at the Lamington Viaduct.Many businesses and householders were affected by flooding in Newton Stewart after the River Cree overflowed into the town.First Minister Nicola Sturgeon visited the area to inspect the damage.The waters breached a retaining wall, flooding many commercial properties on Victoria Street - the main shopping thoroughfare.Jeanette Tate, who owns the Cinnamon Cafe which was badly affected, said she could not fault the multi-agency response once the flood hit.However, she said more preventative work could have been carried out to ensure the retaining wall did not fail.'It is difficult but I do think there is so much publicity for Dumfries and the Nith - and I totally appreciate that - but it is almost like we're neglected or forgotten,' she said.'That may not be true but it is perhaps my perspective over the last few days.'Why were you not ready to help us a bit more when the warning and the alarm alerts had gone out?'Meanwhile, a flood alert remains in place across the Borders because of the constant rain.Peebles was badly hit by problems, sparking calls to introduce more defences in the area.Scottish Borders Council has put a list on its website of the roads worst affected and drivers have been urged not to ignore closure signs.The Labour Party's deputy Scottish leader Alex Rowley was in Hawick on Monday to see the situation first hand.He said it was important to get the flood protection plan right but backed calls to speed up the process.'I was quite taken aback by the amount of damage that has been done,' he said.'Obviously it is heart-breaking for people who have been forced out of their homes and the impact on businesses.'He said it was important that 'immediate steps' were taken to protect the areas most vulnerable and a clear timetable put in place for flood prevention plans.Have you been affected by flooding in Dumfries and Galloway or the Borders? Tell us about your experience of the situation and how it was handled. Email us on selkirk.news@bbc.co.uk or dumfries@bbc.co.uk." tokenized_text = tokenizer.encode(src, return_tensors="pt").cuda() model.eval() summary_ids = model.generate(tokenized_text, max_length=150) output = tokenizer.decode(summary_ids[0], skip_special_tokens=True) output ``` ## Author ` Phan Minh Toan `

301359b803bdaedd15196f664c763321

Sebabrata/dof-Rai2-1

Sebabrata

vision-encoder-decoder

14

0

transformers

0

null

true

false

mit

null

['imagefolder']

null

0

['generated_from_trainer']

true

971

false

# dof-Rai2-1 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder 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: 3e-05 - train_batch_size: 2 - 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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

cb9770781e6d7dfffbbe7b7541dddd18

RobertoMCA97/xlm-roberta-base-finetuned-panx-de-fr

RobertoMCA97

xlm-roberta

9

8

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,320

false

# xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1667 - F1: 0.8582 ## 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: 24 - eval_batch_size: 24 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2885 | 1.0 | 715 | 0.1817 | 0.8287 | | 0.1497 | 2.0 | 1430 | 0.1618 | 0.8442 | | 0.0944 | 3.0 | 2145 | 0.1667 | 0.8582 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

e939682469b1b18fc08bff12538bbcef

sd-concepts-library/tron-style

sd-concepts-library

null

8

0

null

6

null

false

mit

null

0

[]

false

true

924

false

### Tron-Style on Stable Diffusion This is the `<tron-style>"` 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`: ![<tron-style>" 0](https://huggingface.co/sd-concepts-library/tron-style/resolve/main/concept_images/1.jpeg) ![<tron-style>" 1](https://huggingface.co/sd-concepts-library/tron-style/resolve/main/concept_images/2.jpeg) ![<tron-style>" 2](https://huggingface.co/sd-concepts-library/tron-style/resolve/main/concept_images/0.jpeg)

37fb82589f643948f873bf6324e6f356

Gladiator/microsoft-deberta-v3-large_ner_wikiann

Gladiator

deberta-v2

13

11

transformers

0

token-classification

true

false

mit

null

['wikiann']

null

0

['generated_from_trainer']

true

1,744

false

# microsoft-deberta-v3-large_ner_wikiann This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.3108 - Precision: 0.8557 - Recall: 0.8738 - F1: 0.8647 - Accuracy: 0.9406 ## 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: cosine - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3005 | 1.0 | 1250 | 0.2462 | 0.8205 | 0.8400 | 0.8301 | 0.9294 | | 0.1931 | 2.0 | 2500 | 0.2247 | 0.8448 | 0.8630 | 0.8538 | 0.9386 | | 0.1203 | 3.0 | 3750 | 0.2341 | 0.8468 | 0.8693 | 0.8579 | 0.9403 | | 0.0635 | 4.0 | 5000 | 0.2948 | 0.8596 | 0.8745 | 0.8670 | 0.9411 | | 0.0451 | 5.0 | 6250 | 0.3108 | 0.8557 | 0.8738 | 0.8647 | 0.9406 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

417487b761663898bf6ab611c3e62356

nandysoham16/18-clustered_aug

nandysoham16

distilbert

8

0

keras

0

null

false

true

false

mit

['en']

null

0

[]

false

true

4,873

false

# Model Card for Model ID  # Model Details ## Model Description  ['Genome', 'Lighting', 'Hydrogen', 'Gene', 'Copper', 'Grape', 'Infrared', 'Uranium', 'Sexual_orientation', 'Asphalt', 'Incandescent_light_bulb', 'Cotton', 'Alloy', 'Annelid', 'Glass', 'Green', 'Zinc', 'Flowering_plant', 'Light-emitting_diode', 'Red'] - **Developed by:** nandysoham - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** mit - **Finetuned from model [optional]:** [More Information Needed] ## Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] # Uses  ## Direct Use  [More Information Needed] ## Downstream Use [optional]  [More Information Needed] ## Out-of-Scope Use  [More Information Needed] # Bias, Risks, and Limitations  [More Information Needed] ## Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] # Training Details ## Training Data  [More Information Needed] ## Training Procedure [optional]  ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times  [More Information Needed] # Evaluation  ## Testing Data, Factors & Metrics ### Testing Data  [More Information Needed] ### Factors  [More Information Needed] ### Metrics  [More Information Needed] ## Results [More Information Needed] ### Summary # Model Examination [optional]  [More Information Needed] # Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] # Technical Specifications [optional] ## Model Architecture and Objective [More Information Needed] ## Compute Infrastructure [More Information Needed] ### Hardware [More Information Needed] ### Software [More Information Needed] # Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] # Glossary [optional]  [More Information Needed] # More Information [optional] [More Information Needed] # Model Card Authors [optional] [More Information Needed] # Model Card Contact [More Information Needed]

8b3961f22db666ff227311d16eaaa5f3

google/bigbird-base-trivia-itc

google

big_bird

8

6,949

transformers

2

question-answering

true

false

true

apache-2.0

['en']

['trivia_qa']

null

0

[]

false

true

1,953

false

# BigBird base trivia-itc This model is a fine-tune checkpoint of `bigbird-roberta-base`, fine-tuned on `trivia_qa` with `BigBirdForQuestionAnsweringHead` on its top. Check out [this](https://colab.research.google.com/drive/1DVOm1VHjW0eKCayFq1N2GpY6GR9M4tJP?usp=sharing) to see how well `google/bigbird-base-trivia-itc` performs on question answering. ## How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BigBirdForQuestionAnswering # by default its in `block_sparse` mode with num_random_blocks=3, block_size=64 model = BigBirdForQuestionAnswering.from_pretrained("google/bigbird-base-trivia-itc") # you can change `attention_type` to full attention like this: model = BigBirdForQuestionAnswering.from_pretrained("google/bigbird-base-trivia-itc", attention_type="original_full") # you can change `block_size` & `num_random_blocks` like this: model = BigBirdForQuestionAnswering.from_pretrained("google/bigbird-base-trivia-itc", block_size=16, num_random_blocks=2) question = "Replace me by any text you'd like." context = "Put some context for answering" encoded_input = tokenizer(question, context, return_tensors='pt') output = model(**encoded_input) ``` # Fine-tuning config & hyper-parameters - No. of global token = 128 - Window length = 192 - No. of random token = 192 - Max. sequence length = 4096 - No. of heads = 12 - No. of hidden layers = 12 - Hidden layer size = 768 - Batch size = 32 - Loss = cross-entropy noisy spans ## BibTeX entry and citation info ```tex @misc{zaheer2021big, title={Big Bird: Transformers for Longer Sequences}, author={Manzil Zaheer and Guru Guruganesh and Avinava Dubey and Joshua Ainslie and Chris Alberti and Santiago Ontanon and Philip Pham and Anirudh Ravula and Qifan Wang and Li Yang and Amr Ahmed}, year={2021}, eprint={2007.14062}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

348b2432c682c6eed33629f260d4f1b4

jayantapaul888/twitter-data-microsoft-xtremedistil-l6-h256-uncased-sentiment-finetuned-memes

jayantapaul888

bert

12

4

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,909

false

# twitter-data-microsoft-xtremedistil-l6-h256-uncased-sentiment-finetuned-memes This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3635 - Accuracy: 0.8756 - Precision: 0.8761 - Recall: 0.8756 - F1: 0.8755 ## 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: 1e-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: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.6142 | 1.0 | 1762 | 0.5396 | 0.8022 | 0.8010 | 0.8022 | 0.8014 | | 0.4911 | 2.0 | 3524 | 0.4588 | 0.8322 | 0.8332 | 0.8322 | 0.8325 | | 0.4511 | 3.0 | 5286 | 0.4072 | 0.8562 | 0.8564 | 0.8562 | 0.8559 | | 0.412 | 4.0 | 7048 | 0.3825 | 0.8673 | 0.8680 | 0.8673 | 0.8672 | | 0.3886 | 5.0 | 8810 | 0.3677 | 0.8745 | 0.8753 | 0.8745 | 0.8745 | | 0.3914 | 6.0 | 10572 | 0.3635 | 0.8756 | 0.8761 | 0.8756 | 0.8755 | ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1

45a5965bbc764bea8c2eba18adc40278

DunnBC22/vit-base-patch16-224-in21k_GI_diagnosis

DunnBC22

vit

14

1

transformers

0

image-classification

true

false

apache-2.0

null

['imagefolder']

null

0

['generated_from_trainer']

true

2,291

false

# vit-base-patch16-224-in21k_GI_diagnosis This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2538 - Accuracy: 0.9375 - Weighted f1: 0.9365 - Micro f1: 0.9375 - Macro f1: 0.9365 - Weighted recall: 0.9375 - Micro recall: 0.9375 - Macro recall: 0.9375 - Weighted precision: 0.9455 - Micro precision: 0.9375 - Macro precision: 0.9455 ## 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: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| | 1.3805 | 1.0 | 200 | 0.5006 | 0.8638 | 0.8531 | 0.8638 | 0.8531 | 0.8638 | 0.8638 | 0.8638 | 0.9111 | 0.8638 | 0.9111 | | 1.3805 | 2.0 | 400 | 0.2538 | 0.9375 | 0.9365 | 0.9375 | 0.9365 | 0.9375 | 0.9375 | 0.9375 | 0.9455 | 0.9375 | 0.9455 | | 0.0628 | 3.0 | 600 | 0.5797 | 0.8812 | 0.8740 | 0.8812 | 0.8740 | 0.8812 | 0.8812 | 0.8813 | 0.9157 | 0.8812 | 0.9157 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1 - Datasets 2.5.2 - Tokenizers 0.12.1

af305c4e1f0d8859fa2dd22c589fec01

visheratin/t5-efficient-mini-grammar-correction

visheratin

t5

13

111

transformers

4

text2text-generation

true

false

mit

['en']

['c4_200m']

null

0

['grammar-correction']

false

true

806

false

# T5-Efficient-MINI for grammar correction This is a [T5-Efficient-MINI](https://huggingface.co/google/t5-efficient-mini) model that was trained on a subset of [C4_200M](https://ai.googleblog.com/2021/08/the-c4200m-synthetic-dataset-for.html) dataset to solve the grammar correction task in English. To bring additional errors, random typos were introduced to the input sentences using the [nlpaug](https://github.com/makcedward/nlpaug) library. Since the model was trained on only one task, there are no prefixes needed. The model was trained as a part of the project during the [Full Stack Deep Learning](https://fullstackdeeplearning.com/course/2022/) course. ONNX version of the model is deployed on the [site](https://edge-ai.vercel.app/models/grammar-check) and can be run directly in the browser.

e86cd80c3da34f63f05c9c13c44653fe

Reggie/distilbert-joke_detector

Reggie

distilbert

7

2

transformers

0

text-classification

true

false

mit

['en']

null

0

['distilbert']

false

true

1,835

false

### What is this? This model has been developed to detect "narrative-style" jokes, stories and anecdotes (i.e. they are narrated as a story) spoken during speeches or conversations etc. It works best when jokes/anecdotes are at least 40 words or longer. It is based on [lvwerra's distilbert](https://huggingface.co/lvwerra/distilbert-imdb). The training dataset was a private collection of around 2000 jokes. This model has not been trained or tested on one-liners, puns or Reddit-style language-manipulation jokes such as knock-knock, Q&A jokes etc. See the example in the inference widget or How to use section for what constitues a narrative-style joke. For a more accurate model (2.4% more) that is slower at inference, see the [Roberta model](https://huggingface.co/Reggie/muppet-roberta-base-joke_detector). For a still more accurate model (2.9% more) that is much slower at inference, see the [Deberta-v3 model](https://huggingface.co/Reggie/DeBERTa-v3-base-joke_detector). ### Install these first You'll need to pip install transformers & maybe sentencepiece ### How to use ```python from transformers import pipeline import torch device = 0 if torch.cuda.is_available() else -1 model_name = 'Reggie/distilbert-joke_detector' max_seq_len = 510 pipe = pipeline(model=model_name, device=device, truncation=True, max_length=max_seq_len) is_it_a_joke = """A nervous passenger is about to book a flight ticket, and he asks the airlines' ticket seller, "I hope your planes are safe. Do they have a good track record for safety?" The airline agent replies, "Sir, I can guarantee you, we've never had a plane that has crashed more than once." """ result = pipe(is_it_a_joke) # [{'label': 'POSITIVE', 'score': 0.7313136458396912}] print('This is a joke') if result[0]['label'] == 'POSITIVE' else print('This is not a joke') ```

c68a91bed9636a5c9276612c71245926

Hate-speech-CNERG/deoffxlmr-mono-kannada

Hate-speech-CNERG

xlm-roberta

7

1

transformers

0

text-classification

true

false

apache-2.0

['kn']

null

0

[]

false

true

2,473

false

This model is used to detect **Offensive Content** in **Kannada Code-Mixed language**. The mono in the name refers to the monolingual setting, where the model is trained using only Kannada(pure and code-mixed) data. The weights are initialized from pretrained XLM-Roberta-Base and pretrained using Masked Language Modelling on the target dataset before fine-tuning using Cross-Entropy Loss. This model is the best of multiple trained for **EACL 2021 Shared Task on Offensive Language Identification in Dravidian Languages**. Genetic-Algorithm based ensembled test predictions got the second-highest weighted F1 score at the leaderboard (Weighted F1 score on hold out test set: This model - 0.73, Ensemble - 0.74) ### For more details about our paper Debjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. "[Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection](https://www.aclweb.org/anthology/2021.dravidianlangtech-1.38/)". ***Please cite our paper in any published work that uses any of these resources.*** ~~~ @inproceedings{saha-etal-2021-hate, title = "Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection", author = "Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh", booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages", month = apr, year = "2021", address = "Kyiv", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.dravidianlangtech-1.38", pages = "270--276", abstract = "Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {``}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.", } ~~~

c794c14e31314ac66ce91c85d245917a

sd-concepts-library/bored-ape-textual-inversion

sd-concepts-library

null

9

0

null

3

null

false

mit

null

0

[]

false

true

1,110

false

### bored_ape_textual_inversion on Stable Diffusion This is the `<bored_ape>` 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`: ![<bored_ape> 0](https://huggingface.co/sd-concepts-library/bored-ape-textual-inversion/resolve/main/concept_images/3.jpeg) ![<bored_ape> 1](https://huggingface.co/sd-concepts-library/bored-ape-textual-inversion/resolve/main/concept_images/1.jpeg) ![<bored_ape> 2](https://huggingface.co/sd-concepts-library/bored-ape-textual-inversion/resolve/main/concept_images/2.jpeg) ![<bored_ape> 3](https://huggingface.co/sd-concepts-library/bored-ape-textual-inversion/resolve/main/concept_images/0.jpeg)

f2bf6565452663cd2cff63f8d42669e1

caidas/swin2SR-realworld-sr-x4-64-bsrgan-psnr

caidas

swin2sr

5

350

transformers

1

image-to-image

true

false

apache-2.0

null

0

['vision', 'image-to-image']

false

true

576

false

# Swin2SR model (image super-resolution) Swin2SR model that upscales images x4. It was introduced in the paper [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) by Conde et al. and first released in [this repository](https://github.com/mv-lab/swin2sr). # Intended use cases This model is intended for real-world image super resolution. # Usage Refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/swin2sr#transformers.Swin2SRForImageSuperResolution.forward.example).

65622a475bb71bafcbdfdb81723826af

jonatasgrosman/whisper-small-pt-cv11-v4

jonatasgrosman

whisper

14

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['pt']

['mozilla-foundation/common_voice_11_0']

null

0

['whisper-event', 'generated_from_trainer']

true

2,207

false

# Whisper Small Portuguese This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 pt dataset. It achieves the following results on the evaluation set: - Loss: 0.3191 - Wer: 14.8844 - Cer: 5.7447 ## 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-06 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:| | 2.9379 | 0.92 | 500 | 0.4783 | 17.3806 | 7.0572 | | 2.1727 | 1.84 | 1000 | 0.3721 | 17.2727 | 6.7975 | | 1.7856 | 2.76 | 1500 | 0.3466 | 16.3790 | 6.4023 | | 1.7803 | 3.68 | 2000 | 0.3372 | 15.9014 | 6.2089 | | 1.8312 | 4.6 | 2500 | 0.3303 | 15.7473 | 6.0901 | | 1.6403 | 5.52 | 3000 | 0.3256 | 15.9476 | 6.1896 | | 1.536 | 6.45 | 3500 | 0.3235 | 15.5008 | 6.0928 | | 1.4223 | 7.37 | 4000 | 0.3209 | 15.3621 | 6.0735 | | 1.4652 | 8.29 | 4500 | 0.3209 | 15.2696 | 5.9326 | | 1.2572 | 9.21 | 5000 | 0.3191 | 14.8844 | 5.7447 | | 1.7142 | 10.13 | 5500 | 0.3182 | 15.0077 | 5.8469 | | 1.4195 | 11.05 | 6000 | 0.3171 | 15.0693 | 5.8856 | | 1.3965 | 11.97 | 6500 | 0.3167 | 15.0539 | 5.8580 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu116 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

ba0e9d8467f8be4795c288b59bdeda29

Z3R069/sick

Z3R069

null

18

2

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

['text-to-image', 'stable-diffusion']

false

true

605

false

### sick Dreambooth model trained by Z3R069 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept:

5645af73aa6b26fdef26aaa654a401b5

jonatasgrosman/exp_w2v2t_de_unispeech-sat_s75

jonatasgrosman

unispeech-sat

10

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['de']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'de']

false

true

462

false

# exp_w2v2t_de_unispeech-sat_s75 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

d035abfb828d12c6755b5833ddade02b

Omerdor/dry_samples_train

Omerdor

null

22

3

diffusers

0

null

false

apache-2.0

['en']

['imagefolder']

null

0

[]

false

true

1,195

false

# dry_samples_train ## 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: 5e-06 - train_batch_size: 16 - eval_batch_size: 4 - gradient_accumulation_steps: 3 - 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/Omerdor/dry_samples_train/tensorboard?#scalars)

7892ad55dec4cf99c6d770790b387c53

Rocketknight1/gpt2-finetuned-wikitext2

Rocketknight1

gpt2

9

12

transformers

0

text-generation

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,174

false

# Rocketknight1/gpt2-finetuned-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 7.3062 - Validation Loss: 6.7676 - 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: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 7.3062 | 6.7676 | 0 | ### Framework versions - Transformers 4.21.0.dev0 - TensorFlow 2.9.1 - Datasets 2.3.3.dev0 - Tokenizers 0.11.0

a2a1d56521fe37c668491dcd9a2bd575

gokuls/distilbert_sa_GLUE_Experiment_data_aug_cola_384

gokuls

distilbert

17

0

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,735

false

# distilbert_sa_GLUE_Experiment_data_aug_cola_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.7008 - Matthews Correlation: 0.1207 ## 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: 256 - seed: 10 - distributed_type: multi-GPU - 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 | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5179 | 1.0 | 835 | 0.7008 | 0.1207 | | 0.3641 | 2.0 | 1670 | 0.9121 | 0.1063 | | 0.2641 | 3.0 | 2505 | 1.0415 | 0.0951 | | 0.1963 | 4.0 | 3340 | 1.2167 | 0.1072 | | 0.1519 | 5.0 | 4175 | 1.3170 | 0.1162 | | 0.1191 | 6.0 | 5010 | 1.4385 | 0.1118 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

1fd52e92c029346c05cb858db393c368

dcae10/distilbert-base-uncased-finetuned-imdb

dcae10

distilbert

9

transformers

0

fill-mask

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,318

false

# distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6627 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.76 | 1.0 | 157 | 0.6640 | | 0.688 | 2.0 | 314 | 0.6581 | | 0.6768 | 3.0 | 471 | 0.6604 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

ae45399301e60ec7b30b4faaeefcc52a

gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53

gary109

wav2vec2

22

3

transformers

1

automatic-speech-recognition

true

false

apache-2.0

null

0

['automatic-speech-recognition', 'gary109/AI_Light_Dance', 'generated_from_trainer']

true

1,962

false

# ai-light-dance_singing_ft_wav2vec2-large-xlsr-53 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING dataset. It achieves the following results on the evaluation set: - Loss: 0.4327 - Wer: 0.2043 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.4089 | 1.0 | 552 | 1.4750 | 0.9054 | | 0.7995 | 2.0 | 1104 | 0.9044 | 0.6163 | | 0.6232 | 3.0 | 1656 | 0.6645 | 0.3980 | | 0.5351 | 4.0 | 2208 | 0.5674 | 0.3120 | | 0.472 | 5.0 | 2760 | 0.5167 | 0.2579 | | 0.3913 | 6.0 | 3312 | 0.4553 | 0.2335 | | 0.3306 | 7.0 | 3864 | 0.4476 | 0.2114 | | 0.3028 | 8.0 | 4416 | 0.4327 | 0.2043 | | 0.317 | 9.0 | 4968 | 0.4355 | 0.2033 | | 0.2494 | 10.0 | 5520 | 0.4405 | 0.2022 | ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.1.dev0 - Tokenizers 0.12.1

5574308bd665106036931ae96955cdc1

reemalyami/AraRoBERTa-DZ

reemalyami

roberta

6

1

transformers

0

fill-mask

true

false

apache-2.0

['ar']

null

0

[]

false

true

1,665

false

The **AraRoBERTa** models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper [click](https://aclanthology.org/2022.wanlp-1.24/). The following are the AraRoBERTa seven dialectal variations: * [AraRoBERTa-SA](https://huggingface.co/reemalyami/AraRoBERTa-SA): Saudi Arabia (SA) dialect. * [AraRoBERTa-EGY](https://huggingface.co/reemalyami/AraRoBERTa-EGY): Egypt (EGY) dialect. * [AraRoBERTa-KU](https://huggingface.co/reemalyami/AraRoBERTa-KU): Kuwait (KU) dialect. * [AraRoBERTa-OM](https://huggingface.co/reemalyami/AraRoBERTa-OM): Oman (OM) dialect. * [AraRoBERTa-LB](https://huggingface.co/reemalyami/AraRoBERTa-LB): Lebanon (LB) dialect. * [AraRoBERTa-JO](https://huggingface.co/reemalyami/AraRoBERTa-JO): Jordan (JO) dialect. * [AraRoBERTa-DZ](https://huggingface.co/reemalyami/AraRoBERTa-DZ): Algeria (DZ) dialect # When using the model, please cite our paper: ```python @inproceedings{alyami-al-zaidy-2022-weakly, title = "Weakly and Semi-Supervised Learning for {A}rabic Text Classification using Monodialectal Language Models", author = "AlYami, Reem and Al-Zaidy, Rabah", booktitle = "Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP)", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates (Hybrid)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.wanlp-1.24", pages = "260--272", } ``` # Contact **Reem AlYami**: [Linkedin](https://www.linkedin.com/in/reem-alyami/) | <reem.yami@kfupm.edu.sa> | <yami.m.reem@gmail.com>

f3c180bc75dfbae9259697b82940b558

SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-0

SetFit

distilbert

10

5

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,153

false

# distilbert-base-uncased__hate_speech_offensive__train-32-0 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7714 - Accuracy: 0.705 ## 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: 4 - eval_batch_size: 4 - seed: 42 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0871 | 1.0 | 19 | 1.0704 | 0.45 | | 1.0019 | 2.0 | 38 | 1.0167 | 0.55 | | 0.8412 | 3.0 | 57 | 0.9134 | 0.55 | | 0.6047 | 4.0 | 76 | 0.8430 | 0.6 | | 0.3746 | 5.0 | 95 | 0.8315 | 0.6 | | 0.1885 | 6.0 | 114 | 0.8585 | 0.6 | | 0.0772 | 7.0 | 133 | 0.9443 | 0.65 | | 0.0312 | 8.0 | 152 | 1.1019 | 0.65 | | 0.0161 | 9.0 | 171 | 1.1420 | 0.65 | | 0.0102 | 10.0 | 190 | 1.2773 | 0.65 | | 0.0077 | 11.0 | 209 | 1.2454 | 0.65 | | 0.0064 | 12.0 | 228 | 1.2785 | 0.65 | | 0.006 | 13.0 | 247 | 1.3834 | 0.65 | | 0.0045 | 14.0 | 266 | 1.4139 | 0.65 | | 0.0043 | 15.0 | 285 | 1.4056 | 0.65 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2 - Tokenizers 0.10.3

34666f37234fded5787b3006269c0da2

Abdo96/whisper-small-ar

Abdo96

whisper

18

4

transformers

1

automatic-speech-recognition

true

false

apache-2.0

['ar']

['mozilla-foundation/common_voice_11_0']

null

0

['hf-asr-leaderboard', 'generated_from_trainer']

true

1,583

false

# Whisper Small Ar - Abdallah Elbohy This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset For short transcription 30s but for long transcription it has some limitations and challenges. It achieves the following results on the evaluation set: - Loss: 0.3791 - Wer: 49.8081 ## 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: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0972 | 0.57 | 1000 | 0.3791 | 49.8081 | | 0.0978 | 1.14 | 2000 | 0.3791 | 49.8081 | | 0.0986 | 1.71 | 3000 | 0.3791 | 49.8081 | | 0.1055 | 2.28 | 4000 | 0.3791 | 49.8081 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

f24dcda4ab1b35cae0579612ce64fbac

SebLih/whisper-SV3

SebLih

whisper

15

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['sv']

null

0

['hf-asr-leaderboard', 'generated_from_trainer']

true

1,593

false

# Whisper Small SV This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3516 - Wer: 23.0598 ## 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3274 | 0.86 | 200 | 0.3552 | 24.7469 | | 0.1395 | 1.72 | 400 | 0.3303 | 23.5038 | | 0.074 | 2.59 | 600 | 0.3349 | 22.6603 | | 0.0199 | 3.45 | 800 | 0.3451 | 22.7935 | | 0.0089 | 4.31 | 1000 | 0.3516 | 23.0598 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

641414f9f841dad5300a1a0e55dbaa11

Oleksandr2003/my_awesome_qa_model

Oleksandr2003

distilbert

8

4

transformers

0

question-answering

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,403

false

# Oleksandr2003/my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.6133 - Validation Loss: 1.8637 - Epoch: 2 ## 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: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.5361 | 2.3102 | 0 | | 1.9179 | 1.8637 | 1 | | 1.6133 | 1.8637 | 2 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

4825f382c31b510a77c5bb34e7e81535

sd-concepts-library/fireworks-over-water

sd-concepts-library

null

8

0

null

2

null

false

mit

null

0

[]

false

true

952

false

### Fireworks Over Water on Stable Diffusion This is the `<firework>` 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`: ![<firework> 0](https://huggingface.co/sd-concepts-library/fireworks-over-water/resolve/main/concept_images/1.jpeg) ![<firework> 1](https://huggingface.co/sd-concepts-library/fireworks-over-water/resolve/main/concept_images/0.jpeg) ![<firework> 2](https://huggingface.co/sd-concepts-library/fireworks-over-water/resolve/main/concept_images/2.jpeg)

cf0aa5dc40e89885f9c158b8c2f85ad4

okho0653/distilbert-base-uncased-finetuned-sst-2-english-finetuned-20pc

okho0653

distilbert

13

4

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,623

false

# distilbert-base-uncased-finetuned-sst-2-english-finetuned-20pc This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5078 - Accuracy: 0.8333 - F1: 0.3721 ## 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 | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 41 | 0.3986 | 0.8272 | 0.0667 | | No log | 2.0 | 82 | 0.3829 | 0.8519 | 0.4 | | No log | 3.0 | 123 | 0.4916 | 0.8333 | 0.2286 | | No log | 4.0 | 164 | 0.4894 | 0.8333 | 0.4490 | | No log | 5.0 | 205 | 0.5078 | 0.8333 | 0.3721 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

0c6e9e067367ebb1caac6dabc9e41a0f

sentence-transformers/average_word_embeddings_komninos

sentence-transformers

null

8

0

sentence-transformers

0

sentence-similarity

false

apache-2.0

null

0

['sentence-transformers', 'feature-extraction', 'sentence-similarity']

false

true

2,000

false

# average_word_embeddings_komninos This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 300 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('sentence-transformers/average_word_embeddings_komninos') embeddings = model.encode(sentences) print(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=sentence-transformers/average_word_embeddings_komninos) ## Full Model Architecture ``` SentenceTransformer( (0): WordEmbeddings( (emb_layer): Embedding(222305, 300) ) (1): Pooling({'word_embedding_dimension': 300, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

aac3522fbc9bd9fc61a1a305c834c568

sail/poolformer_s12

sail

poolformer

5

971

transformers

0

image-classification

true

false

apache-2.0

null

['imagenet']

null

0

['image-classification', 'vision']

false

true

5,124

false

# PoolFormer (S12 model) PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu et al. and first released in [this repository](https://github.com/sail-sg/poolformer). ## Model description PoolFormer is a model that replaces attention token mixer in transfomrers with extremely simple operator, pooling. Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance. To verify this, we deliberately replace the attention module in transformers with an embarrassingly simple spatial pooling operator to conduct only the most basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on multiple computer vision tasks. For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned vision transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 48%/60% fewer MACs. The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of "MetaFormer", a general architecture abstracted from transformers without specifying the token mixer. Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks. This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=sail/poolformer) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import PoolFormerFeatureExtractor, PoolFormerForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = PoolFormerFeatureExtractor.from_pretrained('sail/poolformer_s12') model = PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Currently, both the feature extractor and model support PyTorch. ## Training data The poolformer model was trained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/sail-sg/poolformer/blob/main/train.py#L529-L572). ### Pretraining The model was trained on TPU-v3s. Training resolution is 224. For all hyperparameters (such as batch size and learning rate), please refer to the original paper. ## Evaluation results | Model | ImageNet top-1 accuracy | # params | URL | |---------------------------------------|-------------------------|----------|------------------------------------------------------------------| | **PoolFormer-S12** | **77.2** | **12M** | **https://huggingface.co/sail/poolformer_s12** | | PoolFormer-S24 | 80.3 | 21M | https://huggingface.co/sail/poolformer_s24 | | PoolFormer-S36 | 81.4 | 31M | https://huggingface.co/sail/poolformer_s36 | | PoolFormer-M36 | 82.1 | 56M | https://huggingface.co/sail/poolformer_m36 | | PoolFormer-M48 | 82.5 | 73M | https://huggingface.co/sail/poolformer_m48 | ### BibTeX entry and citation info ```bibtex @article{yu2021metaformer, title={MetaFormer is Actually What You Need for Vision}, author={Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng}, journal={arXiv preprint arXiv:2111.11418}, year={2021} } ```

05e995c4fa1c2134657cafff10562256

custom-diffusion-library/cat

custom-diffusion-library

null

5

0

diffusers

1

null

true

false

other

null

0

['pytorch', 'stable-diffusion', 'stable-diffusion-diffusers', 'diffusers']

false

true

1,144

false

# This is a Custom Diffusion model fine-tuned from the Stable Diffusion v1-4. [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion/index.html) allows you to fine-tune text-to-image diffusion models, such as Stable Diffusion, given a few images of a new concept (~4-20). Here we give an example model fine-tuned using 5 images of a cat downloaded from UnSplash. The example code of inference is shown below. ## Example code of inference ``` git clone https://github.com/adobe-research/custom-diffusion cd custom-diffusion ``` ```python from diffusers import StableDiffusionPipeline from src import diffuser_training device = 'cuda' model_id = "CompVis/stable-diffusion-v1-4" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to(device) diffuser_training.load_model(pipe.text_encoder, pipe.tokenizer, pipe.unet, 'cat.bin') prompt = "<new1> cat swimming in a pool" images = pipe(prompt, num_inference_steps=200, guidance_scale=6., eta=1.).images ``` <center> <img src="https://huggingface.co/custom-diffusion-library/cat/resolve/main/cat.png" width="600" align="center" > </center>

7d3322d569a537f820c9c3fbcb2474a8

sophiestein/experiment_2

sophiestein

distilbert

13

24

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

0

['generated_from_trainer']

true

1,528

false

# experiment_2 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: 0.1211 - Precision: 0.8841 - Recall: 0.8926 - F1: 0.8883 - Accuracy: 0.9747 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2418 | 1.0 | 878 | 0.0695 | 0.9159 | 0.9255 | 0.9207 | 0.9816 | | 0.0541 | 2.0 | 1756 | 0.0592 | 0.9244 | 0.9343 | 0.9293 | 0.9833 | | 0.0303 | 3.0 | 2634 | 0.0602 | 0.9260 | 0.9388 | 0.9323 | 0.9838 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.11.0+cpu - Datasets 2.4.0 - Tokenizers 0.12.1

814697669f44d5d62526610eae4f132f

jojoUla/bert-large-uncased-finetuned-lowR100-5-uncased-DA-20

jojoUla

bert

18

2

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

3,212

false

# bert-large-uncased-finetuned-lowR100-5-uncased-DA-20 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9006 ## 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: 30 - eval_batch_size: 30 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.5116 | 1.0 | 1 | 6.5297 | | 6.6949 | 2.0 | 2 | 6.9289 | | 6.0946 | 3.0 | 3 | 7.6464 | | 5.8742 | 4.0 | 4 | 4.8191 | | 5.4365 | 5.0 | 5 | 6.1273 | | 5.171 | 6.0 | 6 | 4.5528 | | 4.4944 | 7.0 | 7 | 4.8541 | | 4.1146 | 8.0 | 8 | 3.4321 | | 3.4689 | 9.0 | 9 | 2.4818 | | 3.6228 | 10.0 | 10 | 2.4444 | | 3.147 | 11.0 | 11 | 1.0668 | | 2.969 | 12.0 | 12 | 3.5394 | | 2.9788 | 13.0 | 13 | 3.1681 | | 2.9108 | 14.0 | 14 | 1.6325 | | 2.9377 | 15.0 | 15 | 2.0480 | | 2.6179 | 16.0 | 16 | 2.6157 | | 2.8978 | 17.0 | 17 | 3.3663 | | 2.6496 | 18.0 | 18 | 2.6341 | | 2.592 | 19.0 | 19 | 2.6462 | | 2.5212 | 20.0 | 20 | 2.2172 | | 2.402 | 21.0 | 21 | 3.3419 | | 2.3146 | 22.0 | 22 | 1.8095 | | 2.5215 | 23.0 | 23 | 2.7622 | | 2.1736 | 24.0 | 24 | 3.9402 | | 2.4366 | 25.0 | 25 | 2.3742 | | 2.1603 | 26.0 | 26 | 2.4520 | | 2.21 | 27.0 | 27 | 3.8185 | | 2.1954 | 28.0 | 28 | 4.0015 | | 2.6556 | 29.0 | 29 | 2.4132 | | 2.3936 | 30.0 | 30 | 3.8690 | | 2.2442 | 31.0 | 31 | 3.7408 | | 2.2486 | 32.0 | 32 | 2.5657 | | 2.5066 | 33.0 | 33 | 3.6632 | | 2.0527 | 34.0 | 34 | 2.9892 | | 2.6207 | 35.0 | 35 | 3.5594 | | 2.296 | 36.0 | 36 | 2.3785 | | 2.4068 | 37.0 | 37 | 3.6126 | | 2.257 | 38.0 | 38 | 1.0477 | | 2.0597 | 39.0 | 39 | 1.5386 | | 2.1702 | 40.0 | 40 | 2.4686 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

fdd04cabed239324c0711752686699fb

maretamasaeva/thesis-freeform

maretamasaeva

roberta

13

1

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,373

false

# thesis-freeform This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6933 - Accuracy: 0.4636 ## 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 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6922 | 1.0 | 5684 | 0.6928 | 0.4636 | | 0.6946 | 2.0 | 11368 | 0.6918 | 0.4636 | | 0.692 | 3.0 | 17052 | 0.6949 | 0.4636 | | 0.6901 | 4.0 | 22736 | 0.6933 | 0.4636 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

358c817cfd15c1683e144a232c7fb706

willcai/wav2vec2_common_voice_accents_6

willcai

wav2vec2

11

6

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

1,365

false

# wav2vec2_common_voice_accents_6 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: 0.3711 ## 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: 48 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 384 - total_eval_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 | |:-------------:|:-----:|:----:|:---------------:| | 3.8539 | 25.0 | 400 | 0.3711 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.4 - Tokenizers 0.11.6

fd0855d9768a198e09b8975de0693d50

ThomasNLG/t5-weighter_cnndm-en

ThomasNLG

t5

8

444

transformers

0

text2text-generation

true

false

true

mit

['en']

['squad', 'cnndm']

null

0

['qa', 'classification', 'question', 'answering', 'SQuAD', 'metric', 'nlg', 't5-small']

false

true

1,365

false

# t5-weighter_cnndm-en ## Model description This model is a *Classifier* model based on T5-small, that predicts if a answer / question couple is considered as important fact or not (Is this answer enough relevant to appear in a plausible summary?). It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is. ## How to use ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-weighter_cnndm-en") model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-weighter_cnndm-en") ``` You can play with the model using the inference API, the text input format should follow this template (accordingly to the training stage of the model): `text_input = "{ANSWER} </s> {QUESTION} </s> {CONTEXT}"` ## Training data The model was trained on synthetic data as described in [Questeval: Summarization asks for fact-based evaluation](https://arxiv.org/abs/2103.12693). ### Citation info ```bibtex @article{scialom2021questeval, title={Questeval: Summarization asks for fact-based evaluation}, author={Scialom, Thomas and Dray, Paul-Alexis and Gallinari, Patrick and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo and Wang, Alex}, journal={arXiv preprint arXiv:2103.12693}, year={2021} } ```

faf9c3648e9385b72739227e635fa6ac

muhtasham/finetuned-mlm_medium

muhtasham

bert

10

6

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,721

false

# finetuned-mlm_medium This model is a fine-tuned version of [muhtasham/bert-medium-mlm-finetuned-emotion](https://huggingface.co/muhtasham/bert-medium-mlm-finetuned-emotion) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2805 - Accuracy: 0.9542 - F1: 0.9765 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2318 | 2.55 | 500 | 0.1428 | 0.9512 | 0.9750 | | 0.0777 | 5.1 | 1000 | 0.1976 | 0.9513 | 0.9750 | | 0.0362 | 7.65 | 1500 | 0.2704 | 0.9388 | 0.9684 | | 0.0234 | 10.2 | 2000 | 0.2245 | 0.9578 | 0.9784 | | 0.0181 | 12.76 | 2500 | 0.3703 | 0.9310 | 0.9643 | | 0.0158 | 15.31 | 3000 | 0.6137 | 0.9001 | 0.9474 | | 0.013 | 17.86 | 3500 | 0.2805 | 0.9542 | 0.9765 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

d58e0306bbe004ec683c4c7f45162d04

jonatasgrosman/exp_w2v2t_ja_xls-r_s941

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ja']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'ja']

false

true

453

false

# exp_w2v2t_ja_xls-r_s941 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

aa194ad7a7dafa3082fcd7705853f38a

jonaskoenig/topic_classification_04

jonaskoenig

bert

8

5,762

transformers

8

text-classification

false

true

false

mit

null

0

2

0

['generated_from_keras_callback']

true

1,782

false

# topic_classification_04 This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8325 - Train Sparse Categorical Accuracy: 0.7237 - Epoch: 9 ## 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: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:-----:| | 1.0735 | 0.6503 | 0 | | 0.9742 | 0.6799 | 1 | | 0.9424 | 0.6900 | 2 | | 0.9199 | 0.6970 | 3 | | 0.9016 | 0.7026 | 4 | | 0.8853 | 0.7073 | 5 | | 0.8707 | 0.7120 | 6 | | 0.8578 | 0.7160 | 7 | | 0.8448 | 0.7199 | 8 | | 0.8325 | 0.7237 | 9 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.9.1 - Datasets 2.3.2 - Tokenizers 0.12.1

31a86ab3fd2f828c48adaac062413952

likejazz/xlm-roberta-base-finetuned-panx-all

likejazz

xlm-roberta

10

1

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,319

false

# xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1574 - F1: 0.8504 ## 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: 96 - eval_batch_size: 96 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 179 | 0.1897 | 0.8147 | | No log | 2.0 | 358 | 0.1624 | 0.8394 | | No log | 3.0 | 537 | 0.1574 | 0.8504 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.1+cu117 - Datasets 1.16.1 - Tokenizers 0.10.3

6b44d8f92e1a9fc221db4cae22e5baa2

venetis/convnext-tiny-224_album_vit

venetis

convnext

19

2

transformers

0

image-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,442

false

# convnext-tiny-224_album_vit This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3898 - Accuracy: 0.4912 ## 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: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.6659 | 1.0 | 944 | 3.5335 | 0.2607 | | 2.8174 | 2.0 | 1888 | 2.6391 | 0.4418 | | 2.4959 | 3.0 | 2832 | 2.3898 | 0.4912 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

dc4ca0628ad6088f8cae17660bfdae71

hassnain/wav2vec2-base-timit-demo-colab70

hassnain

wav2vec2

12

6

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,462

false

# wav2vec2-base-timit-demo-colab70 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7439 - Wer: 0.5149 ## 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: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8646 | 7.04 | 500 | 3.1467 | 1.0 | | 1.678 | 14.08 | 1000 | 0.8738 | 0.6511 | | 0.5083 | 21.13 | 1500 | 0.7404 | 0.5504 | | 0.2923 | 28.17 | 2000 | 0.7439 | 0.5149 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

d2716387728e5fa9f4514c9685318cff

muhtasham/olm-bert-tiny-december-2022-target-glue-mrpc

muhtasham

bert

11

4

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,604

false

# olm-bert-tiny-december-2022-target-glue-mrpc This model is a fine-tuned version of [muhtasham/olm-bert-tiny-december-2022](https://huggingface.co/muhtasham/olm-bert-tiny-december-2022) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9243 - Accuracy: 0.6299 - F1: 0.7146 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.6093 | 4.35 | 500 | 0.5848 | 0.7034 | 0.7980 | | 0.5487 | 8.7 | 1000 | 0.5863 | 0.7206 | 0.8087 | | 0.4724 | 13.04 | 1500 | 0.6881 | 0.6544 | 0.7294 | | 0.3752 | 17.39 | 2000 | 0.7549 | 0.6520 | 0.7331 | | 0.276 | 21.74 | 2500 | 0.9243 | 0.6299 | 0.7146 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

1c2429d01dcd478d44ea05217f637fa7

KoichiYasuoka/roberta-classical-chinese-large-ud-goeswith

KoichiYasuoka

roberta

10

11

transformers

0

token-classification

true

false

apache-2.0

['lzh']

['universal_dependencies']

null

0

['classical chinese', 'literary chinese', 'ancient chinese', 'token-classification', 'pos', 'dependency-parsing']

false

true

2,864

false

# roberta-classical-chinese-large-ud-goeswith ## Model Description This is a RoBERTa model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-classical-chinese-large-char](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-large-char) and [UD_Classical_Chinese-Kyoto](https://github.com/UniversalDependencies/UD_Classical_Chinese-Kyoto). ## How to Use ```py class UDgoeswith(object): def __init__(self,bert): from transformers import AutoTokenizer,AutoModelForTokenClassification self.tokenizer=AutoTokenizer.from_pretrained(bert) self.model=AutoModelForTokenClassification.from_pretrained(bert) def __call__(self,text): import numpy,torch,ufal.chu_liu_edmonds w=self.tokenizer(text,return_offsets_mapping=True) v=w["input_ids"] x=[v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)] with torch.no_grad(): e=self.model(input_ids=torch.tensor(x)).logits.numpy()[:,1:-2,:] r=[1 if i==0 else -1 if j.endswith("|root") else 0 for i,j in sorted(self.model.config.id2label.items())] e+=numpy.where(numpy.add.outer(numpy.identity(e.shape[0]),r)==0,0,numpy.nan) g=self.model.config.label2id["X|_|goeswith"] r=numpy.tri(e.shape[0]) for i in range(e.shape[0]): for j in range(i+2,e.shape[1]): r[i,j]=r[i,j-1] if numpy.nanargmax(e[i,j-1])==g else 1 e[:,:,g]+=numpy.where(r==0,0,numpy.nan) m=numpy.full((e.shape[0]+1,e.shape[1]+1),numpy.nan) m[1:,1:]=numpy.nanmax(e,axis=2).transpose() p=numpy.zeros(m.shape) p[1:,1:]=numpy.nanargmax(e,axis=2).transpose() for i in range(1,m.shape[0]): m[i,0],m[i,i],p[i,0]=m[i,i],numpy.nan,p[i,i] h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] if [0 for i in h if i==0]!=[0]: m[:,0]+=numpy.where(m[:,0]==numpy.nanmax(m[[i for i,j in enumerate(h) if j==0],0]),0,numpy.nan) m[[i for i,j in enumerate(h) if j==0]]+=[0 if i==0 or j==0 else numpy.nan for i,j in enumerate(h)] h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] u="# text = "+text+"\n" v=[(s,e) for s,e in w["offset_mapping"] if s<e] for i,(s,e) in enumerate(v,1): q=self.model.config.id2label[p[i,h[i]]].split("|") u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=UDgoeswith("KoichiYasuoka/roberta-classical-chinese-large-ud-goeswith") print(nlp("孟子見梁惠王")) ``` with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/). Or without ufal.chu-liu-edmonds: ``` from transformers import pipeline nlp=pipeline("universal-dependencies","KoichiYasuoka/roberta-classical-chinese-large-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple") print(nlp("孟子見梁惠王")) ```

f64d93247ebb2ed06a86ef4e9989ed99

mateocolina/xlm-roberta-base-finetuned-marc-en

mateocolina

xlm-roberta

12

3

transformers

0

text-classification

true

false

mit

null

['amazon_reviews_multi']

null

0

['generated_from_trainer']

true

1,275

false

# xlm-roberta-base-finetuned-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9276 - Mae: 0.5366 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.0992 | 1.0 | 235 | 0.9340 | 0.5122 | | 0.945 | 2.0 | 470 | 0.9276 | 0.5366 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

8b3febad6aeff95800e413b87671e412

jonatasgrosman/exp_w2v2t_nl_vp-fr_s226

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['nl']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'nl']

false

true

469

false

# exp_w2v2t_nl_vp-fr_s226 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

14b77201a9bb79cec12c514bd0f52bc2

sd-concepts-library/wildkat

sd-concepts-library

null

14

0

null

0

null

false

mit

null

0

[]

false

true

1,510

false

### Wildkat on Stable Diffusion This is the `<wildkat>` 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`: ![<wildkat> 0](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/7.jpeg) ![<wildkat> 1](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/1.jpeg) ![<wildkat> 2](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/2.jpeg) ![<wildkat> 3](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/8.jpeg) ![<wildkat> 4](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/0.jpeg) ![<wildkat> 5](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/3.jpeg) ![<wildkat> 6](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/4.jpeg) ![<wildkat> 7](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/5.jpeg) ![<wildkat> 8](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/6.jpeg)

ce4b0c8aa6f417aec7ef31d231bc9b8c

Helsinki-NLP/opus-mt-fr-vi

Helsinki-NLP

marian

11

80

transformers

0

translation

true

false

apache-2.0

['fr', 'vi']

null

1

0

['translation']

false

true

2,002

false

### fra-vie * source group: French * target group: Vietnamese * OPUS readme: [fra-vie](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-vie/README.md) * model: transformer-align * source language(s): fra * target language(s): vie * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-vie/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-vie/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-vie/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.fra.vie | 31.1 | 0.486 | ### System Info: - hf_name: fra-vie - source_languages: fra - target_languages: vie - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-vie/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['fr', 'vi'] - src_constituents: {'fra'} - tgt_constituents: {'vie', 'vie_Hani'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/fra-vie/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/fra-vie/opus-2020-06-17.test.txt - src_alpha3: fra - tgt_alpha3: vie - short_pair: fr-vi - chrF2_score: 0.486 - bleu: 31.1 - brevity_penalty: 0.985 - ref_len: 13219.0 - src_name: French - tgt_name: Vietnamese - train_date: 2020-06-17 - src_alpha2: fr - tgt_alpha2: vi - prefer_old: False - long_pair: fra-vie - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

75bd0b5f577e84e44d8149c3589857c7

muhtasham/finetuned-self_mlm_mini

muhtasham

bert

10

6

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,714

false

# finetuned-self_mlm_mini This model is a fine-tuned version of [muhtasham/bert-tiny-mlm-finetuned-imdb](https://huggingface.co/muhtasham/bert-tiny-mlm-finetuned-imdb) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6150 - Accuracy: 0.8224 - F1: 0.9025 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4426 | 2.55 | 500 | 0.4673 | 0.7928 | 0.8844 | | 0.2845 | 5.1 | 1000 | 0.3099 | 0.8697 | 0.9303 | | 0.2282 | 7.65 | 1500 | 0.3432 | 0.8589 | 0.9241 | | 0.1819 | 10.2 | 2000 | 0.2702 | 0.8998 | 0.9472 | | 0.1461 | 12.76 | 2500 | 0.4852 | 0.8344 | 0.9097 | | 0.111 | 15.31 | 3000 | 0.6807 | 0.7950 | 0.8858 | | 0.0883 | 17.86 | 3500 | 0.6150 | 0.8224 | 0.9025 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

e8ed5b359ef6ac77bf05bb1804a94c75

Helsinki-NLP/opus-mt-lu-es

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

768

false

### opus-mt-lu-es * source languages: lu * target languages: es * OPUS readme: [lu-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lu-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/lu-es/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lu-es/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lu-es/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.lu.es | 22.4 | 0.400 |

15b9c308ee215eb4407c02b00e31bc16

tangoqash/SAM

tangoqash

distilbert

13

11

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,045

false

# SAM This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3061 - Accuracy: {'accuracy': 0.8733333333333333} - F1: 0.8742 ## 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: 2 ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

e152bc1ba69dd2dd337b3461b439c80f

leander/bert-finetuned-ner

leander

bert

12

3

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

0

['generated_from_trainer']

true

1,518

false

# bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0589 - Precision: 0.9329 - Recall: 0.9507 - F1: 0.9417 - Accuracy: 0.9870 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0867 | 1.0 | 1756 | 0.0639 | 0.9140 | 0.9386 | 0.9261 | 0.9831 | | 0.0398 | 2.0 | 3512 | 0.0586 | 0.9326 | 0.9480 | 0.9402 | 0.9858 | | 0.0212 | 3.0 | 5268 | 0.0589 | 0.9329 | 0.9507 | 0.9417 | 0.9870 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

47dfe92e6e406a583d6e7da18ee5fda8

Elytum/tiny-classification-fast

Elytum

bert

64

10

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,308

false

# tiny-classification-fast This model is a fine-tuned version of [cross-encoder/ms-marco-TinyBERT-L-2-v2](https://huggingface.co/cross-encoder/ms-marco-TinyBERT-L-2-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8673 - Accuracy: 0.7786 ## 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: 128 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9077 | 1.0 | 785 | 1.0466 | 0.7482 | | 1.0061 | 2.0 | 1570 | 0.8673 | 0.7786 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

9eb789b27976a4104e5727c2c43ee674

Sushant45/Adult_contemporary_music-clustered

Sushant45

distilbert

8

28

transformers

0

question-answering

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,879

false

# Sushant45/Adult_contemporary_music-clustered This model is a fine-tuned version of [nandysoham16/15-clustered_aug](https://huggingface.co/nandysoham16/15-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2951 - Train End Logits Accuracy: 0.9375 - Train Start Logits Accuracy: 0.9028 - Validation Loss: 0.5855 - Validation End Logits Accuracy: 0.7143 - Validation Start Logits Accuracy: 0.8571 - 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: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.2951 | 0.9375 | 0.9028 | 0.5855 | 0.7143 | 0.8571 | 0 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

38a4e5ed81cdfd2b7cbe4b999fe3c65b

ali2066/finetuned_token_itr0_3e-05_webDiscourse_16_02_2022-20_59_50

ali2066

distilbert

13

10

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,805

false

# finetuned_token_itr0_3e-05_webDiscourse_16_02_2022-20_59_50 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5450 - Precision: 0.0049 - Recall: 0.0146 - F1: 0.0074 - Accuracy: 0.7431 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 10 | 0.6830 | 0.0109 | 0.0323 | 0.0163 | 0.5685 | | No log | 2.0 | 20 | 0.7187 | 0.0256 | 0.0323 | 0.0286 | 0.5668 | | No log | 3.0 | 30 | 0.6839 | 0.0076 | 0.0484 | 0.0131 | 0.5848 | | No log | 4.0 | 40 | 0.6988 | 0.0092 | 0.0484 | 0.0155 | 0.5918 | | No log | 5.0 | 50 | 0.7055 | 0.0100 | 0.0484 | 0.0165 | 0.5946 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

fda1915a0be8f2eb479a1a7d30ed2f68

sd-concepts-library/crinos-form-garou

sd-concepts-library

null

9

0

null

0

null

false

mit

null

0

[]

false

true

1,045

false

### crinos form garou on Stable Diffusion This is the `<crinos>` 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`: ![<crinos> 0](https://huggingface.co/sd-concepts-library/crinos-form-garou/resolve/main/concept_images/1.jpeg) ![<crinos> 1](https://huggingface.co/sd-concepts-library/crinos-form-garou/resolve/main/concept_images/0.jpeg) ![<crinos> 2](https://huggingface.co/sd-concepts-library/crinos-form-garou/resolve/main/concept_images/2.jpeg) ![<crinos> 3](https://huggingface.co/sd-concepts-library/crinos-form-garou/resolve/main/concept_images/3.jpeg)

60cf63aed9fda58dbb71176dfc92a0ed

svo2/roberta-finetuned-location

svo2

roberta

15

16

transformers

0

question-answering

true

false

cc-by-4.0

null

0

['generated_from_trainer']

true

983

false

# roberta-finetuned-location This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) 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: 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: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

8393c69afdaa92844f7f462103086137

ligerre/xlm-roberta-base-finetuned-panx-it

ligerre

xlm-roberta

10

23

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,320

false

# xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2401 - F1: 0.8246 ## 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: 24 - eval_batch_size: 24 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8187 | 1.0 | 70 | 0.3325 | 0.7337 | | 0.2829 | 2.0 | 140 | 0.2554 | 0.8003 | | 0.1894 | 3.0 | 210 | 0.2401 | 0.8246 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

fe29efb116e4c67750b0d46d721a126b

Fulccrum/trainii_ac94u-label-classification

Fulccrum

null

4

0

sklearn

0

tabular-classification

false

apache-2.0

null

0

['tabular-classification', 'baseline-trainer']

false

true

7,270

false

## Baseline Model trained on trainii_ac94u to apply classification on label **Metrics of the best model:** accuracy 0.361046 recall_macro 0.353192 precision_macro 0.240667 f1_macro 0.278231 Name: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: float64 **See model plot below:** <style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-9" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless id True False False ... False False False text False False False ... False True False[2 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-27" type="checkbox" ><label for="sk-estimator-id-27" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless id True False False ... False False False text False False False ... False True False[2 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-28" type="checkbox" ><label for="sk-estimator-id-28" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless id True False False ... False False False text False False False ... False True False[2 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-29" type="checkbox" ><label for="sk-estimator-id-29" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)</pre></div></div></div></div></div></div></div> **Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). **Logs of training** including the models tried in the process can be found in logs.txt

18d7c9200c9cfd0a23a08dfe1b044127

jhmin/finetuning-sentiment-model-3000-samples

jhmin

distilbert

13

10

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,055

false

# finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3144 - Accuracy: 0.8667 - F1: 0.8667 ## 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: 2 ### Training results ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

99f71ce38065efc9999690d1b88ed1e6

elopezlopez/Bio_ClinicalBERT_fold_7_ternary_v1

elopezlopez

bert

13

1

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

2,668

false

# Bio_ClinicalBERT_fold_7_ternary_v1 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: 1.9612 - F1: 0.7939 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 291 | 0.5762 | 0.7593 | | 0.5434 | 2.0 | 582 | 0.5577 | 0.7939 | | 0.5434 | 3.0 | 873 | 0.6501 | 0.7951 | | 0.2198 | 4.0 | 1164 | 0.8661 | 0.7939 | | 0.2198 | 5.0 | 1455 | 1.1493 | 0.7900 | | 0.0953 | 6.0 | 1746 | 1.1999 | 0.7977 | | 0.0375 | 7.0 | 2037 | 1.4623 | 0.7759 | | 0.0375 | 8.0 | 2328 | 1.4526 | 0.7900 | | 0.0246 | 9.0 | 2619 | 1.6915 | 0.7734 | | 0.0246 | 10.0 | 2910 | 1.6097 | 0.7913 | | 0.0113 | 11.0 | 3201 | 1.7091 | 0.8015 | | 0.0113 | 12.0 | 3492 | 1.7252 | 0.7990 | | 0.0103 | 13.0 | 3783 | 1.7305 | 0.8015 | | 0.0079 | 14.0 | 4074 | 1.7932 | 0.8003 | | 0.0079 | 15.0 | 4365 | 1.7800 | 0.8028 | | 0.0071 | 16.0 | 4656 | 1.7000 | 0.7977 | | 0.0071 | 17.0 | 4947 | 1.8342 | 0.8003 | | 0.0077 | 18.0 | 5238 | 1.8517 | 0.7990 | | 0.0044 | 19.0 | 5529 | 1.8633 | 0.7964 | | 0.0044 | 20.0 | 5820 | 1.8813 | 0.7926 | | 0.0028 | 21.0 | 6111 | 1.8914 | 0.7964 | | 0.0028 | 22.0 | 6402 | 1.9412 | 0.7926 | | 0.0043 | 23.0 | 6693 | 1.9760 | 0.7939 | | 0.0043 | 24.0 | 6984 | 1.9509 | 0.7977 | | 0.0002 | 25.0 | 7275 | 1.9612 | 0.7939 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

2a88b2acea22a3ba17b2d5005c1d3994

jwhe/prompt-extend-1epoch

jwhe

gpt2

12

0

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,195

false

# prompt-extend-1epoch This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9530 ## 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: 512 - eval_batch_size: 1024 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7574 | 1.0 | 3199 | 2.9530 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.11.0+cu102 - Datasets 2.9.0 - Tokenizers 0.13.2

89f96548e4a9f77fc3f5bd764e4485b0

faisito/xlm-roberta-base-finetuned-panx-it

faisito

xlm-roberta

9

5

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,319

false

# xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2532 - F1: 0.8222 ## 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: 24 - eval_batch_size: 24 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8114 | 1.0 | 70 | 0.3235 | 0.7548 | | 0.2825 | 2.0 | 140 | 0.2749 | 0.7913 | | 0.1932 | 3.0 | 210 | 0.2532 | 0.8222 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

8a8fe827cc1b20fb798a9d76e0c1450b

thu-coai/CDial-GPT_LCCC-large

thu-coai

null

5

275

transformers

6

conversational

true

false

mit

null

0

1

0

['conversational']

false

true

1,084

false

## Chinese pre-trained dialogue model (CDial-GPT) This project provides a large-scale Chinese GPT model pre-trained on the dataset [LCCC](https://huggingface.co/datasets/silver/lccc). We present a series of Chinese GPT model that are first pre-trained on a Chinese novel dataset and then post-trained on our LCCC dataset. Similar to [TransferTransfo](https://arxiv.org/abs/1901.08149), we concatenate all dialogue histories into one context sentence, and use this sentence to predict the response. The input of our model consists of word embedding, speaker embedding, and positional embedding of each word. Paper: [A Large-Scale Chinese Short-Text Conversation Dataset](https://arxiv.org/pdf/2008.03946.pdf) ### How to use ```python from transformers import OpenAIGPTLMHeadModel, GPT2LMHeadModel, BertTokenizer import torch tokenizer = BertTokenizer.from_pretrained("thu-coai/CDial-GPT_LCCC-large") model = OpenAIGPTLMHeadModel.from_pretrained("thu-coai/CDial-GPT_LCCC-large") ``` For more details, please refer to our [repo.](https://github.com/thu-coai/CDial-GPT) on github.

c72d1c2e72e284628e143a696fff33a3

arjunchandra/ddpm-butterflies-128

arjunchandra

null

13

3

diffusers

0

null

false

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

null

0

[]

false

true

1,234

false

# ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` 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/arjunchandra/ddpm-butterflies-128/tensorboard?#scalars)

5b8cd52b0b65b02a8d93b7bfaa277e02

jimregan/psst-partial-timit

jimregan

wav2vec2

6

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['en']

['jimregan/psst', 'timit_asr']

null

0

['automatic-speech-recognition']

false

true

7,035

false

This repository contains a number of experiments for the [PSST Challenge](https://psst.study/). As the test set is unavailable, all numbers are based on the validation set. The experiments in the tables below were finetuned on [Wav2vec 2.0 Base, No finetuning](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec) Our overall best performing model (**FER** 9\.2%, **PER:** 21\.0%) was based on [Wav2vec 2.0 Large, No finetuning](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec) (git tag: `larger-rir`), with the TIMIT subset augmented with Room Impulse Response, based on the experiments below, on the base model. ## Augmented TIMIT subset Using a subset of TIMIT that could map easily to the phoneset used by the PSST Challenge data (a list of IDs are in the repository), we experimented with augmenting the data to better match the PSST data. The best results were obtained using Room Impulse Response (tag: `rir`) | **Augmentation** | **FER** | **PER** | **Git tag** | | :----------------------------------------------- | :-------- | :--------- | :---------------------------------- | | unaugmented | 10\.2% | 22\.5% | huggingface-unaugmented | | Gaussian noise | 10\.0% | 22\.1% | gaussian | | Pitchshift | 9\.6% | 22\.9% | pitchshift | | RIR | **9\.6%** | **21\.8%** | rir | | Time stretch | 10\.1% | 22\.8% | timestretch | | Gaussian noise + RIR | 10\.0% | 23\.4% | gaussian-rir | | Pitchshift + Gaussian noise | 9\.9% | 22\.9% | pitchshift-gaussian | | Pitchshift + RIR | 9\.9% | 22\.8% | pitchshift-rir | | Tim estretch + Gaussian noise | 10\.2% | 22\.8% | timestretch-gaussian | | Time stretch + Pitchshift | 9\.8% | 22\.0% | timestretch-pitchshift | | Time stretch + RIR | 9\.7% | 22\.2% | timestretch-rir | | Pitchshift + Gaussian noise + RIR | 10\.1% | 23\.5% | pitchshift-gaussian-rir | | Time stretch + Gaussian noise + RIR | 9\.7% | 22\.3% | timestretch-gaussian-rir | | Time stretch + Pitchshift + Gaussian noise | 10\.2% | 22\.9% | timestretch-pitchshift-gaussian | | Time stretch + Pitchshift + RIR | 10\.2% | 22\.5% | timestretch-pitchshift-rir | | Time stretch + Pitchshift + Gaussian noise + RIR | 10\.9% | 24\.1% | timestretch-pitchshift-gaussian-rir | ## LM experiments We experimented with a number of language model configurations, combining the data from the PSST challenge, the subset of TIMIT we used, and CMUdict. We tried combining CMUdict data in a number of ways: unmodified, with a silence token added at the start of the pronunciation, at the end, and at both the start and the end. The best result was from a 5-gram model, with silences added at the end of the CMUdict data (git tag: `lm-nosil-cmudict-sile.5`). Evaluation was performed using scripts provided by the PSST Challenge's organisers, so there are no scripts in place to automatically use the LM with the transformers library. | | **n-gram** | **FER** | **PER** | **Tag** | | :----------------------------- | :--------- | :--------- | :--------- | :--------- | | Baseline + TIMIT | --- | **10\.2%** | 22\.5% | huggingface-unaugmented | | All silences | 4 | 10\.5% | 23\.0% | lm-allsil.4 | | | 5 | 10\.5% | 22\.6% | lm-allsil.5 | | | 6 | 10\.3% | 22\.3% | lm-allsil.6 | | No silences | 4 | 10\.3% | 22\.6% | lm-nosil.4 | | | 5 | **10\.2%** | 22\.2% | lm-nosil.5 | | | 6 | **10\.2%** | 22\.4% | lm-nosil.6 | | PSST and TIMIT without silence | | | | | | Unmodified CMUdict | 4 | 10\.3% | 22\.6% | lm-nosil-cmudict-nosil.4 | | | 5 | 10\.2% | 22\.2% | lm-nosil-cmudict-nosil.5 | | | 6 | **10\.2%** | 22\.4% | lm-nosil-cmudict-nosil.6 | | CMUdict-end | 4 | 10\.3% | 22\.6% | lm-nosil-cmudict-sile.4 | | | 5 | **10\.2%** | **22\.1%** | lm-nosil-cmudict-sile.5 | | | 6 | **10\.2%** | 22\.3% | lm-nosil-cmudict-sile.6 | | CMUdict-start | 4 | 10\.4% | 22\.6% | lm-nosil-cmudict-sils.4 | | | 5 | 10\.3% | 22\.4% | lm-nosil-cmudict-sils.5 | | | 6 | 10\.3% | 22\.3% | lm-nosil-cmudict-sils.6 | | CMUdict-both | 4 | 10\.4% | 22\.7% | lm-nosil-cmudict-silb.4 | | | 5 | 10\.4% | 22\.3% | lm-nosil-cmudict-silb.5 | | | 6 | 10\.3% | 22\.3% | lm-nosil-cmudict-silb.6 | | Unmodified PSST and TIMIT | | | | | | Unmodified CMUdict | 4 | 10\.3% | 22\.8% | lm-orig-cmudict-nosil.4 | | | 5 | 10\.3% | 22\.4% | lm-orig-cmudict-nosil.5 | | | 6 | **10\.2%** | 22\.4% | lm-orig-cmudict-nosil.6 | | CMUdict-end | 4 | 10\.3% | 22\.7% | lm-orig-cmudict-sile.4 | | | 5 | **10\.2%** | 22\.2% | lm-orig-cmudict-sile.5 | | | 6 | **10\.2%** | 22\.3% | lm-orig-cmudict-sile.6 | | CMUdict-start | 4 | 10\.5% | 22\.8% | lm-orig-cmudict-sils.4 | | | 5 | 10\.4% | 22\.5% | lm-orig-cmudict-sils.5 | | | 6 | 10\.3% | 22\.4% | lm-orig-cmudict-sils.6 | | CMUdict-both | 4 | 10\.5% | 22\.8% | lm-orig-cmudict-silb.4 | | | 5 | 10\.4% | 22\.4% | lm-orig-cmudict-silb.5 | | | 6 | 10\.4% | 22\.4% | lm-orig-cmudict-silb.6 |

cb911a75a9de3ddac897f2313cce55b1

takehiro067/distilbert-base-uncased-finetuned-emotion

takehiro067

distilbert

16

1

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,338

false

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2213 - Accuracy: 0.9255 - F1: 0.9255 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8391 | 1.0 | 250 | 0.3177 | 0.9035 | 0.9006 | | 0.2526 | 2.0 | 500 | 0.2213 | 0.9255 | 0.9255 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

0deda740cab344ce8b8a6dc3005f3c99