davanstrien/model_cards_with_readmes · Datasets at Hugging Face

Prem11100/donut-base-Label-studio

Prem11100

vision-encoder-decoder

14

3

transformers

0

null

true

false

mit

null

['imagefolder']

null

0

['generated_from_trainer']

true

989

false

# donut-base-Label-studio 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: 2e-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: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

1350e397a200a50f60b8fe5b4768ddba

GItaf/gpt2-finetuned-mbti-0901

GItaf

gpt2

12

2

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,326

false

# gpt2-finetuned-mbti-0901 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: 3.9470 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 4.1073 | 1.0 | 9906 | 4.0111 | | 4.0302 | 2.0 | 19812 | 3.9761 | | 3.9757 | 3.0 | 29718 | 3.9578 | | 3.9471 | 4.0 | 39624 | 3.9495 | | 3.9187 | 5.0 | 49530 | 3.9470 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

d51b4f6e17cd94e3c54e2ed029adf1ad

paola-md/distilr-lr1e05-wd0.02-bs32

paola-md

roberta

6

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,674

false

# distilr-lr1e05-wd0.02-bs32 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2742 - Rmse: 0.5236 - Mse: 0.2742 - Mae: 0.4137 ## 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: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2775 | 1.0 | 623 | 0.2735 | 0.5230 | 0.2735 | 0.4187 | | 0.2738 | 2.0 | 1246 | 0.2727 | 0.5222 | 0.2727 | 0.4120 | | 0.2722 | 3.0 | 1869 | 0.2728 | 0.5223 | 0.2728 | 0.4168 | | 0.2701 | 4.0 | 2492 | 0.2751 | 0.5245 | 0.2751 | 0.4000 | | 0.2684 | 5.0 | 3115 | 0.2770 | 0.5263 | 0.2770 | 0.4236 | | 0.2668 | 6.0 | 3738 | 0.2742 | 0.5236 | 0.2742 | 0.4137 | ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 2.4.0 - Tokenizers 0.12.1

ea1f36b266a8411351c41bba1fbf7fb4

google/multiberts-seed_4-step_1500k

google

bert

8

12

transformers

0

null

true

false

apache-2.0

['en']

null

0

['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_1500k']

false

true

3,527

false

# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 1500k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model #4, captured at step 1500k (max: 2000k, i.e., 2M steps). ## Model Description This model was captured during a reproduction of [BERT-base uncased](https://github.com/google-research/bert), for English: it is a Transformers model pretrained on a large corpus of English data, using the Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP) objectives. The intended uses, limitations, training data and training procedure for the fully trained model are similar to [BERT-base uncased](https://github.com/google-research/bert). Two major differences with the original model: * We pre-trained the MultiBERTs models for 2 million steps using sequence length 512 (instead of 1 million steps using sequence length 128 then 512). * We used an alternative version of Wikipedia and Books Corpus, initially collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962). This is a best-effort reproduction, and so it is probable that differences with the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT). See our [technical report](https://arxiv.org/abs/2106.16163) for more details. ### How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_1500k') model = TFBertModel.from_pretrained("google/multiberts-seed_4-step_1500k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_1500k') model = BertModel.from_pretrained("google/multiberts-seed_4-step_1500k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ## Citation info ```bibtex @article{sellam2021multiberts, title={The MultiBERTs: BERT Reproductions for Robustness Analysis}, author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, journal={arXiv preprint arXiv:2106.16163}, year={2021} } ```

9121bc1eacac65b5fb8e8750749743af

ken11/bert-japanese-ner

ken11

bert

4

2,529

transformers

3

token-classification

true

false

mit

['ja']

null

0

['ner', 'token-classification', 'japanese', 'bert']

false

true

1,827

false

## bert-japanese-ner このモデルは日本語の固有表現抽出タスクを目的として、[京都大学黒橋・褚・村脇研究室が公開しているBERT日本語Pretrainedモデル](https://nlp.ist.i.kyoto-u.ac.jp/?ku_bert_japanese)をベースに[ストックマーク株式会社が公開しているner-wikipedia-dataset](https://github.com/stockmarkteam/ner-wikipedia-dataset)でファインチューニングしたものです。 ## How to use このモデルはTokenizerに上述の京都大学BERT日本語PretrainedモデルのTokenizerを利用します。当リポジトリにTokenizerは含まれていません。利用する際は別途ダウンロードしてご用意ください。また、Tokenizerとは別に[Juman++](https://nlp.ist.i.kyoto-u.ac.jp/?JUMAN%2B%2B)と[pyknp](https://nlp.ist.i.kyoto-u.ac.jp/?PyKNP)を利用します。予めインストールしておいてください。 ```py from transformers import ( BertForTokenClassification, BertTokenizer ) from pyknp import Juman jumanpp = Juman() tokenizer = BertTokenizer.from_pretrained("ダウンロードした京都大学のTokenizerのファイルパス") model = BertForTokenClassification.from_pretrained("ken11/bert-japanese-ner") text = "なにか文章" juman_result = jumanpp.analysis(text) tokenized_text = [mrph.midasi for mrph in juman_result.mrph_list()] inputs = tokenizer(tokenized_text, return_tensors="pt", padding='max_length', truncation=True, max_length=64, is_split_into_words=True) pred = model(**inputs).logits[0] pred = np.argmax(pred.detach().numpy(), axis=-1) labels = [] for i, label in enumerate(pred): if i + 1 > len(tokenized_text): continue labels.append(model.config.id2label[label]) print(f"{tokenized_text[i]}: {model.config.id2label[label]}") print(tokenized_text) print(labels) ``` ## Training Data 学習には[ストックマーク株式会社が公開しているner-wikipedia-dataset](https://github.com/stockmarkteam/ner-wikipedia-dataset)を利用しました。便利なデータセットを公開していただきありがとうございます。 ## Note 固有表現抽出のラベルは学習データセットのものをBILUO形式に変換して使用しています。ラベルの詳細については[ner-wikipedia-datasetの概要](https://github.com/stockmarkteam/ner-wikipedia-dataset#%E6%A6%82%E8%A6%81)をご確認ください。 ## Licenese [The MIT license](https://opensource.org/licenses/MIT)

0deb65adbd7de32bc944bd97ae5823d9

muhtasham/tiny-mlm-glue-rte-target-glue-qnli

muhtasham

bert

10

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,803

false

# tiny-mlm-glue-rte-target-glue-qnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-rte](https://huggingface.co/muhtasham/tiny-mlm-glue-rte) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4684 - Accuracy: 0.7809 ## 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 - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6041 | 0.15 | 500 | 0.5365 | 0.7382 | | 0.5388 | 0.31 | 1000 | 0.5298 | 0.7435 | | 0.5182 | 0.46 | 1500 | 0.4987 | 0.7640 | | 0.5123 | 0.61 | 2000 | 0.5240 | 0.7494 | | 0.5096 | 0.76 | 2500 | 0.4802 | 0.7761 | | 0.5026 | 0.92 | 3000 | 0.4708 | 0.7847 | | 0.4897 | 1.07 | 3500 | 0.4503 | 0.7921 | | 0.4798 | 1.22 | 4000 | 0.4681 | 0.7825 | | 0.4659 | 1.37 | 4500 | 0.4770 | 0.7754 | | 0.4743 | 1.53 | 5000 | 0.4684 | 0.7809 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

8dd90e63737d4ba3da598513ae616986

stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v1

stjiris

bert

16

3

sentence-transformers

1

sentence-similarity

true

false

mit

['pt']

['stjiris/portuguese-legal-sentences-v0', 'assin', 'assin2', 'stsb_multi_mt', 'stjiris/IRIS_sts']

null

0

['sentence-transformers', 'transformers', 'bert', 'pytorch', 'sentence-similarity']

false

true

5,493

false

[![INESC-ID](https://www.inesc-id.pt/wp-content/uploads/2019/06/INESC-ID-logo_01.png)](https://www.inesc-id.pt/projects/PR07005/) [![A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/_static/logo.png)](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/) Work developed as part of [Project IRIS](https://www.inesc-id.pt/projects/PR07005/). Thesis: [A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/) # stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v1 (Legal BERTimbau) This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. stjiris/bert-large-portuguese-cased-legal-tsdae derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large. It was trained using the TSDAE technique with a learning rate 1e-5 [Legal Sentences from +-30000 documents](https://huggingface.co/datasets/stjiris/portuguese-legal-sentences-v1.0) 212k training steps (best performance for our semantic search system implementation) It was presented to Generative Pseudo Labeling training. The model was presented to NLI data. 16 batch size, 2e-5 lr It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the [assin](https://huggingface.co/datasets/assin), [assin2](https://huggingface.co/datasets/assin2), [stsb_multi_mt pt](https://huggingface.co/datasets/stsb_multi_mt) and [IRIS STS](https://huggingface.co/datasets/stjiris/IRIS_sts) datasets. 'lr': 1e-5 ## 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 = ["Isto é um exemplo", "Isto é um outro exemplo"] model = SentenceTransformer('stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v1') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v1') model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v1') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1028, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ### Contributions [@rufimelo99](https://github.com/rufimelo99) If you use this work, please cite: ```bibtex @inproceedings{MeloSemantic, author = {Melo, Rui and Santos, Professor Pedro Alexandre and Dias, Professor Jo{\~ a}o}, title = {A {Semantic} {Search} {System} for {Supremo} {Tribunal} de {Justi}{\c c}a}, } @inproceedings{souza2020bertimbau, author = {F{\'a}bio Souza and Rodrigo Nogueira and Roberto Lotufo}, title = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese}, booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)}, year = {2020} } @inproceedings{fonseca2016assin, title={ASSIN: Avaliacao de similaridade semantica e inferencia textual}, author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S}, booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal}, pages={13--15}, year={2016} } @inproceedings{real2020assin, title={The assin 2 shared task: a quick overview}, author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo}, booktitle={International Conference on Computational Processing of the Portuguese Language}, pages={406--412}, year={2020}, organization={Springer} } @InProceedings{huggingface:dataset:stsb_multi_mt, title = {Machine translated multilingual STS benchmark dataset.}, author={Philip May}, year={2021}, url={https://github.com/PhilipMay/stsb-multi-mt} } ```

2f8de519b3d21834e9ae6b42b207511b

huggingnft/hedgies

huggingnft

null

5

4

transformers

0

unconditional-image-generation

false

mit

null

['huggingnft/hedgies']

null

0

['huggingnft', 'nft', 'huggan', 'gan', 'image', 'images', 'unconditional-image-generation']

false

true

2,170

false

# Hugging NFT: hedgies ## Disclaimer All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright holder. ## Model description LightWeight GAN model for unconditional generation. NFT collection available [here](https://opensea.io/collection/hedgies). Dataset is available [here](https://huggingface.co/datasets/huggingnft/hedgies). Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). Project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft) ## Intended uses & limitations #### How to use Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). #### Limitations and bias Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). ## Training data Dataset is available [here](https://huggingface.co/datasets/huggingnft/hedgies). ## Training procedure Training script is available [here](https://github.com/AlekseyKorshuk/huggingnft). ## Generated Images Check results with Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft) ### BibTeX entry and citation info ```bibtex @InProceedings{huggingnft, author={Aleksey Korshuk} year=2022 } ```

72c6c3ca7179c37a6351530b7f288cb8

IDEA-CCNL/Taiyi-Diffusion-532M-Cyberpunk-Chinese

IDEA-CCNL

null

6

4

transformers

4

feature-extraction

true

false

apache-2.0

null

0

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

false

true

2,422

false

# Taiyi-Diffusion-532M-Cyberpunk-Chinese - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) ## 简介 Brief Introduction 该模型由[Katherine Crowson's](https://github.com/openai/guided-diffusion)的无条件扩散模型在1k+张收集的赛博朋克风的图上微调而来。结合[IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese](https://huggingface.co/IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese)可以实现中文Guided Diffusion的生成方式。 This model is finetune from Katherine Crowson's fine-tuned 512x512 diffusion model (https://github.com/openai/guided-diffusion), using 1k+ cyberpunk-style image crawled from the InterNet. Combine With [IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese](https://huggingface.co/IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese) can generate image via guided diffusion in Chinese. ## 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 特殊 Special | 多模态 Multimodal | 太乙 Taiyi | Diffusion Model | 532M | Cyberpunk | ## 使用 Usage 使用示例见：https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/disco_project ## 生成示例 Example | 城市，赛博朋克 | 城市，赛博朋克 | | ---- | ---- | | ![](cyb1.png) | ![](cyb2.png) | ## 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)： If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970): ```text @article{fengshenbang, author = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang}, title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence}, journal = {CoRR}, volume = {abs/2209.02970}, year = {2022} } ``` 也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```

d57ec8b41d722ee0a48138c3b3c88a66

anuragshas/whisper-large-v2-ka

anuragshas

whisper

23

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ka']

['mozilla-foundation/common_voice_11_0']

null

0

['whisper-event', 'generated_from_trainer']

true

1,628

false

# Whisper Large-v2 Georgian This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 ka dataset. It achieves the following results on the evaluation set: - Loss: 0.1187 - Wer: 31.8548 ## 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: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0413 | 2.06 | 200 | 0.0712 | 36.6296 | | 0.006 | 5.04 | 400 | 0.0899 | 33.7467 | | 0.0008 | 8.02 | 600 | 0.1039 | 32.2311 | | 0.0002 | 11.01 | 800 | 0.1141 | 31.9290 | | 0.0001 | 13.06 | 1000 | 0.1187 | 31.8548 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

ae7f36c25f88c3564b8eacd5eeac665d

KedirAhmed/finetuning-sentiment-model-3000-samples

KedirAhmed

distilbert

13

9

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: 0.3069 - Accuracy: 0.8667 - F1: 0.8675 ## 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 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

c47c7cbedea3e4110824f81a7c923e80

sd-dreambooth-library/homelander

sd-dreambooth-library

null

28

2

diffusers

3

null

false

mit

null

2

0

[]

false

true

1,562

false

### Homelander on Stable Diffusion via Dreambooth #### model by Abdifatah This your the Stable Diffusion model fine-tuned the Homelander concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of homelander guy** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). Here are the images used for training this concept: ![image 0](https://huggingface.co/sd-dreambooth-library/homelander/resolve/main/concept_images/8.jpeg) ![image 1](https://huggingface.co/sd-dreambooth-library/homelander/resolve/main/concept_images/3.jpeg) ![image 2](https://huggingface.co/sd-dreambooth-library/homelander/resolve/main/concept_images/1.jpeg) ![image 3](https://huggingface.co/sd-dreambooth-library/homelander/resolve/main/concept_images/5.jpeg) ![image 4](https://huggingface.co/sd-dreambooth-library/homelander/resolve/main/concept_images/0.jpeg) ![image 5](https://huggingface.co/sd-dreambooth-library/homelander/resolve/main/concept_images/7.jpeg) ![image 6](https://huggingface.co/sd-dreambooth-library/homelander/resolve/main/concept_images/6.jpeg) ![image 7](https://huggingface.co/sd-dreambooth-library/homelander/resolve/main/concept_images/4.jpeg) ![image 8](https://huggingface.co/sd-dreambooth-library/homelander/resolve/main/concept_images/9.jpeg) ![image 9](https://huggingface.co/sd-dreambooth-library/homelander/resolve/main/concept_images/2.jpeg)

79e4a8d480204105c6add77510685e20

blackopsmaniac1/gwnt

blackopsmaniac1

null

16

2

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

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

false

true

641

false

### gwnt Dreambooth model trained by blackopsmaniac1 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) MAGIC WORDS: "gwnt style" Sample pictures of this concept:

a3e85fb33fdd955954b06fb6119e9f61

espnet/GunnarThor_talromur_b_tacotron2

espnet

null

18

3

espnet

0

text-to-speech

false

cc-by-4.0

['en']

['talromur']

null

0

['espnet', 'audio', 'text-to-speech']

false

true

6,091

false

## ESPnet2 TTS model ### `espnet/GunnarThor_talromur_b_tacotron2` This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout 49a284e69308d81c142b89795de255b4ce290c54 pip install -e . cd egs2/talromur/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/GunnarThor_talromur_b_tacotron2 ``` ## TTS config <details><summary>expand</summary> ``` config: ./conf/tuning/train_tacotron2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/b/tts_train_tacotron2_raw_phn_none ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 2 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 55403 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: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min - - train - loss - min keep_nbest_models: 5 nbest_averaging_interval: 0 grad_clip: 1.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 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: 500 batch_size: 20 valid_batch_size: null batch_bins: 2560000 valid_batch_bins: null train_shape_file: - exp/b/tts_stats_raw_phn_none/train/text_shape.phn - exp/b/tts_stats_raw_phn_none/train/speech_shape valid_shape_file: - exp/b/tts_stats_raw_phn_none/valid/text_shape.phn - exp/b/tts_stats_raw_phn_none/valid/speech_shape batch_type: numel valid_batch_type: null fold_length: - 150 - 204800 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_b_phn/text - text - text - - dump/raw/train_b_phn/wav.scp - speech - sound valid_data_path_and_name_and_type: - - dump/raw/dev_b_phn/text - text - text - - dump/raw/dev_b_phn/wav.scp - speech - sound allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 eps: 1.0e-06 weight_decay: 0.0 scheduler: null scheduler_conf: {} token_list: - <blank> - <unk> - ',' - . - r - t - n - a0 - s - I0 - D - l - Y0 - m - v - h - E1 - k - a:1 - j - E:1 - f - T - G - a1 - p - c - i:1 - au:1 - O:1 - E0 - I:1 - r_0 - t_h - I1 - k_h - Y1 - i0 - ei1 - u:1 - ou:1 - ei:1 - O1 - N - l_0 - '91' - n_0 - ou0 - ai0 - au1 - ou1 - O0 - '9:1' - ai:1 - ei0 - ai1 - i1 - au0 - c_h - p_h - '90' - C - x - u0 - 9i:1 - Y:1 - u1 - 9i1 - J - N_0 - m_0 - 9i0 - J_0 - Oi1 - Yi0 - Yi1 - Oi0 - au:0 - '9:0' - <sos/eos> odim: null model_conf: {} use_preprocessor: true token_type: phn bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null feats_extract: fbank feats_extract_conf: n_fft: 1024 hop_length: 256 win_length: null fs: 22050 fmin: 80 fmax: 7600 n_mels: 80 normalize: global_mvn normalize_conf: stats_file: exp/b/tts_stats_raw_phn_none/train/feats_stats.npz tts: tacotron2 tts_conf: embed_dim: 512 elayers: 1 eunits: 512 econv_layers: 3 econv_chans: 512 econv_filts: 5 atype: location adim: 512 aconv_chans: 32 aconv_filts: 15 cumulate_att_w: true dlayers: 2 dunits: 1024 prenet_layers: 2 prenet_units: 256 postnet_layers: 5 postnet_chans: 512 postnet_filts: 5 output_activation: null use_batch_norm: true use_concate: true use_residual: false dropout_rate: 0.5 zoneout_rate: 0.1 reduction_factor: 1 spk_embed_dim: null use_masking: true bce_pos_weight: 5.0 use_guided_attn_loss: true guided_attn_loss_sigma: 0.4 guided_attn_loss_lambda: 1.0 pitch_extract: null pitch_extract_conf: {} pitch_normalize: null pitch_normalize_conf: {} energy_extract: null energy_extract_conf: {} energy_normalize: null energy_normalize_conf: {} required: - output_dir - token_list version: 0.10.7a1 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} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` 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} } ```

c665656ba4b8da536cb48f22567fbe71

SIKU-BERT/sikubert

SIKU-BERT

bert

7

1,071

transformers

3

fill-mask

true

false

apache-2.0

['zh']

null

0

['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'roberta', 'pytorch']

false

true

1,318

false

# SikuBERT ## Model description ![SikuBERT](https://raw.githubusercontent.com/SIKU-BERT/SikuBERT-for-digital-humanities-and-classical-Chinese-information-processing/main/appendix/sikubert.png) Digital humanities research needs the support of large-scale corpus and high-performance ancient Chinese natural language processing tools. The pre-training language model has greatly improved the accuracy of text mining in English and modern Chinese texts. At present, there is an urgent need for a pre-training model specifically for the automatic processing of ancient texts. We used the verified high-quality “Siku Quanshu” full-text corpus as the training set, based on the BERT deep language model architecture, we constructed the SikuBERT and SikuRoBERTa pre-training language models for intelligent processing tasks of ancient Chinese. ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SIKU-BERT/sikubert") model = AutoModel.from_pretrained("SIKU-BERT/sikubert") ``` ## About Us We are from Nanjing Agricultural University. > Created with by SIKU-BERT [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/SIKU-BERT/SikuBERT-for-digital-humanities-and-classical-Chinese-information-processing)

0accdfe0c315b9ff4b8ab430a5d81e59

michiyasunaga/LinkBERT-base

michiyasunaga

bert

8

252

transformers

4

text-classification

true

false

apache-2.0

['en']

['wikipedia', 'bookcorpus']

null

0

['bert', 'exbert', 'linkbert', 'feature-extraction', 'fill-mask', 'question-answering', 'text-classification', 'token-classification']

false

true

3,320

false

## LinkBERT-base LinkBERT-base model pretrained on English Wikipedia articles along with hyperlink information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available in [this repository](https://github.com/michiyasunaga/LinkBERT). ## Model description LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document. LinkBERT can be used as a drop-in replacement for BERT. It achieves better performance for general language understanding tasks (e.g. text classification), and is also particularly effective for **knowledge-intensive** tasks (e.g. question answering) and **cross-document** tasks (e.g. reading comprehension, document retrieval). ## Intended uses & limitations The model can be used by fine-tuning on a downstream task, such as question answering, sequence classification, and token classification. You can also use the raw model for feature extraction (i.e. obtaining embeddings for input text). ### How to use To use the model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/LinkBERT-base') model = AutoModel.from_pretrained('michiyasunaga/LinkBERT-base') inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` For fine-tuning, you can use [this repository](https://github.com/michiyasunaga/LinkBERT) or follow any other BERT fine-tuning codebases. ## Evaluation results When fine-tuned on downstream tasks, LinkBERT achieves the following results. **General benchmarks ([MRQA](https://github.com/mrqa/MRQA-Shared-Task-2019) and [GLUE](https://gluebenchmark.com/)):** | | HotpotQA | TriviaQA | SearchQA | NaturalQ | NewsQA | SQuAD | GLUE | | ---------------------- | -------- | -------- | -------- | -------- | ------ | ----- | -------- | | | F1 | F1 | F1 | F1 | F1 | F1 | Avg score | | BERT-base | 76.0 | 70.3 | 74.2 | 76.5 | 65.7 | 88.7 | 79.2 | | **LinkBERT-base** | **78.2** | **73.9** | **76.8** | **78.3** | **69.3** | **90.1** | **79.6** | | BERT-large | 78.1 | 73.7 | 78.3 | 79.0 | 70.9 | 91.1 | 80.7 | | **LinkBERT-large** | **80.8** | **78.2** | **80.5** | **81.0** | **72.6** | **92.7** | **81.1** | ## Citation If you find LinkBERT useful in your project, please cite the following: ```bibtex @InProceedings{yasunaga2022linkbert, author = {Michihiro Yasunaga and Jure Leskovec and Percy Liang}, title = {LinkBERT: Pretraining Language Models with Document Links}, year = {2022}, booktitle = {Association for Computational Linguistics (ACL)}, } ```

0ee24190ca0bbd9c56be4bf1094009e4

fathyshalab/massive_play-roberta-large-v1-5-71

fathyshalab

roberta

14

2

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

['setfit', 'sentence-transformers', 'text-classification']

false

true

1,458

false

# fathyshalab/massive_play-roberta-large-v1-5-71 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/massive_play-roberta-large-v1-5-71") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

a35fa2c96ce75a755d40110391633ae3

Qilex/VirtualPetDiffusion2

Qilex

null

30

4

diffusers

0

null

false

apache-2.0

['en']

['Qilex/private_guys']

null

0

[]

false

true

1,363

false

# VirtualPetDiffusion2 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on a dataset of roughly 8,000 virtual pet thumbnail images. ## Intended uses & limitations This model can be used to generate small (128x128) virtual pet-like thumbnails. The pets are generally somewhat abstract. #### How to use ```python from diffusers import DiffusionPipeline pipeline = DiffusionPipeline.from_pretrained("Qilex/VirtualPetDiffusion2") image = pipeline()["sample"][0] #this line only works in jupyter display(image) ``` ## Training data This model was trained on roughly 8,000 virtual pet thumbnail images (80x80px). The data was randomly flipped, rotated, and perspected using torchvision transforms to prevent some of the issues from the first VirtualPetDiffusion. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: no ### Training results 📈 [TensorBoard logs](https://huggingface.co/Qilex/VirtualPetDiffusion2/tensorboard?#scalars)

e52d7d2ff7ceb55269cb5482c3e1a4f6

heyyai/mikao00

heyyai

null

32

5

diffusers

0

text-to-image

false

mit

null

2

0

['text-to-image']

false

true

2,982

false

### mikao00 on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook #### model by cormacncheese This your the Stable Diffusion model fine-tuned the mikao00 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: **mikao000(1).JPG, mikao000(2).JPG, mikao000(3).JPG, mikao000(4).JPG, mikao000(5).png, mikao000(6).png, mikao000(7).JPG, mikao000(8).JPG, mikao000(9).JPG, mikao000(10).JPG, mikao000(12).JPG, mikao000(13).JPG, mikao000(14).jpeg** You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb). And 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), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: mikao000(14).jpeg mikao000(13).JPG mikao000(12).JPG mikao000(10).JPG mikao000(9).JPG mikao000(8).JPG mikao000(7).JPG mikao000(6).png mikao000(5).png mikao000(4).JPG mikao000(3).JPG mikao000(2).JPG mikao000(1).JPG ![mikao000(1).JPG 0](https://huggingface.co/cormacncheese/mikao00/resolve/main/concept_images/mikao000(1).JPG) ![mikao000(2).JPG 1](https://huggingface.co/cormacncheese/mikao00/resolve/main/concept_images/mikao000(2).JPG) ![mikao000(3).JPG 2](https://huggingface.co/cormacncheese/mikao00/resolve/main/concept_images/mikao000(3).JPG) ![mikao000(4).JPG 3](https://huggingface.co/cormacncheese/mikao00/resolve/main/concept_images/mikao000(4).JPG) ![mikao000(5).png 4](https://huggingface.co/cormacncheese/mikao00/resolve/main/concept_images/mikao000(5).png) ![mikao000(6).png 5](https://huggingface.co/cormacncheese/mikao00/resolve/main/concept_images/mikao000(6).png) ![mikao000(7).JPG 6](https://huggingface.co/cormacncheese/mikao00/resolve/main/concept_images/mikao000(7).JPG) ![mikao000(8).JPG 7](https://huggingface.co/cormacncheese/mikao00/resolve/main/concept_images/mikao000(8).JPG) ![mikao000(9).JPG 8](https://huggingface.co/cormacncheese/mikao00/resolve/main/concept_images/mikao000(9).JPG) ![mikao000(10).JPG 9](https://huggingface.co/cormacncheese/mikao00/resolve/main/concept_images/mikao000(10).JPG) ![mikao000(12).JPG 10](https://huggingface.co/cormacncheese/mikao00/resolve/main/concept_images/mikao000(12).JPG) ![mikao000(13).JPG 11](https://huggingface.co/cormacncheese/mikao00/resolve/main/concept_images/mikao000(13).JPG) ![mikao000(14).jpeg 12](https://huggingface.co/cormacncheese/mikao00/resolve/main/concept_images/mikao000(14).jpeg)

42a2fc174cd7f2bd915cd975382e88b8

IDEA-CCNL/Erlangshen-MegatronBert-1.3B-Similarity

IDEA-CCNL

megatron-bert

5

108

transformers

0

text-classification

true

false

apache-2.0

['zh']

null

0

['bert', 'NLU', 'Similarity']

false

true

3,561

false

# Erlangshen-MegatronBert-1.3B-Similarity - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) ## 简介 Brief Introduction 2021年登顶FewCLUE和ZeroCLUE的中文BERT，在数个相似度任务上微调后的版本 This is the fine-tuned version of the Chinese BERT model on several similarity datasets, which topped FewCLUE and ZeroCLUE benchmark in 2021 ## 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | MegatronBert | 1.3B | 相似度 Similarity | ## 模型信息 Model Information 基于[Erlangshen-MegatronBert-1.3B](https://huggingface.co/IDEA-CCNL/Erlangshen-MegatronBert-1.3B)，我们在收集的20个中文领域的改写数据集，总计2773880个样本上微调了一个Similarity版本。 Based on [Erlangshen-MegatronBert-1.3B](https://huggingface.co/IDEA-CCNL/Erlangshen-MegatronBert-1.3B), we fine-tuned a similarity version on 20 Chinese paraphrase datasets, with totaling 2,773,880 samples. ### 下游效果 Performance | Model | BQ | BUSTM | AFQMC | | :--------: | :-----: | :----: | :-----: | | Erlangshen-Roberta-110M-Similarity | 85.41 | 95.18 | 81.72 | | Erlangshen-Roberta-330M-Similarity | 86.21 | 99.29 | 93.89 | | Erlangshen-MegatronBert-1.3B-Similarity | 86.31 | - | - | ## 使用 Usage ``` python from transformers import AutoModelForSequenceClassification from transformers import BertTokenizer import torch tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-MegatronBert-1.3B-Similarity') model=AutoModelForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-MegatronBert-1.3B-Similarity') texta='今天的饭不好吃' textb='今天心情不好' output=model(torch.tensor([tokenizer.encode(texta,textb)])) print(torch.nn.functional.softmax(output.logits,dim=-1)) ``` ## 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的对该模型的论文： If you are using the resource for your work, please cite the our paper for this model: ```text @article{fengshenbang/erlangshen-megatronbert-sim, author = {Junjie Wang and Yuxiang Zhang and Ping Yang and Ruyi Gan}, title = {Towards No.1 in {CLUE} Semantic Matching Challenge: Pre-trained Language Model Erlangshen with Propensity-Corrected Loss}, journal = {CoRR}, volume = {abs/2208.02959}, year = {2022} } ``` 如果您在您的工作中使用了我们的模型，也可以引用我们的[总论文](https://arxiv.org/abs/2209.02970)： If you are using the resource for your work, please cite the our [overview paper](https://arxiv.org/abs/2209.02970): ```text @article{fengshenbang, author = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang}, title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence}, journal = {CoRR}, volume = {abs/2209.02970}, year = {2022} } ``` 也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```

b40022fa74ed5c29ed2758fa84af62d1

Thilinameths/thilinamethsahann

Thilinameths

null

18

5

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

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

false

true

431

false

### thilinamethsahann Dreambooth model trained by Thilinameths 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) Sample pictures of this concept:

2e94520c0a21d139eb49c7add795f3a6

ALM/whisper-it-small-augmented

ALM

whisper

21

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['it']

['mozilla-foundation/common_voice_11_0']

null

0

['whisper-event', 'generated_from_trainer']

true

2,217

false

# Whisper Small Italian - Robust 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 it dataset. It achieves the following results on the evaluation set: - Loss: 0.1980 - Wer: 8.7457 **IMPORTANT** The model has been trained using *data augmentation* to improve its generalization capabilities and robustness. The results on the eval set during training are biased towards data augmentation applied to evaluation data. **Results on eval set** - Mozilla CV 11.0 - Italian: 8.00 wer (using official script) - TODO ## 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: 32 - 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: 25000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.1927 | 1.0 | 2500 | 0.2506 | 14.9991 | | 0.0736 | 2.01 | 5000 | 0.2258 | 12.7864 | | 0.0413 | 3.01 | 7500 | 0.2144 | 11.4508 | | 0.0201 | 4.02 | 10000 | 0.2146 | 10.8774 | | 0.0129 | 5.02 | 12500 | 0.2127 | 10.6920 | | 0.0091 | 6.03 | 15000 | 0.2117 | 10.2867 | | 0.0043 | 7.03 | 17500 | 0.2076 | 9.6860 | | 0.0018 | 8.04 | 20000 | 0.2065 | 9.4235 | | 0.0013 | 9.04 | 22500 | 0.2003 | 8.9105 | | 0.0009 | 10.05 | 25000 | 0.1978 | 8.7497 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

84ff3aac62e9b1bd81fa6e5272a56341

sgangireddy/whisper-small-fi-full

sgangireddy

whisper

22

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['fi']

['mozilla-foundation/common_voice_11_0']

null

0

['whisper-event', 'generated_from_trainer']

true

1,783

false

# Whisper Small Finnish all 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 fi dataset. It achieves the following results on the evaluation set: - Loss: 0.5334 - Wer: 25.4333 ## Model description The Model is fine-tuned for 5000 steps/updates on CV11 Finnish train+valiation data. - Zero-shot - 30.5 (CV9 test data, even on CV11 the WER is closer a bit higher than this) - Fine-tuned - 25.43 (CV11 test data) ## 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: 32 - 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: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0025 | 19.0 | 1000 | 0.4265 | 24.8493 | | 0.0005 | 38.0 | 2000 | 0.4785 | 25.3203 | | 0.0003 | 57.01 | 3000 | 0.5073 | 25.3956 | | 0.0002 | 76.01 | 4000 | 0.5253 | 25.4333 | | 0.0002 | 96.0 | 5000 | 0.5334 | 25.4333 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

a587461a7f8be5bcd23428827aa44cc9

Helsinki-NLP/opus-mt-kqn-fr

Helsinki-NLP

marian

10

8

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-kqn-fr * source languages: kqn * target languages: fr * OPUS readme: [kqn-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/kqn-fr/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/kqn-fr/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/kqn-fr/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/kqn-fr/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.kqn.fr | 23.2 | 0.400 |

eadf4600f9938e4586a6cb94c4008926

yhavinga/ul2-base-nl36-dutch

yhavinga

t5

33

35

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

11,053

false

# ul2-base-nl36-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-base-nl36-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 The "efficient" T5 architecture findings presented in [this paper](https://arxiv.org/abs/2109.10686) were also applied, which suggests that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count. Specifically, the model depth is defined as the number of transformer blocks that are stacked sequentially. This model uses the [t5-efficient-base-nl36](https://huggingface.co/google/t5-efficient-base-nl36) architecture's layer depth, which means both the encoder and the decoder have 36 transformer layers compared to the original T5 "base" model's architecture of 12 transformer layers. ### 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-base-nl36-dutch", use_fast=False) model = T5ForConditionalGeneration.from_pretrained("yhavinga/ul2-base-nl36-dutch") ``` and in Flax: ```python from transformers import T5Tokenizer, FlaxT5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("yhavinga/ul2-base-nl36-dutch", use_fast=False) model = FlaxT5ForConditionalGeneration.from_pretrained("yhavinga/ul2-base-nl36-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-base-nl36-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-base-nl36-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 2000000 steps with a batch size of 64 (in total 65 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/)

2125838d98e564cc33d140df82429210

Helsinki-NLP/opus-mt-hil-en

Helsinki-NLP

marian

10

14

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-hil-en * source languages: hil * target languages: en * OPUS readme: [hil-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/hil-en/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/hil-en/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/hil-en/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/hil-en/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.hil.en | 49.2 | 0.638 |

b9980ea841b5fbed7276c00a89f535a8

jhaochenz/finetuned_gpt2-large_sst2_negation0.001_pretrainedTrue_epochs2

jhaochenz

gpt2

14

0

transformers

0

text-generation

true

false

mit

null

['sst2']

null

0

['generated_from_trainer']

true

1,215

false

# finetuned_gpt2-large_sst2_negation0.001_pretrainedTrue_epochs2 This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.0769 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0066 | 1.0 | 1322 | 2.9427 | | 1.5196 | 2.0 | 2644 | 3.0769 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.7.0 - Datasets 2.8.0 - Tokenizers 0.13.2

62546fec7f2a6c03371d335c9e5359c8

KoichiYasuoka/roberta-base-thai-char

KoichiYasuoka

roberta

8

604

transformers

0

fill-mask

true

false

apache-2.0

['th']

null

0

['thai', 'masked-lm', 'wikipedia']

false

true

676

false

# roberta-base-thai-char ## Model Description This is a RoBERTa model pre-trained on Thai Wikipedia texts with character-wise embeddings to use BertTokenizerFast. You can fine-tune `roberta-base-thai-char` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-thai-char-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/roberta-base-thai-char-ud-goeswith), and so on. ## How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-thai-char") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-base-thai-char") ```

4fdd4b9a172e9de4073b62633553562e

rossanez/t5-small-finetuned-de-en-256-nofp16

rossanez

t5

12

4

transformers

0

text2text-generation

true

false

apache-2.0

null

['wmt14']

null

0

['generated_from_trainer']

true

1,129

false

# t5-small-finetuned-de-en-256-nofp16 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 188 | 2.1234 | 7.7305 | 17.4033 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

37f8f47504f935212b6d5f62101f753d

stevhliu/my_awesome_eli5_mlm_model

stevhliu

roberta

14

25

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,254

false

# my_awesome_eli5_mlm_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1706 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 20 | 2.2325 | | No log | 2.0 | 40 | 2.1603 | | No log | 3.0 | 60 | 2.2368 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.2 - Tokenizers 0.13.1

c2f439f69c776ef86a2a5546168a44a1

Squirz/phase2

Squirz

null

19

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

414

false

### Phase2 Dreambooth model trained by Squirz 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) Sample pictures of this concept:

2ebf678f0bb865a88057b1796567d5cf

jfarmerphd/bert-finetuned-ner

jfarmerphd

bert

12

1

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: 0.0628 - Precision: 0.9338 - Recall: 0.9490 - F1: 0.9413 - Accuracy: 0.9862 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0893 | 1.0 | 1756 | 0.0765 | 0.9136 | 0.9275 | 0.9205 | 0.9796 | | 0.0334 | 2.0 | 3512 | 0.0627 | 0.9297 | 0.9480 | 0.9388 | 0.9858 | | 0.0167 | 3.0 | 5268 | 0.0628 | 0.9338 | 0.9490 | 0.9413 | 0.9862 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

4694fe66eb1d200c7eed896ae4979744

sd-concepts-library/type

sd-concepts-library

null

10

0

null

1

null

false

mit

null

0

[]

false

true

1,090

false

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

84e5d4d526f1ed08e9e87e619970ddf3

yanaiela/roberta-base-epoch_19

yanaiela

roberta

9

2

transformers

0

fill-mask

true

false

mit

['en']

['wikipedia', 'bookcorpus']

null

0

['roberta-base', 'roberta-base-epoch_19']

false

true

2,102

false

# RoBERTa, Intermediate Checkpoint - Epoch 19 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly initialized weights before the training) to provide the ability to study the training dynamics of such models, and other possible use-cases. These models were trained in part of a work that studies how simple statistics from data, such as co-occurrences affects model predictions, which are described in the paper [Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251). This is RoBERTa-base epoch_19. ## Model Description This model was captured during a reproduction of [RoBERTa-base](https://huggingface.co/roberta-base), for English: it is a Transformers model pretrained on a large corpus of English data, using the Masked Language Modelling (MLM). The intended uses, limitations, training data and training procedure for the fully trained model are similar to [RoBERTa-base](https://huggingface.co/roberta-base). Two major differences with the original model: * We trained our model for 100K steps, instead of 500K * We only use Wikipedia and the Book Corpus, as corpora which are publicly available. ### How to use Using code from [RoBERTa-base](https://huggingface.co/roberta-base), here is an example based on PyTorch: ``` from transformers import pipeline model = pipeline("fill-mask", model='yanaiela/roberta-base-epoch_83', device=-1, top_k=10) model("Hello, I'm the <mask> RoBERTa-base language model") ``` ## Citation info ```bibtex @article{2207.14251, Author = {Yanai Elazar and Nora Kassner and Shauli Ravfogel and Amir Feder and Abhilasha Ravichander and Marius Mosbach and Yonatan Belinkov and Hinrich Schütze and Yoav Goldberg}, Title = {Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions}, Year = {2022}, Eprint = {arXiv:2207.14251}, } ```

e9f13db04477ad4cf60c208ef0df28d1

LeKazuha/distilbert-base-uncased-finetuned-squad

LeKazuha

distilbert

27

3

transformers

0

question-answering

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,874

false

# LeKazuha/distilbert-base-uncased-finetuned-squad 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: 1.1946 - Train End Logits Accuracy: 0.6778 - Train Start Logits Accuracy: 0.6365 - Validation Loss: 1.1272 - Validation End Logits Accuracy: 0.6948 - Validation Start Logits Accuracy: 0.6569 - 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': 11064, '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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.1946 | 0.6778 | 0.6365 | 1.1272 | 0.6948 | 0.6569 | 0 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.7.0 - Datasets 2.1.0 - Tokenizers 0.12.1

fe3d0c1f926f59e7a83a879bb118ecef

carblacac/xlm-roberta-base-finetuned-panx-de

carblacac

xlm-roberta

11

8

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

934

false

# xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. ## 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 ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

5c70fa1ca9b51081388293bfeb9bb084

jonatasgrosman/exp_w2v2r_es_vp-100k_accent_surpeninsular-2_nortepeninsular-8_s578

jonatasgrosman

wav2vec2

10

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['es']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

516

false

# exp_w2v2r_es_vp-100k_accent_surpeninsular-2_nortepeninsular-8_s578 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](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.

f608e8e108a587274def6ebe72823248

google/t5-efficient-base-nl16

google

t5

12

7

transformers

0

text2text-generation

true

apache-2.0

['en']

['c4']

null

0

['deep-narrow']

false

true

6,253

false

# T5-Efficient-BASE-NL16 (Deep-Narrow version) T5-Efficient-BASE-NL16 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-base-nl16** - is of model type **Base** with the following variations: - **nl** is **16** It has **289.02** million parameters and thus requires *ca.* **1156.07 MB** of memory in full precision (*fp32*) or **578.03 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.

76070771c94c3aae7f866ee2677ef229

mathew/layoutlmv2-finetuned-funsd-1024

mathew

layoutlmv2

8

5

transformers

0

token-classification

true

false

cc-by-sa-4.0

null

0

['generated_from_trainer']

true

1,047

false

# layoutlmv2-finetuned-funsd-1024 This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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 - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.8.0+cu101 - Datasets 1.14.0 - Tokenizers 0.10.3

73d2c5a06f1bcc230bf0db5563d2660b

jordyvl/udpos28-sm-all-POS

jordyvl

bert

13

5

transformers

1

token-classification

true

false

apache-2.0

null

['udpos28']

null

0

['generated_from_trainer']

true

1,554

false

# udpos28-sm-all-POS This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the udpos28 dataset. It achieves the following results on the evaluation set: - Loss: 0.1479 - Precision: 0.9587 - Recall: 0.9589 - F1: 0.9588 - Accuracy: 0.9648 ## 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1261 | 1.0 | 4978 | 0.1358 | 0.9513 | 0.9510 | 0.9512 | 0.9581 | | 0.0788 | 2.0 | 9956 | 0.1326 | 0.9578 | 0.9578 | 0.9578 | 0.9642 | | 0.0424 | 3.0 | 14934 | 0.1479 | 0.9587 | 0.9589 | 0.9588 | 0.9648 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1

1d314479acef019c24a168a4208f80b0

jamie613/distilbert-base-uncased-distilled-clinc

jamie613

distilbert

10

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,772

false

# distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1291 - Accuracy: 0.9429 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2296 | 1.0 | 318 | 0.8290 | 0.7571 | | 0.6433 | 2.0 | 636 | 0.4200 | 0.8961 | | 0.3495 | 3.0 | 954 | 0.2493 | 0.9206 | | 0.2254 | 4.0 | 1272 | 0.1835 | 0.9335 | | 0.1726 | 5.0 | 1590 | 0.1576 | 0.9371 | | 0.1467 | 6.0 | 1908 | 0.1442 | 0.9423 | | 0.1318 | 7.0 | 2226 | 0.1360 | 0.9426 | | 0.1229 | 8.0 | 2544 | 0.1323 | 0.9435 | | 0.1185 | 9.0 | 2862 | 0.1299 | 0.9426 | | 0.1151 | 10.0 | 3180 | 0.1291 | 0.9429 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Tokenizers 0.12.1

ecb5623a037fdb494179e5570fee5ef8

CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa

CAMeL-Lab

bert

9

7

transformers

0

token-classification

true

false

apache-2.0

['ar']

null

0

[]

false

true

3,778

false

# CAMeLBERT-MSA POS-MSA Model ## Model description **CAMeLBERT-MSA POS-MSA Model** is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the [CAMeLBERT-MSA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) model. For the fine-tuning, we used the [PATB](https://dl.acm.org/doi/pdf/10.5555/1621804.1621808) dataset . Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT). ## Intended uses You can use the CAMeLBERT-MSA POS-MSA model as part of the transformers pipeline. This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon. #### How to use To use the model with a transformers pipeline: ```python >>> from transformers import pipeline >>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa') >>> text = 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع' >>> pos(text) [{'entity': 'noun', 'score': 0.9999764, 'index': 1, 'word': 'إمارة', 'start': 0, 'end': 5}, {'entity': 'noun_prop', 'score': 0.99991846, 'index': 2, 'word': 'أبوظبي', 'start': 6, 'end': 12}, {'entity': 'pron', 'score': 0.9998356, 'index': 3, 'word': 'هي', 'start': 13, 'end': 15}, {'entity': 'noun', 'score': 0.99368894, 'index': 4, 'word': 'إحدى', 'start': 16, 'end': 20}, {'entity': 'noun', 'score': 0.9999426, 'index': 5, 'word': 'إما', 'start': 21, 'end': 24}, {'entity': 'noun', 'score': 0.9999339, 'index': 6, 'word': '##رات', 'start': 24, 'end': 27}, {'entity': 'noun', 'score': 0.99996775, 'index': 7, 'word': 'دولة', 'start': 28, 'end': 32}, {'entity': 'noun', 'score': 0.99996895, 'index': 8, 'word': 'الإمارات', 'start': 33, 'end': 41}, {'entity': 'adj', 'score': 0.99990183, 'index': 9, 'word': 'العربية', 'start': 42, 'end': 49}, {'entity': 'adj', 'score': 0.9999347, 'index': 10, 'word': 'المتحدة', 'start': 50, 'end': 57}, {'entity': 'noun_num', 'score': 0.99931145, 'index': 11, 'word': 'السبع', 'start': 58, 'end': 63}] ``` *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually. ## Citation ```bibtex @inproceedings{inoue-etal-2021-interplay, title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models", author = "Inoue, Go and Alhafni, Bashar and Baimukan, Nurpeiis and Bouamor, Houda and Habash, Nizar", booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop", month = apr, year = "2021", address = "Kyiv, Ukraine (Online)", publisher = "Association for Computational Linguistics", abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.", } ```

7767d2d7f8cec67bd5fc3dc55e318c2a

pjcordero04/distilbert-base-uncased-finetuned-cola

pjcordero04

distilbert

14

1

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

0

['generated_from_trainer']

true

1,571

false

# distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8348 - Matthews Correlation: 0.5443 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5236 | 1.0 | 535 | 0.5495 | 0.4205 | | 0.3505 | 2.0 | 1070 | 0.5176 | 0.4977 | | 0.2401 | 3.0 | 1605 | 0.5498 | 0.5354 | | 0.1751 | 4.0 | 2140 | 0.7975 | 0.5270 | | 0.1229 | 5.0 | 2675 | 0.8348 | 0.5443 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

cd8d60e94db8d9b83b766c60d38c0e79

Helsinki-NLP/opus-mt-bzs-es

Helsinki-NLP

marian

10

9

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-bzs-es * source languages: bzs * target languages: es * OPUS readme: [bzs-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bzs-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-15.zip](https://object.pouta.csc.fi/OPUS-MT-models/bzs-es/opus-2020-01-15.zip) * test set translations: [opus-2020-01-15.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-es/opus-2020-01-15.test.txt) * test set scores: [opus-2020-01-15.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-es/opus-2020-01-15.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.bzs.es | 28.1 | 0.464 |

262c88179aa6ecd7cd7f872351e2fa60

joeddav/distilbert-base-uncased-go-emotions-student

joeddav

distilbert

8

294,122

transformers

20

text-classification

true

false

mit

['en']

['go_emotions']

null

0

['text-classification', 'pytorch', 'tensorflow']

false

true

903

false

# distilbert-base-uncased-go-emotions-student ## Model Description This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset using [this script](https://github.com/huggingface/transformers/tree/master/examples/research_projects/zero-shot-distillation). It was trained with mixed precision for 10 epochs and otherwise used the default script arguments. ## Intended Usage The model can be used like any other model trained on GoEmotions, but will likely not perform as well as a model trained with full supervision. It is primarily intended as a demo of how an expensive NLI-based zero-shot model can be distilled to a more efficient student, allowing a classifier to be trained with only unlabeled data. Note that although the GoEmotions dataset allow multiple labels per instance, the teacher used single-label classification to create psuedo-labels.

0147b379c458d39b091005e5d74b5323

gokuls/mobilebert_sa_GLUE_Experiment_qnli_256

gokuls

mobilebert

17

5

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,645

false

# mobilebert_sa_GLUE_Experiment_qnli_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6510 - Accuracy: 0.6083 ## 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: 128 - eval_batch_size: 128 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6764 | 1.0 | 819 | 0.6516 | 0.6112 | | 0.6368 | 2.0 | 1638 | 0.6510 | 0.6083 | | 0.6131 | 3.0 | 2457 | 0.6546 | 0.6158 | | 0.5957 | 4.0 | 3276 | 0.6592 | 0.6101 | | 0.5825 | 5.0 | 4095 | 0.6751 | 0.5993 | | 0.5719 | 6.0 | 4914 | 0.6890 | 0.5993 | | 0.5618 | 7.0 | 5733 | 0.7025 | 0.5907 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2

343cc37c796a4f92eba7d81092724112

jmassot/bert-base-uncased-issues-128

jmassot

bert

10

2

transformers

0

fill-mask

true

false

apache-2.0

null

1

0

['generated_from_trainer']

true

1,919

false

# bert-base-uncased-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2512 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1019 | 1.0 | 291 | 1.7019 | | 1.6412 | 2.0 | 582 | 1.4273 | | 1.4844 | 3.0 | 873 | 1.3947 | | 1.4006 | 4.0 | 1164 | 1.3698 | | 1.3382 | 5.0 | 1455 | 1.1941 | | 1.2822 | 6.0 | 1746 | 1.2781 | | 1.2393 | 7.0 | 2037 | 1.2650 | | 1.2009 | 8.0 | 2328 | 1.2082 | | 1.1657 | 9.0 | 2619 | 1.1776 | | 1.1394 | 10.0 | 2910 | 1.2050 | | 1.1276 | 11.0 | 3201 | 1.2067 | | 1.1051 | 12.0 | 3492 | 1.1630 | | 1.0814 | 13.0 | 3783 | 1.2529 | | 1.0757 | 14.0 | 4074 | 1.1699 | | 1.063 | 15.0 | 4365 | 1.1113 | | 1.0637 | 16.0 | 4656 | 1.2512 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.1

04bf077318dbcd59ae025138bbadcb5b

benjamin/roberta-base-wechsel-french

benjamin

roberta

21

13

transformers

1

fill-mask

true

false

mit

['fr']

null

0

[]

false

true

3,652

false

# roberta-base-wechsel-french Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. See the code here: https://github.com/CPJKU/wechsel And the paper here: https://aclanthology.org/2022.naacl-main.293/ ## Performance ### RoBERTa | Model | NLI Score | NER Score | Avg Score | |---|---|---|---| | `roberta-base-wechsel-french` | **82.43** | **90.88** | **86.65** | | `camembert-base` | 80.88 | 90.26 | 85.57 | | Model | NLI Score | NER Score | Avg Score | |---|---|---|---| | `roberta-base-wechsel-german` | **81.79** | **89.72** | **85.76** | | `deepset/gbert-base` | 78.64 | 89.46 | 84.05 | | Model | NLI Score | NER Score | Avg Score | |---|---|---|---| | `roberta-base-wechsel-chinese` | **78.32** | 80.55 | **79.44** | | `bert-base-chinese` | 76.55 | **82.05** | 79.30 | | Model | NLI Score | NER Score | Avg Score | |---|---|---|---| | `roberta-base-wechsel-swahili` | **75.05** | **87.39** | **81.22** | | `xlm-roberta-base` | 69.18 | 87.37 | 78.28 | ### GPT2 | Model | PPL | |---|---| | `gpt2-wechsel-french` | **19.71** | | `gpt2` (retrained from scratch) | 20.47 | | Model | PPL | |---|---| | `gpt2-wechsel-german` | **26.8** | | `gpt2` (retrained from scratch) | 27.63 | | Model | PPL | |---|---| | `gpt2-wechsel-chinese` | **51.97** | | `gpt2` (retrained from scratch) | 52.98 | | Model | PPL | |---|---| | `gpt2-wechsel-swahili` | **10.14** | | `gpt2` (retrained from scratch) | 10.58 | See our paper for details. ## Citation Please cite WECHSEL as ``` @inproceedings{minixhofer-etal-2022-wechsel, title = "{WECHSEL}: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models", author = "Minixhofer, Benjamin and Paischer, Fabian and Rekabsaz, Navid", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.293", pages = "3992--4006", abstract = "Large pretrained language models (LMs) have become the central building block of many NLP applications. Training these models requires ever more computational resources and most of the existing models are trained on English text only. It is exceedingly expensive to train these models in other languages. To alleviate this problem, we introduce a novel method {--} called WECHSEL {--} to efficiently and effectively transfer pretrained LMs to new languages. WECHSEL can be applied to any model which uses subword-based tokenization and learns an embedding for each subword. The tokenizer of the source model (in English) is replaced with a tokenizer in the target language and token embeddings are initialized such that they are semantically similar to the English tokens by utilizing multilingual static word embeddings covering English and the target language. We use WECHSEL to transfer the English RoBERTa and GPT-2 models to four languages (French, German, Chinese and Swahili). We also study the benefits of our method on very low-resource languages. WECHSEL improves over proposed methods for cross-lingual parameter transfer and outperforms models of comparable size trained from scratch with up to 64x less training effort. Our method makes training large language models for new languages more accessible and less damaging to the environment. We make our code and models publicly available.", } ```

3979a09736ffacad494aab242b842571

irenepap/t5-base-asqa-ob

irenepap

t5

10

2

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,151

false

# t5-base-asqa-ob This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the [ASQA](https://huggingface.co/datasets/din0s/asqa) dataset with context (open book). It achieves the following results on the evaluation set: - Loss: 1.7256 - Rougelsum: 13.6463 ## 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: 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:---------:| | 2.5404 | 1.0 | 710 | 1.8160 | 13.0967 | | 2.0048 | 2.0 | 1420 | 1.7752 | 13.2823 | | 1.9116 | 3.0 | 2130 | 1.7574 | 13.3068 | | 1.8722 | 4.0 | 2840 | 1.7469 | 13.3896 | | 1.8298 | 5.0 | 3550 | 1.7395 | 13.4231 | | 1.8397 | 6.0 | 4260 | 1.7347 | 13.5553 | | 1.7575 | 7.0 | 4970 | 1.7303 | 13.5613 | | 1.7433 | 8.0 | 5680 | 1.7266 | 13.5253 | | 1.7502 | 9.0 | 6390 | 1.7254 | 13.5391 | | 1.731 | 10.0 | 7100 | 1.7233 | 13.4958 | | 1.6788 | 11.0 | 7810 | 1.7250 | 13.5977 | | 1.6793 | 12.0 | 8520 | 1.7243 | 13.5956 | | 1.6531 | 13.0 | 9230 | 1.7255 | 13.6186 | | 1.683 | 14.0 | 9940 | 1.7259 | 13.6567 | | 1.6348 | 15.0 | 10650 | 1.7256 | 13.6463 | ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.1+cu102 - Datasets 2.4.0 - Tokenizers 0.12.1

56a446b84e6f5401bc4a5b3686bb140d

lmqg/mt5-small-frquad-qg-ae

lmqg

mt5

40

202

transformers

0

text2text-generation

true

false

cc-by-4.0

['fr']

['lmqg/qg_frquad']

null

0

['question generation', 'answer extraction']

true

7,578

false

# Model Card of `lmqg/mt5-small-frquad-qg-ae` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation and answer extraction jointly on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** fr - **Training data:** [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="fr", model="lmqg/mt5-small-frquad-qg-ae") # model prediction question_answer_pairs = model.generate_qa("Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-frquad-qg-ae") # answer extraction answer = pipe("generate question: Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.") # question generation question = pipe("extract answers: Pourtant, la strophe spensérienne, utilisée cinq fois avant que ne commence le chœur, constitue en soi un vecteur dont les répétitions structurelles, selon Ricks, relèvent du pur lyrisme tout en constituant une menace potentielle. Après les huit sages pentamètres iambiques, l'alexandrin final <hl> permet une pause <hl>, « véritable illusion d'optique » qu'accentuent les nombreuses expressions archaïsantes telles que did swoon, did seem, did go, did receive, did make, qui doublent le prétérit en un temps composé et paraissent à la fois « très précautionneuses et très peu pressées ».") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-frquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 79.9 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_1 | 27.6 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_2 | 16.31 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_3 | 11 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_4 | 7.75 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | METEOR | 17.62 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | MoverScore | 56.44 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | ROUGE_L | 28.06 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-frquad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_frquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 79.7 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | QAAlignedF1Score (MoverScore) | 54.22 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | QAAlignedPrecision (BERTScore) | 77.29 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | QAAlignedPrecision (MoverScore) | 52.84 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | QAAlignedRecall (BERTScore) | 82.36 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | QAAlignedRecall (MoverScore) | 55.76 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-frquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_frquad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:-----------------------------------------------------------------| | AnswerExactMatch | 46.96 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | AnswerF1Score | 67.44 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | BERTScore | 87.84 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_1 | 40.67 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_2 | 35.92 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_3 | 32.1 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_4 | 28.71 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | METEOR | 37.9 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | MoverScore | 76.45 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | ROUGE_L | 43.93 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_frquad - dataset_name: default - input_types: ['paragraph_answer', 'paragraph_sentence'] - output_types: ['question', 'answer'] - prefix_types: ['qg', 'ae'] - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 18 - batch: 64 - lr: 0.0005 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 1 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-frquad-qg-ae/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

23a94029f022bb49e0fee1d968e99b30

IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese

IDEA-CCNL

bert

5

38

transformers

0

fill-mask

true

false

apache-2.0

['zh']

null

0

['classification']

false

true

8,753

false

# IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) ## 简介 Brief Introduction 330M参数的句子表征Topic Classification BERT (TCBert)。 The TCBert with 330M parameters is pre-trained for sentence representation for Chinese topic classification tasks. ## 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 句子表征 | 二郎神 Erlangshen | TCBert (sentence representation) | 330M | Chinese | ## 模型信息 Model Information 为了提高模型在话题分类上句子表征效果，我们收集了大量话题分类数据进行基于prompts的对比学习预训练。 To improve the model performance on sentence representation for the topic classification task, we collected numerous topic classification datasets for contrastive pre-training based on general prompts. ### 下游效果 Performance 我们为每个数据集设计了两个prompt模板。 We customize two prompts templates for each dataset. 第一个prompt模板： For ***prompt template 1***: | Dataset | Prompt template 1 | |---------|:------------------------:| | TNEWS | 下面是一则关于__的新闻： | | CSLDCP | 这一句描述__的内容如下： | | IFLYTEK | 这一句描述__的内容如下： | 第一个prompt模板的微调实验结果： The **fine-tuning** results for prompt template 1: | Model | TNEWS | CLSDCP | IFLYTEK | |-----------------|:------:|:------:|:-------:| | Macbert-base | 55.02 | 57.37 | 51.34 | | Macbert-large | 55.77 | 58.99 | 50.31 | | Erlangshen-1.3B | 57.36 | 62.35 | 53.23 | | TCBert-base110M-Classification-Chinese | 55.57 | 58.60 | 49.63 | | TCBert-large330M-Classification-Chinese | 56.17 | 60.06 | 51.34 | | TCBert-1.3B1.3B-Classification-Chinese | 57.41 | 65.10 | 53.75 | | TCBert-base110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 | | TCBert-large330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 | | TCBert-1.3B1.3B-Sentence-Embedding-Chinese | 57.46 | 65.04 | 53.06 | 第一个prompt模板的句子相似度结果： The **sentence similarity** results for prompt template 1: | | TNEWS | | CSLDCP | | IFLYTEK | | |-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:| | Model | referece | whitening | reference | whitening | reference | whitening | | Macbert-base | 43.53 | 47.16 | 33.50 | 36.53 | 28.99 | 33.85 | | Macbert-large | 46.17 | 49.35 | 37.65 | 39.38 | 32.36 | 35.33 | | Erlangshen-1.3B | 45.72 | 49.60 | 40.56 | 44.26 | 29.33 | 36.48 | | TCBert-base110M-Classification-Chinese | 48.61 | 51.99 | 43.31 | 45.15 | 33.45 | 37.28 | | TCBert-large330M-Classification-Chinese | 50.50 | 52.79 | 52.89 | 53.89 | 34.93 | 38.31 | | TCBert-1.3B1.3B-Classification-Chinese | 50.80 | 51.59 | 51.93 | 54.12 | 33.96 | 38.08 | | TCBert-base110M-Sentence-Embedding-Chinese | 45.82 | 47.06 | 42.91 | 43.87 | 33.28 | 34.76 | | TCBert-large330M-Sentence-Embedding-Chinese | 50.10 | 50.90 | 53.78 | 53.33 | 37.62 | 36.94 | | TCBert-1.3B1.3B-Sentence-Embedding-Chinese | 50.70 | 53.48 | 52.66 | 54.40 | 36.88 | 38.48 | 第二个prompt模板： For ***prompt template 2***: | Dataset | Prompt template 2 | |---------|:------------------------:| | TNEWS | 接下来的新闻，是跟__相关的内容： | | CSLDCP | 接下来的学科，是跟__相关： | | IFLYTEK | 接下来的生活内容，是跟__相关： | 第二个prompt模板的微调结果： The **fine-tuning** results for prompt template 2: | Model | TNEWS | CLSDCP | IFLYTEK | |-----------------|:------:|:------:|:-------:| | Macbert-base | 54.78 | 58.38 | 50.83 | | Macbert-large | 56.77 | 60.22 | 51.63 | | Erlangshen-1.3B | 57.81 | 62.80 | 52.77 | | TCBert-base110M-Classification-Chinese | 54.58 | 59.16 | 49.80 | | TCBert-large330M-Classification-Chinese | 56.22 | 61.23 | 50.77 | | TCBert-1.3B1.3B-Classification-Chinese | 57.41 | 64.82 | 53.34 | | TCBert-base110M-Sentence-Embedding-Chinese | 54.68 | 59.78 | 49.40 | | TCBert-large330M-Sentence-Embedding-Chinese | 55.32 | 62.07 | 51.11 | | TCBert-1.3B1.3B-Sentence-Embedding-Chinese | 56.87 | 65.83 | 52.94 | 第二个prompt模板的句子相似度结果： The **sentence similarity** results for prompt template 2: | | TNEWS | | CSLDCP | | IFLYTEK | | |-----------------|:--------:|:---------:|:---------:|:---------:|:---------:|:---------:| | Model | referece | whitening | reference | whitening | reference | whitening | | Macbert-base | 42.29 | 45.22 | 34.23 | 37.48 | 29.62 | 34.13 | | Macbert-large | 46.22 | 49.60 | 40.11 | 44.26 | 32.36 | 35.16 | | Erlangshen-1.3B | 46.17 | 49.10 | 40.45 | 45.88 | 30.36 | 36.88 | | TCBert-base110M-Classification-Chinese | 48.31 | 51.34 | 43.42 | 45.27 | 33.10 | 36.19 | | TCBert-large330M-Classification-Chinese | 51.19 | 51.69 | 52.55 | 53.28 | 34.31 | 37.45 | | TCBert-1.3B1.3B-Classification-Chinese | 52.14 | 52.39 | 51.71 | 53.89 | 33.62 | 38.14 | | TCBert-base110M-Sentence-Embedding-Chinese | 46.72 | 48.86 | 43.19 | 43.53 | 34.08 | 35.79 | | TCBert-large330M-Sentence-Embedding-Chinese | 50.65 | 51.94 | 53.84 | 53.67 | 37.74 | 36.65 | | TCBert-1.3B1.3B-Sentence-Embedding-Chinese | 50.75 | 54.78 | 51.43 | 54.34 | 36.48 | 38.36 | 更多关于TCBERTs的细节，请参考我们的技术报告。基于新的数据，我们会更新TCBERTs，请留意我们仓库的更新。 For more details about TCBERTs, please refer to our paper. We may regularly update TCBERTs upon new coming data, please keep an eye on the repo! ## 使用 Usage ### 使用示例 Usage Examples ```python # Prompt-based MLM fine-tuning from transformers import BertForMaskedLM, BertTokenizer import torch # Loading models tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese") model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese") # Prepare the data inputs = tokenizer("下面是一则关于[MASK][MASK]的新闻：怎样的房子才算户型方正？", return_tensors="pt") labels = tokenizer("下面是一则关于房产的新闻：怎样的房子才算户型方正？", return_tensors="pt")["input_ids"] labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100) # Output the loss outputs = model(**inputs, labels=labels) loss = outputs.loss ``` ```python # Prompt-based Sentence Similarity # To extract sentence representations. from transformers import BertForMaskedLM, BertTokenizer import torch # Loading models tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese") model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-330M-Sentence-Embedding-Chinese") # Cosine similarity function cos = torch.nn.CosineSimilarity(dim=0, eps=1e-8) with torch.no_grad(): # To extract sentence representations for training data training_input = tokenizer("怎样的房子才算户型方正？", return_tensors="pt") training_output = BertForMaskedLM(**token_text, output_hidden_states=True) training_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0) # To extract sentence representations for training data test_input = tokenizer("下面是一则关于[MASK][MASK]的新闻：股票放量下趺，大资金出逃谁在接盘？", return_tensors="pt") test_output = BertForMaskedLM(**token_text, output_hidden_states=True) test_representation = torch.mean(training_outputs.hidden_states[-1].squeeze(), dim=0) # Calculate similarity scores similarity_score = cos(training_input, test_input) ``` ## 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的[技术报告](https://arxiv.org/abs/2211.11304): If you use for your work, please cite the following paper ``` @article{han2022tcbert, title={TCBERT: A Technical Report for Chinese Topic Classification BERT}, author={Han, Ting and Pan, Kunhao and Chen, Xinyu and Song, Dingjie and Fan, Yuchen and Gao, Xinyu and Gan, Ruyi and Zhang, Jiaxing}, journal={arXiv preprint arXiv:2211.11304}, year={2022} } ``` 如果您在您的工作中使用了我们的模型，可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```

4d99d51fdf01fdfabc3424849eae8d8f

espnet/kan-bayashi_ljspeech_tts_train_joint_conformer_fastspeech2_hifigan_raw-truncated-af8fe0

espnet

null

33

37

espnet

2

text-to-speech

false

cc-by-4.0

['en']

['ljspeech']

null

0

['espnet', 'audio', 'text-to-speech']

false

true

1,896

false

## ESPnet2 TTS pretrained model ### `kan-bayashi/ljspeech_tts_train_joint_conformer_fastspeech2_hifigan_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave` ♻️ Imported from https://zenodo.org/record/5498487/ This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```python # coming soon ``` ### 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 {Enrique Yalta Soplin} 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} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` 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 Enrique Yalta Soplin 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} } ```

0e03f190138ba0f2ace526efd9606934

Gnanesh5/CRS

Gnanesh5

xlnet

6

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

900

false

# CRS This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

09d41b865120444108cd3299a36dbc42

jiobiala24/wav2vec2-base-cv-10000

jiobiala24

wav2vec2

11

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

2,330

false

# wav2vec2-base-cv-10000 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-cv](https://huggingface.co/jiobiala24/wav2vec2-base-cv) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.3393 - Wer: 0.3684 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.4243 | 1.6 | 1000 | 0.7742 | 0.4210 | | 0.3636 | 3.2 | 2000 | 0.8621 | 0.4229 | | 0.2638 | 4.8 | 3000 | 0.9328 | 0.4094 | | 0.2273 | 6.4 | 4000 | 0.9556 | 0.4087 | | 0.187 | 8.0 | 5000 | 0.9093 | 0.4019 | | 0.1593 | 9.6 | 6000 | 0.9842 | 0.4029 | | 0.1362 | 11.2 | 7000 | 1.0651 | 0.4077 | | 0.1125 | 12.8 | 8000 | 1.0550 | 0.3959 | | 0.103 | 14.4 | 9000 | 1.1919 | 0.4002 | | 0.0948 | 16.0 | 10000 | 1.1901 | 0.3983 | | 0.0791 | 17.6 | 11000 | 1.1091 | 0.3860 | | 0.0703 | 19.2 | 12000 | 1.2823 | 0.3904 | | 0.0641 | 20.8 | 13000 | 1.2625 | 0.3817 | | 0.057 | 22.4 | 14000 | 1.2821 | 0.3776 | | 0.0546 | 24.0 | 15000 | 1.2975 | 0.3770 | | 0.0457 | 25.6 | 16000 | 1.2998 | 0.3714 | | 0.0433 | 27.2 | 17000 | 1.3574 | 0.3721 | | 0.0423 | 28.8 | 18000 | 1.3393 | 0.3684 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

e6a71a4aef70e4650c954745534e98a5

jkhan447/language-detection-Bert-base-uncased-additional

jkhan447

bert

13

2

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,039

false

# language-detection-Bert-base-uncased-additional This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2330 - Accuracy: 0.9497 ## 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: 50 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

c2bf7ab792bdaf0467dcd55036f8b29c

timm/efficientformer_l1.snap_dist_in1k

timm

null

4

18

timm

0

image-classification

true

false

apache-2.0

null

['imagenet-1k']

null

0

['image-classification', 'timm']

false

true

3,518

false

# Model card for efficientformer_l1.snap_dist_in1k A EfficientFormer image classification model. Pretrained with distillation on ImageNet-1k. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 12.3 - GMACs: 1.3 - Activations (M): 5.5 - Image size: 224 x 224 - **Original:** https://github.com/snap-research/EfficientFormer - **Papers:** - EfficientFormer: Vision Transformers at MobileNet Speed: https://arxiv.org/abs/2206.01191 - **Dataset:** ImageNet-1k ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('efficientformer_l1.snap_dist_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'efficientformer_l1.snap_dist_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled (ie.e a (batch_size, num_features, H, W) tensor output = model.forward_head(output, pre_logits=True) # output is (batch_size, num_features) tensor ``` ## Model Comparison |model |top1 |top5 |param_count|img_size| |-----------------------------------|------|------|-----------|--------| |efficientformerv2_l.snap_dist_in1k |83.628|96.54 |26.32 |224 | |efficientformer_l7.snap_dist_in1k |83.368|96.534|82.23 |224 | |efficientformer_l3.snap_dist_in1k |82.572|96.24 |31.41 |224 | |efficientformerv2_s2.snap_dist_in1k|82.128|95.902|12.71 |224 | |efficientformer_l1.snap_dist_in1k |80.496|94.984|12.29 |224 | |efficientformerv2_s1.snap_dist_in1k|79.698|94.698|6.19 |224 | |efficientformerv2_s0.snap_dist_in1k|76.026|92.77 |3.6 |224 | ## Citation ```bibtex @article{li2022efficientformer, title={EfficientFormer: Vision Transformers at MobileNet Speed}, author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Ju and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian}, journal={arXiv preprint arXiv:2206.01191}, year={2022} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} } ```

fbc2b3631ad45fc9ac898ab2efa3c3df

Guernika/CoreMLStableDiffusion

Guernika

null

26

0

null

16

null

false

creativeml-openrail-m

null

0

9

4

5

[]

false

true

7,075

false

# Guernika This repository contains [Guernika](https://apps.apple.com/app/id1660407508) compatible models and instructions to convert existing models. While these models and instructions were created for [Guernika](https://apps.apple.com/app/id1660407508), they should work and help with any CoreML based solution. ## <a name="converting-models-to-guernika"></a> Converting Models to Guernika **WARNING:** Xcode is required to convert models: - Make sure you have [Xcode](https://apps.apple.com/app/id497799835) installed. - Once installed run the following commands: ```shell sudo xcode-select --switch /Applications/Xcode.app/Contents/Developer/ sudo xcodebuild -license accept ``` - You should now be ready to start converting models! ### <a name="converting-models-advanced"></a> Easy mode **Step 1:** Download and install [`Guernika Model Converter`](https://huggingface.co/Guernika/CoreMLStableDiffusion/resolve/main/GuernikaModelConverter.dmg). [<img alt="Guernika Model Converter icon" src="https://huggingface.co/Guernika/CoreMLStableDiffusion/resolve/main/GuernikaModelConverter_AppIcon.png" width="256pt" />](https://huggingface.co/Guernika/CoreMLStableDiffusion/resolve/main/GuernikaModelConverter.dmg) **Step 2:** Launch `Guernika Model Converter` from your `Applications` folder, this app may take a few seconds to load. **Step 3:** Once the app has loaded you will be able to select what model you want to convert: - You can input the model identifier (e.g. CompVis/stable-diffusion-v1-4) to download from Hugging Face. You may have to log in to or register for your [Hugging Face account](https://huggingface.co), generate a [User Access Token](https://huggingface.co/settings/tokens) and use this token to set up Hugging Face API access by running `huggingface-cli login` in a Terminal window. - You can select a local model from your machine: `Select local model` - You can select a local .CKPT model from your machine: `Select CKPT` <img alt="Guernika Model Converter interface" src="https://huggingface.co/Guernika/CoreMLStableDiffusion/resolve/main/GuernikaModelConverter_screenshot.png" /> **Step 4:** Once you've chosen the model you want to convert you can choose what modules to convert and/or if you want to chunk the UNet module (recommended for iOS/iPadOS devices). **Step 5:** Once you're happy with your selection click `Convert to Guernika` and wait for the app to complete conversion. **WARNING:** This command may download several GB worth of PyTorch checkpoints from Hugging Face and may take a long time to complete (15-20 minutes on an M1 machine). ### <a name="converting-models-advanced"></a> Advance mode **Step 1:** Create a Python environment and install dependencies: ```bash conda create -n guernika python=3.8 -y conda activate guernika cd /path/to/unziped/scripts/location pip install -e . ``` **Step 2:** Choose what model you want to convert: **Huggin Face model:** Log in to or register for your [Hugging Face account](https://huggingface.co), generate a [User Access Token](https://huggingface.co/settings/tokens) and use this token to set up Hugging Face API access by running `huggingface-cli login` in a Terminal window. Once you know what model you want to convert and have accepted its Terms of Use, run the following command replacing `<model-identifier>` with the desired model's identifier: ```shell python -m python_coreml_stable_diffusion.torch2coreml --model-version <model-identifier> -o <output-directory> --convert-unet --convert-text-encoder --convert-vae-encoder --convert-vae-decoder --convert-safety-checker --bundle-resources-for-guernika --clean-up-mlpackages ``` **Local model:** Run the following command replacing `<model-location>` with the desired model's location path: ```shell python -m python_coreml_stable_diffusion.torch2coreml --model-location <model-location> -o <output-directory> --convert-unet --convert-text-encoder --convert-vae-encoder --convert-vae-decoder --convert-safety-checker --bundle-resources-for-guernika --clean-up-mlpackages ``` **Local CKPT:** Run the following command replacing `<checkpoint-path>` with the desired CKPT's location path: ```shell python -m python_coreml_stable_diffusion.torch2coreml --checkpoint-path <checkpoint-path> -o <output-directory> --convert-unet --convert-text-encoder --convert-vae-encoder --convert-vae-decoder --convert-safety-checker --bundle-resources-for-guernika --clean-up-mlpackages ``` **WARNING:** These commands may download several GB worth of PyTorch checkpoints from Hugging Face. This generally takes 15-20 minutes on an M1 MacBook Pro. Upon successful execution, the neural network models that comprise Stable Diffusion's model will have been converted from PyTorch to Guernika and saved into the specified `<output-directory>`. #### <a name="converting-models--arguments"></a> Notable arguments - `--model-version`: The model version defaults to [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4). Developers may specify other versions that are available on [Hugging Face Hub](https://huggingface.co/models?search=stable-diffusion), e.g. [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) & [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). - `--model-location`: The location of a local model defaults to `None`. - `--checkpoint-path`: The location of a local .CKPT model defaults to `None`. - `--bundle-resources-for-guernika`: Compiles all 4 models and bundles them along with necessary resources for text tokenization into `<output-mlpackages-directory>/Resources` which should provided as input to the Swift package. This flag is not necessary for the diffusers-based Python pipeline. - `--clean-up-mlpackages`: Cleans up created .mlpackages leaving only the compiled model. - `--chunk-unet`: Splits the Unet model in two approximately equal chunks (each with less than 1GB of weights) for mobile-friendly deployment. This is **required** for ANE deployment on iOS and iPadOS. This is not required for macOS. Swift CLI is able to consume both the chunked and regular versions of the Unet model but prioritizes the former. Note that chunked unet is not compatible with the Python pipeline because Python pipeline is intended for macOS only. Chunking is for on-device deployment with Swift only. - `--attention-implementation`: Defaults to `SPLIT_EINSUM` which is the implementation described in [Deploying Transformers on the Apple Neural Engine](https://machinelearning.apple.com/research/neural-engine-transformers). `--attention-implementation ORIGINAL` will switch to an alternative that should be used for non-ANE deployment. Please refer to the [Performance Benchmark](#performance-benchmark) section for further guidance. - `--check-output-correctness`: Compares original PyTorch model's outputs to final Core ML model's outputs. This flag increases RAM consumption significantly so it is recommended only for debugging purposes.

4108f9148d2f9cf25fd474fc487a4c3d

merve/multilabel-v1-replica

merve

bert

13

7

transformers

0

text-classification

true

false

apache-2.0

['tr']

null

0

[]

false

true

1,605

false

**Train-Test Set:** "intent-multilabel-v1-2.zip" **Model:** "dbmdz/bert-base-turkish-cased" ## Tokenizer Params ``` max_length=128 padding="max_length" truncation=True ``` ## Training Params ``` evaluation_strategy = "epoch" save_strategy = "epoch" per_device_train_batch_size = 16 per_device_eval_batch_size = 16 num_train_epochs = 4 load_best_model_at_end = True ``` ## Train-Val Splitting Configuration ``` train_test_split(df_train, test_size=0.1, random_state=1111) ``` ## Training Log ``` Epoch Training Loss Validation Loss 1 No log 0.150276 2 0.195100 0.132906 3 0.107700 0.128633 4 0.107700 0.127795 ``` ## Threshold Optimization - **Best Threshold:** 0.1 - **F1 @ Threshold:** 0.734 ## Eval Results ``` precision recall f1-score support Alakasiz 0.90 0.87 0.89 734 Barinma 0.85 0.80 0.83 207 Elektronik 0.73 0.78 0.75 130 Giysi 0.83 0.66 0.73 94 Kurtarma 0.86 0.79 0.82 362 Lojistik 0.73 0.51 0.60 112 Saglik 0.74 0.74 0.74 108 Su 0.64 0.60 0.62 78 Yagma 0.68 0.55 0.61 31 Yemek 0.80 0.83 0.81 117 micro avg 0.84 0.79 0.81 1973 macro avg 0.78 0.71 0.74 1973 weighted avg 0.84 0.79 0.81 1973 samples avg 0.84 0.82 0.82 1973 ```

018bbd21cd8a5c6e0e4528ed5a13a430

SetFit/MiniLM-L12-H384-uncased__sst2__all-train

SetFit

bert

10

22

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,540

false

# MiniLM-L12-H384-uncased__sst2__all-train This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2632 - Accuracy: 0.9055 ## 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: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4183 | 1.0 | 433 | 0.3456 | 0.8720 | | 0.2714 | 2.0 | 866 | 0.2632 | 0.9055 | | 0.2016 | 3.0 | 1299 | 0.3357 | 0.8990 | | 0.1501 | 4.0 | 1732 | 0.4474 | 0.8863 | | 0.1119 | 5.0 | 2165 | 0.3998 | 0.8979 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3

ccf4f28311f1f78b7ec2dc03840d4013

vishwasgautam/wav2vec2-base-libriSpeech-demo-colab

vishwasgautam

wav2vec2

41

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,345

false

# wav2vec2-base-libriSpeech-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: 0.4627 - Wer: 0.3174 ## 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: 2 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.2349 | 13.51 | 500 | 3.1154 | 1.0 | | 1.5 | 27.03 | 1000 | 0.4627 | 0.3174 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

53f20d202cfa67d04a40df13becf02bb

microsoft/tapex-large-sql-execution

microsoft

bart

8

275

transformers

4

table-question-answering

true

false

mit

['en']

null

0

['tapex', 'table-question-answering']

false

true

3,155

false

# TAPEX (large-sized model) TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraining). ## Model description TAPEX (**Ta**ble **P**re-training via **Ex**ecution) is a conceptually simple and empirically powerful pre-training approach to empower existing models with *table reasoning* skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries. TAPEX is based on the BART architecture, the transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. ## Intended Uses You can use the raw model for simulating neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table. However, the model is mostly meant to be fine-tuned on a supervised dataset. Currently TAPEX can be fine-tuned to tackle table question answering tasks and table fact verification tasks. See the [model hub](https://huggingface.co/models?search=tapex) to look for fine-tuned versions on a task that interests you. ### How to Use Here is how to use this model in transformers: ```python from transformers import TapexTokenizer, BartForConditionalGeneration import pandas as pd tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-sql-execution") model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large-sql-execution") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) # tapex accepts uncased input since it is pre-trained on the uncased corpus query = "select year where city = beijing" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # ['2008'] ``` ### How to Fine-tuning ⚠️ This model checkpoint is **ONLY** used for simulating neural SQL execution (i.e., employ TAPEX to execute a SQL query on a given table), and you **CANNOT** use this model for fine-tuning on downstream tasks. The one that can be used for fine-tuning is at [here](https://huggingface.co/microsoft/tapex-large). > This separation of two models for two kinds of intention is because of a known issue in BART large, and we recommend readers to see [this comment](https://github.com/huggingface/transformers/issues/15559#issuecomment-1062880564) for more details. ### BibTeX entry and citation info ```bibtex @inproceedings{ liu2022tapex, title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor}, author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou}, booktitle={International Conference on Learning Representations}, year={2022}, url={https://openreview.net/forum?id=O50443AsCP} } ```

74ac756cd31bd4018124bf046caa3491

jamesesguerra/distilbart-cnn-12-6-finetuned-1.2.1

jamesesguerra

bart

14

2

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,478

false

# distilbart-cnn-12-6-finetuned-1.2.1 This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9404 - Rouge1: 30.4308 - Rouge2: 13.2594 - Rougel: 25.8203 - Rougelsum: 25.9617 ## 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: 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 2.5124 | 1.0 | 1171 | 2.0753 | 29.493 | 12.3563 | 24.8091 | 24.9317 | | 1.7628 | 2.0 | 2342 | 1.9404 | 30.4308 | 13.2594 | 25.8203 | 25.9617 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.2 - Tokenizers 0.12.1

afcf84a96b3dd90d9f02fa86975b878a

sd-dreambooth-library/marina

sd-dreambooth-library

null

22

4

diffusers

0

null

false

mit

null

2

0

1

0

[]

false

true

1,229

false

### marina on Stable Diffusion via Dreambooth #### model by Eddiefloat This your the Stable Diffusion model fine-tuned the marina concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **marina** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And 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), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/sd-dreambooth-library/marina/resolve/main/concept_images/1.jpeg) ![image 1](https://huggingface.co/sd-dreambooth-library/marina/resolve/main/concept_images/2.jpeg) ![image 2](https://huggingface.co/sd-dreambooth-library/marina/resolve/main/concept_images/3.jpeg) ![image 3](https://huggingface.co/sd-dreambooth-library/marina/resolve/main/concept_images/0.jpeg)

cd087d04b81d151cf82df6b4c8924f68

jonatasgrosman/exp_w2v2t_it_xlsr-53_s237

jonatasgrosman

wav2vec2

10

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['it']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

461

false

# exp_w2v2t_it_xlsr-53_s237 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (it)](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.

c26dc8140018e5f11b10133a07e4ee40

DrishtiSharma/finetuned-ViT-Indian-Food-Classification-v1

DrishtiSharma

vit

17

5

transformers

0

image-classification

true

false

apache-2.0

null

['imagefolder']

null

0

['image-classification', 'generated_from_trainer']

true

2,241

false

# finetuned-ViT-Indian-Food-Classification-v1 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 Human_Action_Recognition dataset. It achieves the following results on the evaluation set: - Loss: 0.2665 - Accuracy: 0.9341 ## 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: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2019 | 0.3 | 100 | 0.9317 | 0.8555 | | 0.6664 | 0.6 | 200 | 0.5432 | 0.8959 | | 0.5096 | 0.9 | 300 | 0.4700 | 0.8990 | | 0.6116 | 1.2 | 400 | 0.4504 | 0.8799 | | 0.4326 | 1.5 | 500 | 0.3856 | 0.8980 | | 0.3349 | 1.8 | 600 | 0.3471 | 0.9129 | | 0.5141 | 2.1 | 700 | 0.3708 | 0.9033 | | 0.32 | 2.4 | 800 | 0.3338 | 0.9139 | | 0.2611 | 2.7 | 900 | 0.3159 | 0.9171 | | 0.1836 | 3.0 | 1000 | 0.2696 | 0.9299 | | 0.2492 | 3.3 | 1100 | 0.2979 | 0.9214 | | 0.1846 | 3.6 | 1200 | 0.3165 | 0.9203 | | 0.1505 | 3.9 | 1300 | 0.2806 | 0.9288 | | 0.1854 | 4.2 | 1400 | 0.2665 | 0.9341 | | 0.124 | 4.5 | 1500 | 0.2695 | 0.9341 | | 0.0719 | 4.8 | 1600 | 0.2668 | 0.9320 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

fac65700dd8fed5d816be3d0b6ef894d

nlp4good/psych-search

nlp4good

bert

10

127

transformers

1

fill-mask

true

false

true

apache-2.0

['en']

['PubMed']

null

0

['mental-health']

false

true

5,241

false

# Psych-Search Psych-Search is a work in progress to bring cutting edge NLP to mental health practitioners. The model detailed here serves as a foundation for traditional classification models as well as NLU models for a Psych-Search application. The goal of the Psych-Search Application is to use a combination of traditional text classification models to expand the scope of the MESH taxonomy with the inclusion of relevant categories for mental health pracitioners designing suicide prevention programs for adolescent communities within the United States, as well as the automatic extraction and standardization of entities such as risk factors and protective factors. Our first expansion efforts to the MESH taxonomy include categories: - Prevention Strategies - Protective Factors We are actively looking for partners on this work and would love to hear from you! Please ping us at [email protected]. ## Model description This model is an extension of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased). Continued pretraining was done using SciBERT as the base model using abstract text only from Pyschology and Psychiatry PubMed research. Training was done on approximately 3.5 million papers for 10 epochs and evaluated on a task similar to BioASQ Task A. ## Intended uses & limitations #### How to use ```python from transformers import AutoTokenizer, AutoModel mname = "nlp4good/psych-search" tokenizer = AutoTokenizer.from_pretrained(mname) model = AutoModel.from_pretrained(mname) ``` ### Limitations and bias This model was trained on all PubMed abstracts categorized under [Psychology and Psychiatry](https://meshb.nlm.nih.gov/treeView). As of March 1, this corresponds to approximately 3.2 million papers that contains abstract text. Of these 3.2 million papers, relevant sparse mental health categories were back translated to increase the representation of certain mental health categories. There are several limitation with this dataset including large discrepancies in the number of papers associated with [Sexual and Gender Minorities](https://meshb.nlm.nih.gov/record/ui?ui=D000072339). The training data consisted of the following breakdown across gender groups: Female | Male | Sexual and Gender Minorities -------|---------|---------- 1,896,301 | 1,945,279 | 4,529 Similar discrepancies are present within [Ethnic Groups](https://meshb.nlm.nih.gov/record/ui?ui=D005006) as defined within the MESH taxonomy: | African Americans | Arabs | Asian Americans | Hispanic Americans | Indians, Central American | Indians, North American | Indians, South American | Indigenous Peoples | Mexican Americans | |-------------------|-------|-----------------|--------------------|---------------------------|-------------------------|-------------------------|--------------------|-------------------| | 31,027 | 2,437 | 5,612 | 18,893 | 124 | 5,657 | 633 | 174 | 3,234 | These discrepancies can have a significant impact on information retrieval systems, downstream machine learning models, and other forms of NLP that leverage these pretrained models. ## Training data This model was trained on all PubMed abstracts categorized under [Psychology and Psychiatry](https://meshb.nlm.nih.gov/treeView). As of March 1, this corresponds to approximately 3.2 million papers that contains abstract text. Of these 3.2 million papers, relevant sparse categories were back translated from english to french and from french to english to increase the representation of sparser mental health categories. This included backtranslating the following papers with the following categories: - Depressive Disorder - Risk Factors - Mental Disorders - Child, Preschool - Mental Health In aggregate, this process added 557,980 additional papers to our training data. ## Training procedure Continued pretraining was done on Psychology and Psychiatry PubMed papers for 10 epochs. Default parameters were used with the exception of gradient accumulation steps which was set at 4, with a per device train batch size of 32. 2 x Nvidia 3090's were used in the development of this model. ## Evaluation results To evaluate the effectiveness of psych-search within the mental health domain, an evaluation task was constructed by finetuning psych-search for a task similar to [BioASQ Task A](http://bioasq.org/). Here we perform large scale biomedical indexing using the MESH taxonomy associated with each paper underneath Psychology and Psychiatry. The evaluation metric is the micro F1 score across all second level descriptors within Psychology and Psychiatry. This corresponds to 38 different MESH categories used during evaluation. bert-base-uncased | SciBERT Scivocab Uncased | Psych-Search -------|---------|---------- 0.7348 | 0.7394 | 0.7415 ## Next Steps If you are interested in continuing to build on this work or have other ideas on how we can build on others work, please let us know! We can be reached at [email protected]. Our goal is to bring state of the art NLP capabilities to underserved areas of research, with mental health being our top priority.

f5cc81f481b321cd2f89fc594cafe839

ScandinavianMrT/gpt2_ONION_prefinetune_4.0

ScandinavianMrT

gpt2

13

6

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,276

false

# gpt2_ONION_prefinetune_4.0 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: 4.6484 ## 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 153 | 4.7368 | | No log | 2.0 | 306 | 4.6732 | | No log | 3.0 | 459 | 4.6527 | | 4.8529 | 4.0 | 612 | 4.6484 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

8b92533afd5a3aa433577a3b938a1fc7

itsGanni/Catalan_language-clustered

itsGanni

distilbert

8

0

transformers

0

question-answering

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,858

false

# itsGanni/Catalan_language-clustered This model is a fine-tuned version of [nandysoham/13-clustered](https://huggingface.co/nandysoham/13-clustered) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7867 - Train End Logits Accuracy: 0.8125 - Train Start Logits Accuracy: 0.7639 - Validation Loss: 0.4452 - Validation End Logits Accuracy: 0.8182 - Validation Start Logits Accuracy: 0.8182 - 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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.7867 | 0.8125 | 0.7639 | 0.4452 | 0.8182 | 0.8182 | 0 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

a8d96b8c8fb100ca55d3b0b8f4dfd0fa

Palak/albert-large-v2_squad

Palak

albert

12

5

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,016

false

# albert-large-v2_squad This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the **squadV1** dataset. - "eval_exact_match": 84.80605487228004 - "eval_f1": 91.80638438705844 - "eval_samples": 10808 ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.14.1 - Pytorch 1.9.0 - Datasets 1.16.1 - Tokenizers 0.10.3

0f4960b83f4ee8d14dbeedfad29b81dd

Helsinki-NLP/opus-mt-fi-de

Helsinki-NLP

marian

10

5,515

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

770

false

### opus-mt-fi-de * source languages: fi * target languages: de * OPUS readme: [fi-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-04.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-de/opus-2019-12-04.zip) * test set translations: [opus-2019-12-04.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-de/opus-2019-12-04.test.txt) * test set scores: [opus-2019-12-04.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-de/opus-2019-12-04.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.fi.de | 45.2 | 0.637 |

2c82aeadb3b79be7f5d06c4a25e86ad0

ubikpt/t5-small-finetuned-cnn-v2

ubikpt

t5

13

10

transformers

0

summarization

true

false

apache-2.0

null

['cnn_dailymail']

null

0

['summarization', 'generated_from_trainer']

true

2,004

false

# t5-small-finetuned-cnn-v2 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.5474 - Rouge1: 35.154 - Rouge2: 18.683 - Rougel: 30.8481 - Rougelsum: 32.9638 ## 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: 5.6e-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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 1.8823 | 1.0 | 35890 | 1.5878 | 34.9676 | 18.4927 | 30.6753 | 32.7702 | | 1.7871 | 2.0 | 71780 | 1.5709 | 34.9205 | 18.5556 | 30.6514 | 32.745 | | 1.7507 | 3.0 | 107670 | 1.5586 | 34.9825 | 18.4964 | 30.6724 | 32.7644 | | 1.7253 | 4.0 | 143560 | 1.5584 | 35.074 | 18.6171 | 30.8007 | 32.9132 | | 1.705 | 5.0 | 179450 | 1.5528 | 35.023 | 18.5787 | 30.7014 | 32.8396 | | 1.6894 | 6.0 | 215340 | 1.5518 | 35.0583 | 18.6754 | 30.791 | 32.8814 | | 1.6776 | 7.0 | 251230 | 1.5468 | 35.2236 | 18.6812 | 30.8944 | 33.0362 | | 1.6687 | 8.0 | 287120 | 1.5474 | 35.154 | 18.683 | 30.8481 | 32.9638 | ### Framework versions - Transformers 4.14.0 - Pytorch 1.5.0 - Datasets 2.3.2 - Tokenizers 0.10.3

797f20ef0fbe4c4c28614ed9d6244967

anmol-chawla/animecharacters-3000

anmol-chawla

null

15

70

diffusers

1

text-to-image

false

creativeml-openrail-m

null

1

0

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

false

true

627

false

### animecharacters_3000 Dreambooth model trained by anmol-chawla 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:

3fcc01f1be2b86e7b645d31fcc19ba71

shuidun/test1

shuidun

gpt2

13

0

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,016

false

# test1 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

5728c5a477266a1ed70e8f1548c58231

sd-concepts-library/toyota-sera

sd-concepts-library

null

15

0

null

0

null

false

mit

null

0

[]

false

true

1,700

false

### Toyota Sera on Stable Diffusion This is the `<toyota-sera>` 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`: ![<toyota-sera> 0](https://huggingface.co/sd-concepts-library/toyota-sera/resolve/main/concept_images/1.jpeg) ![<toyota-sera> 1](https://huggingface.co/sd-concepts-library/toyota-sera/resolve/main/concept_images/8.jpeg) ![<toyota-sera> 2](https://huggingface.co/sd-concepts-library/toyota-sera/resolve/main/concept_images/9.jpeg) ![<toyota-sera> 3](https://huggingface.co/sd-concepts-library/toyota-sera/resolve/main/concept_images/2.jpeg) ![<toyota-sera> 4](https://huggingface.co/sd-concepts-library/toyota-sera/resolve/main/concept_images/0.jpeg) ![<toyota-sera> 5](https://huggingface.co/sd-concepts-library/toyota-sera/resolve/main/concept_images/4.jpeg) ![<toyota-sera> 6](https://huggingface.co/sd-concepts-library/toyota-sera/resolve/main/concept_images/6.jpeg) ![<toyota-sera> 7](https://huggingface.co/sd-concepts-library/toyota-sera/resolve/main/concept_images/5.jpeg) ![<toyota-sera> 8](https://huggingface.co/sd-concepts-library/toyota-sera/resolve/main/concept_images/3.jpeg) ![<toyota-sera> 9](https://huggingface.co/sd-concepts-library/toyota-sera/resolve/main/concept_images/7.jpeg)

1bf5b9f4bcc8a36a37918104510235e6

hammuneer/my_awesome_billsum_model

hammuneer

t5

20

0

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,706

false

# my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.4633 - Rouge1: 0.1168 - Rouge2: 0.0244 - Rougel: 0.0933 - Rougelsum: 0.0933 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 261 | 3.6283 | 0.0812 | 0.0153 | 0.0636 | 0.0637 | 19.0 | | 4.0281 | 2.0 | 522 | 3.5141 | 0.1064 | 0.0206 | 0.0846 | 0.0845 | 19.0 | | 4.0281 | 3.0 | 783 | 3.4741 | 0.1154 | 0.0242 | 0.092 | 0.092 | 19.0 | | 3.7182 | 4.0 | 1044 | 3.4633 | 0.1168 | 0.0244 | 0.0933 | 0.0933 | 19.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

ae6cddced485875bbb97b4c6c0d2935a

desh2608/icefall-asr-spgispeech-pruned-transducer-stateless2

desh2608

null

76

0

k2

0

null

false

mit

['en']

['SPGISpeech']

null

0

['k2', 'icefall']

false

true

1,580

false

# SPGISpeech SPGISpeech consists of 5,000 hours of recorded company earnings calls and their respective transcriptions. The original calls were split into slices ranging from 5 to 15 seconds in length to allow easy training for speech recognition systems. Calls represent a broad cross-section of international business English; SPGISpeech contains approximately 50,000 speakers, one of the largest numbers of any speech corpus, and offers a variety of L1 and L2 English accents. The format of each WAV file is single channel, 16kHz, 16 bit audio. Transcription text represents the output of several stages of manual post-processing. As such, the text contains polished English orthography following a detailed style guide, including proper casing, punctuation, and denormalized non-standard words such as numbers and acronyms, making SPGISpeech suited for training fully formatted end-to-end models. Official reference: O’Neill, P.K., Lavrukhin, V., Majumdar, S., Noroozi, V., Zhang, Y., Kuchaiev, O., Balam, J., Dovzhenko, Y., Freyberg, K., Shulman, M.D., Ginsburg, B., Watanabe, S., & Kucsko, G. (2021). SPGISpeech: 5, 000 hours of transcribed financial audio for fully formatted end-to-end speech recognition. ArXiv, abs/2104.02014. ArXiv link: https://arxiv.org/abs/2104.02014 ## Performance Record | Decoding method | val | |---------------------------|------------| | greedy search | 2.40 | | beam search | 2.24 | | modified beam search | 2.30 | | fast beam search | 2.35 |

b283746ccb3cd56bae069923274a9670

IIIT-L/hing-roberta-finetuned-TRAC-DS

IIIT-L

xlm-roberta

9

1

transformers

0

text-classification

true

false

cc-by-4.0

null

0

['generated_from_trainer']

true

1,472

false

# hing-roberta-finetuned-TRAC-DS This model is a fine-tuned version of [l3cube-pune/hing-roberta](https://huggingface.co/l3cube-pune/hing-roberta) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1610 - Accuracy: 0.7149 - Precision: 0.6921 - Recall: 0.6946 - F1: 0.6932 ## 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: 4.8796394086479776e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 43 - 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 | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.7229 | 2.0 | 1224 | 0.7178 | 0.6928 | 0.6815 | 0.6990 | 0.6780 | | 0.3258 | 3.99 | 2448 | 1.1610 | 0.7149 | 0.6921 | 0.6946 | 0.6932 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.10.1+cu111 - Datasets 2.3.2 - Tokenizers 0.12.1

5a8cd8444190e083ddf279dc07f1f1b1

gokuls/distilbert_sa_GLUE_Experiment_logit_kd_rte_256

gokuls

distilbert

17

2

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,997

false

# distilbert_sa_GLUE_Experiment_logit_kd_rte_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.4233 - Accuracy: 0.4729 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4233 | 1.0 | 10 | 0.4237 | 0.4729 | | 0.4174 | 2.0 | 20 | 0.4245 | 0.4729 | | 0.4184 | 3.0 | 30 | 0.4235 | 0.4729 | | 0.4174 | 4.0 | 40 | 0.4250 | 0.4729 | | 0.4174 | 5.0 | 50 | 0.4241 | 0.4729 | | 0.4169 | 6.0 | 60 | 0.4238 | 0.4729 | | 0.4164 | 7.0 | 70 | 0.4233 | 0.4729 | | 0.4151 | 8.0 | 80 | 0.4233 | 0.4729 | | 0.4109 | 9.0 | 90 | 0.4236 | 0.4729 | | 0.3894 | 10.0 | 100 | 0.4484 | 0.4477 | | 0.3551 | 11.0 | 110 | 0.4821 | 0.4585 | | 0.3256 | 12.0 | 120 | 0.4913 | 0.4477 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

be0d196f34af8d554b0d9c6ba14f9066

juliensimon/distilbert-imdb-mlflow

juliensimon

distilbert

190

34

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,016

false

# distilbert-imdb-mlflow This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the imdb dataset. MLflow logs are included. To visualize them, just clone the repo and run : ``` mlflow ui ``` ## 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: 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: 1 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.10.0 - Datasets 2.3.2 - Tokenizers 0.12.1

5c17e6ce4678345315e6c93a6efb062f

Xuandong/HPD-MiniLM-F128

Xuandong

bert

14

0

transformers

0

feature-extraction

true

false

apache-2.0

null

0

[]

false

true

3,092

false

# HPD-MiniLM-F128 This repository contains the pre-trained models for our paper [Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation](https://arxiv.org/abs/2203.07687). The sentence embedding model contains only 23M parameters and the model size is only 87MB. ## Overview We propose **H**omomorphic **P**rojective **D**istillation (HPD) to learn compressed sentence embeddings. Our method augments a small Transformer encoder model with learnable projection layers to produce compact representations while mimicking a large pre-trained language model to retain the sentence representation quality. ## Details This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search. The teacher model is [`princeton-nlp/sup-simcse-roberta-large`](https://huggingface.co/princeton-nlp/sup-simcse-bert-base-uncased) and the student model is [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased). ## Usage Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` After installing the package, you can simply load our model ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('Xuandong/HPD-MiniLM-F128') ``` Then you can use our model for **encoding sentences into embeddings** ```python sentences = ['He plays guitar.', 'A street vendor is outside.'] sentence_embeddings = model.encode(sentences) for sentence, embedding in zip(sentences, sentence_embeddings): print("Sentence:", sentence) print("Embedding:", embedding) print("") ``` ## Evaluation Results We evaluate our model on semantic textual similarity (STS) tasks. The results are: | STS12 | STS13 | STS14 | STS15 | STS16 | STS-B | SICK-R | Avg. | |-------|-------|-------|-------|-------|--------------|-----------------|-------| | 74.94 | 84.52 | 80.25 | 84.87 | 81.90 | 84.98 | 81.15 | 81.80 | ## Training Please refer to the github repo (https://github.com/XuandongZhao/HPD) for the details about the training. ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 384, 'out_features': 128, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citation Please cite our paper if you use HPD in your work: ```bibtex @article{zhao2022compressing, title={Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation}, author={Zhao, Xuandong and Yu, Zhiguo and Wu, Ming and Li, Lei}, journal={arXiv preprint arXiv:2203.07687}, year={2022} } ```

180e815849356e0912d246d9e3e529d3

LawalAfeez/emotion_detection

LawalAfeez

distilbert

8

3

transformers

0

text-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

963

false

# emotion_detection 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: ## 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 ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

cfdda25c765df23c4c720360ef823c2a

Helsinki-NLP/opus-mt-zne-sv

Helsinki-NLP

marian

10

8

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-zne-sv * source languages: zne * target languages: sv * OPUS readme: [zne-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/zne-sv/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/zne-sv/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-sv/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-sv/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.zne.sv | 25.2 | 0.425 |

5c3758342e1c13d06fa999ef5c9f7743

auro/whisper-cli-small-or

auro

whisper

31

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['or']

['mozilla-foundation/common_voice_11_0']

null

0

['whisper-event', 'generated_from_trainer']

true

1,551

false

# Whisper Small Odia 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 or dataset. It achieves the following results on the evaluation set: - Loss: 0.4245 - Wer: 27.0240 ## 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: 32 - 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: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0021 | 49.0 | 1000 | 0.4245 | 27.0240 | | 0.0001 | 99.0 | 2000 | 0.7338 | 28.1241 | | 0.0 | 149.0 | 3000 | 0.8594 | 28.6601 | | 0.0 | 199.0 | 4000 | 0.9103 | 28.3498 | | 0.0 | 249.0 | 5000 | 0.9329 | 28.2934 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

ad8707774ee16223135c1f475020f673

theojolliffe/bart-paraphrase-v8-e1-rev

theojolliffe

bart

12

1

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,460

false

# bart-paraphrase-v8-e1-rev This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2811 - Rouge1: 59.7973 - Rouge2: 54.3079 - Rougel: 56.5768 - Rougelsum: 59.4379 - Gen Len: 19.8108 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.1123 | 1.0 | 28370 | 0.2811 | 59.7973 | 54.3079 | 56.5768 | 59.4379 | 19.8108 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

f78e2ff1fe5847115f011b570f27f2b0

vaibhav9/roberta-qa

vaibhav9

roberta

15

3

transformers

0

question-answering

true

false

cc-by-4.0

null

0

['generated_from_trainer']

true

1,262

false

# roberta-qa This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7423 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 0.7278 | 1.0 | 3787 | 0.6452 | | 0.4734 | 2.0 | 7574 | 0.6819 | | 0.3543 | 3.0 | 11361 | 0.7423 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.11.0+cu102 - Datasets 2.8.0 - Tokenizers 0.13.2

df65ec4735cabce49ddcee7744dcfb4e

digio/Twitter4SSE

digio

roberta

9

65

transformers

1

sentence-similarity

true

false

apache-2.0

['en']

null

0

['Pytorch', 'Sentence Transformers', 'Transformers']

false

true

1,828

false

# Twitter4SSE This model maps texts to 768 dimensional dense embeddings that encode semantic similarity. It was trained with Multiple Negatives Ranking Loss (MNRL) on a Twitter dataset. It was initialized from [BERTweet](https://huggingface.co/vinai/bertweet-base) and trained with [Sentence-transformers](https://www.sbert.net/). ## Usage The model is easier to use with sentence-trainsformers library ``` pip install -U sentence-transformers ``` ``` from sentence_transformers import SentenceTransformer sentences = ["This is the first tweet", "This is the second tweet"] model = SentenceTransformer('digio/Twitter4SSE') embeddings = model.encode(sentences) print(embeddings) ``` Without sentence-transfomer library, please refer to [this repository](https://huggingface.co/sentence-transformers) for detailed instructions on how to use Sentence Transformers on Huggingface. ## Citing & Authors The official paper [Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings](https://arxiv.org/abs/2110.02030) will be presented at EMNLP 2021. Further details will be available soon. ``` @inproceedings{di-giovanni-brambilla-2021-exploiting, title = "Exploiting {T}witter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings", author = "Di Giovanni, Marco and Brambilla, Marco", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.780", pages = "9902--9910", } ``` The official code is available on [GitHub](https://github.com/marco-digio/Twitter4SSE)

596a5d6eb14946219b8e7595ba9bae03

roshan151/Model_output

roshan151

bert

8

2

transformers

0

fill-mask

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,681

false

# roshan151/Model_output This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.9849 - Validation Loss: 2.8623 - Epoch: 4 ## 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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -82, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 100, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.1673 | 2.8445 | 0 | | 2.9770 | 2.8557 | 1 | | 3.0018 | 2.8612 | 2 | | 2.9625 | 2.8496 | 3 | | 2.9849 | 2.8623 | 4 | ### Framework versions - Transformers 4.21.1 - TensorFlow 2.8.2 - Datasets 2.4.0 - Tokenizers 0.12.1

efc24a288ce7b6b06cdbd62b58d37125

MultiBertGunjanPatrick/multiberts-seed-0-1100k

MultiBertGunjanPatrick

bert

7

2

transformers

0

null

true

false

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

['exbert', 'multiberts', 'multiberts-seed-0']

false

true

6,487

false

# MultiBERTs Seed 0 Checkpoint 1100k (uncased) Seed 0 intermediate checkpoint 1100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-0](https://hf.co/multberts-seed-0). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). ## Model description MultiBERTs models are transformers model pretrained on a large corpus of English 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 labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the MultiBERTs model as inputs. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-0-1100k') model = BertModel.from_pretrained("multiberts-seed-0-1100k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular checkpoint, please try out this checkpoint with the snippet present in the [Limitation and bias section](https://huggingface.co/bert-base-uncased#limitations-and-bias) of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) checkpoint. ## Training data The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size of 256. The sequence length was set to 512 throughout. The optimizer used is Adam with a learning rate of 1e-4, \$\beta_{1} = 0.9\$ and \$\beta_{2} = 0.999\$, a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-16163, author = {Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, title = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis}, journal = {CoRR}, volume = {abs/2106.16163}, year = {2021}, url = {https://arxiv.org/abs/2106.16163}, eprinttype = {arXiv}, eprint = {2106.16163}, timestamp = {Mon, 05 Jul 2021 15:15:50 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=multiberts"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

6d1ba38ae19957f34dbf8dd32862192d

pig4431/Sentiment140_XLNET_5E

pig4431

xlnet

10

13

transformers

0

text-classification

true

false

mit

null

['sentiment140']

null

0

['generated_from_trainer']

true

1,877

false

# Sentiment140_XLNET_5E This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the sentiment140 dataset. It achieves the following results on the evaluation set: - Loss: 0.3797 - Accuracy: 0.84 ## 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: 8 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6687 | 0.08 | 50 | 0.5194 | 0.76 | | 0.5754 | 0.16 | 100 | 0.4500 | 0.7867 | | 0.5338 | 0.24 | 150 | 0.3725 | 0.8333 | | 0.5065 | 0.32 | 200 | 0.4093 | 0.8133 | | 0.4552 | 0.4 | 250 | 0.3910 | 0.8267 | | 0.5352 | 0.48 | 300 | 0.3888 | 0.82 | | 0.415 | 0.56 | 350 | 0.3887 | 0.8267 | | 0.4716 | 0.64 | 400 | 0.3888 | 0.84 | | 0.4565 | 0.72 | 450 | 0.3619 | 0.84 | | 0.4447 | 0.8 | 500 | 0.3758 | 0.8333 | | 0.4407 | 0.88 | 550 | 0.3664 | 0.8133 | | 0.46 | 0.96 | 600 | 0.3797 | 0.84 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.1

64c51aaace60e3618b1b01021cf432ca

DL82/denlip82

DL82

null

47

1

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image']

false

true

3,178

false

### denlip82 Dreambooth model trained by DL82 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You 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). Don't forget to use the concept prompts! Sample pictures of: denlip82 (use that on your prompt) ![denlip82 0](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%281%29.jpg)![denlip82 1](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%282%29.jpg)![denlip82 2](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%283%29.jpg)![denlip82 3](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%284%29.jpg)![denlip82 4](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%285%29.jpg)![denlip82 5](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%286%29.jpg)![denlip82 6](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%287%29.jpg)![denlip82 7](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%288%29.jpg)![denlip82 8](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%289%29.jpg)![denlip82 9](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2810%29.jpg)![denlip82 10](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2811%29.jpg)![denlip82 11](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2812%29.jpg)![denlip82 12](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2813%29.jpg)![denlip82 13](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2814%29.jpg)![denlip82 14](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2815%29.jpg)![denlip82 15](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2816%29.jpg)![denlip82 16](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2817%29.jpg)![denlip82 17](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2818%29.jpg)![denlip82 18](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2819%29.jpg)![denlip82 19](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2820%29.jpg)![denlip82 20](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2821%29.jpg)![denlip82 21](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2822%29.jpg)![denlip82 22](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2823%29.jpg)![denlip82 23](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2824%29.jpg)![denlip82 24](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2825%29.jpg)![denlip82 25](https://huggingface.co/DL82/denlip82/resolve/main/concept_images/denlip82_%2826%29.jpg)

51e718f13a035b9134982385ba837a60

trnt/twitter_emotions

trnt

bert

12

9

transformers

1

text-classification

true

false

apache-2.0

null

['emotion']

null

1

0

['generated_from_trainer']

true

1,405

false

# twitter_emotions This model is a fine-tuned version of [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1647 - Accuracy: 0.9375 ## 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: 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2486 | 1.0 | 2000 | 0.2115 | 0.931 | | 0.135 | 2.0 | 4000 | 0.1725 | 0.936 | | 0.1041 | 3.0 | 6000 | 0.1647 | 0.9375 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

17e2a1fc47e5f5f440945e67c33ded98

sgugger/marian-finetuned-kde4-en-to-fr

sgugger

marian

16

5

transformers

0

translation

true

false

apache-2.0

null

['kde4']

null

0

['translation', 'generated_from_trainer']

true

1,104

false

# marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8666 - Bleu: 53.2503 - Gen Len: 14.7005 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.8.1+cu111 - Datasets 1.12.2.dev0 - Tokenizers 0.10.3

b9162b00ff26acbd78162e04c9c2816f

nielsr/segformer-finetuned-sidewalk-10k-steps

nielsr

segformer

23

3

transformers

1

image-segmentation

true

false

apache-2.0

null

0

['image-segmentation', 'vision', 'generated_from_trainer']

true

185,646

false

# segformer-finetuned-sidewalk-50-epochs This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.6350 - Mean Iou: 0.3022 - Mean Accuracy: 0.3724 - Overall Accuracy: 0.8117 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.8240 - Accuracy Flat-sidewalk: 0.8308 - Accuracy Flat-crosswalk: 0.7789 - Accuracy Flat-cyclinglane: 0.9052 - Accuracy Flat-parkingdriveway: 0.3152 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.4703 - Accuracy Human-person: 0.6444 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.9424 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: 0.0 - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.7116 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.8716 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.4736 - Accuracy Construction-fenceguardrail: 0.5408 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0048 - Accuracy Object-pole: 0.4202 - Accuracy Object-trafficsign: 0.0754 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9437 - Accuracy Nature-terrain: 0.8196 - Accuracy Sky: 0.9525 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.1041 - Accuracy Void-static: 0.2872 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.7413 - Iou Flat-sidewalk: 0.7520 - Iou Flat-crosswalk: 0.7629 - Iou Flat-cyclinglane: 0.4453 - Iou Flat-parkingdriveway: 0.2976 - Iou Flat-railtrack: nan - Iou Flat-curb: 0.3701 - Iou Human-person: 0.4953 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.7962 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.4152 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.6712 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.3749 - Iou Construction-fenceguardrail: 0.4613 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.0048 - Iou Object-pole: 0.2337 - Iou Object-trafficsign: 0.0753 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.8324 - Iou Nature-terrain: 0.7277 - Iou Sky: 0.9234 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0913 - Iou Void-static: 0.1997 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-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: polynomial - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear | 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| 2.4745 | 1.85 | 100 | 1.7861 | 0.1056 | 0.1555 | 0.6397 | nan | 0.2287 | 0.9278 | 0.0 | 0.1406 | 0.0032 | nan | 0.0 | 0.0 | 0.0 | 0.7757 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8764 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8387 | 0.8794 | 0.3057 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.1931 | 0.6432 | 0.0 | 0.1380 | 0.0031 | nan | 0.0 | 0.0 | 0.0 | 0.5312 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4482 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6323 | 0.4860 | 0.3053 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.7294 | 3.7 | 200 | 1.3129 | 0.1517 | 0.1996 | 0.7410 | nan | 0.7928 | 0.8830 | 0.0 | 0.6053 | 0.0089 | nan | 0.0 | 0.0 | 0.0 | 0.7837 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8530 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9138 | 0.7742 | 0.7740 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5519 | 0.7788 | 0.0 | 0.5131 | 0.0088 | nan | 0.0 | 0.0 | 0.0 | 0.5804 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5005 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6747 | 0.5247 | 0.7209 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4479 | 5.56 | 300 | 1.1309 | 0.1608 | 0.2113 | 0.7588 | nan | 0.7973 | 0.9008 | 0.0 | 0.7721 | 0.0269 | nan | 0.0 | 0.0 | 0.0 | 0.8744 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8581 | 0.0 | 0.0007 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8622 | 0.8707 | 0.7985 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5861 | 0.7816 | 0.0 | 0.5877 | 0.0261 | nan | 0.0 | 0.0 | 0.0 | 0.6119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5582 | 0.0 | 0.0007 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7024 | 0.5206 | 0.7706 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.2348 | 7.41 | 400 | 0.9644 | 0.1707 | 0.2170 | 0.7736 | nan | 0.8125 | 0.9218 | 0.0 | 0.7596 | 0.1081 | nan | 0.0000 | 0.0 | 0.0 | 0.9080 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8280 | 0.0 | 0.0334 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8856 | 0.8260 | 0.8612 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6003 | 0.7937 | 0.0 | 0.6538 | 0.0997 | nan | 0.0000 | 0.0 | 0.0 | 0.6189 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5731 | 0.0 | 0.0330 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7147 | 0.5601 | 0.8139 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.0762 | 9.26 | 500 | 0.8819 | 0.1722 | 0.2159 | 0.7748 | nan | 0.7512 | 0.9353 | 0.0 | 0.7565 | 0.1204 | nan | 0.0016 | 0.0 | 0.0 | 0.9115 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8689 | 0.0 | 0.0565 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9098 | 0.7664 | 0.8303 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5993 | 0.7850 | 0.0 | 0.6536 | 0.1052 | nan | 0.0016 | 0.0 | 0.0 | 0.6377 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5767 | 0.0 | 0.0547 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7285 | 0.5709 | 0.7984 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.9933 | 11.11 | 600 | 0.8347 | 0.1814 | 0.2263 | 0.7822 | nan | 0.8064 | 0.9111 | 0.0 | 0.7880 | 0.1443 | nan | 0.0436 | 0.0 | 0.0 | 0.8944 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8970 | 0.0 | 0.1914 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9053 | 0.8080 | 0.8526 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6088 | 0.8045 | 0.0 | 0.6845 | 0.1255 | nan | 0.0419 | 0.0 | 0.0 | 0.6594 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5548 | 0.0 | 0.1585 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7440 | 0.6068 | 0.8176 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.9424 | 12.96 | 700 | 0.8428 | 0.1824 | 0.2271 | 0.7704 | nan | 0.6767 | 0.9270 | 0.0475 | 0.7655 | 0.1322 | nan | 0.2020 | 0.0189 | 0.0 | 0.8410 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9205 | 0.0 | 0.2568 | 0.0 | 0.0 | nan | 0.0 | 0.0023 | 0.0 | 0.0 | 0.8994 | 0.7347 | 0.8413 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5838 | 0.7914 | 0.0475 | 0.6091 | 0.1095 | nan | 0.1597 | 0.0185 | 0.0 | 0.6706 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5131 | 0.0 | 0.1872 | 0.0 | 0.0 | nan | 0.0 | 0.0023 | 0.0 | 0.0 | 0.7525 | 0.5837 | 0.8077 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.8673 | 14.81 | 800 | 0.7934 | 0.2089 | 0.2509 | 0.7818 | nan | 0.6854 | 0.9394 | 0.7072 | 0.7240 | 0.1504 | nan | 0.2013 | 0.0186 | 0.0 | 0.9071 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9037 | 0.0 | 0.3110 | 0.0 | 0.0 | nan | 0.0 | 0.0108 | 0.0 | 0.0 | 0.8990 | 0.7171 | 0.8513 | 0.0 | 0.0 | 0.0013 | 0.0 | nan | 0.5914 | 0.7755 | 0.6900 | 0.6673 | 0.1340 | nan | 0.1542 | 0.0183 | 0.0 | 0.6792 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5639 | 0.0 | 0.2172 | 0.0 | 0.0 | nan | 0.0 | 0.0100 | 0.0 | 0.0 | 0.7615 | 0.6014 | 0.8192 | 0.0 | 0.0 | 0.0013 | 0.0 | | 0.8126 | 16.67 | 900 | 0.7484 | 0.2268 | 0.2784 | 0.7940 | nan | 0.6791 | 0.9397 | 0.7812 | 0.8009 | 0.1532 | nan | 0.3244 | 0.2962 | 0.0 | 0.9018 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8567 | 0.0 | 0.4772 | 0.0002 | 0.0 | nan | 0.0 | 0.0834 | 0.0 | 0.0 | 0.8992 | 0.8280 | 0.8837 | 0.0 | 0.0 | 0.0032 | 0.0 | nan | 0.6303 | 0.7968 | 0.7079 | 0.6095 | 0.1396 | nan | 0.2196 | 0.2638 | 0.0 | 0.7100 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6016 | 0.0 | 0.2860 | 0.0002 | 0.0 | nan | 0.0 | 0.0570 | 0.0 | 0.0 | 0.7678 | 0.6211 | 0.8416 | 0.0 | 0.0 | 0.0032 | 0.0 | | 0.7989 | 18.52 | 1000 | 0.7241 | 0.2279 | 0.2803 | 0.8018 | nan | 0.7224 | 0.9402 | 0.7875 | 0.8234 | 0.1793 | nan | 0.3763 | 0.1974 | 0.0 | 0.9259 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8911 | 0.0 | 0.3994 | 0.0029 | 0.0 | nan | 0.0 | 0.0758 | 0.0 | 0.0 | 0.8619 | 0.8774 | 0.8854 | 0.0 | 0.0 | 0.0225 | 0.0 | nan | 0.6579 | 0.8292 | 0.7198 | 0.6924 | 0.1660 | nan | 0.2392 | 0.1794 | 0.0 | 0.6748 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5766 | 0.0 | 0.2654 | 0.0029 | 0.0 | nan | 0.0 | 0.0636 | 0.0 | 0.0 | 0.7582 | 0.5994 | 0.8455 | 0.0 | 0.0 | 0.0220 | 0.0 | | 0.7429 | 20.37 | 1100 | 0.7321 | 0.2276 | 0.2862 | 0.7876 | nan | 0.8321 | 0.8491 | 0.7958 | 0.8572 | 0.2216 | nan | 0.3030 | 0.2864 | 0.0 | 0.9456 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8668 | 0.0 | 0.3757 | 0.0040 | 0.0 | nan | 0.0 | 0.1140 | 0.0 | 0.0 | 0.8839 | 0.8499 | 0.9228 | 0.0 | 0.0 | 0.0505 | 0.0 | nan | 0.6678 | 0.7848 | 0.7342 | 0.5048 | 0.1995 | nan | 0.2316 | 0.2463 | 0.0 | 0.6379 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5916 | 0.0 | 0.2668 | 0.0040 | 0.0 | nan | 0.0 | 0.0820 | 0.0 | 0.0 | 0.7827 | 0.6428 | 0.8583 | 0.0 | 0.0 | 0.0465 | 0.0 | | 0.7131 | 22.22 | 1200 | 0.7231 | 0.2377 | 0.2995 | 0.7870 | nan | 0.8306 | 0.8458 | 0.7952 | 0.8505 | 0.2218 | nan | 0.3614 | 0.5001 | 0.0 | 0.9504 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7598 | 0.0 | 0.5317 | 0.0405 | 0.0 | nan | 0.0 | 0.1381 | 0.0 | 0.0 | 0.9284 | 0.7938 | 0.9110 | 0.0 | 0.0 | 0.1262 | 0.0 | nan | 0.7038 | 0.7740 | 0.7537 | 0.4538 | 0.1996 | nan | 0.2521 | 0.3853 | 0.0 | 0.6576 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6157 | 0.0 | 0.3046 | 0.0404 | 0.0 | nan | 0.0 | 0.0921 | 0.0 | 0.0 | 0.7846 | 0.6383 | 0.8588 | 0.0 | 0.0 | 0.0911 | 0.0 | | 0.6919 | 24.07 | 1300 | 0.6775 | 0.2361 | 0.2885 | 0.8013 | nan | 0.7728 | 0.9073 | 0.8010 | 0.8366 | 0.1547 | nan | 0.3070 | 0.3428 | 0.0 | 0.9272 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8568 | 0.0 | 0.5009 | 0.0736 | 0.0 | nan | 0.0 | 0.0975 | 0.0 | 0.0 | 0.9297 | 0.7567 | 0.8978 | 0.0 | 0.0 | 0.0682 | 0.0 | nan | 0.6564 | 0.7929 | 0.6932 | 0.6396 | 0.1438 | nan | 0.2385 | 0.2888 | 0.0 | 0.6807 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6085 | 0.0 | 0.3114 | 0.0729 | 0.0 | nan | 0.0 | 0.0803 | 0.0 | 0.0 | 0.7857 | 0.6403 | 0.8601 | 0.0 | 0.0 | 0.0610 | 0.0 | | 0.68 | 25.93 | 1400 | 0.6321 | 0.2575 | 0.3109 | 0.8181 | nan | 0.7851 | 0.9362 | 0.8041 | 0.8438 | 0.1694 | nan | 0.3956 | 0.5626 | 0.0 | 0.9306 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8313 | 0.0 | 0.5073 | 0.2728 | 0.0 | nan | 0.0 | 0.1741 | 0.0 | 0.0 | 0.9221 | 0.7899 | 0.9071 | 0.0 | 0.0 | 0.1157 | 0.0 | nan | 0.6781 | 0.8336 | 0.7386 | 0.7047 | 0.1564 | nan | 0.2789 | 0.4291 | 0.0 | 0.6934 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6062 | 0.0 | 0.3305 | 0.2579 | 0.0 | nan | 0.0 | 0.1228 | 0.0 | 0.0 | 0.7952 | 0.6651 | 0.8631 | 0.0 | 0.0 | 0.0865 | 0.0 | | 0.6644 | 27.78 | 1500 | 0.6568 | 0.2555 | 0.3132 | 0.8074 | nan | 0.7687 | 0.9014 | 0.7631 | 0.8302 | 0.1869 | nan | 0.4841 | 0.4880 | 0.0 | 0.9294 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.8139 | 0.0 | 0.5482 | 0.3042 | 0.0 | nan | 0.0 | 0.1974 | 0.0 | 0.0 | 0.9225 | 0.8543 | 0.9042 | 0.0 | 0.0 | 0.1259 | 0.0 | nan | 0.6723 | 0.8030 | 0.7443 | 0.5873 | 0.1742 | nan | 0.3013 | 0.3813 | 0.0 | 0.7117 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.6159 | 0.0 | 0.3289 | 0.2810 | 0.0 | nan | 0.0 | 0.1295 | 0.0 | 0.0 | 0.8015 | 0.6848 | 0.8665 | 0.0 | 0.0 | 0.0931 | 0.0 | | 0.6153 | 29.63 | 1600 | 0.6157 | 0.2586 | 0.3131 | 0.8188 | nan | 0.8000 | 0.9242 | 0.7980 | 0.8445 | 0.1758 | nan | 0.4143 | 0.6256 | 0.0 | 0.9155 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0007 | 0.0 | 0.0 | 0.8792 | 0.0 | 0.4465 | 0.2182 | 0.0 | nan | 0.0 | 0.1970 | 0.0 | 0.0 | 0.9111 | 0.8171 | 0.9368 | 0.0 | 0.0 | 0.1136 | 0.0 | nan | 0.6844 | 0.8212 | 0.7565 | 0.6537 | 0.1636 | nan | 0.2857 | 0.4354 | 0.0 | 0.7222 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0007 | 0.0 | 0.0 | 0.6274 | 0.0 | 0.3217 | 0.2147 | 0.0 | nan | 0.0 | 0.1313 | 0.0 | 0.0 | 0.8082 | 0.6809 | 0.8737 | 0.0 | 0.0 | 0.0926 | 0.0 | | 0.6154 | 31.48 | 1700 | 0.6397 | 0.2621 | 0.3204 | 0.8117 | nan | 0.8357 | 0.8840 | 0.7908 | 0.8465 | 0.2590 | nan | 0.4050 | 0.5401 | 0.0 | 0.9393 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0105 | 0.0 | 0.0 | 0.8169 | 0.0 | 0.4733 | 0.3188 | 0.0 | nan | 0.0 | 0.2505 | 0.0 | 0.0 | 0.9181 | 0.8473 | 0.9287 | 0.0 | 0.0 | 0.1890 | 0.0 | nan | 0.6774 | 0.8042 | 0.7524 | 0.5662 | 0.2300 | nan | 0.2971 | 0.4050 | 0.0 | 0.6970 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0105 | 0.0 | 0.0 | 0.6489 | 0.0 | 0.3454 | 0.3058 | 0.0 | nan | 0.0 | 0.1441 | 0.0 | 0.0 | 0.8074 | 0.6913 | 0.8820 | 0.0 | 0.0 | 0.1224 | 0.0 | | 0.6305 | 33.33 | 1800 | 0.6131 | 0.2641 | 0.3212 | 0.8194 | nan | 0.8171 | 0.8984 | 0.8212 | 0.8462 | 0.2582 | nan | 0.5051 | 0.5504 | 0.0 | 0.9421 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0221 | 0.0 | 0.0 | 0.8777 | 0.0 | 0.3528 | 0.3169 | 0.0 | nan | 0.0 | 0.2249 | 0.0 | 0.0 | 0.9203 | 0.8499 | 0.9175 | 0.0 | 0.0 | 0.1587 | 0.0 | nan | 0.7209 | 0.8195 | 0.7546 | 0.6166 | 0.2267 | nan | 0.3408 | 0.4000 | 0.0 | 0.6906 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0221 | 0.0 | 0.0 | 0.6055 | 0.0 | 0.2823 | 0.3044 | 0.0 | nan | 0.0 | 0.1545 | 0.0 | 0.0 | 0.8124 | 0.6994 | 0.8799 | 0.0 | 0.0 | 0.1204 | 0.0 | | 0.6083 | 35.19 | 1900 | 0.6224 | 0.2646 | 0.3182 | 0.8171 | nan | 0.7473 | 0.9297 | 0.7826 | 0.8269 | 0.2162 | nan | 0.4556 | 0.4982 | 0.0 | 0.9169 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0865 | 0.0 | 0.0 | 0.9031 | 0.0 | 0.3618 | 0.3583 | 0.0 | nan | 0.0 | 0.2603 | 0.0 | 0.0 | 0.8966 | 0.8828 | 0.9016 | 0.0 | 0.0 | 0.1587 | 0.0 | nan | 0.6824 | 0.8210 | 0.7645 | 0.5950 | 0.2019 | nan | 0.3166 | 0.3895 | 0.0 | 0.7307 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0853 | 0.0 | 0.0 | 0.6063 | 0.0 | 0.2860 | 0.3200 | 0.0 | nan | 0.0 | 0.1659 | 0.0 | 0.0 | 0.8188 | 0.7017 | 0.8695 | 0.0 | 0.0 | 0.1113 | 0.0 | | 0.5847 | 37.04 | 2000 | 0.5906 | 0.2713 | 0.3209 | 0.8281 | nan | 0.7374 | 0.9612 | 0.7764 | 0.8195 | 0.2033 | nan | 0.4219 | 0.4950 | 0.0 | 0.9339 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0960 | 0.0 | 0.0 | 0.8434 | 0.0 | 0.4552 | 0.4437 | 0.0 | nan | 0.0 | 0.2250 | 0.0 | 0.0 | 0.9315 | 0.8612 | 0.9071 | 0.0 | 0.0 | 0.1567 | 0.0 | nan | 0.6883 | 0.8311 | 0.7525 | 0.6838 | 0.1851 | nan | 0.3228 | 0.3780 | 0.0 | 0.7236 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0944 | 0.0 | 0.0 | 0.6338 | 0.0 | 0.3408 | 0.3853 | 0.0 | nan | 0.0 | 0.1586 | 0.0 | 0.0 | 0.8104 | 0.6978 | 0.8800 | 0.0 | 0.0 | 0.1162 | 0.0 | | 0.5764 | 38.89 | 2100 | 0.6088 | 0.2752 | 0.3225 | 0.8255 | nan | 0.7525 | 0.9472 | 0.7709 | 0.8441 | 0.2134 | nan | 0.3932 | 0.5383 | 0.0 | 0.9030 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3470 | 0.0 | 0.0 | 0.9195 | 0.0 | 0.3310 | 0.3215 | 0.0 | nan | 0.0 | 0.2234 | 0.0 | 0.0 | 0.9289 | 0.7964 | 0.9280 | 0.0 | 0.0 | 0.1604 | 0.0 | nan | 0.6993 | 0.8276 | 0.7546 | 0.7234 | 0.1997 | nan | 0.3005 | 0.4222 | 0.0 | 0.7348 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3123 | 0.0 | 0.0 | 0.5918 | 0.0 | 0.2787 | 0.3037 | 0.0 | nan | 0.0 | 0.1585 | 0.0 | 0.0 | 0.8124 | 0.6781 | 0.8844 | 0.0 | 0.0 | 0.1247 | 0.0 | | 0.5787 | 40.74 | 2200 | 0.5706 | 0.2824 | 0.3351 | 0.8347 | nan | 0.8178 | 0.9369 | 0.8003 | 0.8511 | 0.2352 | nan | 0.4838 | 0.5417 | 0.0 | 0.9025 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3689 | 0.0 | 0.0 | 0.8739 | 0.0 | 0.4493 | 0.4040 | 0.0 | nan | 0.0 | 0.2524 | 0.0 | 0.0 | 0.9422 | 0.8182 | 0.9183 | 0.0 | 0.0 | 0.1276 | 0.0 | nan | 0.7292 | 0.8432 | 0.7669 | 0.6897 | 0.2161 | nan | 0.3484 | 0.4230 | 0.0 | 0.7519 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3045 | 0.0 | 0.0 | 0.6407 | 0.0 | 0.3373 | 0.3491 | 0.0 | nan | 0.0 | 0.1557 | 0.0 | 0.0 | 0.8080 | 0.6803 | 0.8850 | 0.0 | 0.0 | 0.1068 | 0.0 | | 0.5724 | 42.59 | 2300 | 0.7562 | 0.2740 | 0.3479 | 0.7662 | nan | 0.8734 | 0.7169 | 0.7809 | 0.8847 | 0.2838 | nan | 0.3742 | 0.6758 | 0.0 | 0.9339 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6048 | 0.0 | 0.0 | 0.8535 | 0.0 | 0.4435 | 0.4729 | 0.0 | nan | 0.0 | 0.2817 | 0.0 | 0.0 | 0.9149 | 0.8765 | 0.9329 | 0.0 | 0.0 | 0.2292 | 0.0 | nan | 0.7041 | 0.6683 | 0.7628 | 0.3371 | 0.2575 | nan | 0.2878 | 0.4639 | 0.0 | 0.7454 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4190 | 0.0 | 0.0 | 0.6387 | 0.0 | 0.3357 | 0.3997 | 0.0 | nan | 0.0 | 0.1776 | 0.0 | 0.0 | 0.8183 | 0.7106 | 0.8911 | 0.0 | 0.0 | 0.1516 | 0.0 | | 0.556 | 44.44 | 2400 | 0.7350 | 0.2665 | 0.3366 | 0.7813 | nan | 0.7897 | 0.7888 | 0.8022 | 0.8878 | 0.2389 | nan | 0.4270 | 0.4859 | 0.0 | 0.9401 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4618 | 0.0 | 0.0 | 0.8866 | 0.0 | 0.3979 | 0.5050 | 0.0 | nan | 0.0 | 0.2580 | 0.0 | 0.0 | 0.9097 | 0.8627 | 0.9337 | 0.0 | 0.0 | 0.1948 | 0.0 | nan | 0.6902 | 0.7286 | 0.7779 | 0.3964 | 0.2231 | nan | 0.3011 | 0.3626 | 0.0 | 0.7078 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3485 | 0.0 | 0.0 | 0.6171 | 0.0 | 0.3044 | 0.3372 | 0.0 | nan | 0.0 | 0.1812 | 0.0 | 0.0 | 0.8195 | 0.7011 | 0.8947 | 0.0 | 0.0 | 0.1378 | 0.0 | | 0.5599 | 46.3 | 2500 | 0.5949 | 0.2846 | 0.3464 | 0.8215 | nan | 0.7919 | 0.9145 | 0.7935 | 0.8679 | 0.2189 | nan | 0.3795 | 0.5589 | 0.0 | 0.9334 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5627 | 0.0 | 0.0 | 0.8536 | 0.0 | 0.4394 | 0.4730 | 0.0 | nan | 0.0 | 0.3260 | 0.0 | 0.0 | 0.9098 | 0.8344 | 0.9487 | 0.0 | 0.0 | 0.2801 | 0.0 | nan | 0.6901 | 0.8199 | 0.7749 | 0.5729 | 0.2084 | nan | 0.3034 | 0.4321 | 0.0 | 0.7422 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4230 | 0.0 | 0.0 | 0.6491 | 0.0 | 0.3237 | 0.3989 | 0.0 | nan | 0.0 | 0.1963 | 0.0 | 0.0 | 0.8232 | 0.7048 | 0.8949 | 0.0 | 0.0 | 0.1489 | 0.0 | | 0.5368 | 48.15 | 2600 | 0.6125 | 0.2829 | 0.3502 | 0.8211 | nan | 0.7798 | 0.9034 | 0.7913 | 0.9079 | 0.2587 | nan | 0.3407 | 0.6423 | 0.0 | 0.9351 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6794 | 0.0 | 0.0 | 0.8554 | 0.0 | 0.3996 | 0.4884 | 0.0 | nan | 0.0 | 0.2870 | 0.0 | 0.0 | 0.9271 | 0.8698 | 0.9424 | 0.0 | 0.0 | 0.1992 | 0.0 | nan | 0.6878 | 0.8122 | 0.7578 | 0.5597 | 0.2427 | nan | 0.2680 | 0.4737 | 0.0 | 0.7517 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3649 | 0.0 | 0.0 | 0.6557 | 0.0 | 0.3130 | 0.4117 | 0.0 | nan | 0.0 | 0.1847 | 0.0 | 0.0 | 0.8236 | 0.7137 | 0.8969 | 0.0 | 0.0 | 0.1361 | 0.0 | | 0.5391 | 50.0 | 2700 | 0.5993 | 0.2877 | 0.3507 | 0.8242 | nan | 0.8174 | 0.8948 | 0.8094 | 0.8896 | 0.2730 | nan | 0.4105 | 0.5570 | 0.0 | 0.9164 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5439 | 0.0 | 0.0 | 0.8772 | 0.0 | 0.5070 | 0.5443 | 0.0 | nan | 0.0 | 0.2691 | 0.0 | 0.0 | 0.9205 | 0.8660 | 0.8975 | 0.0 | 0.0 | 0.2294 | 0.0 | nan | 0.7059 | 0.8214 | 0.7578 | 0.5803 | 0.2537 | nan | 0.2892 | 0.4308 | 0.0 | 0.7548 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4363 | 0.0 | 0.0 | 0.6490 | 0.0 | 0.3579 | 0.4224 | 0.0 | nan | 0.0 | 0.1927 | 0.0 | 0.0 | 0.8239 | 0.7040 | 0.8748 | 0.0 | 0.0 | 0.1516 | 0.0 | | 0.5041 | 51.85 | 2800 | 0.5912 | 0.2859 | 0.3493 | 0.8264 | nan | 0.7593 | 0.9248 | 0.8029 | 0.8780 | 0.2945 | nan | 0.3718 | 0.6308 | 0.0 | 0.9078 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6667 | 0.0 | 0.0 | 0.8945 | 0.0 | 0.3362 | 0.4834 | 0.0 | nan | 0.0 | 0.3167 | 0.0 | 0.0 | 0.9255 | 0.8641 | 0.9382 | 0.0 | 0.0 | 0.1836 | 0.0 | nan | 0.6993 | 0.8205 | 0.7232 | 0.5789 | 0.2712 | nan | 0.2852 | 0.4872 | 0.0 | 0.7747 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3825 | 0.0 | 0.0 | 0.6382 | 0.0 | 0.2862 | 0.4138 | 0.0 | nan | 0.0 | 0.2019 | 0.0 | 0.0 | 0.8284 | 0.7271 | 0.8984 | 0.0 | 0.0 | 0.1316 | 0.0 | | 0.5007 | 53.7 | 2900 | 0.6220 | 0.2839 | 0.3577 | 0.8134 | nan | 0.7302 | 0.8903 | 0.8180 | 0.9098 | 0.3134 | nan | 0.3521 | 0.6870 | 0.0 | 0.9429 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7288 | 0.0 | 0.0 | 0.8340 | 0.0 | 0.5169 | 0.4700 | 0.0 | nan | 0.0 | 0.3105 | 0.0 | 0.0 | 0.9356 | 0.8318 | 0.9437 | 0.0 | 0.0003 | 0.2298 | 0.0 | nan | 0.6722 | 0.8034 | 0.7257 | 0.4922 | 0.2900 | nan | 0.2639 | 0.4741 | 0.0 | 0.7434 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4082 | 0.0 | 0.0 | 0.6635 | 0.0 | 0.3690 | 0.4172 | 0.0 | nan | 0.0 | 0.1981 | 0.0 | 0.0 | 0.8205 | 0.6936 | 0.9015 | 0.0 | 0.0003 | 0.1483 | 0.0 | | 0.4992 | 55.56 | 3000 | 0.5669 | 0.2928 | 0.3647 | 0.8317 | nan | 0.7826 | 0.9171 | 0.8018 | 0.9165 | 0.2758 | nan | 0.5273 | 0.6986 | 0.0 | 0.9410 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6836 | 0.0 | 0.0 | 0.8296 | 0.0 | 0.4717 | 0.4595 | 0.0 | nan | 0.0 | 0.3613 | 0.0 | 0.0 | 0.9272 | 0.8671 | 0.9424 | 0.0 | 0.0017 | 0.2669 | 0.0 | nan | 0.7196 | 0.8377 | 0.7464 | 0.6016 | 0.2573 | nan | 0.3367 | 0.4767 | 0.0 | 0.7565 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4237 | 0.0 | 0.0 | 0.6653 | 0.0 | 0.3438 | 0.4034 | 0.0 | nan | 0.0 | 0.1974 | 0.0 | 0.0 | 0.8287 | 0.7120 | 0.9031 | 0.0 | 0.0017 | 0.1565 | 0.0 | | 0.5151 | 57.41 | 3100 | 0.6131 | 0.2864 | 0.3598 | 0.8169 | nan | 0.7793 | 0.9005 | 0.7894 | 0.8762 | 0.2508 | nan | 0.3852 | 0.6197 | 0.0 | 0.9316 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6506 | 0.0 | 0.0 | 0.7819 | 0.0 | 0.5348 | 0.5782 | 0.0 | nan | 0.0 | 0.3853 | 0.0 | 0.0 | 0.9211 | 0.8624 | 0.9390 | 0.0 | 0.0 | 0.3278 | 0.0 | nan | 0.6967 | 0.8145 | 0.7436 | 0.5453 | 0.2362 | nan | 0.2992 | 0.4656 | 0.0 | 0.7549 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4221 | 0.0 | 0.0 | 0.6246 | 0.0 | 0.3873 | 0.3923 | 0.0 | nan | 0.0 | 0.1937 | 0.0 | 0.0 | 0.8257 | 0.7204 | 0.8994 | 0.0 | 0.0 | 0.1417 | 0.0 | | 0.4688 | 59.26 | 3200 | 0.7342 | 0.2674 | 0.3425 | 0.7758 | nan | 0.6724 | 0.8138 | 0.8211 | 0.8881 | 0.2106 | nan | 0.3435 | 0.4240 | 0.0 | 0.9345 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6881 | 0.0 | 0.0 | 0.8684 | 0.0 | 0.4808 | 0.5494 | 0.0 | nan | 0.0 | 0.2968 | 0.0 | 0.0 | 0.9269 | 0.8322 | 0.9291 | 0.0 | 0.0 | 0.2817 | 0.0 | nan | 0.6227 | 0.7395 | 0.7654 | 0.4008 | 0.1990 | nan | 0.2434 | 0.3473 | 0.0 | 0.7526 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3733 | 0.0 | 0.0 | 0.5567 | 0.0 | 0.3425 | 0.4056 | 0.0 | nan | 0.0 | 0.2033 | 0.0 | 0.0 | 0.8238 | 0.7088 | 0.8978 | 0.0 | 0.0 | 0.1748 | 0.0 | | 0.4657 | 61.11 | 3300 | 0.7162 | 0.2737 | 0.3487 | 0.7884 | nan | 0.6859 | 0.8395 | 0.7919 | 0.8974 | 0.2306 | nan | 0.4086 | 0.6012 | 0.0 | 0.9212 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7186 | 0.0 | 0.0 | 0.8738 | 0.0 | 0.4323 | 0.5271 | 0.0 | nan | 0.0 | 0.3163 | 0.0 | 0.0 | 0.9373 | 0.8107 | 0.9381 | 0.0 | 0.0 | 0.2280 | 0.0 | nan | 0.6253 | 0.7668 | 0.7584 | 0.4350 | 0.2180 | nan | 0.2835 | 0.4646 | 0.0 | 0.7649 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3505 | 0.0 | 0.0 | 0.5817 | 0.0 | 0.3184 | 0.4275 | 0.0 | nan | 0.0 | 0.1989 | 0.0 | 0.0 | 0.8181 | 0.6916 | 0.9021 | 0.0 | 0.0 | 0.1529 | 0.0 | | 0.4789 | 62.96 | 3400 | 0.6510 | 0.2824 | 0.3535 | 0.8065 | nan | 0.7245 | 0.8835 | 0.7760 | 0.8886 | 0.2720 | nan | 0.3709 | 0.6675 | 0.0 | 0.9351 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6668 | 0.0 | 0.0 | 0.8450 | 0.0 | 0.4917 | 0.5508 | 0.0 | nan | 0.0 | 0.3585 | 0.0 | 0.0 | 0.9367 | 0.7684 | 0.9321 | 0.0 | 0.0022 | 0.2404 | 0.0 | nan | 0.6754 | 0.7938 | 0.7682 | 0.4856 | 0.2514 | nan | 0.2841 | 0.4779 | 0.0 | 0.7566 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3801 | 0.0 | 0.0 | 0.6118 | 0.0 | 0.3623 | 0.4464 | 0.0 | nan | 0.0 | 0.1990 | 0.0 | 0.0 | 0.8150 | 0.6727 | 0.9029 | 0.0 | 0.0022 | 0.1516 | 0.0 | | 0.4718 | 64.81 | 3500 | 0.7369 | 0.2741 | 0.3491 | 0.7687 | nan | 0.7886 | 0.7455 | 0.8159 | 0.8865 | 0.2585 | nan | 0.3583 | 0.6014 | 0.0 | 0.9362 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6741 | 0.0 | 0.0 | 0.8728 | 0.0 | 0.4488 | 0.5138 | 0.0 | nan | 0.0 | 0.3533 | 0.0 | 0.0 | 0.9343 | 0.8363 | 0.9345 | 0.0 | 0.0002 | 0.2111 | 0.0 | nan | 0.6800 | 0.6730 | 0.7173 | 0.3412 | 0.2406 | nan | 0.2736 | 0.4651 | 0.0 | 0.7688 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3688 | 0.0 | 0.0 | 0.6494 | 0.0 | 0.3507 | 0.4403 | 0.0 | nan | 0.0 | 0.1950 | 0.0 | 0.0 | 0.8287 | 0.7216 | 0.9039 | 0.0 | 0.0002 | 0.1536 | 0.0 | | 0.4586 | 66.67 | 3600 | 0.7463 | 0.2799 | 0.3515 | 0.7620 | nan | 0.8497 | 0.6965 | 0.7931 | 0.9041 | 0.2737 | nan | 0.3983 | 0.5616 | 0.0 | 0.9365 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5892 | 0.0 | 0.0 | 0.8439 | 0.0 | 0.5213 | 0.4720 | 0.0 | nan | 0.0 | 0.3429 | 0.0 | 0.0 | 0.9332 | 0.8690 | 0.9431 | 0.0 | 0.0 | 0.3213 | 0.0 | nan | 0.7435 | 0.6450 | 0.7808 | 0.3120 | 0.2517 | nan | 0.3134 | 0.4378 | 0.0 | 0.7305 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4349 | 0.0 | 0.0 | 0.6399 | 0.0 | 0.3813 | 0.4243 | 0.0 | nan | 0.0 | 0.2097 | 0.0 | 0.0 | 0.8287 | 0.7225 | 0.9085 | 0.0 | 0.0 | 0.1926 | 0.0 | | 0.4506 | 68.52 | 3700 | 0.6409 | 0.2859 | 0.3587 | 0.8030 | nan | 0.7887 | 0.8394 | 0.8054 | 0.8912 | 0.2518 | nan | 0.3799 | 0.6292 | 0.0 | 0.9273 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7090 | 0.0 | 0.0 | 0.8655 | 0.0 | 0.4989 | 0.5447 | 0.0 | nan | 0.0 | 0.3519 | 0.0 | 0.0 | 0.9335 | 0.8362 | 0.9278 | 0.0 | 0.0 | 0.2975 | 0.0 | nan | 0.7248 | 0.7574 | 0.7649 | 0.4118 | 0.2326 | nan | 0.2996 | 0.4840 | 0.0 | 0.7856 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3424 | 0.0 | 0.0 | 0.6639 | 0.0 | 0.3766 | 0.4576 | 0.0 | nan | 0.0 | 0.2055 | 0.0 | 0.0 | 0.8284 | 0.7274 | 0.9032 | 0.0 | 0.0 | 0.1823 | 0.0 | | 0.4659 | 70.37 | 3800 | 0.6466 | 0.2884 | 0.3577 | 0.8081 | nan | 0.8256 | 0.8420 | 0.7982 | 0.8692 | 0.3484 | nan | 0.4035 | 0.4964 | 0.0 | 0.9489 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6461 | 0.0 | 0.0 | 0.8281 | 0.0 | 0.5593 | 0.5404 | 0.0 | nan | 0.0 | 0.3533 | 0.0 | 0.0 | 0.9345 | 0.7861 | 0.9426 | 0.0 | 0.0 | 0.3225 | 0.0 | nan | 0.7403 | 0.7665 | 0.7649 | 0.4456 | 0.2991 | nan | 0.3198 | 0.3976 | 0.0 | 0.7512 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4217 | 0.0 | 0.0 | 0.6537 | 0.0 | 0.3859 | 0.4470 | 0.0 | nan | 0.0 | 0.2219 | 0.0 | 0.0 | 0.8223 | 0.6908 | 0.9109 | 0.0 | 0.0 | 0.1898 | 0.0 | | 0.4416 | 72.22 | 3900 | 0.6944 | 0.2824 | 0.3648 | 0.7953 | nan | 0.8073 | 0.8044 | 0.8200 | 0.9039 | 0.2713 | nan | 0.4385 | 0.6632 | 0.0 | 0.9435 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7130 | 0.0 | 0.0 | 0.8448 | 0.0 | 0.5050 | 0.5552 | 0.0 | nan | 0.0 | 0.3791 | 0.0 | 0.0 | 0.9316 | 0.8332 | 0.9378 | 0.0 | 0.0047 | 0.3183 | 0.0 | nan | 0.7045 | 0.7445 | 0.6571 | 0.4107 | 0.2536 | nan | 0.3089 | 0.4711 | 0.0 | 0.7504 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3814 | 0.0 | 0.0 | 0.6468 | 0.0 | 0.3800 | 0.4413 | 0.0 | nan | 0.0 | 0.2243 | 0.0 | 0.0 | 0.8294 | 0.7257 | 0.9078 | 0.0 | 0.0047 | 0.1964 | 0.0 | | 0.4347 | 74.07 | 4000 | 0.5742 | 0.2960 | 0.3615 | 0.8319 | nan | 0.8135 | 0.9088 | 0.8067 | 0.8959 | 0.3006 | nan | 0.3611 | 0.6055 | 0.0 | 0.9354 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6851 | 0.0 | 0.0 | 0.8692 | 0.0 | 0.4956 | 0.5065 | 0.0 | nan | 0.0 | 0.3493 | 0.0 | 0.0 | 0.9264 | 0.8500 | 0.9368 | 0.0 | 0.0018 | 0.3210 | 0.0 | nan | 0.7436 | 0.8254 | 0.7615 | 0.5609 | 0.2797 | nan | 0.3045 | 0.4733 | 0.0 | 0.7745 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4006 | 0.0 | 0.0 | 0.6424 | 0.0 | 0.3800 | 0.4600 | 0.0 | nan | 0.0 | 0.2126 | 0.0 | 0.0 | 0.8296 | 0.7251 | 0.9085 | 0.0 | 0.0018 | 0.1876 | 0.0 | | 0.4191 | 75.93 | 4100 | 0.6454 | 0.2879 | 0.3671 | 0.8068 | nan | 0.7757 | 0.8432 | 0.8171 | 0.8803 | 0.3169 | nan | 0.4971 | 0.6474 | 0.0 | 0.9274 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7272 | 0.0 | 0.0 | 0.8520 | 0.0 | 0.4847 | 0.5414 | 0.0 | nan | 0.0 | 0.4113 | 0.0 | 0.0 | 0.9400 | 0.8335 | 0.9348 | 0.0 | 0.0167 | 0.3000 | 0.0 | nan | 0.7112 | 0.7615 | 0.6876 | 0.4533 | 0.2904 | nan | 0.3375 | 0.4768 | 0.0 | 0.7857 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3483 | 0.0 | 0.0 | 0.6544 | 0.0 | 0.3636 | 0.4546 | 0.0 | nan | 0.0 | 0.2086 | 0.0 | 0.0 | 0.8293 | 0.7293 | 0.9093 | 0.0 | 0.0165 | 0.1938 | 0.0 | | 0.4355 | 77.78 | 4200 | 0.5871 | 0.2915 | 0.3601 | 0.8236 | nan | 0.6673 | 0.9324 | 0.8063 | 0.8730 | 0.2988 | nan | 0.5014 | 0.5734 | 0.0 | 0.9480 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6629 | 0.0 | 0.0 | 0.8653 | 0.0 | 0.4649 | 0.5559 | 0.0 | nan | 0.0 | 0.3890 | 0.0 | 0.0 | 0.9183 | 0.8681 | 0.9537 | 0.0 | 0.0088 | 0.2359 | 0.0 | nan | 0.6266 | 0.8175 | 0.7309 | 0.5730 | 0.2746 | nan | 0.3471 | 0.4465 | 0.0 | 0.7567 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4103 | 0.0 | 0.0 | 0.6684 | 0.0 | 0.3482 | 0.4615 | 0.0 | nan | 0.0 | 0.2062 | 0.0 | 0.0 | 0.8356 | 0.7347 | 0.9131 | 0.0 | 0.0088 | 0.1686 | 0.0 | | 0.431 | 79.63 | 4300 | 0.5778 | 0.2902 | 0.3540 | 0.8266 | nan | 0.8325 | 0.9042 | 0.7971 | 0.8575 | 0.2707 | nan | 0.4318 | 0.5731 | 0.0 | 0.9428 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6701 | 0.0 | 0.0 | 0.8781 | 0.0 | 0.4081 | 0.5480 | 0.0 | nan | 0.0 | 0.3573 | 0.0 | 0.0 | 0.9299 | 0.7480 | 0.9397 | 0.0 | 0.0343 | 0.2046 | 0.0 | nan | 0.7428 | 0.8112 | 0.7719 | 0.5907 | 0.2545 | nan | 0.3259 | 0.4272 | 0.0 | 0.7505 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4255 | 0.0 | 0.0 | 0.6496 | 0.0 | 0.3209 | 0.4384 | 0.0 | nan | 0.0 | 0.2061 | 0.0 | 0.0 | 0.8142 | 0.6646 | 0.9118 | 0.0 | 0.0338 | 0.1477 | 0.0 | | 0.4105 | 81.48 | 4400 | 0.7355 | 0.2837 | 0.3547 | 0.7802 | nan | 0.8194 | 0.7548 | 0.8125 | 0.9004 | 0.2421 | nan | 0.4411 | 0.5260 | 0.0 | 0.9344 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6628 | 0.0 | 0.0 | 0.9003 | 0.0 | 0.4114 | 0.5457 | 0.0 | nan | 0.0 | 0.3720 | 0.0 | 0.0 | 0.9386 | 0.8336 | 0.9269 | 0.0 | 0.0905 | 0.2364 | 0.0 | nan | 0.7295 | 0.6964 | 0.7754 | 0.3477 | 0.2325 | nan | 0.3336 | 0.4069 | 0.0 | 0.7641 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4284 | 0.0 | 0.0 | 0.6483 | 0.0 | 0.3512 | 0.4444 | 0.0 | nan | 0.0 | 0.2140 | 0.0 | 0.0 | 0.8260 | 0.7200 | 0.9047 | 0.0 | 0.0883 | 0.1667 | 0.0 | | 0.4102 | 83.33 | 4500 | 0.6431 | 0.2832 | 0.3550 | 0.8023 | nan | 0.6173 | 0.8926 | 0.8233 | 0.8684 | 0.3015 | nan | 0.4774 | 0.5853 | 0.0 | 0.9435 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7118 | 0.0 | 0.0 | 0.8678 | 0.0 | 0.4544 | 0.5288 | 0.0 | nan | 0.0 | 0.3435 | 0.0 | 0.0 | 0.9438 | 0.7934 | 0.9323 | 0.0 | 0.0264 | 0.2495 | 0.0 | nan | 0.5793 | 0.7784 | 0.7849 | 0.5220 | 0.2750 | nan | 0.3433 | 0.4263 | 0.0 | 0.7478 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3651 | 0.0 | 0.0 | 0.6236 | 0.0 | 0.3489 | 0.4347 | 0.0 | nan | 0.0 | 0.2243 | 0.0 | 0.0 | 0.8184 | 0.6879 | 0.9082 | 0.0 | 0.0258 | 0.1674 | 0.0 | | 0.4172 | 85.19 | 4600 | 0.6988 | 0.2875 | 0.3537 | 0.7940 | nan | 0.7505 | 0.8194 | 0.8168 | 0.9128 | 0.2640 | nan | 0.4022 | 0.4961 | 0.0 | 0.9391 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6453 | 0.0 | 0.0 | 0.8769 | 0.0 | 0.4600 | 0.5182 | 0.0 | nan | 0.0 | 0.3740 | 0.0 | 0.0 | 0.9378 | 0.8263 | 0.9455 | 0.0 | 0.0900 | 0.2436 | 0.0 | nan | 0.7048 | 0.7401 | 0.7654 | 0.3938 | 0.2454 | nan | 0.2874 | 0.3973 | 0.0 | 0.7572 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4779 | 0.0 | 0.0 | 0.6427 | 0.0 | 0.3531 | 0.4565 | 0.0 | nan | 0.0 | 0.2402 | 0.0 | 0.0 | 0.8333 | 0.7320 | 0.9149 | 0.0 | 0.0880 | 0.1706 | 0.0 | | 0.3885 | 87.04 | 4700 | 0.5978 | 0.2953 | 0.3647 | 0.8175 | nan | 0.8142 | 0.8718 | 0.8027 | 0.8554 | 0.3059 | nan | 0.3787 | 0.5867 | 0.0 | 0.9403 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6845 | 0.0 | 0.0 | 0.8471 | 0.0 | 0.5315 | 0.5788 | 0.0 | nan | 0.0 | 0.3874 | 0.0 | 0.0 | 0.9354 | 0.8156 | 0.9494 | 0.0 | 0.1221 | 0.2636 | 0.0 | nan | 0.7263 | 0.7825 | 0.7874 | 0.4784 | 0.2859 | nan | 0.2981 | 0.4480 | 0.0 | 0.7604 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3820 | 0.0 | 0.0 | 0.6694 | 0.0 | 0.3781 | 0.4545 | 0.0 | nan | 0.0 | 0.2385 | 0.0 | 0.0 | 0.8301 | 0.7216 | 0.9144 | 0.0 | 0.1131 | 0.1798 | 0.0 | | 0.3949 | 88.89 | 4800 | 0.5747 | 0.2961 | 0.3643 | 0.8282 | nan | 0.8129 | 0.8976 | 0.8121 | 0.8713 | 0.2894 | nan | 0.4694 | 0.5562 | 0.0 | 0.9391 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6947 | 0.0 | 0.0 | 0.8395 | 0.0 | 0.5260 | 0.5481 | 0.0 | nan | 0.0 | 0.3852 | 0.0 | 0.0 | 0.9428 | 0.8221 | 0.9365 | 0.0 | 0.0559 | 0.2580 | 0.0 | nan | 0.7394 | 0.8130 | 0.7924 | 0.5533 | 0.2658 | nan | 0.3447 | 0.4378 | 0.0 | 0.7620 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3851 | 0.0 | 0.0 | 0.6633 | 0.0 | 0.3722 | 0.4533 | 0.0 | nan | 0.0 | 0.2184 | 0.0 | 0.0 | 0.8217 | 0.7122 | 0.9124 | 0.0 | 0.0534 | 0.1742 | 0.0 | | 0.4158 | 90.74 | 4900 | 0.6449 | 0.2916 | 0.3657 | 0.8070 | nan | 0.8043 | 0.8271 | 0.8157 | 0.9192 | 0.3073 | nan | 0.4380 | 0.6344 | 0.0 | 0.9340 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7171 | 0.0 | 0.0 | 0.8572 | 0.0 | 0.5188 | 0.5406 | 0.0 | nan | 0.0 | 0.3852 | 0.0 | 0.0 | 0.9420 | 0.8552 | 0.9459 | 0.0 | 0.0450 | 0.2148 | 0.0 | nan | 0.6975 | 0.7564 | 0.7902 | 0.4563 | 0.2853 | nan | 0.3171 | 0.4654 | 0.0 | 0.7879 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3571 | 0.0 | 0.0 | 0.6623 | 0.0 | 0.3819 | 0.4583 | 0.0 | nan | 0.0 | 0.2243 | 0.0 | 0.0 | 0.8302 | 0.7431 | 0.9150 | 0.0 | 0.0421 | 0.1602 | 0.0 | | 0.3856 | 92.59 | 5000 | 0.7492 | 0.2796 | 0.3559 | 0.7680 | nan | 0.8020 | 0.7250 | 0.8248 | 0.9139 | 0.2500 | nan | 0.3621 | 0.5930 | 0.0 | 0.9411 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6964 | 0.0 | 0.0 | 0.9036 | 0.0 | 0.3460 | 0.5234 | 0.0 | nan | 0.0 | 0.4271 | 0.0 | 0.0 | 0.9255 | 0.8871 | 0.9524 | 0.0 | 0.0666 | 0.2471 | 0.0 | nan | 0.6954 | 0.6697 | 0.7878 | 0.3256 | 0.2365 | nan | 0.2864 | 0.4452 | 0.0 | 0.7724 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3838 | 0.0 | 0.0 | 0.6413 | 0.0 | 0.2968 | 0.4239 | 0.0 | nan | 0.0 | 0.2271 | 0.0 | 0.0 | 0.8382 | 0.7554 | 0.9171 | 0.0 | 0.0624 | 0.1808 | 0.0 | | 0.3915 | 94.44 | 5100 | 0.6402 | 0.2893 | 0.3608 | 0.8012 | nan | 0.7614 | 0.8406 | 0.7898 | 0.9029 | 0.3080 | nan | 0.3857 | 0.6328 | 0.0 | 0.9373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7010 | 0.0 | 0.0 | 0.8626 | 0.0 | 0.5045 | 0.5235 | 0.0 | nan | 0.0 | 0.3802 | 0.0 | 0.0 | 0.9442 | 0.7561 | 0.9401 | 0.0 | 0.1133 | 0.2603 | 0.0 | nan | 0.6850 | 0.7546 | 0.7750 | 0.4451 | 0.2827 | nan | 0.3049 | 0.4715 | 0.0 | 0.7694 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3810 | 0.0 | 0.0 | 0.6626 | 0.0 | 0.3832 | 0.4394 | 0.0 | nan | 0.0 | 0.2214 | 0.0 | 0.0 | 0.8125 | 0.6725 | 0.9138 | 0.0 | 0.1034 | 0.1797 | 0.0 | | 0.3732 | 96.3 | 5200 | 0.7308 | 0.2840 | 0.3598 | 0.7795 | nan | 0.7534 | 0.7741 | 0.8137 | 0.9035 | 0.2614 | nan | 0.4308 | 0.6431 | 0.0 | 0.9315 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7293 | 0.0 | 0.0 | 0.8884 | 0.0 | 0.4166 | 0.5225 | 0.0 | nan | 0.0 | 0.3992 | 0.0 | 0.0 | 0.9329 | 0.8517 | 0.9519 | 0.0 | 0.0756 | 0.2354 | 0.0 | nan | 0.6723 | 0.6942 | 0.7836 | 0.3665 | 0.2474 | nan | 0.3333 | 0.4669 | 0.0 | 0.7857 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3545 | 0.0 | 0.0 | 0.6375 | 0.0 | 0.3443 | 0.4311 | 0.0 | nan | 0.0 | 0.2377 | 0.0 | 0.0 | 0.8346 | 0.7428 | 0.9173 | 0.0 | 0.0659 | 0.1722 | 0.0 | | 0.3843 | 98.15 | 5300 | 0.6580 | 0.2864 | 0.3556 | 0.7962 | nan | 0.7254 | 0.8440 | 0.7996 | 0.8889 | 0.2696 | nan | 0.4320 | 0.6399 | 0.0 | 0.9285 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6708 | 0.0 | 0.0 | 0.8872 | 0.0 | 0.4070 | 0.5262 | 0.0 | nan | 0.0 | 0.3791 | 0.0 | 0.0 | 0.9423 | 0.7462 | 0.9487 | 0.0 | 0.1269 | 0.2159 | 0.0 | nan | 0.6660 | 0.7540 | 0.7836 | 0.4484 | 0.2521 | nan | 0.3307 | 0.4691 | 0.0 | 0.7963 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3896 | 0.0 | 0.0 | 0.6071 | 0.0 | 0.3185 | 0.4568 | 0.0 | nan | 0.0 | 0.2206 | 0.0 | 0.0 | 0.8138 | 0.6608 | 0.9170 | 0.0 | 0.1163 | 0.1644 | 0.0 | | 0.3903 | 100.0 | 5400 | 0.6288 | 0.2881 | 0.3541 | 0.8086 | nan | 0.7763 | 0.8567 | 0.8240 | 0.8951 | 0.2446 | nan | 0.4334 | 0.5553 | 0.0 | 0.9354 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6738 | 0.0 | 0.0 | 0.8901 | 0.0 | 0.4777 | 0.5458 | 0.0 | nan | 0.0 | 0.3297 | 0.0 | 0.0 | 0.9417 | 0.7702 | 0.9457 | 0.0 | 0.0457 | 0.1907 | 0.0 | nan | 0.6906 | 0.7727 | 0.7923 | 0.4705 | 0.2358 | nan | 0.3295 | 0.4509 | 0.0 | 0.7755 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3981 | 0.0 | 0.0 | 0.6528 | 0.0 | 0.3644 | 0.4573 | 0.0 | nan | 0.0 | 0.2197 | 0.0 | 0.0 | 0.8176 | 0.6797 | 0.9157 | 0.0 | 0.0444 | 0.1500 | 0.0 | | 0.355 | 101.85 | 5500 | 0.7112 | 0.2860 | 0.3563 | 0.7844 | nan | 0.7834 | 0.7947 | 0.8123 | 0.8807 | 0.2262 | nan | 0.3408 | 0.6020 | 0.0 | 0.9382 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6759 | 0.0 | 0.0 | 0.8838 | 0.0 | 0.4491 | 0.5845 | 0.0 | nan | 0.0 | 0.4029 | 0.0 | 0.0 | 0.9295 | 0.7890 | 0.9477 | 0.0 | 0.1045 | 0.2564 | 0.0 | nan | 0.7086 | 0.7078 | 0.7825 | 0.3607 | 0.2168 | nan | 0.2792 | 0.4624 | 0.0 | 0.7767 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4366 | 0.0 | 0.0 | 0.6667 | 0.0 | 0.3443 | 0.4351 | 0.0 | nan | 0.0 | 0.2386 | 0.0 | 0.0 | 0.8283 | 0.7060 | 0.9167 | 0.0 | 0.1000 | 0.1847 | 0.0 | | 0.3729 | 103.7 | 5600 | 0.6849 | 0.2835 | 0.3591 | 0.7887 | nan | 0.8150 | 0.7790 | 0.8122 | 0.8834 | 0.2787 | nan | 0.4506 | 0.6270 | 0.0 | 0.9253 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7408 | 0.0 | 0.0 | 0.9180 | 0.0 | 0.3273 | 0.5197 | 0.0 | nan | 0.0 | 0.4167 | 0.0 | 0.0 | 0.9358 | 0.8379 | 0.9406 | 0.0 | 0.0480 | 0.2345 | 0.0 | nan | 0.6989 | 0.7189 | 0.7862 | 0.3939 | 0.2648 | nan | 0.3292 | 0.4851 | 0.0 | 0.7976 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3286 | 0.0 | 0.0 | 0.6202 | 0.0 | 0.2779 | 0.4371 | 0.0 | nan | 0.0 | 0.2402 | 0.0 | 0.0 | 0.8321 | 0.7297 | 0.9140 | 0.0 | 0.0437 | 0.1749 | 0.0 | | 0.3895 | 105.56 | 5700 | 0.6917 | 0.2909 | 0.3669 | 0.7881 | nan | 0.8520 | 0.7575 | 0.8037 | 0.9006 | 0.2858 | nan | 0.4909 | 0.6331 | 0.0 | 0.9365 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6811 | 0.0 | 0.0 | 0.8525 | 0.0 | 0.5087 | 0.5374 | 0.0 | nan | 0.0 | 0.3766 | 0.0 | 0.0 | 0.9432 | 0.8426 | 0.9479 | 0.0 | 0.0982 | 0.2931 | 0.0 | nan | 0.7338 | 0.7000 | 0.7834 | 0.3764 | 0.2683 | nan | 0.3430 | 0.4719 | 0.0 | 0.7841 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3792 | 0.0 | 0.0 | 0.6627 | 0.0 | 0.3815 | 0.4454 | 0.0 | nan | 0.0 | 0.2245 | 0.0 | 0.0 | 0.8273 | 0.7311 | 0.9183 | 0.0 | 0.0894 | 0.1885 | 0.0 | | 0.3602 | 107.41 | 5800 | 0.5475 | 0.3042 | 0.3685 | 0.8353 | nan | 0.7641 | 0.9319 | 0.8055 | 0.8737 | 0.3132 | nan | 0.4868 | 0.6244 | 0.0 | 0.9407 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6873 | 0.0 | 0.0 | 0.8810 | 0.0 | 0.4631 | 0.5387 | 0.0 | nan | 0.0 | 0.4382 | 0.0 | 0.0 | 0.9298 | 0.7866 | 0.9486 | 0.0 | 0.1344 | 0.2454 | 0.0 | nan | 0.7121 | 0.8270 | 0.7806 | 0.6491 | 0.2900 | nan | 0.3497 | 0.4700 | 0.0 | 0.7753 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4480 | 0.0 | 0.0 | 0.6577 | 0.0 | 0.3509 | 0.4582 | 0.0 | nan | 0.0 | 0.2281 | 0.0 | 0.0 | 0.8267 | 0.6946 | 0.9179 | 0.0 | 0.1213 | 0.1782 | 0.0 | | 0.3674 | 109.26 | 5900 | 0.6421 | 0.2919 | 0.3540 | 0.8016 | nan | 0.6932 | 0.8577 | 0.8144 | 0.9018 | 0.3136 | nan | 0.3961 | 0.5655 | 0.0 | 0.9370 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6563 | 0.0 | 0.0 | 0.9140 | 0.0 | 0.3656 | 0.4891 | 0.0 | nan | 0.0 | 0.3775 | 0.0 | 0.0 | 0.9373 | 0.8204 | 0.9427 | 0.0 | 0.1378 | 0.2090 | 0.0 | nan | 0.6366 | 0.7503 | 0.7829 | 0.4541 | 0.2884 | nan | 0.3050 | 0.4442 | 0.0 | 0.7727 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4780 | 0.0 | 0.0 | 0.6644 | 0.0 | 0.3163 | 0.4511 | 0.0 | nan | 0.0 | 0.2316 | 0.0 | 0.0 | 0.8321 | 0.7257 | 0.9157 | 0.0 | 0.1268 | 0.1636 | 0.0 | | 0.3657 | 111.11 | 6000 | 0.5813 | 0.2955 | 0.3637 | 0.8277 | nan | 0.7870 | 0.8975 | 0.7014 | 0.8566 | 0.3741 | nan | 0.4469 | 0.6219 | 0.0 | 0.9403 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7185 | 0.0 | 0.0 | 0.8827 | 0.0 | 0.4503 | 0.5681 | 0.0 | nan | 0.0 | 0.3815 | 0.0 | 0.0 | 0.9397 | 0.8275 | 0.9484 | 0.0 | 0.0968 | 0.1999 | 0.0 | nan | 0.7203 | 0.8097 | 0.6881 | 0.5693 | 0.3405 | nan | 0.3293 | 0.4754 | 0.0 | 0.7846 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3863 | 0.0 | 0.0 | 0.6346 | 0.0 | 0.3557 | 0.4385 | 0.0 | nan | 0.0 | 0.2181 | 0.0 | 0.0 | 0.8287 | 0.7172 | 0.9189 | 0.0 | 0.0846 | 0.1578 | 0.0 | | 0.367 | 112.96 | 6100 | 0.6609 | 0.2897 | 0.3661 | 0.7984 | nan | 0.7903 | 0.8284 | 0.8039 | 0.9016 | 0.2212 | nan | 0.4163 | 0.6816 | 0.0 | 0.9453 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7209 | 0.0 | 0.0 | 0.8372 | 0.0 | 0.4577 | 0.5511 | 0.0 | nan | 0.0 | 0.4283 | 0.0 | 0.0 | 0.9390 | 0.7875 | 0.9493 | 0.0 | 0.1399 | 0.3157 | 0.0 | nan | 0.7203 | 0.7408 | 0.7738 | 0.4105 | 0.2117 | nan | 0.3182 | 0.4784 | 0.0 | 0.7828 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3859 | 0.0 | 0.0 | 0.6672 | 0.0 | 0.3588 | 0.4378 | 0.0 | nan | 0.0 | 0.2244 | 0.0 | 0.0 | 0.8282 | 0.7032 | 0.9187 | 0.0 | 0.1137 | 0.1958 | 0.0 | | 0.3638 | 114.81 | 6200 | 0.7997 | 0.2803 | 0.3592 | 0.7547 | nan | 0.8092 | 0.6782 | 0.8102 | 0.9284 | 0.2905 | nan | 0.3691 | 0.6185 | 0.0 | 0.9403 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7520 | 0.0 | 0.0 | 0.8609 | 0.0 | 0.4178 | 0.5567 | 0.0 | nan | 0.0 | 0.3931 | 0.0 | 0.0 | 0.9474 | 0.8770 | 0.9435 | 0.0000 | 0.0667 | 0.2347 | 0.0 | nan | 0.7091 | 0.6261 | 0.7837 | 0.2942 | 0.2753 | nan | 0.2928 | 0.4552 | 0.0 | 0.7808 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3801 | 0.0 | 0.0 | 0.6648 | 0.0 | 0.3421 | 0.4315 | 0.0 | nan | 0.0 | 0.2152 | 0.0 | 0.0 | 0.8297 | 0.7448 | 0.9168 | 0.0000 | 0.0595 | 0.1680 | 0.0 | | 0.3654 | 116.67 | 6300 | 0.6019 | 0.2956 | 0.3645 | 0.8175 | nan | 0.8244 | 0.8533 | 0.6788 | 0.8927 | 0.3058 | nan | 0.4950 | 0.6003 | 0.0 | 0.9396 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6930 | 0.0 | 0.0 | 0.8964 | 0.0 | 0.3647 | 0.5196 | 0.0 | nan | 0.0 | 0.4113 | 0.0 | 0.0 | 0.9257 | 0.8551 | 0.9594 | 0.0 | 0.1310 | 0.3167 | 0.0 | nan | 0.7337 | 0.7732 | 0.6601 | 0.4748 | 0.2853 | nan | 0.3520 | 0.4685 | 0.0 | 0.7868 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4121 | 0.0 | 0.0 | 0.6708 | 0.0 | 0.3117 | 0.4434 | 0.0 | nan | 0.0 | 0.2326 | 0.0 | 0.0 | 0.8405 | 0.7541 | 0.9187 | 0.0 | 0.1205 | 0.2201 | 0.0 | | 0.3652 | 118.52 | 6400 | 0.5981 | 0.2967 | 0.3649 | 0.8205 | nan | 0.7551 | 0.8909 | 0.6342 | 0.9054 | 0.3093 | nan | 0.4234 | 0.6313 | 0.0 | 0.9387 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6751 | 0.0 | 0.0 | 0.8700 | 0.0 | 0.4187 | 0.5633 | 0.0 | nan | 0.0 | 0.4465 | 0.0 | 0.0 | 0.9262 | 0.8528 | 0.9534 | 0.0002 | 0.1437 | 0.3398 | 0.0 | nan | 0.6956 | 0.7948 | 0.6246 | 0.4963 | 0.2861 | nan | 0.3171 | 0.4870 | 0.0 | 0.7941 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4467 | 0.0 | 0.0 | 0.6719 | 0.0 | 0.3338 | 0.4473 | 0.0 | nan | 0.0 | 0.2377 | 0.0 | 0.0 | 0.8417 | 0.7531 | 0.9198 | 0.0002 | 0.1302 | 0.2180 | 0.0 | | 0.3559 | 120.37 | 6500 | 0.5780 | 0.3026 | 0.3668 | 0.8256 | nan | 0.7517 | 0.9024 | 0.8103 | 0.8905 | 0.3788 | nan | 0.3990 | 0.5648 | 0.0 | 0.9522 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6491 | 0.0 | 0.0 | 0.8623 | 0.0 | 0.5208 | 0.5227 | 0.0 | nan | 0.0 | 0.4095 | 0.0 | 0.0 | 0.9315 | 0.8073 | 0.9531 | 0.0 | 0.1367 | 0.2937 | 0.0 | nan | 0.6917 | 0.8084 | 0.7831 | 0.5645 | 0.3365 | nan | 0.3195 | 0.4446 | 0.0 | 0.7603 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4620 | 0.0 | 0.0 | 0.6310 | 0.0 | 0.3859 | 0.4599 | 0.0 | nan | 0.0 | 0.2286 | 0.0 | 0.0 | 0.8329 | 0.7236 | 0.9192 | 0.0 | 0.1259 | 0.2064 | 0.0 | | 0.3348 | 122.22 | 6600 | 0.5522 | 0.3023 | 0.3735 | 0.8379 | nan | 0.8289 | 0.9088 | 0.6882 | 0.8947 | 0.3594 | nan | 0.4373 | 0.6918 | 0.0 | 0.9448 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7098 | 0.0 | 0.0 | 0.8356 | 0.0 | 0.5156 | 0.5832 | 0.0 | nan | 0.0 | 0.4059 | 0.0 | 0.0 | 0.9417 | 0.8359 | 0.9578 | 0.0009 | 0.1308 | 0.2812 | 0.0 | nan | 0.7433 | 0.8257 | 0.6716 | 0.5930 | 0.3306 | nan | 0.3517 | 0.4956 | 0.0 | 0.7897 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3747 | 0.0 | 0.0 | 0.6736 | 0.0 | 0.3802 | 0.4271 | 0.0 | nan | 0.0 | 0.2180 | 0.0 | 0.0 | 0.8323 | 0.7373 | 0.9200 | 0.0008 | 0.1171 | 0.1906 | 0.0 | | 0.3653 | 124.07 | 6700 | 0.6070 | 0.2986 | 0.3679 | 0.8216 | nan | 0.6919 | 0.9133 | 0.8114 | 0.8786 | 0.3306 | nan | 0.4558 | 0.6517 | 0.0 | 0.9455 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7183 | 0.0 | 0.0 | 0.8672 | 0.0 | 0.5019 | 0.5472 | 0.0 | nan | 0.0 | 0.4162 | 0.0 | 0.0 | 0.9390 | 0.8019 | 0.9414 | 0.0 | 0.0957 | 0.2664 | 0.0 | nan | 0.6394 | 0.8000 | 0.7821 | 0.6011 | 0.3025 | nan | 0.3359 | 0.4969 | 0.0 | 0.7887 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3803 | 0.0 | 0.0 | 0.6386 | 0.0 | 0.3855 | 0.4427 | 0.0 | nan | 0.0 | 0.2268 | 0.0 | 0.0 | 0.8298 | 0.7136 | 0.9170 | 0.0 | 0.0886 | 0.1861 | 0.0 | | 0.3216 | 125.93 | 6800 | 0.6091 | 0.3003 | 0.3729 | 0.8176 | nan | 0.8300 | 0.8429 | 0.8233 | 0.9193 | 0.3587 | nan | 0.4900 | 0.6837 | 0.0 | 0.9439 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7272 | 0.0 | 0.0 | 0.8781 | 0.0 | 0.4143 | 0.5307 | 0.0 | nan | 0.0 | 0.4051 | 0.0116 | 0.0 | 0.9314 | 0.8400 | 0.9539 | 0.0 | 0.0921 | 0.2558 | 0.0 | nan | 0.7584 | 0.7706 | 0.7892 | 0.4626 | 0.3268 | nan | 0.3678 | 0.5054 | 0.0 | 0.7811 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3947 | 0.0 | 0.0 | 0.6604 | 0.0 | 0.3306 | 0.4515 | 0.0 | nan | 0.0 | 0.2265 | 0.0116 | 0.0 | 0.8386 | 0.7409 | 0.9204 | 0.0 | 0.0850 | 0.1887 | 0.0 | | 0.358 | 127.78 | 6900 | 0.5287 | 0.3110 | 0.3729 | 0.8465 | nan | 0.8062 | 0.9359 | 0.8173 | 0.8927 | 0.3346 | nan | 0.4527 | 0.6392 | 0.0 | 0.9354 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6945 | 0.0 | 0.0 | 0.8722 | 0.0 | 0.4896 | 0.5317 | 0.0 | nan | 0.0 | 0.4070 | 0.0 | 0.0 | 0.9436 | 0.8467 | 0.9449 | 0.0 | 0.1243 | 0.2646 | 0.0 | nan | 0.7567 | 0.8356 | 0.7873 | 0.6388 | 0.3087 | nan | 0.3575 | 0.4948 | 0.0 | 0.7958 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4146 | 0.0 | 0.0 | 0.6798 | 0.0 | 0.3797 | 0.4630 | 0.0 | nan | 0.0 | 0.2283 | 0.0 | 0.0 | 0.8356 | 0.7467 | 0.9182 | 0.0 | 0.1175 | 0.1940 | 0.0 | | 0.3402 | 129.63 | 7000 | 0.6208 | 0.2946 | 0.3637 | 0.8141 | nan | 0.7658 | 0.8754 | 0.8158 | 0.9118 | 0.2322 | nan | 0.4017 | 0.6637 | 0.0 | 0.9438 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6933 | 0.0 | 0.0 | 0.8763 | 0.0 | 0.3895 | 0.5601 | 0.0 | nan | 0.0 | 0.4252 | 0.0043 | 0.0 | 0.9423 | 0.7810 | 0.9448 | 0.0000 | 0.1253 | 0.2865 | 0.0 | nan | 0.7060 | 0.7779 | 0.7885 | 0.4813 | 0.2236 | nan | 0.3133 | 0.4921 | 0.0 | 0.7863 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4236 | 0.0 | 0.0 | 0.6817 | 0.0 | 0.3292 | 0.4440 | 0.0 | nan | 0.0 | 0.2236 | 0.0043 | 0.0 | 0.8247 | 0.6964 | 0.9178 | 0.0000 | 0.1163 | 0.1976 | 0.0 | | 0.3218 | 131.48 | 7100 | 0.5444 | 0.3108 | 0.3748 | 0.8443 | nan | 0.8296 | 0.9244 | 0.8276 | 0.8878 | 0.2774 | nan | 0.4782 | 0.6750 | 0.0 | 0.9366 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6983 | 0.0 | 0.0 | 0.8664 | 0.0 | 0.4743 | 0.5451 | 0.0 | nan | 0.0 | 0.4187 | 0.0113 | 0.0 | 0.9391 | 0.8642 | 0.9558 | 0.0 | 0.1166 | 0.2684 | 0.0 | nan | 0.7636 | 0.8260 | 0.7984 | 0.6281 | 0.2647 | nan | 0.3705 | 0.5066 | 0.0 | 0.8001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4217 | 0.0 | 0.0 | 0.6783 | 0.0 | 0.3686 | 0.4581 | 0.0 | nan | 0.0 | 0.2178 | 0.0113 | 0.0 | 0.8396 | 0.7666 | 0.9213 | 0.0 | 0.1113 | 0.1943 | 0.0 | | 0.3413 | 133.33 | 7200 | 0.5473 | 0.3063 | 0.3680 | 0.8412 | nan | 0.8038 | 0.9272 | 0.7396 | 0.8885 | 0.2742 | nan | 0.4489 | 0.5761 | 0.0 | 0.9434 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6970 | 0.0 | 0.0 | 0.8722 | 0.0 | 0.5185 | 0.5545 | 0.0 | nan | 0.0 | 0.4060 | 0.0241 | 0.0 | 0.9384 | 0.8611 | 0.9453 | 0.0 | 0.1082 | 0.2489 | 0.0 | nan | 0.7450 | 0.8245 | 0.7280 | 0.6104 | 0.2595 | nan | 0.3532 | 0.4660 | 0.0 | 0.7846 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4313 | 0.0 | 0.0 | 0.6807 | 0.0 | 0.3896 | 0.4684 | 0.0 | nan | 0.0 | 0.2284 | 0.0241 | 0.0 | 0.8397 | 0.7610 | 0.9186 | 0.0 | 0.1022 | 0.1871 | 0.0 | | 0.3463 | 135.19 | 7300 | 0.6341 | 0.2922 | 0.3603 | 0.8106 | nan | 0.8087 | 0.8519 | 0.8052 | 0.9145 | 0.2425 | nan | 0.3711 | 0.5676 | 0.0 | 0.9336 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7046 | 0.0 | 0.0 | 0.8888 | 0.0 | 0.3923 | 0.5815 | 0.0 | nan | 0.0 | 0.4055 | 0.0319 | 0.0 | 0.9344 | 0.8036 | 0.9503 | 0.0 | 0.1152 | 0.2276 | 0.0 | nan | 0.7410 | 0.7674 | 0.7870 | 0.4522 | 0.2330 | nan | 0.3152 | 0.4495 | 0.0 | 0.7851 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4247 | 0.0 | 0.0 | 0.6553 | 0.0 | 0.3108 | 0.4330 | 0.0 | nan | 0.0 | 0.2290 | 0.0319 | 0.0 | 0.8273 | 0.7106 | 0.9198 | 0.0 | 0.1051 | 0.1720 | 0.0 | | 0.317 | 137.04 | 7400 | 0.5689 | 0.2996 | 0.3673 | 0.8346 | nan | 0.8380 | 0.9048 | 0.7202 | 0.8874 | 0.2300 | nan | 0.4682 | 0.6001 | 0.0 | 0.9282 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7278 | 0.0 | 0.0 | 0.8811 | 0.0 | 0.4430 | 0.5714 | 0.0 | nan | 0.0 | 0.4115 | 0.0148 | 0.0 | 0.9311 | 0.8477 | 0.9517 | 0.0 | 0.1019 | 0.2961 | 0.0 | nan | 0.7600 | 0.8107 | 0.7092 | 0.5843 | 0.2243 | nan | 0.3634 | 0.4741 | 0.0 | 0.7839 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3683 | 0.0 | 0.0 | 0.6667 | 0.0 | 0.3433 | 0.4519 | 0.0 | nan | 0.0 | 0.2331 | 0.0148 | 0.0 | 0.8387 | 0.7448 | 0.9201 | 0.0 | 0.0930 | 0.2020 | 0.0 | | 0.3241 | 138.89 | 7500 | 0.5921 | 0.3030 | 0.3698 | 0.8264 | nan | 0.7560 | 0.9038 | 0.8054 | 0.8993 | 0.2921 | nan | 0.4358 | 0.6497 | 0.0 | 0.9426 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6843 | 0.0 | 0.0 | 0.8596 | 0.0 | 0.4666 | 0.5531 | 0.0 | nan | 0.0014 | 0.4125 | 0.0280 | 0.0 | 0.9419 | 0.8345 | 0.9468 | 0.0005 | 0.1478 | 0.2726 | 0.0 | nan | 0.6935 | 0.8021 | 0.7869 | 0.5437 | 0.2719 | nan | 0.3428 | 0.4933 | 0.0 | 0.7917 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4134 | 0.0 | 0.0 | 0.6707 | 0.0 | 0.3632 | 0.4528 | 0.0 | nan | 0.0014 | 0.2150 | 0.0280 | 0.0 | 0.8367 | 0.7422 | 0.9203 | 0.0005 | 0.1346 | 0.1914 | 0.0 | | 0.3341 | 140.74 | 7600 | 0.5641 | 0.3038 | 0.3702 | 0.8325 | nan | 0.7624 | 0.9172 | 0.8114 | 0.8959 | 0.2940 | nan | 0.5063 | 0.6105 | 0.0 | 0.9434 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7179 | 0.0 | 0.0 | 0.8732 | 0.0 | 0.5230 | 0.5420 | 0.0 | nan | 0.0 | 0.4148 | 0.0425 | 0.0 | 0.9411 | 0.7719 | 0.9528 | 0.0 | 0.0840 | 0.2431 | 0.0 | nan | 0.7064 | 0.8174 | 0.7877 | 0.6132 | 0.2760 | nan | 0.3594 | 0.4823 | 0.0 | 0.7859 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4116 | 0.0 | 0.0 | 0.6715 | 0.0 | 0.3953 | 0.4613 | 0.0 | nan | 0.0 | 0.2236 | 0.0425 | 0.0 | 0.8241 | 0.6840 | 0.9219 | 0.0 | 0.0790 | 0.1794 | 0.0 | | 0.3135 | 142.59 | 7700 | 0.5712 | 0.3062 | 0.3709 | 0.8300 | nan | 0.7952 | 0.8986 | 0.8100 | 0.8619 | 0.3084 | nan | 0.4715 | 0.6006 | 0.0 | 0.9439 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6837 | 0.0 | 0.0 | 0.8669 | 0.0 | 0.5083 | 0.5475 | 0.0 | nan | 0.0 | 0.4053 | 0.0384 | 0.0 | 0.9443 | 0.8124 | 0.9524 | 0.0 | 0.1181 | 0.3029 | 0.0 | nan | 0.7270 | 0.8042 | 0.7907 | 0.5385 | 0.2877 | nan | 0.3610 | 0.4689 | 0.0 | 0.7784 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4431 | 0.0 | 0.0 | 0.6764 | 0.0 | 0.3905 | 0.4659 | 0.0 | nan | 0.0 | 0.2280 | 0.0384 | 0.0 | 0.8312 | 0.7224 | 0.9227 | 0.0 | 0.1114 | 0.2117 | 0.0 | | 0.2985 | 144.44 | 7800 | 0.5705 | 0.3063 | 0.3739 | 0.8331 | nan | 0.7844 | 0.9061 | 0.8011 | 0.8987 | 0.3105 | nan | 0.4674 | 0.6336 | 0.0 | 0.9448 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7174 | 0.0 | 0.0 | 0.8645 | 0.0 | 0.4836 | 0.5414 | 0.0 | nan | 0.0 | 0.4277 | 0.0445 | 0.0 | 0.9390 | 0.8448 | 0.9518 | 0.0003 | 0.1004 | 0.3014 | 0.0 | nan | 0.7238 | 0.8110 | 0.7871 | 0.5506 | 0.2869 | nan | 0.3545 | 0.4901 | 0.0 | 0.7879 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4047 | 0.0 | 0.0 | 0.6872 | 0.0 | 0.3776 | 0.4572 | 0.0 | nan | 0.0 | 0.2263 | 0.0445 | 0.0 | 0.8392 | 0.7464 | 0.9226 | 0.0003 | 0.0950 | 0.2101 | 0.0 | | 0.3083 | 146.3 | 7900 | 0.6255 | 0.3029 | 0.3735 | 0.8173 | nan | 0.7919 | 0.8576 | 0.8118 | 0.9101 | 0.3017 | nan | 0.4374 | 0.6462 | 0.0 | 0.9461 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7137 | 0.0 | 0.0 | 0.8706 | 0.0 | 0.5111 | 0.5445 | 0.0 | nan | 0.0001 | 0.4282 | 0.0589 | 0.0 | 0.9317 | 0.8537 | 0.9628 | 0.0000 | 0.1030 | 0.2713 | 0.0 | nan | 0.7389 | 0.7675 | 0.7857 | 0.4623 | 0.2774 | nan | 0.3477 | 0.4815 | 0.0 | 0.7777 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4220 | 0.0 | 0.0 | 0.6797 | 0.0 | 0.3926 | 0.4652 | 0.0 | nan | 0.0001 | 0.2292 | 0.0588 | 0.0 | 0.8421 | 0.7549 | 0.9219 | 0.0000 | 0.0939 | 0.1926 | 0.0 | | 0.3132 | 148.15 | 8000 | 0.6407 | 0.2987 | 0.3697 | 0.8084 | nan | 0.8056 | 0.8366 | 0.8045 | 0.9187 | 0.2881 | nan | 0.3901 | 0.6494 | 0.0 | 0.9456 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7065 | 0.0 | 0.0 | 0.8674 | 0.0 | 0.4835 | 0.5578 | 0.0 | nan | 0.0 | 0.4107 | 0.0690 | 0.0 | 0.9364 | 0.8069 | 0.9579 | 0.0 | 0.1392 | 0.2549 | 0.0 | nan | 0.7400 | 0.7511 | 0.7860 | 0.4288 | 0.2705 | nan | 0.3211 | 0.4907 | 0.0 | 0.7845 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4064 | 0.0 | 0.0 | 0.6776 | 0.0 | 0.3750 | 0.4463 | 0.0 | nan | 0.0 | 0.2323 | 0.0689 | 0.0 | 0.8346 | 0.7221 | 0.9215 | 0.0 | 0.1189 | 0.1827 | 0.0 | | 0.3227 | 150.0 | 8100 | 0.6215 | 0.3010 | 0.3747 | 0.8154 | nan | 0.8072 | 0.8523 | 0.7987 | 0.9122 | 0.3387 | nan | 0.4049 | 0.6521 | 0.0 | 0.9464 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7268 | 0.0 | 0.0 | 0.8526 | 0.0 | 0.5301 | 0.5632 | 0.0 | nan | 0.0015 | 0.4353 | 0.0597 | 0.0 | 0.9352 | 0.8036 | 0.9574 | 0.0 | 0.1202 | 0.2916 | 0.0 | nan | 0.7319 | 0.7712 | 0.7839 | 0.4639 | 0.3115 | nan | 0.3235 | 0.4815 | 0.0 | 0.7813 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3954 | 0.0 | 0.0 | 0.6800 | 0.0 | 0.3930 | 0.4522 | 0.0 | nan | 0.0015 | 0.2349 | 0.0596 | 0.0 | 0.8319 | 0.7106 | 0.9225 | 0.0 | 0.1071 | 0.1947 | 0.0 | | 0.3041 | 151.85 | 8200 | 0.6365 | 0.2982 | 0.3695 | 0.8091 | nan | 0.7813 | 0.8516 | 0.8100 | 0.9057 | 0.2989 | nan | 0.4138 | 0.6557 | 0.0 | 0.9422 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7155 | 0.0 | 0.0 | 0.8717 | 0.0 | 0.5273 | 0.5454 | 0.0 | nan | 0.0 | 0.4293 | 0.0595 | 0.0 | 0.9354 | 0.7484 | 0.9557 | 0.0 | 0.1301 | 0.2483 | 0.0 | nan | 0.7117 | 0.7612 | 0.7891 | 0.4543 | 0.2787 | nan | 0.3305 | 0.4950 | 0.0 | 0.7874 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4007 | 0.0 | 0.0 | 0.6772 | 0.0 | 0.3923 | 0.4632 | 0.0 | nan | 0.0 | 0.2342 | 0.0594 | 0.0 | 0.8230 | 0.6691 | 0.9227 | 0.0 | 0.1142 | 0.1800 | 0.0 | | 0.3295 | 153.7 | 8300 | 0.5763 | 0.3064 | 0.3745 | 0.8319 | nan | 0.8091 | 0.9000 | 0.8155 | 0.8927 | 0.3048 | nan | 0.4385 | 0.6734 | 0.0 | 0.9391 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7114 | 0.0 | 0.0 | 0.8707 | 0.0 | 0.4884 | 0.5694 | 0.0 | nan | 0.0032 | 0.4179 | 0.0581 | 0.0 | 0.9385 | 0.8107 | 0.9552 | 0.0006 | 0.1316 | 0.2550 | 0.0 | nan | 0.7460 | 0.8059 | 0.7926 | 0.5582 | 0.2844 | nan | 0.3545 | 0.5009 | 0.0 | 0.7892 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4184 | 0.0 | 0.0 | 0.6741 | 0.0 | 0.3769 | 0.4455 | 0.0 | nan | 0.0032 | 0.2317 | 0.0581 | 0.0 | 0.8317 | 0.7120 | 0.9232 | 0.0005 | 0.1162 | 0.1807 | 0.0 | | 0.3057 | 155.56 | 8400 | 0.6602 | 0.2967 | 0.3669 | 0.8053 | nan | 0.7862 | 0.8400 | 0.8012 | 0.9083 | 0.2761 | nan | 0.3977 | 0.6548 | 0.0 | 0.9399 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7262 | 0.0 | 0.0 | 0.8830 | 0.0 | 0.4582 | 0.5390 | 0.0 | nan | 0.0 | 0.4382 | 0.0696 | 0.0 | 0.9380 | 0.7676 | 0.9517 | 0.0 | 0.1204 | 0.2454 | 0.0 | nan | 0.7257 | 0.7493 | 0.7832 | 0.4331 | 0.2603 | nan | 0.3344 | 0.4909 | 0.0 | 0.7899 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4164 | 0.0 | 0.0 | 0.6631 | 0.0 | 0.3619 | 0.4610 | 0.0 | nan | 0.0 | 0.2358 | 0.0695 | 0.0 | 0.8268 | 0.6858 | 0.9224 | 0.0 | 0.1038 | 0.1798 | 0.0 | | 0.3152 | 157.41 | 8500 | 0.6195 | 0.2986 | 0.3661 | 0.8115 | nan | 0.7876 | 0.8570 | 0.7994 | 0.8920 | 0.2891 | nan | 0.4035 | 0.6056 | 0.0 | 0.9417 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7090 | 0.0 | 0.0 | 0.8719 | 0.0 | 0.4959 | 0.5413 | 0.0 | nan | 0.0 | 0.4136 | 0.0566 | 0.0 | 0.9414 | 0.7717 | 0.9517 | 0.0 | 0.1198 | 0.2672 | 0.0 | nan | 0.7263 | 0.7633 | 0.7814 | 0.4550 | 0.2715 | nan | 0.3352 | 0.4721 | 0.0 | 0.7820 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4233 | 0.0 | 0.0 | 0.6671 | 0.0 | 0.3757 | 0.4677 | 0.0 | nan | 0.0 | 0.2407 | 0.0565 | 0.0 | 0.8255 | 0.6891 | 0.9216 | 0.0 | 0.1083 | 0.1912 | 0.0 | | 0.3041 | 159.26 | 8600 | 0.5761 | 0.3071 | 0.3735 | 0.8297 | nan | 0.8077 | 0.8910 | 0.8053 | 0.8839 | 0.3353 | nan | 0.4603 | 0.6015 | 0.0 | 0.9489 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6966 | 0.0 | 0.0 | 0.8701 | 0.0 | 0.4933 | 0.5427 | 0.0 | nan | 0.0082 | 0.4481 | 0.0761 | 0.0 | 0.9301 | 0.8454 | 0.9544 | 0.0005 | 0.1062 | 0.2469 | 0.0 | nan | 0.7406 | 0.7982 | 0.7855 | 0.5184 | 0.3024 | nan | 0.3652 | 0.4669 | 0.0 | 0.7807 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4413 | 0.0 | 0.0 | 0.6853 | 0.0 | 0.3815 | 0.4553 | 0.0 | nan | 0.0082 | 0.2312 | 0.0759 | 0.0 | 0.8414 | 0.7507 | 0.9229 | 0.0005 | 0.0961 | 0.1775 | 0.0 | | 0.3185 | 161.11 | 8700 | 0.5760 | 0.3058 | 0.3698 | 0.8296 | nan | 0.8094 | 0.8946 | 0.7956 | 0.8887 | 0.2897 | nan | 0.4223 | 0.5895 | 0.0 | 0.9357 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6889 | 0.0 | 0.0 | 0.8908 | 0.0 | 0.4640 | 0.5538 | 0.0 | nan | 0.0 | 0.4239 | 0.0692 | 0.0 | 0.9305 | 0.8418 | 0.9519 | 0.0001 | 0.1431 | 0.2510 | 0.0 | nan | 0.7455 | 0.7997 | 0.7789 | 0.5321 | 0.2717 | nan | 0.3473 | 0.4756 | 0.0 | 0.8013 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4311 | 0.0 | 0.0 | 0.6576 | 0.0 | 0.3605 | 0.4511 | 0.0 | nan | 0.0 | 0.2412 | 0.0691 | 0.0 | 0.8410 | 0.7459 | 0.9223 | 0.0001 | 0.1284 | 0.1839 | 0.0 | | 0.2908 | 162.96 | 8800 | 0.5655 | 0.3075 | 0.3717 | 0.8316 | nan | 0.8548 | 0.8841 | 0.7997 | 0.8745 | 0.3118 | nan | 0.4610 | 0.6024 | 0.0 | 0.9410 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6931 | 0.0 | 0.0 | 0.8861 | 0.0 | 0.4534 | 0.5383 | 0.0 | nan | 0.0015 | 0.4266 | 0.0689 | 0.0 | 0.9366 | 0.8053 | 0.9554 | 0.0 | 0.1346 | 0.2641 | 0.0 | nan | 0.7595 | 0.8021 | 0.7817 | 0.5396 | 0.2919 | nan | 0.3717 | 0.4720 | 0.0 | 0.7905 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4462 | 0.0 | 0.0 | 0.6634 | 0.0 | 0.3562 | 0.4639 | 0.0 | nan | 0.0015 | 0.2393 | 0.0688 | 0.0 | 0.8346 | 0.7212 | 0.9232 | 0.0 | 0.1193 | 0.1923 | 0.0 | | 0.3137 | 164.81 | 8900 | 0.5829 | 0.3094 | 0.3784 | 0.8279 | nan | 0.8476 | 0.8674 | 0.8118 | 0.9018 | 0.3237 | nan | 0.4801 | 0.6610 | 0.0 | 0.9387 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6851 | 0.0 | 0.0 | 0.8696 | 0.0 | 0.5109 | 0.5681 | 0.0 | nan | 0.0260 | 0.4276 | 0.0709 | 0.0 | 0.9330 | 0.8416 | 0.9554 | 0.0012 | 0.1333 | 0.2547 | 0.0 | nan | 0.7562 | 0.7893 | 0.7902 | 0.5123 | 0.3055 | nan | 0.3768 | 0.4921 | 0.0 | 0.7978 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 0.0 | 0.0 | 0.6754 | 0.0 | 0.3867 | 0.4408 | 0.0 | nan | 0.0260 | 0.2316 | 0.0708 | 0.0 | 0.8396 | 0.7418 | 0.9237 | 0.0010 | 0.1173 | 0.1797 | 0.0 | | 0.3219 | 166.67 | 9000 | 0.5812 | 0.3065 | 0.3750 | 0.8278 | nan | 0.8354 | 0.8788 | 0.8041 | 0.8834 | 0.2990 | nan | 0.4594 | 0.6655 | 0.0 | 0.9395 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6980 | 0.0 | 0.0 | 0.8601 | 0.0 | 0.5069 | 0.5685 | 0.0 | nan | 0.0113 | 0.4156 | 0.0664 | 0.0 | 0.9440 | 0.8108 | 0.9521 | 0.0001 | 0.1291 | 0.2716 | 0.0 | nan | 0.7565 | 0.7902 | 0.7828 | 0.5219 | 0.2845 | nan | 0.3688 | 0.4922 | 0.0 | 0.7966 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4240 | 0.0 | 0.0 | 0.6768 | 0.0 | 0.3877 | 0.4481 | 0.0 | nan | 0.0113 | 0.2327 | 0.0664 | 0.0 | 0.8308 | 0.7154 | 0.9230 | 0.0001 | 0.1124 | 0.1869 | 0.0 | | 0.3181 | 168.52 | 9100 | 0.5632 | 0.3112 | 0.3765 | 0.8367 | nan | 0.8125 | 0.9072 | 0.8124 | 0.8963 | 0.3044 | nan | 0.4647 | 0.6697 | 0.0 | 0.9359 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6879 | 0.0 | 0.0 | 0.8771 | 0.0 | 0.5085 | 0.5560 | 0.0 | nan | 0.0039 | 0.4244 | 0.0703 | 0.0 | 0.9367 | 0.8280 | 0.9532 | 0.0 | 0.1309 | 0.2672 | 0.0 | nan | 0.7474 | 0.8113 | 0.7892 | 0.5707 | 0.2882 | nan | 0.3704 | 0.5031 | 0.0 | 0.7988 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4314 | 0.0 | 0.0 | 0.6778 | 0.0 | 0.3900 | 0.4604 | 0.0 | nan | 0.0039 | 0.2372 | 0.0702 | 0.0 | 0.8390 | 0.7407 | 0.9234 | 0.0 | 0.1173 | 0.1872 | 0.0 | | 0.3009 | 170.37 | 9200 | 0.5671 | 0.3095 | 0.3743 | 0.8326 | nan | 0.7939 | 0.9018 | 0.7926 | 0.8902 | 0.3160 | nan | 0.4603 | 0.6415 | 0.0 | 0.9414 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6804 | 0.0 | 0.0 | 0.8815 | 0.0 | 0.4974 | 0.5528 | 0.0 | nan | 0.0000 | 0.4233 | 0.0749 | 0.0 | 0.9339 | 0.8322 | 0.9566 | 0.0 | 0.1296 | 0.2770 | 0.0 | nan | 0.7279 | 0.8041 | 0.7736 | 0.5652 | 0.2951 | nan | 0.3698 | 0.4960 | 0.0 | 0.7938 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4395 | 0.0 | 0.0 | 0.6714 | 0.0 | 0.3837 | 0.4627 | 0.0 | nan | 0.0000 | 0.2368 | 0.0747 | 0.0 | 0.8379 | 0.7389 | 0.9235 | 0.0 | 0.1161 | 0.1946 | 0.0 | | 0.2873 | 172.22 | 9300 | 0.6113 | 0.3047 | 0.3720 | 0.8176 | nan | 0.8107 | 0.8536 | 0.7603 | 0.8949 | 0.3232 | nan | 0.4761 | 0.6422 | 0.0 | 0.9415 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6799 | 0.0 | 0.0 | 0.8720 | 0.0 | 0.5023 | 0.5457 | 0.0 | nan | 0.0034 | 0.4146 | 0.0717 | 0.0 | 0.9439 | 0.8035 | 0.9521 | 0.0 | 0.1299 | 0.2839 | 0.0 | nan | 0.7355 | 0.7675 | 0.7422 | 0.4826 | 0.3027 | nan | 0.3715 | 0.4933 | 0.0 | 0.7896 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4421 | 0.0 | 0.0 | 0.6666 | 0.0 | 0.3881 | 0.4723 | 0.0 | nan | 0.0034 | 0.2350 | 0.0716 | 0.0 | 0.8305 | 0.7183 | 0.9229 | 0.0 | 0.1152 | 0.1992 | 0.0 | | 0.2856 | 174.07 | 9400 | 0.6091 | 0.3045 | 0.3713 | 0.8183 | nan | 0.8177 | 0.8508 | 0.7884 | 0.9070 | 0.3274 | nan | 0.4412 | 0.5971 | 0.0 | 0.9437 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6904 | 0.0 | 0.0 | 0.8760 | 0.0 | 0.5037 | 0.5471 | 0.0 | nan | 0.0023 | 0.4093 | 0.0729 | 0.0 | 0.9395 | 0.8289 | 0.9513 | 0.0000 | 0.1123 | 0.2745 | 0.0 | nan | 0.7401 | 0.7694 | 0.7705 | 0.4745 | 0.3070 | nan | 0.3570 | 0.4797 | 0.0 | 0.7901 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4370 | 0.0 | 0.0 | 0.6642 | 0.0 | 0.3879 | 0.4663 | 0.0 | nan | 0.0023 | 0.2356 | 0.0728 | 0.0 | 0.8358 | 0.7333 | 0.9230 | 0.0000 | 0.1034 | 0.1937 | 0.0 | | 0.2803 | 175.93 | 9500 | 0.6404 | 0.3009 | 0.3704 | 0.8084 | nan | 0.8365 | 0.8208 | 0.7833 | 0.9062 | 0.3050 | nan | 0.4405 | 0.6203 | 0.0 | 0.9443 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6940 | 0.0 | 0.0 | 0.8667 | 0.0 | 0.5055 | 0.5494 | 0.0 | nan | 0.0084 | 0.4148 | 0.0772 | 0.0 | 0.9424 | 0.8074 | 0.9551 | 0.0001 | 0.1077 | 0.2664 | 0.0 | nan | 0.7454 | 0.7459 | 0.7680 | 0.4316 | 0.2897 | nan | 0.3571 | 0.4866 | 0.0 | 0.7930 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4255 | 0.0 | 0.0 | 0.6652 | 0.0 | 0.3877 | 0.4601 | 0.0 | nan | 0.0084 | 0.2306 | 0.0771 | 0.0 | 0.8314 | 0.7178 | 0.9235 | 0.0001 | 0.0969 | 0.1889 | 0.0 | | 0.2924 | 177.78 | 9600 | 0.6156 | 0.3045 | 0.3723 | 0.8156 | nan | 0.8293 | 0.8420 | 0.8051 | 0.8964 | 0.3365 | nan | 0.4651 | 0.6281 | 0.0 | 0.9443 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6806 | 0.0 | 0.0 | 0.8777 | 0.0 | 0.4957 | 0.5434 | 0.0 | nan | 0.0043 | 0.4293 | 0.0774 | 0.0 | 0.9387 | 0.7942 | 0.9562 | 0.0 | 0.1178 | 0.2514 | 0.0 | nan | 0.7508 | 0.7606 | 0.7848 | 0.4617 | 0.3134 | nan | 0.3712 | 0.4903 | 0.0 | 0.7912 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4384 | 0.0 | 0.0 | 0.6666 | 0.0 | 0.3850 | 0.4648 | 0.0 | nan | 0.0043 | 0.2308 | 0.0773 | 0.0 | 0.8320 | 0.7126 | 0.9232 | 0.0 | 0.1028 | 0.1836 | 0.0 | | 0.2911 | 179.63 | 9700 | 0.6039 | 0.3051 | 0.3743 | 0.8197 | nan | 0.8161 | 0.8573 | 0.8009 | 0.9013 | 0.3091 | nan | 0.4597 | 0.6407 | 0.0 | 0.9406 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7191 | 0.0 | 0.0 | 0.8787 | 0.0 | 0.5007 | 0.5561 | 0.0 | nan | 0.0046 | 0.4187 | 0.0825 | 0.0 | 0.9325 | 0.8335 | 0.9578 | 0.0000 | 0.1036 | 0.2642 | 0.0 | nan | 0.7434 | 0.7687 | 0.7825 | 0.4751 | 0.2917 | nan | 0.3667 | 0.4994 | 0.0 | 0.7998 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4127 | 0.0 | 0.0 | 0.6761 | 0.0 | 0.3878 | 0.4561 | 0.0 | nan | 0.0046 | 0.2352 | 0.0823 | 0.0 | 0.8393 | 0.7401 | 0.9235 | 0.0000 | 0.0883 | 0.1885 | 0.0 | | 0.3093 | 181.48 | 9800 | 0.6244 | 0.3021 | 0.3707 | 0.8132 | nan | 0.8240 | 0.8367 | 0.7819 | 0.9031 | 0.3158 | nan | 0.4523 | 0.6336 | 0.0 | 0.9419 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7047 | 0.0 | 0.0 | 0.8782 | 0.0 | 0.5024 | 0.5478 | 0.0 | nan | 0.0 | 0.4039 | 0.0761 | 0.0 | 0.9422 | 0.8036 | 0.9524 | 0.0 | 0.0992 | 0.2629 | 0.0 | nan | 0.7414 | 0.7575 | 0.7666 | 0.4537 | 0.2990 | nan | 0.3642 | 0.4913 | 0.0 | 0.7906 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4261 | 0.0 | 0.0 | 0.6655 | 0.0 | 0.3892 | 0.4639 | 0.0 | nan | 0.0 | 0.2339 | 0.0760 | 0.0 | 0.8311 | 0.7168 | 0.9226 | 0.0 | 0.0873 | 0.1892 | 0.0 | | 0.3194 | 183.33 | 9900 | 0.6384 | 0.3015 | 0.3707 | 0.8106 | nan | 0.8269 | 0.8295 | 0.7809 | 0.9036 | 0.3169 | nan | 0.4373 | 0.6407 | 0.0 | 0.9394 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7004 | 0.0 | 0.0 | 0.8774 | 0.0 | 0.4936 | 0.5511 | 0.0 | nan | 0.0004 | 0.4210 | 0.0726 | 0.0 | 0.9434 | 0.8072 | 0.9462 | 0.0 | 0.1149 | 0.2605 | 0.0 | nan | 0.7423 | 0.7508 | 0.7639 | 0.4418 | 0.2988 | nan | 0.3584 | 0.4963 | 0.0 | 0.7976 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4212 | 0.0 | 0.0 | 0.6662 | 0.0 | 0.3830 | 0.4618 | 0.0 | nan | 0.0004 | 0.2347 | 0.0725 | 0.0 | 0.8311 | 0.7208 | 0.9214 | 0.0 | 0.0993 | 0.1875 | 0.0 | | 0.3174 | 185.19 | 10000 | 0.6350 | 0.3022 | 0.3724 | 0.8117 | nan | 0.8240 | 0.8308 | 0.7789 | 0.9052 | 0.3152 | nan | 0.4703 | 0.6444 | 0.0 | 0.9424 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7116 | 0.0 | 0.0 | 0.8716 | 0.0 | 0.4736 | 0.5408 | 0.0 | nan | 0.0048 | 0.4202 | 0.0754 | 0.0 | 0.9437 | 0.8196 | 0.9525 | 0.0 | 0.1041 | 0.2872 | 0.0 | nan | 0.7413 | 0.7520 | 0.7629 | 0.4453 | 0.2976 | nan | 0.3701 | 0.4953 | 0.0 | 0.7962 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4152 | 0.0 | 0.0 | 0.6712 | 0.0 | 0.3749 | 0.4613 | 0.0 | nan | 0.0048 | 0.2337 | 0.0753 | 0.0 | 0.8324 | 0.7277 | 0.9234 | 0.0 | 0.0913 | 0.1997 | 0.0 | ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6

bcd67bfd3b7536cb7108dd262af7691c

mkhairil/autotrain-text-sentiment-indonlu-smse-2885384370

mkhairil

bert

8

49

transformers

0

text-classification

true

false

apache-2.0

['unk', 'id']

['mkhairil/autotrain-data-text-sentiment-indonlu-smse']

{'emissions': 5.395117116799661}

0

['autotrain', 'text-classification']

false

true

1,265

false

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - fine tuned with indonlp/indonlu dataset. (10000 rows from https://huggingface.co/datasets/indonlp/indonlu/viewer/smsa/train) - Model ID: 2885384370 - CO2 Emissions (in grams): 5.3951 ## Validation Metrics - Loss: 0.270 - Accuracy: 0.900 - Macro F1: 0.866 - Micro F1: 0.900 - Weighted F1: 0.899 - Macro Precision: 0.874 - Micro Precision: 0.900 - Weighted Precision: 0.899 - Macro Recall: 0.859 - Micro Recall: 0.900 - Weighted Recall: 0.900 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/mkhairil/autotrain-text-sentiment-indonlu-smse-2885384370 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("mkhairil/autotrain-text-sentiment-indonlu-smse-2885384370", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("mkhairil/autotrain-text-sentiment-indonlu-smse-2885384370", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

200b8d91ed50cc92bd9df8466cb35e3c

gokuls/distilbert_add_GLUE_Experiment_logit_kd_qnli_256

gokuls

distilbert

17

2

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,752

false

# distilbert_add_GLUE_Experiment_logit_kd_qnli_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3989 - Accuracy: 0.5874 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4156 | 1.0 | 410 | 0.4111 | 0.5054 | | 0.4078 | 2.0 | 820 | 0.4018 | 0.5799 | | 0.3962 | 3.0 | 1230 | 0.3989 | 0.5874 | | 0.3899 | 4.0 | 1640 | 0.4018 | 0.5867 | | 0.3851 | 5.0 | 2050 | 0.4032 | 0.5799 | | 0.3802 | 6.0 | 2460 | 0.4118 | 0.5728 | | 0.3762 | 7.0 | 2870 | 0.4093 | 0.5718 | | 0.3717 | 8.0 | 3280 | 0.4100 | 0.5737 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

04091592db31fd9b89de156f45235ecb

DrishtiSharma/whisper-large-v2-kk-v1

DrishtiSharma

whisper

15

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['kk']

['mozilla-foundation/common_voice_11_0']

null

0

['whisper-event', 'generated_from_trainer']

true

1,313

false

# Whisper Large Kazakh - Drishti Sharma This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4794 - Wer: 35.5486 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0007 | 10.0 | 1000 | 0.4794 | 35.5486 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

7667c53bd3bf3c3fdbd9f1ffaaa5e962

seonghyeonye/flipped_3B

seonghyeonye

t5

9

transformers

3

text2text-generation

true

false

apache-2.0

['en']

['bigscience/P3']

null

2

0

2

0

[]

false

true

5,687

false

**Official repository**: [seonghyeonye/Flipped-Learning](https://github.com/seonghyeonye/Flipped-Learning) # Model Description FLIPPED uses a unique meta-learning method to show zero-shot task generalization on classification natural language prompts, outperforming GPT-3 and T0-11B on many tasks with a 4x smaller scale. It is a series of encoder-decoder model trained on a numerous classification dataset. We show inputs and its corresponding outputs of each instances in each dataset to FLIPPED, and train it to generate its possible instruction. We add unlikelihood loss in order **not** to generate the instruction when given the same input, but a wrong output. To obtain FLIPPED, we fine-tune a T5 model in a given scale on a multitask mixture covering many different classification NLP tasks. # Intended uses You can use the models to perform inference on tasks by specifying your input-output NLP query in a "input: {input}\noutput: {output}" form , and the model will predict the instruction. For example, You can try *"input: <extra_id_0> this is the best cast iron skillet you will ever buy<extra_id_1>\noutput: Positive"* as an input, and the model will hopefully generate *"Title: Review:"*. # How to use Our overall explanation models along with ablations can be found in our [paper](https://arxiv.org/abs/2210.02969). We recommend using the [FLIPPED-11B](seonghyeonye/flipped_11B) checkpoint as it leads (on average) to the best performances on a variety of NLP tasks. |Model|Number of parameters| |-|-| |[Flipped_11B](https://huggingface.co/seonghyeonye/flipped_11B)|11 billion| |[Flipped_3B](https://huggingface.co/seonghyeonye/flipped_3B)|3 billion| Here is how to download the model in PyTorch: ```python import torch from transformers import T5Tokenizer, T5ForConditionalGeneration model = T5ForConditionalGeneration.from_pretrained("seonghyeonye/flipped_3B") tokenizer = T5Tokenizer.from_pretrained("seonghyeonye/flipped_3B") ``` If you want to use another checkpoint, please replace the path in `T5Tokenizer` and `T5ForConditionalGeneration`. We also provide a quick [Jupyter Notebook](https://github.com/seonghyeonye/Flipped-Learning/blob/master/flipped_inference.ipynb) where you can inference with our method. **Note: the model was trained with fp32 activations. As such, we highly discourage running inference with fp16.** # Training procedure FLIPPED models are based on [T5](https://huggingface.co/google/t5-v1_1-xl), a Transformer-based encoder-decoder language model pre-trained with a masked language modeling-style objective on [C4](https://huggingface.co/datasets/c4). At a high level, the input text along with output label is fed to the encoder and the instruction text is produced by the decoder. The model is fine-tuned to autoregressively generate the target. We also feed input text along with a wrong input, adding an unlikelihood loss in order not to make model produce the proper instruction in that case. Here are our training details. Training details: - Fine-tuning steps: 5'000 - Input sequence length: 512 - Target sequence length: 128 - Batch size: 240 - Optimizer: Adafactor - Learning rate: 5e-5 - Dropout: 0.1 - Sampling strategy: proportional to the number of examples in each dataset (we randomly sampled any dataset if it has over 500'000 examples so that it has at most 500'000 examples. Also, we randomly choose which instruction to generate for each training steps, so ideally each instruction appears *num_examples/num_templates* while training.) # Training data We trained different variants T0 with different mixtures of datasets. |Model|Training datasets| |--|--| |FLIPPED_11B|- Multiple-Choice QA: CommonsenseQA, DREAM, QUAIL, QuaRTz, Social IQA, WiQA, Cosmos, QASC, Quarel, SciQ - Sentiment: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp - Topic Classification: AG News, DBPedia - Paraphrase Identification: MRPC, PAWS, QQP| |FLIPPED_3B|Same as FLIPPED_11B| We only choose prompts examples that has output lables, which can be found on the dataset page. # Evaluation data We evaluate our models on following datasets: |Task category|Datasets| |-|-| |Natural language inference|ANLI(R1, R2, R3), CB, RTE| |Coreference resolution|WSC, Winogrande| |Word sense disambiguation|WiC| |Sentence completion|COPA, HellaSwag, Story Cloze| |QA|PIQA, ARC-Challenge, OpenbookQA| We also evaluate FLIPPED on a subset of [BIG-bench benchmark](https://github.com/google/BIG-bench): - Code description task - Conceptual combinations - Hindu knowledge json - Known unknowns - Language identification - Logic grid puzzle task - Logical deduction - Common misconceptions - Movie dialog same or different - Novel concepts - Strategyqa - Formal fallacies syllogisms negation - VitaminC - Winowhy multiple choice # Label generalization We evaluate the robustness of models on following datasets with changing the output label of the datasets. The substitute words can be found in our [paper](https://arxiv.org/abs/2210.02969). |Task category|(Datasets, Template name)| |-|-| |Unseen tasks|(WSC, does the pronoun refer to), (CB, can we infer), (RTE, MNLI crowdsource)| |Seen tasks|(IMDB, Reviewer Enjoyment Yes No), (PAWS, Meaning) | The template name we used can be found in the [promptsource template library](https://github.com/bigscience-workshop/promptsource/tree/main/promptsource/templates). # BibTeX entry and citation info ```bibtex @article{ye2022guess, title={Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners}, author={Ye, Seonghyeon and Kim, Doyoung and Jang, Joel and Shin, Joongbo and Seo, Minjoon}, journal={arXiv preprint arXiv:2210.02969}, year={2022} } ```

40038816cad97d3801810e8f78f405f9

robkayinto/xlm-roberta-base-finetuned-panx-de

robkayinto

xlm-roberta

12

1

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,320

false

# xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1397 - F1: 0.8609 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2593 | 1.0 | 525 | 0.1628 | 0.8254 | | 0.1291 | 2.0 | 1050 | 0.1420 | 0.8450 | | 0.0817 | 3.0 | 1575 | 0.1397 | 0.8609 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.2+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

438afdb581992f6317404ce0ac5cbdcc

jonatasgrosman/exp_w2v2t_es_unispeech-sat_s42

jonatasgrosman

unispeech-sat

10

340

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['es']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

462

false

# exp_w2v2t_es_unispeech-sat_s42 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 (es)](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.

2aea92017845a667a4ff308c33445914

AnnihilationOperator/ofa-huge-caption

AnnihilationOperator

ofa

6

0

transformers

0

null

true

false

apache-2.0

null

0

[]

false

true

3,728

false

# OFA-huge-caption This is the **huge** version of OFA pretrained model finetuned on COCO captioning task, forked & converted from the [original fairseq version](https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_huge_best.pt) and compressed into float16. The conversion script is custom, but the procedure described [Issue #171](https://github.com/OFA-Sys/OFA/issues/171) should also apply (quantization is not performed, but that's trivial). You will need a [OFA modified version of transformers](https://github.com/OFA-Sys/OFA/tree/feature/add_transformers) to use this model. No idea why it is still not in master. Tips: You can just copy-paste the `transformers` folder into your project and rename it, then monkey-patch the `transformers` module to point to your local copy to avoid having to install it. ## Original README below ## Introduction This is the **huge** version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple sequence-to-sequence learning framework. The directory includes 4 files, namely `config.json` which consists of model configuration, `vocab.json` and `merge.txt` for our OFA tokenizer, and lastly `pytorch_model.bin` which consists of model weights. There is no need to worry about the mismatch between Fairseq and transformers, since we have addressed the issue yet. ## How to use To use it in transformers, please refer to <https://github.com/OFA-Sys/OFA/tree/feature/add_transformers>. Install the transformers and download the models as shown below. ```bash git clone --single-branch --branch feature/add_transformers https://github.com/OFA-Sys/OFA.git pip install OFA/transformers/ git clone https://huggingface.co/OFA-Sys/OFA-huge ``` After, refer the path to OFA-huge to `ckpt_dir`, and prepare an image for the testing example below. Also, ensure that you have pillow and torchvision in your environment. ```python >>> from PIL import Image >>> from torchvision import transforms >>> from transformers import OFATokenizer, OFAModel >>> from generate import sequence_generator >>> mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] >>> resolution = 480 >>> patch_resize_transform = transforms.Compose([ lambda image: image.convert("RGB"), transforms.Resize((resolution, resolution), interpolation=Image.BICUBIC), transforms.ToTensor(), transforms.Normalize(mean=mean, std=std) ]) >>> tokenizer = OFATokenizer.from_pretrained(ckpt_dir) >>> txt = " what does the image describe?" >>> inputs = tokenizer([txt], return_tensors="pt").input_ids >>> img = Image.open(path_to_image) >>> patch_img = patch_resize_transform(img).unsqueeze(0) # using the generator of fairseq version >>> model = OFAModel.from_pretrained(ckpt_dir, use_cache=True) >>> generator = sequence_generator.SequenceGenerator( tokenizer=tokenizer, beam_size=5, max_len_b=16, min_len=0, no_repeat_ngram_size=3, ) >>> data = {} >>> data["net_input"] = {"input_ids": inputs, 'patch_images': patch_img, 'patch_masks':torch.tensor([True])} >>> gen_output = generator.generate([model], data) >>> gen = [gen_output[i][0]["tokens"] for i in range(len(gen_output))] # using the generator of huggingface version >>> model = OFAModel.from_pretrained(ckpt_dir, use_cache=False) >>> gen = model.generate(inputs, patch_images=patch_img, num_beams=5, no_repeat_ngram_size=3) >>> print(tokenizer.batch_decode(gen, skip_special_tokens=True)) ```

82a3a5d796f4566cc3498ee89933c14f