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davanstrien
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pytorchmodelmetadata

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argv947059/example-based-ner-bert
0405e52e4ff75f97262c9e3241a4fcfac5bee3c9
2021-05-19T00:02:49.000Z
[ "pytorch", "jax", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
argv947059
null
argv947059/example-based-ner-bert
2
null
transformers
23,700
hello
aristotletan/t5-small-finetuned-xsum
826e7dccf2be1edf69846f244b8db983b4ef4019
2021-07-22T00:18:39.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "dataset:wsj_markets", "transformers", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible" ]
text2text-generation
false
aristotletan
null
aristotletan/t5-small-finetuned-xsum
2
null
transformers
23,701
--- license: apache-2.0 tags: - generated_from_trainer datasets: - wsj_markets metrics: - rouge model_index: - name: t5-small-finetuned-xsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wsj_markets type: wsj_markets args: default metric: name: Rouge1 type: rouge value: 10.4492 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wsj_markets dataset. It achieves the following results on the evaluation set: - Loss: 1.1447 - Rouge1: 10.4492 - Rouge2: 3.9563 - Rougel: 9.3368 - Rougelsum: 9.9828 - 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: 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: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:| | 2.2742 | 1.0 | 868 | 1.3135 | 9.4644 | 2.618 | 8.4048 | 8.9764 | 19.0 | | 1.4607 | 2.0 | 1736 | 1.2134 | 9.6327 | 3.8535 | 9.0703 | 9.2466 | 19.0 | | 1.3579 | 3.0 | 2604 | 1.1684 | 10.1616 | 3.5498 | 9.2294 | 9.4507 | 19.0 | | 1.3314 | 4.0 | 3472 | 1.1514 | 10.0621 | 3.6907 | 9.1635 | 9.4955 | 19.0 | | 1.3084 | 5.0 | 4340 | 1.1447 | 10.4492 | 3.9563 | 9.3368 | 9.9828 | 19.0 | ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Datasets 1.10.0 - Tokenizers 0.10.3
arjunth2001/priv_sum
349d0b1f365406eb05a8afc428ec1bb50cf8255f
2021-10-07T07:04:17.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
arjunth2001
null
arjunth2001/priv_sum
2
null
transformers
23,702
Entry not found
arman0320/bert-base-cased-wikitext2
0c077326d4943da35e4891f0160999c086cab2ae
2022-01-25T05:51:08.000Z
[ "pytorch", "tensorboard", "bert", "fill-mask", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
fill-mask
false
arman0320
null
arman0320/bert-base-cased-wikitext2
2
null
transformers
23,703
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-cased-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-wikitext2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.8596 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 7.0963 | 1.0 | 2346 | 7.0570 | | 6.9063 | 2.0 | 4692 | 6.8721 | | 6.8585 | 3.0 | 7038 | 6.8931 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.0 - Tokenizers 0.10.3
arnolfokam/roberta-base-kin
68c304d76bc5dcf1042f46e4174fc5972f5ad5d7
2021-11-24T11:46:30.000Z
[ "pytorch", "roberta", "token-classification", "kin", "dataset:masakhaner", "transformers", "NER", "license:apache-2.0", "autotrain_compatible" ]
token-classification
false
arnolfokam
null
arnolfokam/roberta-base-kin
2
null
transformers
23,704
--- language: - kin tags: - NER datasets: - masakhaner metrics: - f1 - precision - recall license: apache-2.0 widget: - text: "Ambasaderi Bellomo yavuze ko bishimira ubufatanye burambye hagati ya EU n’u Rwanda, bushingiye nanone ku bufatanye hagati y’imigabane ya Afurika n’u Burayi." --- # Model description **roberta-base-kin** is a model based on the fine-tuned RoBERTa base model. It has been trained to recognize four types of entities: - dates & time (DATE) - Location (LOC) - Organizations (ORG) - Person (PER) # Intended Use - Intended to be used for research purposes concerning Named Entity Recognition for African Languages. - Not intended for practical purposes. # Training Data This model was fine-tuned on the Kinyarwanda corpus **(kin)** of the [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset. However, we thresholded the number of entity groups per sentence in this dataset to 10 entity groups. # Training procedure This model was trained on a single NVIDIA P5000 from [Paperspace](https://www.paperspace.com) #### Hyperparameters - **Learning Rate:** 5e-5 - **Batch Size:** 32 - **Maximum Sequence Length:** 164 - **Epochs:** 30 # Evaluation Data We evaluated this model on the test split of the Kinyarwandan corpus **(kin)** present in the [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) with no thresholding. # Metrics - Precision - Recall - F1-score # Limitations - The size of the pre-trained language model prevents its usage in anything other than research. - Lack of analysis concerning the bias and fairness in these models may make them dangerous if deployed into production system. - The train data is a less populated version of the original dataset in terms of entity groups per sentence. Therefore, this can negatively impact the performance. # Caveats and Recommendations - The topics in the dataset corpus are centered around **News**. Future training could be done with a more diverse corpus. # Results Model Name| Precision | Recall | F1-score -|-|-|- **roberta-base-kin**| 76.26 | 80.58 |78.36 # Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("arnolfokam/roberta-base-kin") model = AutoModelForTokenClassification.from_pretrained("arnolfokam/roberta-base-kin") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Rayon Sports yasinyishije rutahizamu w’Umurundi" ner_results = nlp(example) print(ner_results) ```
artursz/wav2vec2-large-xls-r-300m-lv-v05
786710d3426c87d89f70a0fb46dcb8a1b07c604c
2021-11-23T02:47:04.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
artursz
null
artursz/wav2vec2-large-xls-r-300m-lv-v05
2
null
transformers
23,705
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-lv-v05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-lv-v05 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3862 - Wer: 0.2588 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8836 | 2.81 | 400 | 0.8722 | 0.7244 | | 0.5365 | 5.63 | 800 | 0.4622 | 0.4812 | | 0.277 | 8.45 | 1200 | 0.4348 | 0.4056 | | 0.1947 | 11.27 | 1600 | 0.4223 | 0.3636 | | 0.1655 | 14.08 | 2000 | 0.4084 | 0.3465 | | 0.1441 | 16.9 | 2400 | 0.4329 | 0.3497 | | 0.121 | 19.72 | 2800 | 0.4371 | 0.3324 | | 0.1062 | 22.53 | 3200 | 0.4202 | 0.3198 | | 0.0937 | 25.35 | 3600 | 0.4063 | 0.3265 | | 0.0871 | 28.17 | 4000 | 0.4253 | 0.3255 | | 0.0755 | 30.98 | 4400 | 0.4368 | 0.3194 | | 0.0627 | 33.8 | 4800 | 0.4067 | 0.2908 | | 0.0595 | 36.62 | 5200 | 0.3929 | 0.2973 | | 0.0523 | 39.44 | 5600 | 0.3748 | 0.2817 | | 0.0434 | 42.25 | 6000 | 0.3769 | 0.2711 | | 0.0391 | 45.07 | 6400 | 0.3901 | 0.2653 | | 0.0319 | 47.88 | 6800 | 0.3862 | 0.2588 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
arunkumar629/distilbert-base-uncased-finetuned-squad
40857b79c934055573a668ef5b7b4c706506d6dd
2022-07-01T15:39:27.000Z
[ "pytorch", "tensorboard", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
arunkumar629
null
arunkumar629/distilbert-base-uncased-finetuned-squad
2
null
transformers
23,706
Entry not found
arvalinno/distilbert-base-uncased-finetuned-squad
7da41471e3ad86123c9dda68d5a6db670555a84e
2021-11-20T17:31:23.000Z
[ "pytorch", "tensorboard", "distilbert", "question-answering", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
arvalinno
null
arvalinno/distilbert-base-uncased-finetuned-squad
2
null
transformers
23,707
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-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: - Loss: 1.4232 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.7604 | 1.0 | 6366 | 1.5329 | | 1.4784 | 2.0 | 12732 | 1.3930 | | 1.3082 | 3.0 | 19098 | 1.4232 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
asahi417/lmqg-mt5-small-jaquad
66ae0f8701ec85fe6a4bc2d9fc265838598c71a8
2022-06-09T00:45:37.000Z
[ "pytorch", "mt5", "text2text-generation", "ja", "dataset:asahi417/qg_jaquad", "transformers", "question generation", "license:cc-by-4.0", "autotrain_compatible" ]
text2text-generation
false
asahi417
null
asahi417/lmqg-mt5-small-jaquad
2
null
transformers
23,708
--- language: ja tags: - question generation license: cc-by-4.0 datasets: - asahi417/qg_jaquad metrics: - bleu - meteor - rouge - bertscore widget: - text: "ゾフィーは貴族出身ではあったが王族出身ではなく、ハプスブルク家の皇位継承者であるフランツ・フェルディナントとの結婚は貴賤結婚となった。皇帝フランツ・ヨーゼフは、2人の間に生まれた子孫が皇位を継がないことを条件として結婚を承認していた。視察が予定されている<hl>6月28日<hl>は2人の14回目の結婚記念日であった。" example_title: "Question Generation Example 1" - text: "『クマのプーさん』の物語はまず1925年12月24日、『イヴニング・ニュース』紙のクリスマス特集号に短編作品として掲載された。これは『クマのプーさん』の第一章にあたる作品で、このときだけは挿絵をJ.H.ダウドがつけている。その後作品10話と挿絵が整い、刊行に先駆けて「イーヨーの誕生日」のエピソードが1926年8月に『ロイヤルマガジン』に、同年10月9日に『ニューヨーク・イヴニング・ポスト』紙に掲載されたあと、同年10月14日にロンドンで(メシュエン社)、21日にニューヨークで(ダットン社)『クマのプーさん』が刊行された。前著『ぼくたちがとてもちいさかったころ』がすでに大きな成功を収めていたこともあり、イギリスでは初版は前著の7倍に当たる<hl>3万5000部<hl>が刷られた。他方のアメリカでもその年の終わりまでに15万部を売り上げている。ただし依然として人気のあった前著を売り上げで追い越すには数年の時間を要した。" example_title: "Question Generation Example 2" - text: "フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め<hl>30数点<hl>しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。以下には若干の疑問作も含め、37点の基本情報を記載し、各作品について略説する。収録順序、推定制作年代は『「フェルメールとその時代展」図録』による。日本語の作品タイトルについては、上掲図録のほか、『「フェルメール展」図録』、『フェルメール生涯と作品』による。便宜上「1650年代の作品」「1660年代の作品」「1670年代の作品」の3つの節を設けたが、フェルメールの作品には制作年代不明のものが多く、推定制作年代については研究者や文献によって若干の差がある。" example_title: "Question Generation Example 3" - text: "東大寺は、六宗兼学の場として世に広く知られるようになった。六宗とはすなわち、法相宗(法性宗)、三論宗、倶舎宗(薩婆多宗)、成実宗、華厳宗(花厳宗)、律宗のことであり、すべて<hl>中国<hl>から起こり、伝来したものであった。当時の宗とは、教団というよりは仏教教理の学派に近い。それゆえ、兼学の場ができたとも言える。この様な兼学の形態は、南都の寺院では広く見られたものである。この六宗兼学の場(後、真言、天台加わって八宗兼学の場)の性格は、現在の東大寺でも見られるが、中でも重んじられたのが、本尊の大仏の性格が華厳経の教えに則ったものであることからも分かるように、華厳宗である。" example_title: "Question Generation Example 4" pipeline_tag: text2text-generation --- # MT5 SMALL fine-tuned for Japanese Question Generation MT5 SMALL Model fine-tuned on Japanese question generation dataset (JaQuAD) with an extensive hyper-parameter search. - [Online Demo](https://autoqg.net/) - [Project Repository](https://github.com/asahi417/lm-question-generation) ## Overview **Language model:** mt5-small **Language:** Japanese (ja) **Downstream-task:** Question Generation **Training data:** JaQuAD **Eval data:** JaQuAD **Code:** See [our repository](https://github.com/asahi417/lm-question-generation) ## Usage ### In Transformers ```python from transformers import pipeline model_path = 'asahi417/lmqg-mt5-small-squad' pipe = pipeline("text2text-generation", model_path) # Question Genration paragraph = '東大寺は、六宗兼学の場として世に広く知られるようになった。六宗とはすなわち、法相宗(法性宗)、三論宗、倶舎宗(薩婆多宗)、成実宗、華厳宗(花厳宗)、律宗のことであり、すべて中国から起こり、伝来したものであった。' # highlight an answer in the paragraph to generate question answer = '中国' highlight_token = '<hl>' input_text = paragraph.replace(answer, '{0} {1} {0}'.format(highlight_token, answer)) generation = pipe(input_text) print(generation) >>> [{'generated_text': '六宗はどこから始まったの?'}] ``` ## Evaluations Evaluation on the test set of [JaQuAD QG dataset](https://huggingface.co/datasets/asahi417/qg_jaquad). All evaluations were done using our [evaluation script](https://github.com/asahi417/lm-question-generation). | BLEU 4 | ROUGE L | METEOR | BERTScore | | ------ | -------- | ------ | --------- | | 30.49 | 50.87 | 29.03 | 80.87 | - [metric file](https://huggingface.co/asahi417/lmqg-mt5-small-jaquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.asahi417_qg_jaquad.default.json) ## Fine-tuning Parameters We ran grid search to find the best hyper-parameters and continued fine-tuning until the validation metric decrease. The best hyper-parameters can be found [here](https://huggingface.co/asahi417/lmqg-mt5-small-jaquad/raw/main/trainer_config.json), and fine-tuning script is released in [our repository](https://github.com/asahi417/lm-question-generation). ## Citation TBA
asahi417/lmqg-t5-small-squad
cf9e060a61fd91b5c0edcb9c99756b7b6be6a2e5
2022-06-09T18:16:32.000Z
[ "pytorch", "t5", "text2text-generation", "en", "dataset:asahi417/qg_squad", "transformers", "question generation", "license:cc-by-4.0", "autotrain_compatible" ]
text2text-generation
false
asahi417
null
asahi417/lmqg-t5-small-squad
2
null
transformers
23,709
--- language: en tags: - question generation license: cc-by-4.0 datasets: - asahi417/qg_squad metrics: - bleu - meteor - rouge - bertscore - moverscore widget: - text: "generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records." example_title: "Question Generation Example 1" - text: "generate question: Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records." example_title: "Question Generation Example 2" - text: "generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, <hl> Cadillac Records <hl> ." example_title: "Question Generation Example 3" pipeline_tag: text2text-generation --- # T5 SMALL fine-tuned for English Question Generation T5 SMALL Model fine-tuned on English question generation dataset (SQuAD) with an extensive hyper-parameter search. - [Online Demo](https://autoqg.net/) - [Project Repository](https://github.com/asahi417/lm-question-generation) ## Overview **Language model:** t5-small **Language:** English (en) **Downstream-task:** Question Generation **Training data:** SQuAD **Eval data:** SQuAD **Code:** See [our repository](https://github.com/asahi417/lm-question-generation) ## Usage ### In Transformers ```python from transformers import pipeline model_path = 'asahi417/lmqg-t5-small-squad' pipe = pipeline("text2text-generation", model_path) paragraph = 'Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.' # highlight an answer in the paragraph to generate question answer = 'Etta James' highlight_token = '<hl>' input_text = paragraph.replace(answer, '{0} {1} {0}'.format(highlight_token, answer)) input_text = 'generate question: {}'.format(input_text) # add task specific prefix generation = pipe(input_text) print(generation) >>> [{'generated_text': 'What is the name of the biopic that Beyonce starred in?'}] ``` ## Evaluations Evaluation on the test set of [SQuAD QG dataset](https://huggingface.co/datasets/asahi417/qg_squad). The results are comparable with the [leaderboard](https://paperswithcode.com/sota/question-generation-on-squad11) and previous works. All evaluations were done using our [evaluation script](https://github.com/asahi417/lm-question-generation). | BLEU 4 | ROUGE L | METEOR | BERTScore | MoverScore | | ------ | -------- | ------ | --------- | ---------- | | 24.39 | 51.43 | 25.83 | 90.20 | 63.88 | - [metric file](https://huggingface.co/asahi417/lmqg-t5-small-squad/raw/main/eval/metric.first.sentence.paragraph_answer.question.asahi417_qg_squad.default.json) ## Fine-tuning Parameters We ran grid search to find the best hyper-parameters and continued fine-tuning until the validation metric decrease. The best hyper-parameters can be found [here](https://huggingface.co/asahi417/lmqg-t5-small-squad/raw/main/trainer_config.json), and fine-tuning script is released in [our repository](https://github.com/asahi417/lm-question-generation). ## Citation TBA
tner/xlm-roberta-base-conll2003
51b23257995bf6cd353476522327b7c11cb47a30
2021-02-13T00:07:07.000Z
[ "pytorch", "xlm-roberta", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
tner
null
tner/xlm-roberta-base-conll2003
2
null
transformers
23,710
# XLM-RoBERTa for NER XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner). ## Usage ``` from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-conll2003") model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-conll2003") ```
tner/xlm-roberta-base-uncased-all-english
376fa301095f3615bf063b96863412aadab9fd9c
2021-02-12T23:35:06.000Z
[ "pytorch", "xlm-roberta", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
tner
null
tner/xlm-roberta-base-uncased-all-english
2
null
transformers
23,711
# XLM-RoBERTa for NER XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner). ## Usage ``` from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-all-english") model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-all-english") ```
tner/xlm-roberta-base-uncased-panx-dataset-en
502e2a70262e6190b1cbff176c15391c7260b948
2021-02-13T00:10:50.000Z
[ "pytorch", "xlm-roberta", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
tner
null
tner/xlm-roberta-base-uncased-panx-dataset-en
2
null
transformers
23,712
# XLM-RoBERTa for NER XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner). ## Usage ``` from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-panx-dataset-en") model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-panx-dataset-en") ```
tner/xlm-roberta-large-panx-dataset-es
d616f069e855ac85d86abd0cdc506b2308ff6456
2021-02-13T00:04:53.000Z
[ "pytorch", "xlm-roberta", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
tner
null
tner/xlm-roberta-large-panx-dataset-es
2
null
transformers
23,713
# XLM-RoBERTa for NER XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner). ## Usage ``` from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-panx-dataset-es") model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-large-panx-dataset-es") ```
tner/xlm-roberta-large-uncased-all-english
3b20a99f250c04c3b591406cef825ddcb190472a
2021-02-13T00:05:18.000Z
[ "pytorch", "xlm-roberta", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
tner
null
tner/xlm-roberta-large-uncased-all-english
2
null
transformers
23,714
# XLM-RoBERTa for NER XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner). ## Usage ``` from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-all-english") model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-all-english") ```
asakawa/distilroberta-base-finetuned-wikitext2
9242bb38a3a48ba2760395841c32dba6138fde84
2022-01-06T02:13:38.000Z
[ "pytorch", "tensorboard", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
asakawa
null
asakawa/distilroberta-base-finetuned-wikitext2
2
null
transformers
23,715
Entry not found
aschvin/english_wav2_vec_classification
e1cdece4e744c1ae54cbf1efb750155441381113
2022-01-23T09:43:23.000Z
[ "pytorch", "wav2vec2", "transformers" ]
null
false
aschvin
null
aschvin/english_wav2_vec_classification
2
null
transformers
23,716
Entry not found
assij/wav2vec2-common_voice-tr-demo-dist
490f083e2d0e0a1be0282cd38a367bdb40e153ee
2021-12-29T10:04:41.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
assij
null
assij/wav2vec2-common_voice-tr-demo-dist
2
null
transformers
23,717
Entry not found
aszidon/distilbertcustom
add1cce72b600f43acd0144923529bf2f8a3c888
2021-11-04T03:40:07.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
aszidon
null
aszidon/distilbertcustom
2
null
transformers
23,718
Entry not found
aszidon/distilbertcustom2
c284132d422004b400ad985068749394d9b5a19c
2021-11-05T03:06:40.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
aszidon
null
aszidon/distilbertcustom2
2
null
transformers
23,719
Entry not found
aszidon/distilbertcustom5
75f7dfb1dd8e460751978fe4e3901b5cd4630fc4
2021-11-08T04:00:23.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
aszidon
null
aszidon/distilbertcustom5
2
null
transformers
23,720
Entry not found
avioo1/distilbert-base-uncased-finetuned-squad
35c81a4d95ae768fcd25594950645e9c5353e2f4
2021-09-12T01:58:39.000Z
[ "pytorch", "tensorboard", "distilbert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
avioo1
null
avioo1/distilbert-base-uncased-finetuned-squad
2
null
transformers
23,721
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: - task: name: Question Answering type: question-answering dataset: name: squad type: squad args: plain_text --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.2125 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 1.2637 | 1.0 | 5533 | 1.2125 | ### Framework versions - Transformers 4.10.2 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3
avnish100/DialoGPT-small-rick
0200c757cb1f11b6e33dc1adcaafc6927e3c7201
2021-09-10T12:49:46.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
avnish100
null
avnish100/DialoGPT-small-rick
2
null
transformers
23,722
---- tags: - conversational --- #Rick DialoGPT model
aws-ai/pairsupcon-bert-base-uncased
80c78efa4f84fc7e3a5daba80ac9a128b7760aa4
2021-12-18T19:27:33.000Z
[ "pytorch", "bert", "transformers" ]
null
false
aws-ai
null
aws-ai/pairsupcon-bert-base-uncased
2
null
transformers
23,723
Entry not found
ayameRushia/wav2vec2-large-xlsr-indo-base
6c443cc964dfe10cfec172b1d799685d7fd7b0e3
2021-07-05T22:13:24.000Z
[ "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "id", "dataset:common_voice", "transformers", "audio", "speech", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
ayameRushia
null
ayameRushia/wav2vec2-large-xlsr-indo-base
2
null
transformers
23,724
--- language: id datasets: - common_voice tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Indonesia by Ayame Rushia results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice id type: common_voice args: id metrics: - name: Test WER type: wer value: ??? --- # Wav2Vec2-Large-XLSR-53-Indonesia Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Indonesia using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "id", split="test[:2%]"). processor = Wav2Vec2Processor.from_pretrained("ayameRushia/wav2vec2-large-xlsr-indonesia-demo") model = Wav2Vec2ForCTC.from_pretrained("ayameRushia/wav2vec2-large-xlsr-indonesia-demo") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "id", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("ayameRushia/wav2vec2-large-xlsr-indonesia-demo") model = Wav2Vec2ForCTC.from_pretrained("ayameRushia/wav2vec2-large-xlsr-indonesia-demo") model.to("cuda") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: WER = 20.072720 % ## Training Training using common voice dataset
azwierzc/plt5-base-pl-to-sql
35e11c61a081e6c56e3afdf59e2823e792c4d57d
2022-02-04T14:29:14.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
azwierzc
null
azwierzc/plt5-base-pl-to-sql
2
null
transformers
23,725
Entry not found
baby-oogway/wav2vec2-timit_asr-oogway
dedeb9372ebd873cd5ff68614e7a82da4dfdbdb7
2021-11-27T20:14:26.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
baby-oogway
null
baby-oogway/wav2vec2-timit_asr-oogway
2
null
transformers
23,726
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-timit_asr-oogway results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-timit_asr-oogway This model is a fine-tuned version of [OthmaneJ/distil-wav2vec2](https://huggingface.co/OthmaneJ/distil-wav2vec2) 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: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
bada/test
4eaa674bbdd8e1bedc030d34ee2457dcdfce3397
2021-05-19T12:06:17.000Z
[ "pytorch", "jax", "bert", "pretraining", "transformers" ]
null
false
bada
null
bada/test
2
null
transformers
23,727
"hello"
bala1802/model_1_test
a24589e0ebbc9c6ab5109d1bbac819cdca1288da
2021-05-21T13:59:23.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]
text-generation
false
bala1802
null
bala1802/model_1_test
2
null
transformers
23,728
Entry not found
balamariannmt/LanguageModel_Trial_2
f6c6b45e9b78119addff734adaade47845b800ef
2021-05-21T14:01:08.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]
text-generation
false
balamariannmt
null
balamariannmt/LanguageModel_Trial_2
2
null
transformers
23,729
Entry not found
balawmt/LanguageModel_Trial_1
5bc9f3a1789d7e6b04976500f91b79c9e8ba5626
2021-05-21T14:03:49.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]
text-generation
false
balawmt
null
balawmt/LanguageModel_Trial_1
2
null
transformers
23,730
Entry not found
begar/distilgpt2-finetuned
e76d73de91316bc11c42cfa9ce69fb4e3b8c3047
2022-01-14T22:01:35.000Z
[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-generation
false
begar
null
begar/distilgpt2-finetuned
2
null
transformers
23,731
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) 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: 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 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
beiluo/nlpload
1b70bc5574b173ce92d3daeb624043498d11433a
2021-11-04T06:47:17.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
beiluo
null
beiluo/nlpload
2
null
transformers
23,732
Entry not found
benjamin/roberta-large-wechsel-hindi
998bd9a3e9309e407544c6840ac9573d1f3a17d7
2021-11-11T10:31:36.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
benjamin
null
benjamin/roberta-large-wechsel-hindi
2
null
transformers
23,733
Entry not found
benjamin/roberta-large-wechsel-tamil
31629076c935eb169f71f5dd594d34b896179428
2021-11-11T10:39:28.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
benjamin
null
benjamin/roberta-large-wechsel-tamil
2
null
transformers
23,734
Entry not found
benyong/testmodel
fcde8ce3a9cf059ed3485d4034f7a4593876548b
2021-11-07T01:35:56.000Z
[ "pytorch", "tf", "jax", "rust", "bert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1810.04805", "transformers", "exbert", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
benyong
null
benyong/testmodel
2
null
transformers
23,735
--- language: en tags: - exbert license: apache-2.0 datasets: - bookcorpus - wikipedia --- # BERT base model (uncased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BERT is a 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 BERT 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=bert) 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 You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-base-uncased') >>> unmasker("Hello I'm a [MASK] model.") [{'sequence': "[CLS] hello i'm a fashion model. [SEP]", 'score': 0.1073106899857521, 'token': 4827, 'token_str': 'fashion'}, {'sequence': "[CLS] hello i'm a role model. [SEP]", 'score': 0.08774490654468536, 'token': 2535, 'token_str': 'role'}, {'sequence': "[CLS] hello i'm a new model. [SEP]", 'score': 0.05338378623127937, 'token': 2047, 'token_str': 'new'}, {'sequence': "[CLS] hello i'm a super model. [SEP]", 'score': 0.04667217284440994, 'token': 3565, 'token_str': 'super'}, {'sequence': "[CLS] hello i'm a fine model. [SEP]", 'score': 0.027095865458250046, 'token': 2986, 'token_str': 'fine'}] ``` 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('bert-base-uncased') model = BertModel.from_pretrained("bert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = TFBertModel.from_pretrained("bert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') 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: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-base-uncased') >>> unmasker("The man worked as a [MASK].") [{'sequence': '[CLS] the man worked as a carpenter. [SEP]', 'score': 0.09747550636529922, 'token': 10533, 'token_str': 'carpenter'}, {'sequence': '[CLS] the man worked as a waiter. [SEP]', 'score': 0.0523831807076931, 'token': 15610, 'token_str': 'waiter'}, {'sequence': '[CLS] the man worked as a barber. [SEP]', 'score': 0.04962705448269844, 'token': 13362, 'token_str': 'barber'}, {'sequence': '[CLS] the man worked as a mechanic. [SEP]', 'score': 0.03788609802722931, 'token': 15893, 'token_str': 'mechanic'}, {'sequence': '[CLS] the man worked as a salesman. [SEP]', 'score': 0.037680890411138535, 'token': 18968, 'token_str': 'salesman'}] >>> unmasker("The woman worked as a [MASK].") [{'sequence': '[CLS] the woman worked as a nurse. [SEP]', 'score': 0.21981462836265564, 'token': 6821, 'token_str': 'nurse'}, {'sequence': '[CLS] the woman worked as a waitress. [SEP]', 'score': 0.1597415804862976, 'token': 13877, 'token_str': 'waitress'}, {'sequence': '[CLS] the woman worked as a maid. [SEP]', 'score': 0.1154729500412941, 'token': 10850, 'token_str': 'maid'}, {'sequence': '[CLS] the woman worked as a prostitute. [SEP]', 'score': 0.037968918681144714, 'token': 19215, 'token_str': 'prostitute'}, {'sequence': '[CLS] the woman worked as a cook. [SEP]', 'score': 0.03042375110089779, 'token': 5660, 'token_str': 'cook'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The BERT model was 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 model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. 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. ## Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Glue test results: | Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average | |:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:| | | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 | ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805}, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=bert-base-uncased"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
beomi/exKcBERT-kowiki
dae012c6b8af853c95005e246edea8d034aa5d9e
2021-06-14T13:45:28.000Z
[ "pytorch", "exbert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
beomi
null
beomi/exKcBERT-kowiki
2
null
transformers
23,736
Entry not found
bergurth/IceBERT-finetuned-ner
a9d634422f1c8e876a74d7b1b2667daaef9ce9b5
2021-10-05T21:48:37.000Z
[ "pytorch", "tensorboard", "roberta", "token-classification", "dataset:mim_gold_ner", "transformers", "generated_from_trainer", "license:gpl-3.0", "model-index", "autotrain_compatible" ]
token-classification
false
bergurth
null
bergurth/IceBERT-finetuned-ner
2
null
transformers
23,737
--- license: gpl-3.0 tags: - generated_from_trainer datasets: - mim_gold_ner metrics: - precision - recall - f1 - accuracy widget: - text: Bob Dillan beit Maríu Markan á barkann. model-index: - name: IceBERT-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: mim_gold_ner type: mim_gold_ner args: mim-gold-ner metrics: - name: Precision type: precision value: 0.8873049035270985 - name: Recall type: recall value: 0.8627076114231091 - name: F1 type: f1 value: 0.8748333939173634 - name: Accuracy type: accuracy value: 0.9848076353832492 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # IceBERT-finetuned-ner This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the mim_gold_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0783 - Precision: 0.8873 - Recall: 0.8627 - F1: 0.8748 - Accuracy: 0.9848 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0539 | 1.0 | 2904 | 0.0768 | 0.8732 | 0.8453 | 0.8590 | 0.9833 | | 0.0281 | 2.0 | 5808 | 0.0737 | 0.8781 | 0.8492 | 0.8634 | 0.9838 | | 0.0166 | 3.0 | 8712 | 0.0783 | 0.8873 | 0.8627 | 0.8748 | 0.9848 | ### Framework versions - Transformers 4.11.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
bertin-project/bertin-base-pos-conll2002-es
25fcd8ff6777f8a02ced43eea7aa239d56ddf202
2021-09-23T13:41:54.000Z
[ "pytorch", "roberta", "token-classification", "es", "transformers", "spanish", "ner", "license:cc-by-4.0", "autotrain_compatible" ]
token-classification
false
bertin-project
null
bertin-project/bertin-base-pos-conll2002-es
2
1
transformers
23,738
--- language: es license: cc-by-4.0 tags: - spanish - roberta - ner --- This checkpoint has been trained for the POS task using the CoNLL 2002-es dataset. This checkpoint was created from **Bertin Gaussian 512**, which is a **RoBERTa-base** model trained from scratch in Spanish. Information on this base model may be found at [its own card](https://huggingface.co/bertin-project/bertin-base-gaussian-exp-512seqlen) and at deeper detail on [the main project card](https://huggingface.co/bertin-project/bertin-roberta-base-spanish). The training dataset for the base model is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (using a Gaussian function), discarding more often documents with very large values (poor quality) of very small values (short, repetitive texts). This is part of the [Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google. ## Team members - Eduardo González ([edugp](https://huggingface.co/edugp)) - Javier de la Rosa ([versae](https://huggingface.co/versae)) - Manu Romero ([mrm8488](https://huggingface.co/)) - María Grandury ([mariagrandury](https://huggingface.co/)) - Pablo González de Prado ([Pablogps](https://huggingface.co/Pablogps)) - Paulo Villegas ([paulo](https://huggingface.co/paulo))
bhavikardeshna/multilingual-bert-base-cased-chinese
c2efd38ab167f2a69570417e2afa644dc33c7948
2021-12-21T11:41:47.000Z
[ "pytorch", "bert", "question-answering", "arxiv:2112.09866", "transformers", "autotrain_compatible" ]
question-answering
false
bhavikardeshna
null
bhavikardeshna/multilingual-bert-base-cased-chinese
2
null
transformers
23,739
# BibTeX entry and citation info ``` @misc{pandya2021cascading, title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages}, author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt}, year={2021}, eprint={2112.09866}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
bhavikardeshna/multilingual-bert-base-cased-spanish
dfff6cecd37758bad0e82b5f966e352f84f4cb0b
2021-12-21T11:43:55.000Z
[ "pytorch", "bert", "question-answering", "arxiv:2112.09866", "transformers", "autotrain_compatible" ]
question-answering
false
bhavikardeshna
null
bhavikardeshna/multilingual-bert-base-cased-spanish
2
null
transformers
23,740
# BibTeX entry and citation info ``` @misc{pandya2021cascading, title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages}, author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt}, year={2021}, eprint={2112.09866}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
bill/bert_finetuning_test1
45bf626e078ad4ad00e433f81175e1210f8b999f
2021-09-03T15:39:15.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
bill
null
bill/bert_finetuning_test1
2
null
transformers
23,741
Entry not found
birgermoell/lm-swedish
894b1777b8e8326a5aa268fbb2f26b86189b4105
2022-02-08T21:37:51.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "sv", "dataset:common_voice", "dataset:NST Swedish ASR Database", "dataset:P4", "transformers", "audio", "speech", "license:cc0-1.0", "model-index" ]
automatic-speech-recognition
false
birgermoell
null
birgermoell/lm-swedish
2
null
transformers
23,742
--- language: sv datasets: - common_voice - NST Swedish ASR Database - P4 metrics: - wer tags: - audio - automatic-speech-recognition - speech license: cc0-1.0 model-index: - name: Wav2vec 2.0 large VoxRex Swedish results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice type: common_voice args: sv-SE metrics: - name: Test WER type: wer value: 9.914 --- # Wav2vec 2.0 large VoxRex Swedish (C) Experiment with LM model. **Disclaimer:** This is a work in progress. See [VoxRex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) for more details. **Update 2022-01-10:** Updated to VoxRex-C version. Finetuned version of KBs [VoxRex large](https://huggingface.co/KBLab/wav2vec2-large-voxrex) model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is **2.5%**. WER for Common Voice test set is **8.49%** directly and **7.37%** with a 4-gram language model. When using this model, make sure that your speech input is sampled at 16kHz. # Performance\* ![Comparison](comparison.png "Comparison") <center><del>*<i>Chart shows performance without the additional 20k steps of Common Voice fine-tuning</i></del></center> ## Training This model has been fine-tuned for 120000 updates on NST + CommonVoice<del> and then for an additional 20000 updates on CommonVoice only. The additional fine-tuning on CommonVoice hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed]</del>. ![WER during training](chart_1.svg "WER") ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "sv-SE", split="test[:2%]"). processor = Wav2Vec2Processor.from_pretrained("KBLab/wav2vec2-large-voxrex-swedish") model = Wav2Vec2ForCTC.from_pretrained("KBLab/wav2vec2-large-voxrex-swedish") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ```
birgermoell/wav2vec2-large-xlrs-estonian
96927200e328b887edee4c8adf73ef35527193e0
2021-07-05T23:07:04.000Z
[ "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "et", "dataset:common_voice", "transformers", "audio", "speech", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
birgermoell
null
birgermoell/wav2vec2-large-xlrs-estonian
2
null
transformers
23,743
--- language: et datasets: - common_voice tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Estonian by Birger Moell results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice Estonian type: common_voice args: et metrics: - name: Test WER type: wer value: 36.951816 --- # Wav2Vec2-Large-XLSR-53-Estonian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Luganda using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "et", split="test[:2%]"). processor = Wav2Vec2Processor.from_pretrained("birgermoell/wav2vec2-large-xlrs-estonian") model = Wav2Vec2ForCTC.from_pretrained("birgermoell/wav2vec2-large-xlrs-estonian") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\tbatch["speech"] = resampler(speech_array).squeeze().numpy() \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Luganda test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "fi", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("birgermoell/wav2vec2-large-xlrs-estonian") model = Wav2Vec2ForCTC.from_pretrained("birgermoell/wav2vec2-large-xlrs-estonian") model.to("cuda") chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\?\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\.\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\!\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\-\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\;\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\:\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\“]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\tbatch["speech"] = resampler(speech_array).squeeze().numpy() \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\twith torch.no_grad(): \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\t\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\tbatch["pred_strings"] = processor.batch_decode(pred_ids) \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\treturn batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: WER: 36.951816 ## Training The Common Voice `train` and `validation` datasets were used for training. The script used for training can be found here https://colab.research.google.com/drive/1VcWT92vBCwVn-5d-mkYxhgILPr11OHfR?usp=sharing
birgermoell/wav2vec2-speechdat
884b9dd709fa74e56f59a2fe055e526007a731da
2022-02-08T06:44:20.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "sv-SE", "transformers", "common_voice", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
birgermoell
null
birgermoell/wav2vec2-speechdat
2
null
transformers
23,744
--- language: - sv-SE license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer model-index: - name: wav2vec2-speechdat results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-speechdat This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 0.4578 - Wer: 0.2927 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:------:|:---------------:|:------:| | No log | 0.01 | 100 | 3.6252 | 1.0 | | No log | 0.02 | 200 | 3.1906 | 1.0 | | No log | 0.03 | 300 | 3.1090 | 1.0 | | No log | 0.04 | 400 | 1.8796 | 0.9955 | | 6.2575 | 0.05 | 500 | 1.3515 | 0.9058 | | 6.2575 | 0.06 | 600 | 1.1209 | 0.8328 | | 6.2575 | 0.07 | 700 | 1.1404 | 0.8309 | | 6.2575 | 0.09 | 800 | 1.0599 | 0.8021 | | 6.2575 | 0.1 | 900 | 0.9901 | 0.8335 | | 0.7737 | 0.11 | 1000 | 0.8846 | 0.7400 | | 0.7737 | 0.12 | 1100 | 0.9971 | 0.7820 | | 0.7737 | 0.13 | 1200 | 0.8665 | 0.7123 | | 0.7737 | 0.14 | 1300 | 0.8490 | 0.7366 | | 0.7737 | 0.15 | 1400 | 0.8250 | 0.6765 | | 0.6183 | 0.16 | 1500 | 0.8291 | 0.6965 | | 0.6183 | 0.17 | 1600 | 0.7946 | 0.6823 | | 0.6183 | 0.18 | 1700 | 0.8239 | 0.6894 | | 0.6183 | 0.19 | 1800 | 0.8282 | 0.6796 | | 0.6183 | 0.2 | 1900 | 0.7645 | 0.6518 | | 0.561 | 0.21 | 2000 | 0.7530 | 0.6367 | | 0.561 | 0.22 | 2100 | 0.7296 | 0.6177 | | 0.561 | 0.24 | 2200 | 0.7527 | 0.6498 | | 0.561 | 0.25 | 2300 | 0.7210 | 0.6316 | | 0.561 | 0.26 | 2400 | 0.7938 | 0.6757 | | 0.5402 | 0.27 | 2500 | 0.7485 | 0.6372 | | 0.5402 | 0.28 | 2600 | 0.7146 | 0.6133 | | 0.5402 | 0.29 | 2700 | 0.7308 | 0.6626 | | 0.5402 | 0.3 | 2800 | 0.7078 | 0.5949 | | 0.5402 | 0.31 | 2900 | 0.7679 | 0.6373 | | 0.5303 | 0.32 | 3000 | 0.7263 | 0.6502 | | 0.5303 | 0.33 | 3100 | 0.6613 | 0.5846 | | 0.5303 | 0.34 | 3200 | 0.6784 | 0.5783 | | 0.5303 | 0.35 | 3300 | 0.6908 | 0.5833 | | 0.5303 | 0.36 | 3400 | 0.6595 | 0.5826 | | 0.503 | 0.37 | 3500 | 0.6717 | 0.5938 | | 0.503 | 0.39 | 3600 | 0.6938 | 0.5791 | | 0.503 | 0.4 | 3700 | 0.6677 | 0.6052 | | 0.503 | 0.41 | 3800 | 0.6544 | 0.5554 | | 0.503 | 0.42 | 3900 | 0.6514 | 0.5728 | | 0.4959 | 0.43 | 4000 | 0.6847 | 0.6188 | | 0.4959 | 0.44 | 4100 | 0.6626 | 0.5869 | | 0.4959 | 0.45 | 4200 | 0.6670 | 0.5700 | | 0.4959 | 0.46 | 4300 | 0.6596 | 0.5846 | | 0.4959 | 0.47 | 4400 | 0.6523 | 0.5468 | | 0.4824 | 0.48 | 4500 | 0.6392 | 0.5688 | | 0.4824 | 0.49 | 4600 | 0.6561 | 0.5687 | | 0.4824 | 0.5 | 4700 | 0.6697 | 0.5817 | | 0.4824 | 0.51 | 4800 | 0.6348 | 0.5608 | | 0.4824 | 0.52 | 4900 | 0.6561 | 0.5600 | | 0.4714 | 0.54 | 5000 | 0.6522 | 0.6181 | | 0.4714 | 0.55 | 5100 | 0.6858 | 0.5921 | | 0.4714 | 0.56 | 5200 | 0.6706 | 0.5497 | | 0.4714 | 0.57 | 5300 | 0.7123 | 0.5768 | | 0.4714 | 0.58 | 5400 | 0.6599 | 0.6100 | | 0.471 | 0.59 | 5500 | 0.6421 | 0.5626 | | 0.471 | 0.6 | 5600 | 0.6395 | 0.5753 | | 0.471 | 0.61 | 5700 | 0.6788 | 0.5481 | | 0.471 | 0.62 | 5800 | 0.6386 | 0.5516 | | 0.471 | 0.63 | 5900 | 0.6694 | 0.5913 | | 0.4707 | 0.64 | 6000 | 0.6251 | 0.5699 | | 0.4707 | 0.65 | 6100 | 0.6243 | 0.5567 | | 0.4707 | 0.66 | 6200 | 0.6645 | 0.5629 | | 0.4707 | 0.67 | 6300 | 0.6296 | 0.5895 | | 0.4707 | 0.69 | 6400 | 0.6078 | 0.5183 | | 0.4632 | 0.7 | 6500 | 0.6270 | 0.5619 | | 0.4632 | 0.71 | 6600 | 0.6050 | 0.5336 | | 0.4632 | 0.72 | 6700 | 0.6185 | 0.5449 | | 0.4632 | 0.73 | 6800 | 0.6281 | 0.5645 | | 0.4632 | 0.74 | 6900 | 0.5877 | 0.5084 | | 0.4514 | 0.75 | 7000 | 0.6199 | 0.5403 | | 0.4514 | 0.76 | 7100 | 0.6293 | 0.5275 | | 0.4514 | 0.77 | 7200 | 0.6290 | 0.5447 | | 0.4514 | 0.78 | 7300 | 0.6130 | 0.5373 | | 0.4514 | 0.79 | 7400 | 0.6138 | 0.5285 | | 0.4457 | 0.8 | 7500 | 0.6040 | 0.5259 | | 0.4457 | 0.81 | 7600 | 0.6220 | 0.5686 | | 0.4457 | 0.82 | 7700 | 0.5915 | 0.5164 | | 0.4457 | 0.84 | 7800 | 0.6270 | 0.5289 | | 0.4457 | 0.85 | 7900 | 0.6224 | 0.5515 | | 0.4458 | 0.86 | 8000 | 0.6161 | 0.5323 | | 0.4458 | 0.87 | 8100 | 0.5827 | 0.5122 | | 0.4458 | 0.88 | 8200 | 0.6067 | 0.5202 | | 0.4458 | 0.89 | 8300 | 0.6087 | 0.5192 | | 0.4458 | 0.9 | 8400 | 0.6859 | 0.5796 | | 0.4409 | 0.91 | 8500 | 0.6180 | 0.5131 | | 0.4409 | 0.92 | 8600 | 0.5945 | 0.4948 | | 0.4409 | 0.93 | 8700 | 0.5967 | 0.5532 | | 0.4409 | 0.94 | 8800 | 0.5770 | 0.4961 | | 0.4409 | 0.95 | 8900 | 0.5809 | 0.5203 | | 0.4305 | 0.96 | 9000 | 0.5805 | 0.5039 | | 0.4305 | 0.97 | 9100 | 0.5873 | 0.5188 | | 0.4305 | 0.98 | 9200 | 0.6277 | 0.5516 | | 0.4305 | 1.0 | 9300 | 0.5727 | 0.5052 | | 0.4305 | 1.01 | 9400 | 0.5858 | 0.5123 | | 0.4264 | 1.02 | 9500 | 0.5692 | 0.4968 | | 0.4264 | 1.03 | 9600 | 0.5954 | 0.5117 | | 0.4264 | 1.04 | 9700 | 0.5904 | 0.5076 | | 0.4264 | 1.05 | 9800 | 0.6046 | 0.5101 | | 0.4264 | 1.06 | 9900 | 0.5616 | 0.4926 | | 0.4176 | 1.07 | 10000 | 0.5971 | 0.5368 | | 0.4176 | 1.08 | 10100 | 0.5706 | 0.4940 | | 0.4176 | 1.09 | 10200 | 0.5612 | 0.5032 | | 0.4176 | 1.1 | 10300 | 0.5672 | 0.4944 | | 0.4176 | 1.11 | 10400 | 0.5915 | 0.5218 | | 0.4033 | 1.12 | 10500 | 0.5706 | 0.5051 | | 0.4033 | 1.13 | 10600 | 0.5661 | 0.4934 | | 0.4033 | 1.15 | 10700 | 0.5724 | 0.4903 | | 0.4033 | 1.16 | 10800 | 0.5792 | 0.4940 | | 0.4033 | 1.17 | 10900 | 0.5744 | 0.4911 | | 0.392 | 1.18 | 11000 | 0.5767 | 0.5162 | | 0.392 | 1.19 | 11100 | 0.5588 | 0.4835 | | 0.392 | 1.2 | 11200 | 0.5609 | 0.4922 | | 0.392 | 1.21 | 11300 | 0.5890 | 0.4914 | | 0.392 | 1.22 | 11400 | 0.5525 | 0.4897 | | 0.387 | 1.23 | 11500 | 0.5704 | 0.5051 | | 0.387 | 1.24 | 11600 | 0.5539 | 0.5014 | | 0.387 | 1.25 | 11700 | 0.5473 | 0.4882 | | 0.387 | 1.26 | 11800 | 0.5662 | 0.5004 | | 0.387 | 1.27 | 11900 | 0.5785 | 0.5220 | | 0.3956 | 1.28 | 12000 | 0.5990 | 0.5114 | | 0.3956 | 1.3 | 12100 | 0.5497 | 0.4895 | | 0.3956 | 1.31 | 12200 | 0.5538 | 0.4895 | | 0.3956 | 1.32 | 12300 | 0.5652 | 0.4913 | | 0.3956 | 1.33 | 12400 | 0.5682 | 0.5128 | | 0.4043 | 1.34 | 12500 | 0.5830 | 0.4999 | | 0.4043 | 1.35 | 12600 | 0.5686 | 0.4865 | | 0.4043 | 1.36 | 12700 | 0.5688 | 0.4937 | | 0.4043 | 1.37 | 12800 | 0.5753 | 0.5034 | | 0.4043 | 1.38 | 12900 | 0.5898 | 0.4865 | | 0.3997 | 1.39 | 13000 | 0.5723 | 0.4963 | | 0.3997 | 1.4 | 13100 | 0.5767 | 0.4986 | | 0.3997 | 1.41 | 13200 | 0.5960 | 0.5084 | | 0.3997 | 1.42 | 13300 | 0.5859 | 0.5096 | | 0.3997 | 1.43 | 13400 | 0.5491 | 0.4784 | | 0.3997 | 1.45 | 13500 | 0.5636 | 0.5049 | | 0.3997 | 1.46 | 13600 | 0.5667 | 0.4708 | | 0.3997 | 1.47 | 13700 | 0.5757 | 0.4862 | | 0.3997 | 1.48 | 13800 | 0.5444 | 0.4816 | | 0.3997 | 1.49 | 13900 | 0.5557 | 0.4792 | | 0.3954 | 1.5 | 14000 | 0.5437 | 0.4810 | | 0.3954 | 1.51 | 14100 | 0.5489 | 0.4674 | | 0.3954 | 1.52 | 14200 | 0.5415 | 0.4674 | | 0.3954 | 1.53 | 14300 | 0.5481 | 0.4902 | | 0.3954 | 1.54 | 14400 | 0.5474 | 0.4763 | | 0.3814 | 1.55 | 14500 | 0.5588 | 0.4731 | | 0.3814 | 1.56 | 14600 | 0.5746 | 0.4820 | | 0.3814 | 1.57 | 14700 | 0.5676 | 0.4884 | | 0.3814 | 1.58 | 14800 | 0.5495 | 0.4711 | | 0.3814 | 1.6 | 14900 | 0.5565 | 0.4782 | | 0.3877 | 1.61 | 15000 | 0.5671 | 0.5135 | | 0.3877 | 1.62 | 15100 | 0.5512 | 0.4868 | | 0.3877 | 1.63 | 15200 | 0.5683 | 0.4650 | | 0.3877 | 1.64 | 15300 | 0.5427 | 0.4717 | | 0.3877 | 1.65 | 15400 | 0.5519 | 0.4651 | | 0.387 | 1.66 | 15500 | 0.5327 | 0.4456 | | 0.387 | 1.67 | 15600 | 0.5371 | 0.4673 | | 0.387 | 1.68 | 15700 | 0.5337 | 0.4705 | | 0.387 | 1.69 | 15800 | 0.5606 | 0.4992 | | 0.387 | 1.7 | 15900 | 0.5254 | 0.4613 | | 0.3877 | 1.71 | 16000 | 0.5619 | 0.4882 | | 0.3877 | 1.72 | 16100 | 0.5212 | 0.4560 | | 0.3877 | 1.73 | 16200 | 0.5369 | 0.4696 | | 0.3877 | 1.75 | 16300 | 0.5392 | 0.4677 | | 0.3877 | 1.76 | 16400 | 0.5353 | 0.4768 | | 0.3739 | 1.77 | 16500 | 0.5435 | 0.4777 | | 0.3739 | 1.78 | 16600 | 0.5343 | 0.4884 | | 0.3739 | 1.79 | 16700 | 0.5309 | 0.4942 | | 0.3739 | 1.8 | 16800 | 0.5373 | 0.4727 | | 0.3739 | 1.81 | 16900 | 0.5550 | 0.4686 | | 0.3884 | 1.82 | 17000 | 0.5486 | 0.4826 | | 0.3884 | 1.83 | 17100 | 0.5508 | 0.4862 | | 0.3884 | 1.84 | 17200 | 0.5423 | 0.4855 | | 0.3884 | 1.85 | 17300 | 0.5478 | 0.4730 | | 0.3884 | 1.86 | 17400 | 0.5438 | 0.4938 | | 0.3842 | 1.87 | 17500 | 0.5571 | 0.4818 | | 0.3842 | 1.88 | 17600 | 0.5402 | 0.4753 | | 0.3842 | 1.9 | 17700 | 0.5679 | 0.4827 | | 0.3842 | 1.91 | 17800 | 0.5385 | 0.4642 | | 0.3842 | 1.92 | 17900 | 0.5519 | 0.4942 | | 0.3953 | 1.93 | 18000 | 0.5559 | 0.4745 | | 0.3953 | 1.94 | 18100 | 0.5657 | 0.4963 | | 0.3953 | 1.95 | 18200 | 0.5296 | 0.4642 | | 0.3953 | 1.96 | 18300 | 0.5529 | 0.4907 | | 0.3953 | 1.97 | 18400 | 0.5380 | 0.4536 | | 0.3745 | 1.98 | 18500 | 0.5276 | 0.4678 | | 0.3745 | 1.99 | 18600 | 0.5544 | 0.4854 | | 0.3745 | 2.0 | 18700 | 0.5195 | 0.4535 | | 0.3745 | 2.01 | 18800 | 0.5165 | 0.4635 | | 0.3745 | 2.02 | 18900 | 0.5062 | 0.4431 | | 0.3538 | 2.03 | 19000 | 0.5255 | 0.4509 | | 0.3538 | 2.04 | 19100 | 0.5125 | 0.4512 | | 0.3538 | 2.06 | 19200 | 0.5105 | 0.4504 | | 0.3538 | 2.07 | 19300 | 0.5000 | 0.4490 | | 0.3538 | 2.08 | 19400 | 0.5150 | 0.4520 | | 0.356 | 2.09 | 19500 | 0.5053 | 0.4383 | | 0.356 | 2.1 | 19600 | 0.5085 | 0.4417 | | 0.356 | 2.11 | 19700 | 0.5229 | 0.4490 | | 0.356 | 2.12 | 19800 | 0.5326 | 0.4492 | | 0.356 | 2.13 | 19900 | 0.5139 | 0.4491 | | 0.3474 | 2.14 | 20000 | 0.5134 | 0.4384 | | 0.3474 | 2.15 | 20100 | 0.5498 | 0.4606 | | 0.3474 | 2.16 | 20200 | 0.5324 | 0.4540 | | 0.3474 | 2.17 | 20300 | 0.5338 | 0.4548 | | 0.3474 | 2.18 | 20400 | 0.5076 | 0.4425 | | 0.345 | 2.19 | 20500 | 0.5253 | 0.4550 | | 0.345 | 2.21 | 20600 | 0.5125 | 0.4618 | | 0.345 | 2.22 | 20700 | 0.5171 | 0.4487 | | 0.345 | 2.23 | 20800 | 0.5232 | 0.4464 | | 0.345 | 2.24 | 20900 | 0.5298 | 0.4588 | | 0.341 | 2.25 | 21000 | 0.5342 | 0.4576 | | 0.341 | 2.26 | 21100 | 0.5515 | 0.4678 | | 0.341 | 2.27 | 21200 | 0.5041 | 0.4495 | | 0.341 | 2.28 | 21300 | 0.5169 | 0.4473 | | 0.341 | 2.29 | 21400 | 0.5227 | 0.4494 | | 0.354 | 2.3 | 21500 | 0.5214 | 0.4458 | | 0.354 | 2.31 | 21600 | 0.5303 | 0.4587 | | 0.354 | 2.32 | 21700 | 0.5237 | 0.4597 | | 0.354 | 2.33 | 21800 | 0.5067 | 0.4460 | | 0.354 | 2.34 | 21900 | 0.5117 | 0.4560 | | 0.3333 | 2.36 | 22000 | 0.5104 | 0.4359 | | 0.3333 | 2.37 | 22100 | 0.5326 | 0.4679 | | 0.3333 | 2.38 | 22200 | 0.5098 | 0.4510 | | 0.3333 | 2.39 | 22300 | 0.5044 | 0.4445 | | 0.3333 | 2.4 | 22400 | 0.5219 | 0.4489 | | 0.3514 | 2.41 | 22500 | 0.4987 | 0.4433 | | 0.3514 | 2.42 | 22600 | 0.5009 | 0.4338 | | 0.3514 | 2.43 | 22700 | 0.5252 | 0.4444 | | 0.3514 | 2.44 | 22800 | 0.4861 | 0.4269 | | 0.3514 | 2.45 | 22900 | 0.5157 | 0.4421 | | 0.3444 | 2.46 | 23000 | 0.5277 | 0.4426 | | 0.3444 | 2.47 | 23100 | 0.5213 | 0.4378 | | 0.3444 | 2.48 | 23200 | 0.5172 | 0.4482 | | 0.3444 | 2.49 | 23300 | 0.5142 | 0.4376 | | 0.3444 | 2.51 | 23400 | 0.5044 | 0.4231 | | 0.3536 | 2.52 | 23500 | 0.5268 | 0.4496 | | 0.3536 | 2.53 | 23600 | 0.5176 | 0.4326 | | 0.3536 | 2.54 | 23700 | 0.5032 | 0.4296 | | 0.3536 | 2.55 | 23800 | 0.5211 | 0.4460 | | 0.3536 | 2.56 | 23900 | 0.5093 | 0.4379 | | 0.337 | 2.57 | 24000 | 0.4990 | 0.4311 | | 0.337 | 2.58 | 24100 | 0.4962 | 0.4329 | | 0.337 | 2.59 | 24200 | 0.5033 | 0.4289 | | 0.337 | 2.6 | 24300 | 0.5260 | 0.4534 | | 0.337 | 2.61 | 24400 | 0.5309 | 0.4441 | | 0.3393 | 2.62 | 24500 | 0.5132 | 0.4346 | | 0.3393 | 2.63 | 24600 | 0.5189 | 0.4233 | | 0.3393 | 2.64 | 24700 | 0.5074 | 0.4326 | | 0.3393 | 2.66 | 24800 | 0.5111 | 0.4254 | | 0.3393 | 2.67 | 24900 | 0.4933 | 0.4254 | | 0.3334 | 2.68 | 25000 | 0.5046 | 0.4407 | | 0.3334 | 2.69 | 25100 | 0.5010 | 0.4404 | | 0.3334 | 2.7 | 25200 | 0.5045 | 0.4236 | | 0.3334 | 2.71 | 25300 | 0.4938 | 0.4305 | | 0.3334 | 2.72 | 25400 | 0.5021 | 0.4383 | | 0.3366 | 2.73 | 25500 | 0.4953 | 0.4202 | | 0.3366 | 2.74 | 25600 | 0.4985 | 0.4338 | | 0.3366 | 2.75 | 25700 | 0.4765 | 0.4161 | | 0.3366 | 2.76 | 25800 | 0.4873 | 0.4292 | | 0.3366 | 2.77 | 25900 | 0.4998 | 0.4189 | | 0.3359 | 2.78 | 26000 | 0.4991 | 0.4248 | | 0.3359 | 2.79 | 26100 | 0.5012 | 0.4307 | | 0.3359 | 2.81 | 26200 | 0.5081 | 0.4151 | | 0.3359 | 2.82 | 26300 | 0.4997 | 0.4305 | | 0.3359 | 2.83 | 26400 | 0.4969 | 0.4302 | | 0.3396 | 2.84 | 26500 | 0.4784 | 0.4271 | | 0.3396 | 2.85 | 26600 | 0.4804 | 0.4149 | | 0.3396 | 2.86 | 26700 | 0.4900 | 0.4192 | | 0.3396 | 2.87 | 26800 | 0.5044 | 0.4325 | | 0.3396 | 2.88 | 26900 | 0.4935 | 0.4376 | | 0.3356 | 2.89 | 27000 | 0.5007 | 0.4269 | | 0.3356 | 2.9 | 27100 | 0.4887 | 0.4178 | | 0.3356 | 2.91 | 27200 | 0.4770 | 0.4170 | | 0.3356 | 2.92 | 27300 | 0.4847 | 0.4167 | | 0.3356 | 2.93 | 27400 | 0.4861 | 0.4139 | | 0.3395 | 2.94 | 27500 | 0.4975 | 0.4291 | | 0.3395 | 2.95 | 27600 | 0.5056 | 0.4471 | | 0.3395 | 2.97 | 27700 | 0.5111 | 0.4375 | | 0.3395 | 2.98 | 27800 | 0.5327 | 0.4577 | | 0.3395 | 2.99 | 27900 | 0.5067 | 0.4393 | | 0.3332 | 3.0 | 28000 | 0.4898 | 0.4188 | | 0.3332 | 3.01 | 28100 | 0.4790 | 0.4093 | | 0.3332 | 3.02 | 28200 | 0.4828 | 0.4202 | | 0.3332 | 3.03 | 28300 | 0.4836 | 0.4146 | | 0.3332 | 3.04 | 28400 | 0.4901 | 0.4242 | | 0.2984 | 3.05 | 28500 | 0.4772 | 0.4118 | | 0.2984 | 3.06 | 28600 | 0.5055 | 0.4213 | | 0.2984 | 3.07 | 28700 | 0.4911 | 0.4100 | | 0.2984 | 3.08 | 28800 | 0.4737 | 0.4087 | | 0.2984 | 3.09 | 28900 | 0.4930 | 0.4216 | | 0.3056 | 3.1 | 29000 | 0.4736 | 0.4109 | | 0.3056 | 3.12 | 29100 | 0.4863 | 0.4058 | | 0.3056 | 3.13 | 29200 | 0.4784 | 0.4184 | | 0.3056 | 3.14 | 29300 | 0.4923 | 0.4240 | | 0.3056 | 3.15 | 29400 | 0.4846 | 0.4226 | | 0.2995 | 3.16 | 29500 | 0.4829 | 0.4086 | | 0.2995 | 3.17 | 29600 | 0.4934 | 0.4240 | | 0.2995 | 3.18 | 29700 | 0.4893 | 0.4152 | | 0.2995 | 3.19 | 29800 | 0.4730 | 0.4227 | | 0.2995 | 3.2 | 29900 | 0.5027 | 0.4330 | | 0.2926 | 3.21 | 30000 | 0.4903 | 0.4112 | | 0.2926 | 3.22 | 30100 | 0.4961 | 0.4157 | | 0.2926 | 3.23 | 30200 | 0.4980 | 0.4269 | | 0.2926 | 3.24 | 30300 | 0.4896 | 0.4126 | | 0.2926 | 3.25 | 30400 | 0.4726 | 0.4062 | | 0.301 | 3.27 | 30500 | 0.4733 | 0.3985 | | 0.301 | 3.28 | 30600 | 0.4772 | 0.4047 | | 0.301 | 3.29 | 30700 | 0.4806 | 0.4082 | | 0.301 | 3.3 | 30800 | 0.4683 | 0.4011 | | 0.301 | 3.31 | 30900 | 0.4775 | 0.4079 | | 0.2933 | 3.32 | 31000 | 0.4729 | 0.4083 | | 0.2933 | 3.33 | 31100 | 0.4628 | 0.4016 | | 0.2933 | 3.34 | 31200 | 0.4753 | 0.4192 | | 0.2933 | 3.35 | 31300 | 0.4687 | 0.4185 | | 0.2933 | 3.36 | 31400 | 0.4806 | 0.4106 | | 0.2957 | 3.37 | 31500 | 0.4889 | 0.4240 | | 0.2957 | 3.38 | 31600 | 0.4882 | 0.4182 | | 0.2957 | 3.39 | 31700 | 0.4798 | 0.4162 | | 0.2957 | 3.4 | 31800 | 0.4718 | 0.4108 | | 0.2957 | 3.42 | 31900 | 0.4685 | 0.4101 | | 0.3039 | 3.43 | 32000 | 0.4816 | 0.4188 | | 0.3039 | 3.44 | 32100 | 0.4874 | 0.4139 | | 0.3039 | 3.45 | 32200 | 0.4899 | 0.4115 | | 0.3039 | 3.46 | 32300 | 0.4852 | 0.4180 | | 0.3039 | 3.47 | 32400 | 0.5074 | 0.4129 | | 0.3006 | 3.48 | 32500 | 0.4837 | 0.4076 | | 0.3006 | 3.49 | 32600 | 0.4927 | 0.4098 | | 0.3006 | 3.5 | 32700 | 0.4999 | 0.4172 | | 0.3006 | 3.51 | 32800 | 0.4773 | 0.4194 | | 0.3006 | 3.52 | 32900 | 0.4859 | 0.4058 | | 0.3089 | 3.53 | 33000 | 0.4783 | 0.4104 | | 0.3089 | 3.54 | 33100 | 0.4622 | 0.4020 | | 0.3089 | 3.55 | 33200 | 0.4840 | 0.4065 | | 0.3089 | 3.57 | 33300 | 0.4756 | 0.4241 | | 0.3089 | 3.58 | 33400 | 0.4831 | 0.4170 | | 0.3061 | 3.59 | 33500 | 0.4794 | 0.4068 | | 0.3061 | 3.6 | 33600 | 0.4730 | 0.4037 | | 0.3061 | 3.61 | 33700 | 0.4808 | 0.4138 | | 0.3061 | 3.62 | 33800 | 0.4924 | 0.4248 | | 0.3061 | 3.63 | 33900 | 0.4749 | 0.4112 | | 0.3047 | 3.64 | 34000 | 0.4924 | 0.4326 | | 0.3047 | 3.65 | 34100 | 0.4745 | 0.4104 | | 0.3047 | 3.66 | 34200 | 0.4760 | 0.4123 | | 0.3047 | 3.67 | 34300 | 0.4788 | 0.4066 | | 0.3047 | 3.68 | 34400 | 0.4627 | 0.4158 | | 0.3042 | 3.69 | 34500 | 0.4974 | 0.4131 | | 0.3042 | 3.7 | 34600 | 0.4593 | 0.4063 | | 0.3042 | 3.72 | 34700 | 0.4549 | 0.3928 | | 0.3042 | 3.73 | 34800 | 0.4690 | 0.3898 | | 0.3042 | 3.74 | 34900 | 0.4560 | 0.4007 | | 0.2963 | 3.75 | 35000 | 0.4606 | 0.3959 | | 0.2963 | 3.76 | 35100 | 0.4762 | 0.4057 | | 0.2963 | 3.77 | 35200 | 0.4750 | 0.4034 | | 0.2963 | 3.78 | 35300 | 0.4772 | 0.4114 | | 0.2963 | 3.79 | 35400 | 0.4669 | 0.3995 | | 0.3012 | 3.8 | 35500 | 0.4709 | 0.4090 | | 0.3012 | 3.81 | 35600 | 0.4722 | 0.4123 | | 0.3012 | 3.82 | 35700 | 0.4913 | 0.4165 | | 0.3012 | 3.83 | 35800 | 0.4814 | 0.4063 | | 0.3012 | 3.84 | 35900 | 0.4869 | 0.4171 | | 0.3015 | 3.85 | 36000 | 0.4791 | 0.4059 | | 0.3015 | 3.87 | 36100 | 0.4535 | 0.3976 | | 0.3015 | 3.88 | 36200 | 0.4706 | 0.4009 | | 0.3015 | 3.89 | 36300 | 0.4679 | 0.4012 | | 0.3015 | 3.9 | 36400 | 0.4736 | 0.4096 | | 0.2965 | 3.91 | 36500 | 0.4756 | 0.4106 | | 0.2965 | 3.92 | 36600 | 0.4669 | 0.4085 | | 0.2965 | 3.93 | 36700 | 0.4796 | 0.4054 | | 0.2965 | 3.94 | 36800 | 0.4583 | 0.3932 | | 0.2965 | 3.95 | 36900 | 0.4430 | 0.3969 | | 0.2993 | 3.96 | 37000 | 0.4560 | 0.3914 | | 0.2993 | 3.97 | 37100 | 0.4739 | 0.4002 | | 0.2993 | 3.98 | 37200 | 0.4598 | 0.3912 | | 0.2993 | 3.99 | 37300 | 0.4607 | 0.3907 | | 0.2993 | 4.0 | 37400 | 0.4709 | 0.3986 | | 0.2886 | 4.01 | 37500 | 0.4642 | 0.4067 | | 0.2886 | 4.03 | 37600 | 0.4684 | 0.3984 | | 0.2886 | 4.04 | 37700 | 0.4690 | 0.3979 | | 0.2886 | 4.05 | 37800 | 0.4722 | 0.3980 | | 0.2886 | 4.06 | 37900 | 0.4734 | 0.3927 | | 0.2534 | 4.07 | 38000 | 0.4724 | 0.3988 | | 0.2534 | 4.08 | 38100 | 0.4665 | 0.3986 | | 0.2534 | 4.09 | 38200 | 0.4659 | 0.4036 | | 0.2534 | 4.1 | 38300 | 0.4694 | 0.3952 | | 0.2534 | 4.11 | 38400 | 0.4719 | 0.3891 | | 0.2596 | 4.12 | 38500 | 0.4687 | 0.3994 | | 0.2596 | 4.13 | 38600 | 0.4705 | 0.3903 | | 0.2596 | 4.14 | 38700 | 0.4601 | 0.3975 | | 0.2596 | 4.15 | 38800 | 0.4666 | 0.3971 | | 0.2596 | 4.16 | 38900 | 0.4772 | 0.3892 | | 0.2643 | 4.18 | 39000 | 0.4810 | 0.4071 | | 0.2643 | 4.19 | 39100 | 0.4980 | 0.4167 | | 0.2643 | 4.2 | 39200 | 0.4657 | 0.3996 | | 0.2643 | 4.21 | 39300 | 0.4869 | 0.4002 | | 0.2643 | 4.22 | 39400 | 0.4656 | 0.3913 | | 0.265 | 4.23 | 39500 | 0.4720 | 0.3947 | | 0.265 | 4.24 | 39600 | 0.4711 | 0.3970 | | 0.265 | 4.25 | 39700 | 0.4689 | 0.3933 | | 0.265 | 4.26 | 39800 | 0.4728 | 0.4017 | | 0.265 | 4.27 | 39900 | 0.4673 | 0.3847 | | 0.2644 | 4.28 | 40000 | 0.4636 | 0.3960 | | 0.2644 | 4.29 | 40100 | 0.4699 | 0.3864 | | 0.2644 | 4.3 | 40200 | 0.4580 | 0.3874 | | 0.2644 | 4.31 | 40300 | 0.4763 | 0.3951 | | 0.2644 | 4.33 | 40400 | 0.4752 | 0.4141 | | 0.2633 | 4.34 | 40500 | 0.4918 | 0.3994 | | 0.2633 | 4.35 | 40600 | 0.4783 | 0.4026 | | 0.2633 | 4.36 | 40700 | 0.4739 | 0.4034 | | 0.2633 | 4.37 | 40800 | 0.4750 | 0.4000 | | 0.2633 | 4.38 | 40900 | 0.4608 | 0.3943 | | 0.2679 | 4.39 | 41000 | 0.4615 | 0.3891 | | 0.2679 | 4.4 | 41100 | 0.4730 | 0.3984 | | 0.2679 | 4.41 | 41200 | 0.4728 | 0.4011 | | 0.2679 | 4.42 | 41300 | 0.4675 | 0.3932 | | 0.2679 | 4.43 | 41400 | 0.4662 | 0.3929 | | 0.2682 | 4.44 | 41500 | 0.4490 | 0.3837 | | 0.2682 | 4.45 | 41600 | 0.4611 | 0.3838 | | 0.2682 | 4.46 | 41700 | 0.4605 | 0.3945 | | 0.2682 | 4.48 | 41800 | 0.4730 | 0.3938 | | 0.2682 | 4.49 | 41900 | 0.4567 | 0.3874 | | 0.2658 | 4.5 | 42000 | 0.4715 | 0.3869 | | 0.2658 | 4.51 | 42100 | 0.4514 | 0.3833 | | 0.2658 | 4.52 | 42200 | 0.4602 | 0.3898 | | 0.2658 | 4.53 | 42300 | 0.4846 | 0.4022 | | 0.2658 | 4.54 | 42400 | 0.4474 | 0.3810 | | 0.2676 | 4.55 | 42500 | 0.4513 | 0.3820 | | 0.2676 | 4.56 | 42600 | 0.4588 | 0.3928 | | 0.2676 | 4.57 | 42700 | 0.4601 | 0.3894 | | 0.2676 | 4.58 | 42800 | 0.4516 | 0.3792 | | 0.2676 | 4.59 | 42900 | 0.4482 | 0.3848 | | 0.2693 | 4.6 | 43000 | 0.4695 | 0.4008 | | 0.2693 | 4.61 | 43100 | 0.4580 | 0.3871 | | 0.2693 | 4.63 | 43200 | 0.4419 | 0.3857 | | 0.2693 | 4.64 | 43300 | 0.4534 | 0.3796 | | 0.2693 | 4.65 | 43400 | 0.4532 | 0.3856 | | 0.2641 | 4.66 | 43500 | 0.4421 | 0.3809 | | 0.2641 | 4.67 | 43600 | 0.4400 | 0.3844 | | 0.2641 | 4.68 | 43700 | 0.4515 | 0.3833 | | 0.2641 | 4.69 | 43800 | 0.4462 | 0.3808 | | 0.2641 | 4.7 | 43900 | 0.4741 | 0.3926 | | 0.2626 | 4.71 | 44000 | 0.4542 | 0.3931 | | 0.2626 | 4.72 | 44100 | 0.4555 | 0.3885 | | 0.2626 | 4.73 | 44200 | 0.4505 | 0.3845 | | 0.2626 | 4.74 | 44300 | 0.4593 | 0.3871 | | 0.2626 | 4.75 | 44400 | 0.4359 | 0.3830 | | 0.2648 | 4.76 | 44500 | 0.4387 | 0.3736 | | 0.2648 | 4.78 | 44600 | 0.4529 | 0.3807 | | 0.2648 | 4.79 | 44700 | 0.4566 | 0.3837 | | 0.2648 | 4.8 | 44800 | 0.4557 | 0.4067 | | 0.2648 | 4.81 | 44900 | 0.4609 | 0.3852 | | 0.2603 | 4.82 | 45000 | 0.4667 | 0.4005 | | 0.2603 | 4.83 | 45100 | 0.4666 | 0.3836 | | 0.2603 | 4.84 | 45200 | 0.4775 | 0.3946 | | 0.2603 | 4.85 | 45300 | 0.4701 | 0.3925 | | 0.2603 | 4.86 | 45400 | 0.4579 | 0.3889 | | 0.2626 | 4.87 | 45500 | 0.4516 | 0.3884 | | 0.2626 | 4.88 | 45600 | 0.4605 | 0.3878 | | 0.2626 | 4.89 | 45700 | 0.4576 | 0.3802 | | 0.2626 | 4.9 | 45800 | 0.4553 | 0.3780 | | 0.2626 | 4.91 | 45900 | 0.4336 | 0.3752 | | 0.2602 | 4.93 | 46000 | 0.4419 | 0.3881 | | 0.2602 | 4.94 | 46100 | 0.4601 | 0.3843 | | 0.2602 | 4.95 | 46200 | 0.4437 | 0.3956 | | 0.2602 | 4.96 | 46300 | 0.4524 | 0.3844 | | 0.2602 | 4.97 | 46400 | 0.4709 | 0.4031 | | 0.2609 | 4.98 | 46500 | 0.4500 | 0.3872 | | 0.2609 | 4.99 | 46600 | 0.4366 | 0.3846 | | 0.2609 | 5.0 | 46700 | 0.4653 | 0.3884 | | 0.2609 | 5.01 | 46800 | 0.4602 | 0.3932 | | 0.2609 | 5.02 | 46900 | 0.4668 | 0.3854 | | 0.2472 | 5.03 | 47000 | 0.4616 | 0.3891 | | 0.2472 | 5.04 | 47100 | 0.4543 | 0.3836 | | 0.2472 | 5.05 | 47200 | 0.4526 | 0.3822 | | 0.2472 | 5.06 | 47300 | 0.4539 | 0.3741 | | 0.2472 | 5.07 | 47400 | 0.4776 | 0.3818 | | 0.2278 | 5.09 | 47500 | 0.4771 | 0.3794 | | 0.2278 | 5.1 | 47600 | 0.4662 | 0.3831 | | 0.2278 | 5.11 | 47700 | 0.4558 | 0.4032 | | 0.2278 | 5.12 | 47800 | 0.4904 | 0.3918 | | 0.2278 | 5.13 | 47900 | 0.4765 | 0.3890 | | 0.2311 | 5.14 | 48000 | 0.4674 | 0.3882 | | 0.2311 | 5.15 | 48100 | 0.4609 | 0.3947 | | 0.2311 | 5.16 | 48200 | 0.4588 | 0.3837 | | 0.2311 | 5.17 | 48300 | 0.4827 | 0.3845 | | 0.2311 | 5.18 | 48400 | 0.4711 | 0.3839 | | 0.229 | 5.19 | 48500 | 0.4583 | 0.3873 | | 0.229 | 5.2 | 48600 | 0.4800 | 0.3858 | | 0.229 | 5.21 | 48700 | 0.4611 | 0.3800 | | 0.229 | 5.22 | 48800 | 0.4504 | 0.3889 | | 0.229 | 5.24 | 48900 | 0.4569 | 0.3761 | | 0.2313 | 5.25 | 49000 | 0.4732 | 0.3915 | | 0.2313 | 5.26 | 49100 | 0.4728 | 0.3832 | | 0.2313 | 5.27 | 49200 | 0.4667 | 0.3815 | | 0.2313 | 5.28 | 49300 | 0.4912 | 0.3856 | | 0.2313 | 5.29 | 49400 | 0.4790 | 0.3946 | | 0.2266 | 5.3 | 49500 | 0.4597 | 0.3763 | | 0.2266 | 5.31 | 49600 | 0.4580 | 0.3778 | | 0.2266 | 5.32 | 49700 | 0.4439 | 0.3721 | | 0.2266 | 5.33 | 49800 | 0.4611 | 0.3704 | | 0.2266 | 5.34 | 49900 | 0.4599 | 0.3769 | | 0.235 | 5.35 | 50000 | 0.4543 | 0.3808 | | 0.235 | 5.36 | 50100 | 0.4555 | 0.3773 | | 0.235 | 5.37 | 50200 | 0.4525 | 0.3815 | | 0.235 | 5.39 | 50300 | 0.4557 | 0.3814 | | 0.235 | 5.4 | 50400 | 0.4604 | 0.3754 | | 0.2299 | 5.41 | 50500 | 0.4658 | 0.3770 | | 0.2299 | 5.42 | 50600 | 0.4658 | 0.3884 | | 0.2299 | 5.43 | 50700 | 0.4701 | 0.3919 | | 0.2299 | 5.44 | 50800 | 0.4495 | 0.3818 | | 0.2299 | 5.45 | 50900 | 0.4703 | 0.3886 | | 0.2307 | 5.46 | 51000 | 0.4395 | 0.3743 | | 0.2307 | 5.47 | 51100 | 0.4487 | 0.3751 | | 0.2307 | 5.48 | 51200 | 0.4355 | 0.3733 | | 0.2307 | 5.49 | 51300 | 0.4622 | 0.3811 | | 0.2307 | 5.5 | 51400 | 0.4443 | 0.3801 | | 0.2383 | 5.51 | 51500 | 0.4411 | 0.3743 | | 0.2383 | 5.52 | 51600 | 0.4438 | 0.3778 | | 0.2383 | 5.54 | 51700 | 0.4559 | 0.3784 | | 0.2383 | 5.55 | 51800 | 0.4309 | 0.3656 | | 0.2383 | 5.56 | 51900 | 0.4455 | 0.3660 | | 0.23 | 5.57 | 52000 | 0.4436 | 0.3598 | | 0.23 | 5.58 | 52100 | 0.4344 | 0.3685 | | 0.23 | 5.59 | 52200 | 0.4282 | 0.3690 | | 0.23 | 5.6 | 52300 | 0.4464 | 0.3800 | | 0.23 | 5.61 | 52400 | 0.4458 | 0.3909 | | 0.2305 | 5.62 | 52500 | 0.4483 | 0.3756 | | 0.2305 | 5.63 | 52600 | 0.4547 | 0.3785 | | 0.2305 | 5.64 | 52700 | 0.4671 | 0.3820 | | 0.2305 | 5.65 | 52800 | 0.4449 | 0.3658 | | 0.2305 | 5.66 | 52900 | 0.4596 | 0.3716 | | 0.2237 | 5.67 | 53000 | 0.4399 | 0.3669 | | 0.2237 | 5.69 | 53100 | 0.4410 | 0.3719 | | 0.2237 | 5.7 | 53200 | 0.4574 | 0.3619 | | 0.2237 | 5.71 | 53300 | 0.4443 | 0.3690 | | 0.2237 | 5.72 | 53400 | 0.4381 | 0.3678 | | 0.2337 | 5.73 | 53500 | 0.4490 | 0.3687 | | 0.2337 | 5.74 | 53600 | 0.4427 | 0.3752 | | 0.2337 | 5.75 | 53700 | 0.4423 | 0.3858 | | 0.2337 | 5.76 | 53800 | 0.4702 | 0.3825 | | 0.2337 | 5.77 | 53900 | 0.4724 | 0.3800 | | 0.23 | 5.78 | 54000 | 0.4476 | 0.3827 | | 0.23 | 5.79 | 54100 | 0.4508 | 0.3919 | | 0.23 | 5.8 | 54200 | 0.4564 | 0.3788 | | 0.23 | 5.81 | 54300 | 0.4602 | 0.3888 | | 0.23 | 5.82 | 54400 | 0.4538 | 0.3732 | | 0.2334 | 5.84 | 54500 | 0.4500 | 0.3808 | | 0.2334 | 5.85 | 54600 | 0.4475 | 0.3705 | | 0.2334 | 5.86 | 54700 | 0.4415 | 0.3772 | | 0.2334 | 5.87 | 54800 | 0.4515 | 0.3771 | | 0.2334 | 5.88 | 54900 | 0.4410 | 0.3677 | | 0.2259 | 5.89 | 55000 | 0.4555 | 0.3702 | | 0.2259 | 5.9 | 55100 | 0.4509 | 0.3894 | | 0.2259 | 5.91 | 55200 | 0.4472 | 0.3692 | | 0.2259 | 5.92 | 55300 | 0.4438 | 0.3754 | | 0.2259 | 5.93 | 55400 | 0.4399 | 0.3698 | | 0.2289 | 5.94 | 55500 | 0.4496 | 0.3753 | | 0.2289 | 5.95 | 55600 | 0.4506 | 0.3752 | | 0.2289 | 5.96 | 55700 | 0.4482 | 0.3766 | | 0.2289 | 5.97 | 55800 | 0.4415 | 0.3772 | | 0.2289 | 5.98 | 55900 | 0.4447 | 0.3750 | | 0.2281 | 6.0 | 56000 | 0.4566 | 0.3842 | | 0.2281 | 6.01 | 56100 | 0.4694 | 0.3774 | | 0.2281 | 6.02 | 56200 | 0.4454 | 0.3788 | | 0.2281 | 6.03 | 56300 | 0.4676 | 0.3718 | | 0.2281 | 6.04 | 56400 | 0.4650 | 0.3751 | | 0.1979 | 6.05 | 56500 | 0.4601 | 0.3765 | | 0.1979 | 6.06 | 56600 | 0.4647 | 0.3840 | | 0.1979 | 6.07 | 56700 | 0.4782 | 0.3756 | | 0.1979 | 6.08 | 56800 | 0.4709 | 0.3736 | | 0.1979 | 6.09 | 56900 | 0.4707 | 0.3734 | | 0.1923 | 6.1 | 57000 | 0.4704 | 0.3751 | | 0.1923 | 6.11 | 57100 | 0.4542 | 0.3721 | | 0.1923 | 6.12 | 57200 | 0.4542 | 0.3735 | | 0.1923 | 6.13 | 57300 | 0.4587 | 0.3804 | | 0.1923 | 6.15 | 57400 | 0.4428 | 0.3687 | | 0.2012 | 6.16 | 57500 | 0.4456 | 0.3748 | | 0.2012 | 6.17 | 57600 | 0.4578 | 0.3762 | | 0.2012 | 6.18 | 57700 | 0.4699 | 0.3722 | | 0.2012 | 6.19 | 57800 | 0.4499 | 0.3756 | | 0.2012 | 6.2 | 57900 | 0.4633 | 0.3680 | | 0.1951 | 6.21 | 58000 | 0.4548 | 0.3712 | | 0.1951 | 6.22 | 58100 | 0.4520 | 0.3759 | | 0.1951 | 6.23 | 58200 | 0.4458 | 0.3616 | | 0.1951 | 6.24 | 58300 | 0.4307 | 0.3637 | | 0.1951 | 6.25 | 58400 | 0.4546 | 0.3621 | | 0.1967 | 6.26 | 58500 | 0.4459 | 0.3623 | | 0.1967 | 6.27 | 58600 | 0.4535 | 0.3690 | | 0.1967 | 6.28 | 58700 | 0.4574 | 0.3771 | | 0.1967 | 6.3 | 58800 | 0.4493 | 0.3744 | | 0.1967 | 6.31 | 58900 | 0.4494 | 0.3769 | | 0.1998 | 6.32 | 59000 | 0.4529 | 0.3644 | | 0.1998 | 6.33 | 59100 | 0.4416 | 0.3662 | | 0.1998 | 6.34 | 59200 | 0.4468 | 0.3785 | | 0.1998 | 6.35 | 59300 | 0.4377 | 0.3664 | | 0.1998 | 6.36 | 59400 | 0.4647 | 0.3755 | | 0.2009 | 6.37 | 59500 | 0.4700 | 0.3824 | | 0.2009 | 6.38 | 59600 | 0.4488 | 0.3685 | | 0.2009 | 6.39 | 59700 | 0.4649 | 0.3804 | | 0.2009 | 6.4 | 59800 | 0.4389 | 0.3689 | | 0.2009 | 6.41 | 59900 | 0.4456 | 0.3531 | | 0.2007 | 6.42 | 60000 | 0.4572 | 0.3658 | | 0.2007 | 6.43 | 60100 | 0.4464 | 0.3669 | | 0.2007 | 6.45 | 60200 | 0.4666 | 0.3711 | | 0.2007 | 6.46 | 60300 | 0.4399 | 0.3660 | | 0.2007 | 6.47 | 60400 | 0.4445 | 0.3631 | | 0.2005 | 6.48 | 60500 | 0.4450 | 0.3621 | | 0.2005 | 6.49 | 60600 | 0.4346 | 0.3571 | | 0.2005 | 6.5 | 60700 | 0.4358 | 0.3581 | | 0.2005 | 6.51 | 60800 | 0.4344 | 0.3646 | | 0.2005 | 6.52 | 60900 | 0.4377 | 0.3621 | | 0.2038 | 6.53 | 61000 | 0.4262 | 0.3570 | | 0.2038 | 6.54 | 61100 | 0.4269 | 0.3614 | | 0.2038 | 6.55 | 61200 | 0.4297 | 0.3592 | | 0.2038 | 6.56 | 61300 | 0.4433 | 0.3682 | | 0.2038 | 6.57 | 61400 | 0.4474 | 0.3644 | | 0.199 | 6.58 | 61500 | 0.4464 | 0.3678 | | 0.199 | 6.6 | 61600 | 0.4397 | 0.3562 | | 0.199 | 6.61 | 61700 | 0.4415 | 0.3612 | | 0.199 | 6.62 | 61800 | 0.4362 | 0.3601 | | 0.199 | 6.63 | 61900 | 0.4442 | 0.3623 | | 0.1995 | 6.64 | 62000 | 0.4558 | 0.3662 | | 0.1995 | 6.65 | 62100 | 0.4477 | 0.3647 | | 0.1995 | 6.66 | 62200 | 0.4542 | 0.3699 | | 0.1995 | 6.67 | 62300 | 0.4411 | 0.3632 | | 0.1995 | 6.68 | 62400 | 0.4408 | 0.3658 | | 0.2014 | 6.69 | 62500 | 0.4426 | 0.3691 | | 0.2014 | 6.7 | 62600 | 0.4246 | 0.3645 | | 0.2014 | 6.71 | 62700 | 0.4466 | 0.3676 | | 0.2014 | 6.72 | 62800 | 0.4493 | 0.3566 | | 0.2014 | 6.73 | 62900 | 0.4336 | 0.3621 | | 0.2015 | 6.75 | 63000 | 0.4367 | 0.3604 | | 0.2015 | 6.76 | 63100 | 0.4424 | 0.3754 | | 0.2015 | 6.77 | 63200 | 0.4679 | 0.3733 | | 0.2015 | 6.78 | 63300 | 0.4483 | 0.3752 | | 0.2015 | 6.79 | 63400 | 0.4746 | 0.3822 | | 0.2048 | 6.8 | 63500 | 0.4340 | 0.3731 | | 0.2048 | 6.81 | 63600 | 0.4346 | 0.3631 | | 0.2048 | 6.82 | 63700 | 0.4525 | 0.3680 | | 0.2048 | 6.83 | 63800 | 0.4360 | 0.3641 | | 0.2048 | 6.84 | 63900 | 0.4299 | 0.3558 | | 0.2017 | 6.85 | 64000 | 0.4370 | 0.3533 | | 0.2017 | 6.86 | 64100 | 0.4293 | 0.3617 | | 0.2017 | 6.87 | 64200 | 0.4431 | 0.3660 | | 0.2017 | 6.88 | 64300 | 0.4362 | 0.3688 | | 0.2017 | 6.9 | 64400 | 0.4507 | 0.3648 | | 0.2045 | 6.91 | 64500 | 0.4439 | 0.3613 | | 0.2045 | 6.92 | 64600 | 0.4249 | 0.3493 | | 0.2045 | 6.93 | 64700 | 0.4362 | 0.3612 | | 0.2045 | 6.94 | 64800 | 0.4336 | 0.3585 | | 0.2045 | 6.95 | 64900 | 0.4387 | 0.3568 | | 0.1977 | 6.96 | 65000 | 0.4313 | 0.3542 | | 0.1977 | 6.97 | 65100 | 0.4287 | 0.3552 | | 0.1977 | 6.98 | 65200 | 0.4372 | 0.3586 | | 0.1977 | 6.99 | 65300 | 0.4378 | 0.3629 | | 0.1977 | 7.0 | 65400 | 0.4518 | 0.3640 | | 0.1971 | 7.01 | 65500 | 0.4480 | 0.3557 | | 0.1971 | 7.02 | 65600 | 0.4530 | 0.3560 | | 0.1971 | 7.03 | 65700 | 0.4581 | 0.3582 | | 0.1971 | 7.04 | 65800 | 0.4492 | 0.3543 | | 0.1971 | 7.06 | 65900 | 0.4448 | 0.3608 | | 0.1672 | 7.07 | 66000 | 0.4469 | 0.3543 | | 0.1672 | 7.08 | 66100 | 0.4262 | 0.3488 | | 0.1672 | 7.09 | 66200 | 0.4289 | 0.3570 | | 0.1672 | 7.1 | 66300 | 0.4455 | 0.3545 | | 0.1672 | 7.11 | 66400 | 0.4449 | 0.3563 | | 0.169 | 7.12 | 66500 | 0.4555 | 0.3565 | | 0.169 | 7.13 | 66600 | 0.4432 | 0.3656 | | 0.169 | 7.14 | 66700 | 0.4399 | 0.3610 | | 0.169 | 7.15 | 66800 | 0.4383 | 0.3554 | | 0.169 | 7.16 | 66900 | 0.4376 | 0.3536 | | 0.1724 | 7.17 | 67000 | 0.4383 | 0.3572 | | 0.1724 | 7.18 | 67100 | 0.4452 | 0.3535 | | 0.1724 | 7.19 | 67200 | 0.4610 | 0.3668 | | 0.1724 | 7.21 | 67300 | 0.4534 | 0.3546 | | 0.1724 | 7.22 | 67400 | 0.4506 | 0.3604 | | 0.1729 | 7.23 | 67500 | 0.4463 | 0.3507 | | 0.1729 | 7.24 | 67600 | 0.4440 | 0.3630 | | 0.1729 | 7.25 | 67700 | 0.4361 | 0.3550 | | 0.1729 | 7.26 | 67800 | 0.4397 | 0.3643 | | 0.1729 | 7.27 | 67900 | 0.4328 | 0.3548 | | 0.1736 | 7.28 | 68000 | 0.4546 | 0.3614 | | 0.1736 | 7.29 | 68100 | 0.4506 | 0.3558 | | 0.1736 | 7.3 | 68200 | 0.4361 | 0.3513 | | 0.1736 | 7.31 | 68300 | 0.4223 | 0.3500 | | 0.1736 | 7.32 | 68400 | 0.4474 | 0.3497 | | 0.1733 | 7.33 | 68500 | 0.4303 | 0.3549 | | 0.1733 | 7.34 | 68600 | 0.4265 | 0.3483 | | 0.1733 | 7.36 | 68700 | 0.4339 | 0.3558 | | 0.1733 | 7.37 | 68800 | 0.4266 | 0.3491 | | 0.1733 | 7.38 | 68900 | 0.4423 | 0.3565 | | 0.1764 | 7.39 | 69000 | 0.4410 | 0.3554 | | 0.1764 | 7.4 | 69100 | 0.4482 | 0.3703 | | 0.1764 | 7.41 | 69200 | 0.4480 | 0.3641 | | 0.1764 | 7.42 | 69300 | 0.4361 | 0.3500 | | 0.1764 | 7.43 | 69400 | 0.4399 | 0.3632 | | 0.1711 | 7.44 | 69500 | 0.4383 | 0.3591 | | 0.1711 | 7.45 | 69600 | 0.4523 | 0.3636 | | 0.1711 | 7.46 | 69700 | 0.4388 | 0.3502 | | 0.1711 | 7.47 | 69800 | 0.4305 | 0.3565 | | 0.1711 | 7.48 | 69900 | 0.4290 | 0.3538 | | 0.1748 | 7.49 | 70000 | 0.4359 | 0.3511 | | 0.1748 | 7.51 | 70100 | 0.4315 | 0.3460 | | 0.1748 | 7.52 | 70200 | 0.4268 | 0.3555 | | 0.1748 | 7.53 | 70300 | 0.4267 | 0.3455 | | 0.1748 | 7.54 | 70400 | 0.4359 | 0.3517 | | 0.1739 | 7.55 | 70500 | 0.4299 | 0.3491 | | 0.1739 | 7.56 | 70600 | 0.4423 | 0.3409 | | 0.1739 | 7.57 | 70700 | 0.4251 | 0.3420 | | 0.1739 | 7.58 | 70800 | 0.4300 | 0.3414 | | 0.1739 | 7.59 | 70900 | 0.4349 | 0.3422 | | 0.1763 | 7.6 | 71000 | 0.4328 | 0.3418 | | 0.1763 | 7.61 | 71100 | 0.4313 | 0.3452 | | 0.1763 | 7.62 | 71200 | 0.4240 | 0.3534 | | 0.1763 | 7.63 | 71300 | 0.4274 | 0.3474 | | 0.1763 | 7.64 | 71400 | 0.4304 | 0.3467 | | 0.171 | 7.66 | 71500 | 0.4331 | 0.3510 | | 0.171 | 7.67 | 71600 | 0.4263 | 0.3478 | | 0.171 | 7.68 | 71700 | 0.4301 | 0.3447 | | 0.171 | 7.69 | 71800 | 0.4046 | 0.3452 | | 0.171 | 7.7 | 71900 | 0.4300 | 0.3528 | | 0.1792 | 7.71 | 72000 | 0.4253 | 0.3492 | | 0.1792 | 7.72 | 72100 | 0.4296 | 0.3491 | | 0.1792 | 7.73 | 72200 | 0.4118 | 0.3451 | | 0.1792 | 7.74 | 72300 | 0.4348 | 0.3345 | | 0.1792 | 7.75 | 72400 | 0.4283 | 0.3447 | | 0.1801 | 7.76 | 72500 | 0.4232 | 0.3449 | | 0.1801 | 7.77 | 72600 | 0.4491 | 0.3486 | | 0.1801 | 7.78 | 72700 | 0.4261 | 0.3343 | | 0.1801 | 7.79 | 72800 | 0.4382 | 0.3455 | | 0.1801 | 7.81 | 72900 | 0.4301 | 0.3415 | | 0.1731 | 7.82 | 73000 | 0.4236 | 0.3438 | | 0.1731 | 7.83 | 73100 | 0.4257 | 0.3419 | | 0.1731 | 7.84 | 73200 | 0.4368 | 0.3410 | | 0.1731 | 7.85 | 73300 | 0.4207 | 0.3398 | | 0.1731 | 7.86 | 73400 | 0.4118 | 0.3418 | | 0.1748 | 7.87 | 73500 | 0.4357 | 0.3429 | | 0.1748 | 7.88 | 73600 | 0.4277 | 0.3452 | | 0.1748 | 7.89 | 73700 | 0.4173 | 0.3476 | | 0.1748 | 7.9 | 73800 | 0.4191 | 0.3478 | | 0.1748 | 7.91 | 73900 | 0.4197 | 0.3457 | | 0.1745 | 7.92 | 74000 | 0.4197 | 0.3436 | | 0.1745 | 7.93 | 74100 | 0.4253 | 0.3512 | | 0.1745 | 7.94 | 74200 | 0.4217 | 0.3463 | | 0.1745 | 7.95 | 74300 | 0.4305 | 0.3473 | | 0.1745 | 7.97 | 74400 | 0.4215 | 0.3507 | | 0.1743 | 7.98 | 74500 | 0.4127 | 0.3408 | | 0.1743 | 7.99 | 74600 | 0.4191 | 0.3468 | | 0.1743 | 8.0 | 74700 | 0.4381 | 0.3491 | | 0.1743 | 8.01 | 74800 | 0.4510 | 0.3477 | | 0.1743 | 8.02 | 74900 | 0.4482 | 0.3471 | | 0.1588 | 8.03 | 75000 | 0.4471 | 0.3430 | | 0.1588 | 8.04 | 75100 | 0.4296 | 0.3393 | | 0.1588 | 8.05 | 75200 | 0.4480 | 0.3398 | | 0.1588 | 8.06 | 75300 | 0.4302 | 0.3452 | | 0.1588 | 8.07 | 75400 | 0.4410 | 0.3431 | | 0.144 | 8.08 | 75500 | 0.4263 | 0.3455 | | 0.144 | 8.09 | 75600 | 0.4523 | 0.3495 | | 0.144 | 8.1 | 75700 | 0.4455 | 0.3511 | | 0.144 | 8.12 | 75800 | 0.4379 | 0.3445 | | 0.144 | 8.13 | 75900 | 0.4418 | 0.3411 | | 0.1483 | 8.14 | 76000 | 0.4491 | 0.3463 | | 0.1483 | 8.15 | 76100 | 0.4386 | 0.3467 | | 0.1483 | 8.16 | 76200 | 0.4327 | 0.3524 | | 0.1483 | 8.17 | 76300 | 0.4360 | 0.3613 | | 0.1483 | 8.18 | 76400 | 0.4352 | 0.3498 | | 0.1541 | 8.19 | 76500 | 0.4376 | 0.3414 | | 0.1541 | 8.2 | 76600 | 0.4408 | 0.3464 | | 0.1541 | 8.21 | 76700 | 0.4415 | 0.3445 | | 0.1541 | 8.22 | 76800 | 0.4455 | 0.3482 | | 0.1541 | 8.23 | 76900 | 0.4542 | 0.3415 | | 0.1479 | 8.24 | 77000 | 0.4462 | 0.3426 | | 0.1479 | 8.25 | 77100 | 0.4460 | 0.3413 | | 0.1479 | 8.27 | 77200 | 0.4434 | 0.3375 | | 0.1479 | 8.28 | 77300 | 0.4397 | 0.3473 | | 0.1479 | 8.29 | 77400 | 0.4379 | 0.3484 | | 0.1479 | 8.3 | 77500 | 0.4441 | 0.3494 | | 0.1479 | 8.31 | 77600 | 0.4301 | 0.3466 | | 0.1479 | 8.32 | 77700 | 0.4420 | 0.3474 | | 0.1479 | 8.33 | 77800 | 0.4520 | 0.3589 | | 0.1479 | 8.34 | 77900 | 0.4283 | 0.3482 | | 0.1531 | 8.35 | 78000 | 0.4325 | 0.3446 | | 0.1531 | 8.36 | 78100 | 0.4380 | 0.3469 | | 0.1531 | 8.37 | 78200 | 0.4463 | 0.3503 | | 0.1531 | 8.38 | 78300 | 0.4479 | 0.3499 | | 0.1531 | 8.39 | 78400 | 0.4477 | 0.3529 | | 0.1507 | 8.4 | 78500 | 0.4709 | 0.3551 | | 0.1507 | 8.42 | 78600 | 0.4533 | 0.3531 | | 0.1507 | 8.43 | 78700 | 0.4507 | 0.3522 | | 0.1507 | 8.44 | 78800 | 0.4562 | 0.3583 | | 0.1507 | 8.45 | 78900 | 0.4421 | 0.3577 | | 0.1545 | 8.46 | 79000 | 0.4485 | 0.3547 | | 0.1545 | 8.47 | 79100 | 0.4389 | 0.3465 | | 0.1545 | 8.48 | 79200 | 0.4397 | 0.3502 | | 0.1545 | 8.49 | 79300 | 0.4403 | 0.3471 | | 0.1545 | 8.5 | 79400 | 0.4394 | 0.3482 | | 0.153 | 8.51 | 79500 | 0.4393 | 0.3474 | | 0.153 | 8.52 | 79600 | 0.4343 | 0.3495 | | 0.153 | 8.53 | 79700 | 0.4395 | 0.3539 | | 0.153 | 8.54 | 79800 | 0.4497 | 0.3535 | | 0.153 | 8.55 | 79900 | 0.4443 | 0.3540 | | 0.1558 | 8.57 | 80000 | 0.4495 | 0.3554 | | 0.1558 | 8.58 | 80100 | 0.4387 | 0.3460 | | 0.1558 | 8.59 | 80200 | 0.4378 | 0.3520 | | 0.1558 | 8.6 | 80300 | 0.4446 | 0.3527 | | 0.1558 | 8.61 | 80400 | 0.4513 | 0.3508 | | 0.1527 | 8.62 | 80500 | 0.4396 | 0.3537 | | 0.1527 | 8.63 | 80600 | 0.4405 | 0.3507 | | 0.1527 | 8.64 | 80700 | 0.4398 | 0.3450 | | 0.1527 | 8.65 | 80800 | 0.4458 | 0.3508 | | 0.1527 | 8.66 | 80900 | 0.4380 | 0.3465 | | 0.1522 | 8.67 | 81000 | 0.4373 | 0.3482 | | 0.1522 | 8.68 | 81100 | 0.4363 | 0.3410 | | 0.1522 | 8.69 | 81200 | 0.4290 | 0.3447 | | 0.1522 | 8.7 | 81300 | 0.4409 | 0.3515 | | 0.1522 | 8.72 | 81400 | 0.4363 | 0.3433 | | 0.1502 | 8.73 | 81500 | 0.4313 | 0.3429 | | 0.1502 | 8.74 | 81600 | 0.4263 | 0.3451 | | 0.1502 | 8.75 | 81700 | 0.4297 | 0.3452 | | 0.1502 | 8.76 | 81800 | 0.4449 | 0.3411 | | 0.1502 | 8.77 | 81900 | 0.4465 | 0.3455 | | 0.151 | 8.78 | 82000 | 0.4274 | 0.3425 | | 0.151 | 8.79 | 82100 | 0.4525 | 0.3532 | | 0.151 | 8.8 | 82200 | 0.4282 | 0.3502 | | 0.151 | 8.81 | 82300 | 0.4189 | 0.3507 | | 0.151 | 8.82 | 82400 | 0.4379 | 0.3451 | | 0.1529 | 8.83 | 82500 | 0.4378 | 0.3419 | | 0.1529 | 8.84 | 82600 | 0.4283 | 0.3392 | | 0.1529 | 8.85 | 82700 | 0.4359 | 0.3399 | | 0.1529 | 8.87 | 82800 | 0.4308 | 0.3358 | | 0.1529 | 8.88 | 82900 | 0.4296 | 0.3335 | | 0.151 | 8.89 | 83000 | 0.4387 | 0.3372 | | 0.151 | 8.9 | 83100 | 0.4335 | 0.3420 | | 0.151 | 8.91 | 83200 | 0.4329 | 0.3374 | | 0.151 | 8.92 | 83300 | 0.4353 | 0.3404 | | 0.151 | 8.93 | 83400 | 0.4384 | 0.3447 | | 0.1522 | 8.94 | 83500 | 0.4444 | 0.3353 | | 0.1522 | 8.95 | 83600 | 0.4413 | 0.3481 | | 0.1522 | 8.96 | 83700 | 0.4247 | 0.3474 | | 0.1522 | 8.97 | 83800 | 0.4197 | 0.3386 | | 0.1522 | 8.98 | 83900 | 0.4216 | 0.3384 | | 0.1511 | 8.99 | 84000 | 0.4159 | 0.3396 | | 0.1511 | 9.0 | 84100 | 0.4213 | 0.3416 | | 0.1511 | 9.01 | 84200 | 0.4399 | 0.3379 | | 0.1511 | 9.03 | 84300 | 0.4318 | 0.3437 | | 0.1511 | 9.04 | 84400 | 0.4356 | 0.3371 | | 0.1336 | 9.05 | 84500 | 0.4403 | 0.3373 | | 0.1336 | 9.06 | 84600 | 0.4545 | 0.3381 | | 0.1336 | 9.07 | 84700 | 0.4313 | 0.3331 | | 0.1336 | 9.08 | 84800 | 0.4257 | 0.3360 | | 0.1336 | 9.09 | 84900 | 0.4285 | 0.3372 | | 0.1315 | 9.1 | 85000 | 0.4378 | 0.3332 | | 0.1315 | 9.11 | 85100 | 0.4352 | 0.3282 | | 0.1315 | 9.12 | 85200 | 0.4360 | 0.3339 | | 0.1315 | 9.13 | 85300 | 0.4404 | 0.3365 | | 0.1315 | 9.14 | 85400 | 0.4345 | 0.3356 | | 0.1272 | 9.15 | 85500 | 0.4468 | 0.3375 | | 0.1272 | 9.16 | 85600 | 0.4331 | 0.3363 | | 0.1272 | 9.18 | 85700 | 0.4330 | 0.3309 | | 0.1272 | 9.19 | 85800 | 0.4424 | 0.3301 | | 0.1272 | 9.2 | 85900 | 0.4520 | 0.3326 | | 0.1289 | 9.21 | 86000 | 0.4421 | 0.3326 | | 0.1289 | 9.22 | 86100 | 0.4480 | 0.3335 | | 0.1289 | 9.23 | 86200 | 0.4351 | 0.3380 | | 0.1289 | 9.24 | 86300 | 0.4350 | 0.3427 | | 0.1289 | 9.25 | 86400 | 0.4362 | 0.3320 | | 0.1333 | 9.26 | 86500 | 0.4260 | 0.3342 | | 0.1333 | 9.27 | 86600 | 0.4357 | 0.3360 | | 0.1333 | 9.28 | 86700 | 0.4505 | 0.3372 | | 0.1333 | 9.29 | 86800 | 0.4342 | 0.3359 | | 0.1333 | 9.3 | 86900 | 0.4295 | 0.3367 | | 0.1318 | 9.31 | 87000 | 0.4320 | 0.3335 | | 0.1318 | 9.33 | 87100 | 0.4332 | 0.3344 | | 0.1318 | 9.34 | 87200 | 0.4373 | 0.3330 | | 0.1318 | 9.35 | 87300 | 0.4490 | 0.3316 | | 0.1318 | 9.36 | 87400 | 0.4188 | 0.3429 | | 0.1275 | 9.37 | 87500 | 0.4502 | 0.3383 | | 0.1275 | 9.38 | 87600 | 0.4463 | 0.3387 | | 0.1275 | 9.39 | 87700 | 0.4385 | 0.3308 | | 0.1275 | 9.4 | 87800 | 0.4464 | 0.3414 | | 0.1275 | 9.41 | 87900 | 0.4563 | 0.3405 | | 0.1331 | 9.42 | 88000 | 0.4286 | 0.3374 | | 0.1331 | 9.43 | 88100 | 0.4389 | 0.3352 | | 0.1331 | 9.44 | 88200 | 0.4301 | 0.3340 | | 0.1331 | 9.45 | 88300 | 0.4417 | 0.3373 | | 0.1331 | 9.46 | 88400 | 0.4450 | 0.3425 | | 0.1266 | 9.48 | 88500 | 0.4456 | 0.3451 | | 0.1266 | 9.49 | 88600 | 0.4517 | 0.3403 | | 0.1266 | 9.5 | 88700 | 0.4447 | 0.3419 | | 0.1266 | 9.51 | 88800 | 0.4486 | 0.3428 | | 0.1266 | 9.52 | 88900 | 0.4591 | 0.3411 | | 0.1316 | 9.53 | 89000 | 0.4481 | 0.3387 | | 0.1316 | 9.54 | 89100 | 0.4308 | 0.3349 | | 0.1316 | 9.55 | 89200 | 0.4411 | 0.3405 | | 0.1316 | 9.56 | 89300 | 0.4378 | 0.3390 | | 0.1316 | 9.57 | 89400 | 0.4448 | 0.3365 | | 0.1325 | 9.58 | 89500 | 0.4575 | 0.3416 | | 0.1325 | 9.59 | 89600 | 0.4608 | 0.3422 | | 0.1325 | 9.6 | 89700 | 0.4396 | 0.3350 | | 0.1325 | 9.61 | 89800 | 0.4380 | 0.3398 | | 0.1325 | 9.63 | 89900 | 0.4337 | 0.3388 | | 0.1324 | 9.64 | 90000 | 0.4376 | 0.3388 | | 0.1324 | 9.65 | 90100 | 0.4185 | 0.3380 | | 0.1324 | 9.66 | 90200 | 0.4394 | 0.3384 | | 0.1324 | 9.67 | 90300 | 0.4472 | 0.3400 | | 0.1324 | 9.68 | 90400 | 0.4523 | 0.3390 | | 0.1361 | 9.69 | 90500 | 0.4466 | 0.3389 | | 0.1361 | 9.7 | 90600 | 0.4414 | 0.3383 | | 0.1361 | 9.71 | 90700 | 0.4288 | 0.3348 | | 0.1361 | 9.72 | 90800 | 0.4445 | 0.3374 | | 0.1361 | 9.73 | 90900 | 0.4252 | 0.3322 | | 0.1353 | 9.74 | 91000 | 0.4312 | 0.3338 | | 0.1353 | 9.75 | 91100 | 0.4326 | 0.3319 | | 0.1353 | 9.76 | 91200 | 0.4212 | 0.3400 | | 0.1353 | 9.78 | 91300 | 0.4191 | 0.3374 | | 0.1353 | 9.79 | 91400 | 0.4399 | 0.3332 | | 0.1308 | 9.8 | 91500 | 0.4340 | 0.3349 | | 0.1308 | 9.81 | 91600 | 0.4280 | 0.3379 | | 0.1308 | 9.82 | 91700 | 0.4419 | 0.3376 | | 0.1308 | 9.83 | 91800 | 0.4309 | 0.3333 | | 0.1308 | 9.84 | 91900 | 0.4274 | 0.3352 | | 0.1321 | 9.85 | 92000 | 0.4147 | 0.3337 | | 0.1321 | 9.86 | 92100 | 0.4252 | 0.3316 | | 0.1321 | 9.87 | 92200 | 0.4378 | 0.3381 | | 0.1321 | 9.88 | 92300 | 0.4265 | 0.3355 | | 0.1321 | 9.89 | 92400 | 0.4247 | 0.3331 | | 0.1358 | 9.9 | 92500 | 0.4099 | 0.3379 | | 0.1358 | 9.91 | 92600 | 0.4142 | 0.3356 | | 0.1358 | 9.93 | 92700 | 0.4220 | 0.3332 | | 0.1358 | 9.94 | 92800 | 0.4219 | 0.3369 | | 0.1358 | 9.95 | 92900 | 0.4178 | 0.3332 | | 0.1331 | 9.96 | 93000 | 0.4305 | 0.3353 | | 0.1331 | 9.97 | 93100 | 0.4324 | 0.3307 | | 0.1331 | 9.98 | 93200 | 0.4315 | 0.3344 | | 0.1331 | 9.99 | 93300 | 0.4212 | 0.3314 | | 0.1331 | 10.0 | 93400 | 0.4203 | 0.3332 | | 0.1304 | 10.01 | 93500 | 0.4424 | 0.3351 | | 0.1304 | 10.02 | 93600 | 0.4474 | 0.3341 | | 0.1304 | 10.03 | 93700 | 0.4466 | 0.3378 | | 0.1304 | 10.04 | 93800 | 0.4388 | 0.3327 | | 0.1304 | 10.05 | 93900 | 0.4312 | 0.3360 | | 0.1152 | 10.06 | 94000 | 0.4471 | 0.3307 | | 0.1152 | 10.07 | 94100 | 0.4472 | 0.3316 | | 0.1152 | 10.09 | 94200 | 0.4462 | 0.3324 | | 0.1152 | 10.1 | 94300 | 0.4383 | 0.3344 | | 0.1152 | 10.11 | 94400 | 0.4671 | 0.3365 | | 0.1097 | 10.12 | 94500 | 0.4596 | 0.3307 | | 0.1097 | 10.13 | 94600 | 0.4517 | 0.3382 | | 0.1097 | 10.14 | 94700 | 0.4285 | 0.3380 | | 0.1097 | 10.15 | 94800 | 0.4628 | 0.3363 | | 0.1097 | 10.16 | 94900 | 0.4478 | 0.3365 | | 0.1153 | 10.17 | 95000 | 0.4464 | 0.3346 | | 0.1153 | 10.18 | 95100 | 0.4432 | 0.3392 | | 0.1153 | 10.19 | 95200 | 0.4326 | 0.3330 | | 0.1153 | 10.2 | 95300 | 0.4480 | 0.3327 | | 0.1153 | 10.21 | 95400 | 0.4436 | 0.3260 | | 0.1149 | 10.22 | 95500 | 0.4549 | 0.3311 | | 0.1149 | 10.24 | 95600 | 0.4573 | 0.3353 | | 0.1149 | 10.25 | 95700 | 0.4373 | 0.3369 | | 0.1149 | 10.26 | 95800 | 0.4459 | 0.3358 | | 0.1149 | 10.27 | 95900 | 0.4288 | 0.3270 | | 0.1169 | 10.28 | 96000 | 0.4474 | 0.3330 | | 0.1169 | 10.29 | 96100 | 0.4524 | 0.3298 | | 0.1169 | 10.3 | 96200 | 0.4517 | 0.3258 | | 0.1169 | 10.31 | 96300 | 0.4366 | 0.3288 | | 0.1169 | 10.32 | 96400 | 0.4574 | 0.3324 | | 0.1137 | 10.33 | 96500 | 0.4507 | 0.3343 | | 0.1137 | 10.34 | 96600 | 0.4414 | 0.3301 | | 0.1137 | 10.35 | 96700 | 0.4524 | 0.3366 | | 0.1137 | 10.36 | 96800 | 0.4563 | 0.3435 | | 0.1137 | 10.37 | 96900 | 0.4315 | 0.3375 | | 0.1162 | 10.39 | 97000 | 0.4429 | 0.3365 | | 0.1162 | 10.4 | 97100 | 0.4489 | 0.3380 | | 0.1162 | 10.41 | 97200 | 0.4352 | 0.3357 | | 0.1162 | 10.42 | 97300 | 0.4390 | 0.3319 | | 0.1162 | 10.43 | 97400 | 0.4570 | 0.3303 | | 0.1151 | 10.44 | 97500 | 0.4692 | 0.3344 | | 0.1151 | 10.45 | 97600 | 0.4605 | 0.3332 | | 0.1151 | 10.46 | 97700 | 0.4457 | 0.3238 | | 0.1151 | 10.47 | 97800 | 0.4298 | 0.3304 | | 0.1151 | 10.48 | 97900 | 0.4619 | 0.3274 | | 0.1105 | 10.49 | 98000 | 0.4362 | 0.3244 | | 0.1105 | 10.5 | 98100 | 0.4568 | 0.3289 | | 0.1105 | 10.51 | 98200 | 0.4522 | 0.3336 | | 0.1105 | 10.52 | 98300 | 0.4302 | 0.3257 | | 0.1105 | 10.54 | 98400 | 0.4505 | 0.3238 | | 0.1164 | 10.55 | 98500 | 0.4430 | 0.3301 | | 0.1164 | 10.56 | 98600 | 0.4575 | 0.3283 | | 0.1164 | 10.57 | 98700 | 0.4447 | 0.3277 | | 0.1164 | 10.58 | 98800 | 0.4400 | 0.3301 | | 0.1164 | 10.59 | 98900 | 0.4427 | 0.3288 | | 0.1113 | 10.6 | 99000 | 0.4538 | 0.3248 | | 0.1113 | 10.61 | 99100 | 0.4519 | 0.3298 | | 0.1113 | 10.62 | 99200 | 0.4290 | 0.3249 | | 0.1113 | 10.63 | 99300 | 0.4501 | 0.3220 | | 0.1113 | 10.64 | 99400 | 0.4410 | 0.3218 | | 0.1159 | 10.65 | 99500 | 0.4478 | 0.3211 | | 0.1159 | 10.66 | 99600 | 0.4462 | 0.3250 | | 0.1159 | 10.67 | 99700 | 0.4543 | 0.3302 | | 0.1159 | 10.69 | 99800 | 0.4462 | 0.3301 | | 0.1159 | 10.7 | 99900 | 0.4468 | 0.3229 | | 0.1161 | 10.71 | 100000 | 0.4515 | 0.3241 | | 0.1161 | 10.72 | 100100 | 0.4404 | 0.3276 | | 0.1161 | 10.73 | 100200 | 0.4439 | 0.3222 | | 0.1161 | 10.74 | 100300 | 0.4392 | 0.3257 | | 0.1161 | 10.75 | 100400 | 0.4476 | 0.3314 | | 0.1199 | 10.76 | 100500 | 0.4493 | 0.3270 | | 0.1199 | 10.77 | 100600 | 0.4462 | 0.3224 | | 0.1199 | 10.78 | 100700 | 0.4467 | 0.3311 | | 0.1199 | 10.79 | 100800 | 0.4198 | 0.3228 | | 0.1199 | 10.8 | 100900 | 0.4349 | 0.3225 | | 0.1146 | 10.81 | 101000 | 0.4371 | 0.3272 | | 0.1146 | 10.82 | 101100 | 0.4525 | 0.3210 | | 0.1146 | 10.84 | 101200 | 0.4293 | 0.3219 | | 0.1146 | 10.85 | 101300 | 0.4238 | 0.3216 | | 0.1146 | 10.86 | 101400 | 0.4377 | 0.3252 | | 0.118 | 10.87 | 101500 | 0.4371 | 0.3208 | | 0.118 | 10.88 | 101600 | 0.4216 | 0.3174 | | 0.118 | 10.89 | 101700 | 0.4312 | 0.3189 | | 0.118 | 10.9 | 101800 | 0.4317 | 0.3204 | | 0.118 | 10.91 | 101900 | 0.4303 | 0.3235 | | 0.114 | 10.92 | 102000 | 0.4416 | 0.3158 | | 0.114 | 10.93 | 102100 | 0.4240 | 0.3195 | | 0.114 | 10.94 | 102200 | 0.4340 | 0.3149 | | 0.114 | 10.95 | 102300 | 0.4311 | 0.3215 | | 0.114 | 10.96 | 102400 | 0.4261 | 0.3238 | | 0.1152 | 10.97 | 102500 | 0.4263 | 0.3206 | | 0.1152 | 10.98 | 102600 | 0.4325 | 0.3294 | | 0.1152 | 11.0 | 102700 | 0.4327 | 0.3187 | | 0.1152 | 11.01 | 102800 | 0.4423 | 0.3195 | | 0.1152 | 11.02 | 102900 | 0.4341 | 0.3277 | | 0.1084 | 11.03 | 103000 | 0.4232 | 0.3243 | | 0.1084 | 11.04 | 103100 | 0.4355 | 0.3184 | | 0.1084 | 11.05 | 103200 | 0.4374 | 0.3274 | | 0.1084 | 11.06 | 103300 | 0.4484 | 0.3305 | | 0.1084 | 11.07 | 103400 | 0.4423 | 0.3226 | | 0.1003 | 11.08 | 103500 | 0.4518 | 0.3224 | | 0.1003 | 11.09 | 103600 | 0.4518 | 0.3243 | | 0.1003 | 11.1 | 103700 | 0.4282 | 0.3207 | | 0.1003 | 11.11 | 103800 | 0.4418 | 0.3220 | | 0.1003 | 11.12 | 103900 | 0.4411 | 0.3216 | | 0.1009 | 11.13 | 104000 | 0.4474 | 0.3238 | | 0.1009 | 11.15 | 104100 | 0.4406 | 0.3245 | | 0.1009 | 11.16 | 104200 | 0.4384 | 0.3242 | | 0.1009 | 11.17 | 104300 | 0.4702 | 0.3265 | | 0.1009 | 11.18 | 104400 | 0.4611 | 0.3266 | | 0.0992 | 11.19 | 104500 | 0.4425 | 0.3211 | | 0.0992 | 11.2 | 104600 | 0.4575 | 0.3222 | | 0.0992 | 11.21 | 104700 | 0.4449 | 0.3208 | | 0.0992 | 11.22 | 104800 | 0.4715 | 0.3208 | | 0.0992 | 11.23 | 104900 | 0.4469 | 0.3223 | | 0.1021 | 11.24 | 105000 | 0.4536 | 0.3225 | | 0.1021 | 11.25 | 105100 | 0.4629 | 0.3234 | | 0.1021 | 11.26 | 105200 | 0.4550 | 0.3205 | | 0.1021 | 11.27 | 105300 | 0.4598 | 0.3213 | | 0.1021 | 11.28 | 105400 | 0.4522 | 0.3179 | | 0.1021 | 11.3 | 105500 | 0.4658 | 0.3211 | | 0.1021 | 11.31 | 105600 | 0.4664 | 0.3196 | | 0.1021 | 11.32 | 105700 | 0.4736 | 0.3177 | | 0.1021 | 11.33 | 105800 | 0.4587 | 0.3158 | | 0.1021 | 11.34 | 105900 | 0.4589 | 0.3194 | | 0.1025 | 11.35 | 106000 | 0.4692 | 0.3214 | | 0.1025 | 11.36 | 106100 | 0.4382 | 0.3181 | | 0.1025 | 11.37 | 106200 | 0.4556 | 0.3185 | | 0.1025 | 11.38 | 106300 | 0.4445 | 0.3191 | | 0.1025 | 11.39 | 106400 | 0.4379 | 0.3163 | | 0.104 | 11.4 | 106500 | 0.4454 | 0.3220 | | 0.104 | 11.41 | 106600 | 0.4463 | 0.3201 | | 0.104 | 11.42 | 106700 | 0.4550 | 0.3173 | | 0.104 | 11.43 | 106800 | 0.4404 | 0.3168 | | 0.104 | 11.45 | 106900 | 0.4569 | 0.3170 | | 0.1016 | 11.46 | 107000 | 0.4529 | 0.3168 | | 0.1016 | 11.47 | 107100 | 0.4587 | 0.3173 | | 0.1016 | 11.48 | 107200 | 0.4505 | 0.3172 | | 0.1016 | 11.49 | 107300 | 0.4489 | 0.3159 | | 0.1016 | 11.5 | 107400 | 0.4528 | 0.3130 | | 0.1001 | 11.51 | 107500 | 0.4473 | 0.3181 | | 0.1001 | 11.52 | 107600 | 0.4434 | 0.3176 | | 0.1001 | 11.53 | 107700 | 0.4597 | 0.3186 | | 0.1001 | 11.54 | 107800 | 0.4351 | 0.3159 | | 0.1001 | 11.55 | 107900 | 0.4471 | 0.3185 | | 0.1005 | 11.56 | 108000 | 0.4457 | 0.3191 | | 0.1005 | 11.57 | 108100 | 0.4544 | 0.3293 | | 0.1005 | 11.58 | 108200 | 0.4436 | 0.3221 | | 0.1005 | 11.6 | 108300 | 0.4642 | 0.3270 | | 0.1005 | 11.61 | 108400 | 0.4474 | 0.3270 | | 0.1031 | 11.62 | 108500 | 0.4458 | 0.3196 | | 0.1031 | 11.63 | 108600 | 0.4723 | 0.3205 | | 0.1031 | 11.64 | 108700 | 0.4507 | 0.3226 | | 0.1031 | 11.65 | 108800 | 0.4424 | 0.3213 | | 0.1031 | 11.66 | 108900 | 0.4511 | 0.3213 | | 0.1014 | 11.67 | 109000 | 0.4422 | 0.3205 | | 0.1014 | 11.68 | 109100 | 0.4498 | 0.3180 | | 0.1014 | 11.69 | 109200 | 0.4303 | 0.3167 | | 0.1014 | 11.7 | 109300 | 0.4483 | 0.3108 | | 0.1014 | 11.71 | 109400 | 0.4548 | 0.3169 | | 0.0981 | 11.72 | 109500 | 0.4406 | 0.3122 | | 0.0981 | 11.73 | 109600 | 0.4293 | 0.3114 | | 0.0981 | 11.75 | 109700 | 0.4369 | 0.3159 | | 0.0981 | 11.76 | 109800 | 0.4364 | 0.3164 | | 0.0981 | 11.77 | 109900 | 0.4358 | 0.3189 | | 0.1023 | 11.78 | 110000 | 0.4281 | 0.3183 | | 0.1023 | 11.79 | 110100 | 0.4404 | 0.3159 | | 0.1023 | 11.8 | 110200 | 0.4471 | 0.3135 | | 0.1023 | 11.81 | 110300 | 0.4498 | 0.3201 | | 0.1023 | 11.82 | 110400 | 0.4527 | 0.3161 | | 0.0988 | 11.83 | 110500 | 0.4440 | 0.3173 | | 0.0988 | 11.84 | 110600 | 0.4356 | 0.3136 | | 0.0988 | 11.85 | 110700 | 0.4308 | 0.3135 | | 0.0988 | 11.86 | 110800 | 0.4294 | 0.3192 | | 0.0988 | 11.87 | 110900 | 0.4241 | 0.3168 | | 0.1022 | 11.88 | 111000 | 0.4420 | 0.3157 | | 0.1022 | 11.9 | 111100 | 0.4313 | 0.3125 | | 0.1022 | 11.91 | 111200 | 0.4213 | 0.3168 | | 0.1022 | 11.92 | 111300 | 0.4352 | 0.3135 | | 0.1022 | 11.93 | 111400 | 0.4297 | 0.3116 | | 0.1032 | 11.94 | 111500 | 0.4218 | 0.3137 | | 0.1032 | 11.95 | 111600 | 0.4334 | 0.3123 | | 0.1032 | 11.96 | 111700 | 0.4373 | 0.3175 | | 0.1032 | 11.97 | 111800 | 0.4299 | 0.3160 | | 0.1032 | 11.98 | 111900 | 0.4326 | 0.3189 | | 0.0969 | 11.99 | 112000 | 0.4208 | 0.3186 | | 0.0969 | 12.0 | 112100 | 0.4385 | 0.3169 | | 0.0969 | 12.01 | 112200 | 0.4453 | 0.3156 | | 0.0969 | 12.02 | 112300 | 0.4596 | 0.3133 | | 0.0969 | 12.03 | 112400 | 0.4509 | 0.3093 | | 0.0901 | 12.04 | 112500 | 0.4535 | 0.3138 | | 0.0901 | 12.06 | 112600 | 0.4371 | 0.3144 | | 0.0901 | 12.07 | 112700 | 0.4499 | 0.3154 | | 0.0901 | 12.08 | 112800 | 0.4615 | 0.3198 | | 0.0901 | 12.09 | 112900 | 0.4523 | 0.3177 | | 0.0889 | 12.1 | 113000 | 0.4412 | 0.3130 | | 0.0889 | 12.11 | 113100 | 0.4471 | 0.3181 | | 0.0889 | 12.12 | 113200 | 0.4530 | 0.3169 | | 0.0889 | 12.13 | 113300 | 0.4670 | 0.3149 | | 0.0889 | 12.14 | 113400 | 0.4594 | 0.3141 | | 0.0917 | 12.15 | 113500 | 0.4623 | 0.3127 | | 0.0917 | 12.16 | 113600 | 0.4460 | 0.3133 | | 0.0917 | 12.17 | 113700 | 0.4512 | 0.3191 | | 0.0917 | 12.18 | 113800 | 0.4681 | 0.3136 | | 0.0917 | 12.19 | 113900 | 0.4564 | 0.3129 | | 0.0906 | 12.21 | 114000 | 0.4482 | 0.3107 | | 0.0906 | 12.22 | 114100 | 0.4595 | 0.3133 | | 0.0906 | 12.23 | 114200 | 0.4510 | 0.3118 | | 0.0906 | 12.24 | 114300 | 0.4472 | 0.3131 | | 0.0906 | 12.25 | 114400 | 0.4499 | 0.3130 | | 0.0918 | 12.26 | 114500 | 0.4503 | 0.3138 | | 0.0918 | 12.27 | 114600 | 0.4518 | 0.3135 | | 0.0918 | 12.28 | 114700 | 0.4493 | 0.3114 | | 0.0918 | 12.29 | 114800 | 0.4574 | 0.3133 | | 0.0918 | 12.3 | 114900 | 0.4683 | 0.3200 | | 0.0869 | 12.31 | 115000 | 0.4608 | 0.3165 | | 0.0869 | 12.32 | 115100 | 0.4618 | 0.3183 | | 0.0869 | 12.33 | 115200 | 0.4689 | 0.3173 | | 0.0869 | 12.34 | 115300 | 0.4681 | 0.3224 | | 0.0869 | 12.36 | 115400 | 0.4576 | 0.3231 | | 0.0885 | 12.37 | 115500 | 0.4831 | 0.3176 | | 0.0885 | 12.38 | 115600 | 0.4602 | 0.3181 | | 0.0885 | 12.39 | 115700 | 0.4493 | 0.3168 | | 0.0885 | 12.4 | 115800 | 0.4564 | 0.3149 | | 0.0885 | 12.41 | 115900 | 0.4585 | 0.3158 | | 0.091 | 12.42 | 116000 | 0.4713 | 0.3193 | | 0.091 | 12.43 | 116100 | 0.4581 | 0.3139 | | 0.091 | 12.44 | 116200 | 0.4637 | 0.3131 | | 0.091 | 12.45 | 116300 | 0.4572 | 0.3124 | | 0.091 | 12.46 | 116400 | 0.4489 | 0.3163 | | 0.0886 | 12.47 | 116500 | 0.4679 | 0.3159 | | 0.0886 | 12.48 | 116600 | 0.4712 | 0.3151 | | 0.0886 | 12.49 | 116700 | 0.4750 | 0.3186 | | 0.0886 | 12.51 | 116800 | 0.4673 | 0.3176 | | 0.0886 | 12.52 | 116900 | 0.4601 | 0.3113 | | 0.0917 | 12.53 | 117000 | 0.4341 | 0.3125 | | 0.0917 | 12.54 | 117100 | 0.4462 | 0.3077 | | 0.0917 | 12.55 | 117200 | 0.4502 | 0.3099 | | 0.0917 | 12.56 | 117300 | 0.4482 | 0.3116 | | 0.0917 | 12.57 | 117400 | 0.4459 | 0.3131 | | 0.0881 | 12.58 | 117500 | 0.4464 | 0.3122 | | 0.0881 | 12.59 | 117600 | 0.4471 | 0.3125 | | 0.0881 | 12.6 | 117700 | 0.4319 | 0.3122 | | 0.0881 | 12.61 | 117800 | 0.4421 | 0.3103 | | 0.0881 | 12.62 | 117900 | 0.4326 | 0.3108 | | 0.0913 | 12.63 | 118000 | 0.4414 | 0.3068 | | 0.0913 | 12.64 | 118100 | 0.4421 | 0.3083 | | 0.0913 | 12.66 | 118200 | 0.4449 | 0.3103 | | 0.0913 | 12.67 | 118300 | 0.4380 | 0.3128 | | 0.0913 | 12.68 | 118400 | 0.4390 | 0.3136 | | 0.0921 | 12.69 | 118500 | 0.4452 | 0.3104 | | 0.0921 | 12.7 | 118600 | 0.4378 | 0.3122 | | 0.0921 | 12.71 | 118700 | 0.4459 | 0.3080 | | 0.0921 | 12.72 | 118800 | 0.4369 | 0.3051 | | 0.0921 | 12.73 | 118900 | 0.4474 | 0.3076 | | 0.0886 | 12.74 | 119000 | 0.4508 | 0.3066 | | 0.0886 | 12.75 | 119100 | 0.4456 | 0.3097 | | 0.0886 | 12.76 | 119200 | 0.4503 | 0.3078 | | 0.0886 | 12.77 | 119300 | 0.4460 | 0.3081 | | 0.0886 | 12.78 | 119400 | 0.4404 | 0.3080 | | 0.0897 | 12.79 | 119500 | 0.4351 | 0.3100 | | 0.0897 | 12.81 | 119600 | 0.4446 | 0.3120 | | 0.0897 | 12.82 | 119700 | 0.4407 | 0.3098 | | 0.0897 | 12.83 | 119800 | 0.4406 | 0.3084 | | 0.0897 | 12.84 | 119900 | 0.4492 | 0.3067 | | 0.09 | 12.85 | 120000 | 0.4546 | 0.3098 | | 0.09 | 12.86 | 120100 | 0.4547 | 0.3074 | | 0.09 | 12.87 | 120200 | 0.4517 | 0.3111 | | 0.09 | 12.88 | 120300 | 0.4320 | 0.3064 | | 0.09 | 12.89 | 120400 | 0.4294 | 0.3072 | | 0.0898 | 12.9 | 120500 | 0.4412 | 0.3050 | | 0.0898 | 12.91 | 120600 | 0.4254 | 0.3074 | | 0.0898 | 12.92 | 120700 | 0.4409 | 0.3071 | | 0.0898 | 12.93 | 120800 | 0.4362 | 0.3071 | | 0.0898 | 12.94 | 120900 | 0.4579 | 0.3090 | | 0.0892 | 12.95 | 121000 | 0.4492 | 0.3059 | | 0.0892 | 12.97 | 121100 | 0.4404 | 0.3105 | | 0.0892 | 12.98 | 121200 | 0.4365 | 0.3066 | | 0.0892 | 12.99 | 121300 | 0.4368 | 0.3048 | | 0.0892 | 13.0 | 121400 | 0.4410 | 0.3033 | | 0.085 | 13.01 | 121500 | 0.4450 | 0.3047 | | 0.085 | 13.02 | 121600 | 0.4633 | 0.3013 | | 0.085 | 13.03 | 121700 | 0.4600 | 0.3054 | | 0.085 | 13.04 | 121800 | 0.4541 | 0.3047 | | 0.085 | 13.05 | 121900 | 0.4546 | 0.3058 | | 0.0791 | 13.06 | 122000 | 0.4536 | 0.3045 | | 0.0791 | 13.07 | 122100 | 0.4589 | 0.3066 | | 0.0791 | 13.08 | 122200 | 0.4581 | 0.3057 | | 0.0791 | 13.09 | 122300 | 0.4546 | 0.3048 | | 0.0791 | 13.1 | 122400 | 0.4673 | 0.3006 | | 0.0789 | 13.12 | 122500 | 0.4551 | 0.3019 | | 0.0789 | 13.13 | 122600 | 0.4467 | 0.3025 | | 0.0789 | 13.14 | 122700 | 0.4593 | 0.3015 | | 0.0789 | 13.15 | 122800 | 0.4598 | 0.3037 | | 0.0789 | 13.16 | 122900 | 0.4532 | 0.3038 | | 0.077 | 13.17 | 123000 | 0.4607 | 0.3015 | | 0.077 | 13.18 | 123100 | 0.4385 | 0.3005 | | 0.077 | 13.19 | 123200 | 0.4590 | 0.3041 | | 0.077 | 13.2 | 123300 | 0.4359 | 0.3047 | | 0.077 | 13.21 | 123400 | 0.4458 | 0.3039 | | 0.0771 | 13.22 | 123500 | 0.4506 | 0.3075 | | 0.0771 | 13.23 | 123600 | 0.4457 | 0.3079 | | 0.0771 | 13.24 | 123700 | 0.4448 | 0.3048 | | 0.0771 | 13.25 | 123800 | 0.4398 | 0.3036 | | 0.0771 | 13.27 | 123900 | 0.4510 | 0.3055 | | 0.0804 | 13.28 | 124000 | 0.4507 | 0.3059 | | 0.0804 | 13.29 | 124100 | 0.4544 | 0.3076 | | 0.0804 | 13.3 | 124200 | 0.4534 | 0.3073 | | 0.0804 | 13.31 | 124300 | 0.4441 | 0.3061 | | 0.0804 | 13.32 | 124400 | 0.4391 | 0.3075 | | 0.0774 | 13.33 | 124500 | 0.4527 | 0.3063 | | 0.0774 | 13.34 | 124600 | 0.4638 | 0.3057 | | 0.0774 | 13.35 | 124700 | 0.4541 | 0.3064 | | 0.0774 | 13.36 | 124800 | 0.4617 | 0.3078 | | 0.0774 | 13.37 | 124900 | 0.4584 | 0.3041 | | 0.0795 | 13.38 | 125000 | 0.4663 | 0.3032 | | 0.0795 | 13.39 | 125100 | 0.4546 | 0.3025 | | 0.0795 | 13.4 | 125200 | 0.4616 | 0.3021 | | 0.0795 | 13.42 | 125300 | 0.4603 | 0.3016 | | 0.0795 | 13.43 | 125400 | 0.4616 | 0.3040 | | 0.0791 | 13.44 | 125500 | 0.4548 | 0.3021 | | 0.0791 | 13.45 | 125600 | 0.4560 | 0.3025 | | 0.0791 | 13.46 | 125700 | 0.4516 | 0.3037 | | 0.0791 | 13.47 | 125800 | 0.4500 | 0.3013 | | 0.0791 | 13.48 | 125900 | 0.4540 | 0.3009 | | 0.0776 | 13.49 | 126000 | 0.4581 | 0.3026 | | 0.0776 | 13.5 | 126100 | 0.4598 | 0.3028 | | 0.0776 | 13.51 | 126200 | 0.4587 | 0.3038 | | 0.0776 | 13.52 | 126300 | 0.4514 | 0.3024 | | 0.0776 | 13.53 | 126400 | 0.4495 | 0.3036 | | 0.0793 | 13.54 | 126500 | 0.4556 | 0.3016 | | 0.0793 | 13.55 | 126600 | 0.4603 | 0.3025 | | 0.0793 | 13.57 | 126700 | 0.4496 | 0.2995 | | 0.0793 | 13.58 | 126800 | 0.4483 | 0.2969 | | 0.0793 | 13.59 | 126900 | 0.4462 | 0.2980 | | 0.0816 | 13.6 | 127000 | 0.4521 | 0.2982 | | 0.0816 | 13.61 | 127100 | 0.4580 | 0.3019 | | 0.0816 | 13.62 | 127200 | 0.4669 | 0.3009 | | 0.0816 | 13.63 | 127300 | 0.4513 | 0.3017 | | 0.0816 | 13.64 | 127400 | 0.4602 | 0.3015 | | 0.0779 | 13.65 | 127500 | 0.4592 | 0.2998 | | 0.0779 | 13.66 | 127600 | 0.4700 | 0.2981 | | 0.0779 | 13.67 | 127700 | 0.4727 | 0.2978 | | 0.0779 | 13.68 | 127800 | 0.4600 | 0.2983 | | 0.0779 | 13.69 | 127900 | 0.4472 | 0.2978 | | 0.0779 | 13.7 | 128000 | 0.4483 | 0.2984 | | 0.0779 | 13.72 | 128100 | 0.4512 | 0.2968 | | 0.0779 | 13.73 | 128200 | 0.4549 | 0.2988 | | 0.0779 | 13.74 | 128300 | 0.4576 | 0.2992 | | 0.0779 | 13.75 | 128400 | 0.4400 | 0.2974 | | 0.0793 | 13.76 | 128500 | 0.4433 | 0.3009 | | 0.0793 | 13.77 | 128600 | 0.4456 | 0.2982 | | 0.0793 | 13.78 | 128700 | 0.4560 | 0.3019 | | 0.0793 | 13.79 | 128800 | 0.4551 | 0.3008 | | 0.0793 | 13.8 | 128900 | 0.4513 | 0.3007 | | 0.0769 | 13.81 | 129000 | 0.4518 | 0.3008 | | 0.0769 | 13.82 | 129100 | 0.4567 | 0.2981 | | 0.0769 | 13.83 | 129200 | 0.4437 | 0.2985 | | 0.0769 | 13.84 | 129300 | 0.4424 | 0.2970 | | 0.0769 | 13.85 | 129400 | 0.4423 | 0.3010 | | 0.0785 | 13.87 | 129500 | 0.4495 | 0.2999 | | 0.0785 | 13.88 | 129600 | 0.4483 | 0.2975 | | 0.0785 | 13.89 | 129700 | 0.4485 | 0.2982 | | 0.0785 | 13.9 | 129800 | 0.4429 | 0.2972 | | 0.0785 | 13.91 | 129900 | 0.4430 | 0.2958 | | 0.0792 | 13.92 | 130000 | 0.4495 | 0.2954 | | 0.0792 | 13.93 | 130100 | 0.4485 | 0.2947 | | 0.0792 | 13.94 | 130200 | 0.4395 | 0.2972 | | 0.0792 | 13.95 | 130300 | 0.4379 | 0.2973 | | 0.0792 | 13.96 | 130400 | 0.4428 | 0.2989 | | 0.0795 | 13.97 | 130500 | 0.4385 | 0.3000 | | 0.0795 | 13.98 | 130600 | 0.4490 | 0.2983 | | 0.0795 | 13.99 | 130700 | 0.4568 | 0.2970 | | 0.0795 | 14.0 | 130800 | 0.4482 | 0.2963 | | 0.0795 | 14.01 | 130900 | 0.4479 | 0.2962 | | 0.075 | 14.03 | 131000 | 0.4565 | 0.2968 | | 0.075 | 14.04 | 131100 | 0.4623 | 0.2962 | | 0.075 | 14.05 | 131200 | 0.4617 | 0.2965 | | 0.075 | 14.06 | 131300 | 0.4687 | 0.2949 | | 0.075 | 14.07 | 131400 | 0.4718 | 0.2929 | | 0.0709 | 14.08 | 131500 | 0.4720 | 0.2945 | | 0.0709 | 14.09 | 131600 | 0.4604 | 0.2953 | | 0.0709 | 14.1 | 131700 | 0.4655 | 0.2955 | | 0.0709 | 14.11 | 131800 | 0.4695 | 0.2958 | | 0.0709 | 14.12 | 131900 | 0.4666 | 0.2945 | | 0.0705 | 14.13 | 132000 | 0.4605 | 0.2959 | | 0.0705 | 14.14 | 132100 | 0.4581 | 0.2947 | | 0.0705 | 14.15 | 132200 | 0.4597 | 0.2948 | | 0.0705 | 14.16 | 132300 | 0.4612 | 0.2943 | | 0.0705 | 14.18 | 132400 | 0.4611 | 0.2959 | | 0.0727 | 14.19 | 132500 | 0.4569 | 0.2958 | | 0.0727 | 14.2 | 132600 | 0.4556 | 0.2951 | | 0.0727 | 14.21 | 132700 | 0.4597 | 0.2955 | | 0.0727 | 14.22 | 132800 | 0.4472 | 0.2935 | | 0.0727 | 14.23 | 132900 | 0.4573 | 0.2943 | | 0.0723 | 14.24 | 133000 | 0.4572 | 0.2943 | | 0.0723 | 14.25 | 133100 | 0.4582 | 0.2956 | | 0.0723 | 14.26 | 133200 | 0.4599 | 0.2968 | | 0.0723 | 14.27 | 133300 | 0.4633 | 0.2962 | | 0.0723 | 14.28 | 133400 | 0.4604 | 0.2972 | | 0.071 | 14.29 | 133500 | 0.4587 | 0.2971 | | 0.071 | 14.3 | 133600 | 0.4598 | 0.2973 | | 0.071 | 14.31 | 133700 | 0.4579 | 0.2976 | | 0.071 | 14.33 | 133800 | 0.4539 | 0.2969 | | 0.071 | 14.34 | 133900 | 0.4628 | 0.2961 | | 0.0703 | 14.35 | 134000 | 0.4627 | 0.2974 | | 0.0703 | 14.36 | 134100 | 0.4611 | 0.2974 | | 0.0703 | 14.37 | 134200 | 0.4607 | 0.2977 | | 0.0703 | 14.38 | 134300 | 0.4638 | 0.2983 | | 0.0703 | 14.39 | 134400 | 0.4628 | 0.2969 | | 0.0736 | 14.4 | 134500 | 0.4543 | 0.2965 | | 0.0736 | 14.41 | 134600 | 0.4585 | 0.2963 | | 0.0736 | 14.42 | 134700 | 0.4636 | 0.2950 | | 0.0736 | 14.43 | 134800 | 0.4636 | 0.2964 | | 0.0736 | 14.44 | 134900 | 0.4630 | 0.2958 | | 0.0715 | 14.45 | 135000 | 0.4611 | 0.2968 | | 0.0715 | 14.46 | 135100 | 0.4633 | 0.2966 | | 0.0715 | 14.48 | 135200 | 0.4664 | 0.2954 | | 0.0715 | 14.49 | 135300 | 0.4670 | 0.2945 | | 0.0715 | 14.5 | 135400 | 0.4638 | 0.2961 | | 0.073 | 14.51 | 135500 | 0.4635 | 0.2965 | | 0.073 | 14.52 | 135600 | 0.4639 | 0.2956 | | 0.073 | 14.53 | 135700 | 0.4617 | 0.2948 | | 0.073 | 14.54 | 135800 | 0.4609 | 0.2933 | | 0.073 | 14.55 | 135900 | 0.4614 | 0.2947 | | 0.0717 | 14.56 | 136000 | 0.4567 | 0.2958 | | 0.0717 | 14.57 | 136100 | 0.4615 | 0.2934 | | 0.0717 | 14.58 | 136200 | 0.4606 | 0.2929 | | 0.0717 | 14.59 | 136300 | 0.4652 | 0.2934 | | 0.0717 | 14.6 | 136400 | 0.4664 | 0.2934 | | 0.0717 | 14.61 | 136500 | 0.4657 | 0.2923 | | 0.0717 | 14.63 | 136600 | 0.4633 | 0.2931 | | 0.0717 | 14.64 | 136700 | 0.4624 | 0.2943 | | 0.0717 | 14.65 | 136800 | 0.4615 | 0.2949 | | 0.0717 | 14.66 | 136900 | 0.4619 | 0.2930 | | 0.0707 | 14.67 | 137000 | 0.4608 | 0.2936 | | 0.0707 | 14.68 | 137100 | 0.4615 | 0.2945 | | 0.0707 | 14.69 | 137200 | 0.4605 | 0.2941 | | 0.0707 | 14.7 | 137300 | 0.4598 | 0.2931 | | 0.0707 | 14.71 | 137400 | 0.4596 | 0.2943 | | 0.0694 | 14.72 | 137500 | 0.4624 | 0.2927 | | 0.0694 | 14.73 | 137600 | 0.4614 | 0.2931 | | 0.0694 | 14.74 | 137700 | 0.4621 | 0.2924 | | 0.0694 | 14.75 | 137800 | 0.4589 | 0.2920 | | 0.0694 | 14.76 | 137900 | 0.4590 | 0.2926 | | 0.0706 | 14.78 | 138000 | 0.4588 | 0.2931 | | 0.0706 | 14.79 | 138100 | 0.4583 | 0.2928 | | 0.0706 | 14.8 | 138200 | 0.4552 | 0.2934 | | 0.0706 | 14.81 | 138300 | 0.4551 | 0.2923 | | 0.0706 | 14.82 | 138400 | 0.4555 | 0.2927 | | 0.0717 | 14.83 | 138500 | 0.4547 | 0.2930 | | 0.0717 | 14.84 | 138600 | 0.4546 | 0.2930 | | 0.0717 | 14.85 | 138700 | 0.4553 | 0.2934 | | 0.0717 | 14.86 | 138800 | 0.4554 | 0.2924 | | 0.0717 | 14.87 | 138900 | 0.4573 | 0.2924 | | 0.0722 | 14.88 | 139000 | 0.4582 | 0.2927 | | 0.0722 | 14.89 | 139100 | 0.4586 | 0.2926 | | 0.0722 | 14.9 | 139200 | 0.4570 | 0.2926 | | 0.0722 | 14.91 | 139300 | 0.4571 | 0.2923 | | 0.0722 | 14.93 | 139400 | 0.4564 | 0.2925 | | 0.0698 | 14.94 | 139500 | 0.4573 | 0.2927 | | 0.0698 | 14.95 | 139600 | 0.4574 | 0.2927 | | 0.0698 | 14.96 | 139700 | 0.4573 | 0.2927 | | 0.0698 | 14.97 | 139800 | 0.4576 | 0.2921 | | 0.0698 | 14.98 | 139900 | 0.4578 | 0.2923 | | 0.0705 | 14.99 | 140000 | 0.4579 | 0.2928 | | 0.0705 | 15.0 | 140100 | 0.4578 | 0.2927 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
bochaowei/t5-small-finetuned-xsum-wei0
95438d8769ee6482bb65eb0da78515c71f9e7095
2021-10-20T15:10:46.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "dataset:xsum", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
bochaowei
null
bochaowei/t5-small-finetuned-xsum-wei0
2
null
transformers
23,745
--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: t5-small-finetuned-xsum-wei0 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum args: default metrics: - name: Rouge1 type: rouge value: 25.7398 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-xsum-wei0 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.6289 - Rouge1: 25.7398 - Rouge2: 6.1361 - Rougel: 19.8262 - Rougelsum: 19.8284 - Gen Len: 18.7984 ## 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: 12 - eval_batch_size: 12 - 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 | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.858 | 1.0 | 1701 | 2.6289 | 25.7398 | 6.1361 | 19.8262 | 19.8284 | 18.7984 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
bochaowei/t5-small-finetuned-xsum-wei1
95099d64137cbd722bd284e6a5f73a263d4032b4
2021-10-20T18:33:31.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "dataset:xsum", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
bochaowei
null
bochaowei/t5-small-finetuned-xsum-wei1
2
null
transformers
23,746
20% of the training data --- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: t5-small-finetuned-xsum-wei1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum args: default metrics: - name: Rouge1 type: rouge value: 27.5875 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-xsum-wei1 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5287 - Rouge1: 27.5875 - Rouge2: 7.4083 - Rougel: 21.5654 - Rougelsum: 21.5716 - Gen Len: 18.8205 ## 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: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.7677 | 1.0 | 3401 | 2.5441 | 27.4235 | 7.2208 | 21.3535 | 21.3636 | 18.8311 | | 2.735 | 2.0 | 6802 | 2.5287 | 27.5875 | 7.4083 | 21.5654 | 21.5716 | 18.8205 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
bookemdan/DialoGPT-small-harrypotter
4c0b7dc33d592551406706f3f5bed7cd9047e22e
2021-08-30T19:54:26.000Z
[ "pytorch", "conversational" ]
conversational
false
bookemdan
null
bookemdan/DialoGPT-small-harrypotter
2
null
null
23,747
--- tags: - conversational --- # Harry Potter DialoGPT Model
boran/berkbot
6fcd660132afad37604066baa16848c5226a9ab1
2021-08-27T19:37:21.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
boran
null
boran/berkbot
2
null
transformers
23,748
--- tags: - conversational --- #berk
brimeggi/inexis-bot
937b226ebb11d3561841e89e59af64dffd07d240
2021-10-21T04:40:05.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
brimeggi
null
brimeggi/inexis-bot
2
null
transformers
23,749
Entry not found
briverse/vi-electra-base-cased
bee53523c11e6d8daa4e092237ffac1175e368bb
2021-02-04T15:28:43.000Z
[ "pytorch", "electra", "pretraining", "transformers" ]
null
false
briverse
null
briverse/vi-electra-base-cased
2
null
transformers
23,750
Entry not found
briverse/vi-electra-large-uncased-800
19df6287829d37b05efdc3da90b2c4a5f99ad0bc
2021-02-04T15:22:00.000Z
[ "pytorch", "electra", "pretraining", "transformers" ]
null
false
briverse
null
briverse/vi-electra-large-uncased-800
2
null
transformers
23,751
Entry not found
brokentx/newbrokiev2
ff11d73e86f22e1c68e610c901123a0463fb6000
2021-06-05T11:14:49.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
brokentx
null
brokentx/newbrokiev2
2
null
transformers
23,752
--- tags: - conversational --- # My Awesome Model
bryan6aero/wav2vec2-base-timit-demo-colab
7b515433f658e58d0ea974474db2ae0cbe01e9a4
2022-02-17T22:00:53.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
bryan6aero
null
bryan6aero/wav2vec2-base-timit-demo-colab
2
null
transformers
23,753
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4779 - Wer: 0.3453 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4307 | 4.0 | 500 | 1.4129 | 0.9980 | | 0.626 | 8.0 | 1000 | 0.4605 | 0.4499 | | 0.2199 | 12.0 | 1500 | 0.4457 | 0.3898 | | 0.1303 | 16.0 | 2000 | 0.4418 | 0.3771 | | 0.0851 | 20.0 | 2500 | 0.4647 | 0.3548 | | 0.0604 | 24.0 | 3000 | 0.4603 | 0.3499 | | 0.0461 | 28.0 | 3500 | 0.4779 | 0.3453 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
btk/gpt2_data_random
9b5d53aa00caa8751efdfe3cf03b23bc34ebac8d
2021-05-21T14:30:55.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]
text-generation
false
btk
null
btk/gpt2_data_random
2
null
transformers
23,754
Entry not found
btk/gpt2jt
8cfe24765354b62c143afd3e87eff53805059444
2021-05-21T14:33:54.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]
text-generation
false
btk
null
btk/gpt2jt
2
null
transformers
23,755
Entry not found
cahya/wav2vec2-base-artificial
1f30a9d4bacf4e818664b45f454606161ed4cd7b
2021-07-05T23:38:02.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
cahya
null
cahya/wav2vec2-base-artificial
2
null
transformers
23,756
Entry not found
cahya/wav2vec2-large-xlsr-sundanese
beffe9e905ecd0787c3e87f271f8df7142f23b5b
2021-07-06T00:00:07.000Z
[ "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "su", "dataset:openslr", "transformers", "audio", "speech", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
cahya
null
cahya/wav2vec2-large-xlsr-sundanese
2
null
transformers
23,757
--- language: su datasets: - openslr metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Sundanese by cahya results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: OpenSLR High quality TTS data for Sundanese type: OpenSLR args: su metrics: - name: Test WER type: wer value: 6.19 --- # Wav2Vec2-Large-XLSR-Sundanese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the [OpenSLR High quality TTS data for Sundanese](https://openslr.org/44/). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset, load_metric, Dataset from datasets.utils.download_manager import DownloadManager from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from pathlib import Path import pandas as pd def load_dataset_sundanese(): urls = [ "https://www.openslr.org/resources/44/su_id_female.zip", "https://www.openslr.org/resources/44/su_id_male.zip" ] dm = DownloadManager() download_dirs = dm.download_and_extract(urls) data_dirs = [ Path(download_dirs[0])/"su_id_female/wavs", Path(download_dirs[1])/"su_id_male/wavs", ] filenames = [ Path(download_dirs[0])/"su_id_female/line_index.tsv", Path(download_dirs[1])/"su_id_male/line_index.tsv", ] dfs = [] dfs.append(pd.read_csv(filenames[0], sep='\t4?\t', names=["path", "sentence"])) dfs.append(pd.read_csv(filenames[1], sep='\t\t', names=["path", "sentence"])) for i, dir in enumerate(data_dirs): dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1) df = pd.concat(dfs) # df = df.sample(frac=1, random_state=1).reset_index(drop=True) dataset = Dataset.from_pandas(df) dataset = dataset.remove_columns('__index_level_0__') return dataset.train_test_split(test_size=0.1, seed=1) dataset = load_dataset_sundanese() test_dataset = dataset['test'] processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset[:2]["sentence"]) ``` ## Evaluation The model can be evaluated as follows or using the [notebook](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb). ```python import torch import torchaudio from datasets import load_dataset, load_metric, Dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets.utils.download_manager import DownloadManager import re from pathlib import Path import pandas as pd def load_dataset_sundanese(): urls = [ "https://www.openslr.org/resources/44/su_id_female.zip", "https://www.openslr.org/resources/44/su_id_male.zip" ] dm = DownloadManager() download_dirs = dm.download_and_extract(urls) data_dirs = [ Path(download_dirs[0])/"su_id_female/wavs", Path(download_dirs[1])/"su_id_male/wavs", ] filenames = [ Path(download_dirs[0])/"su_id_female/line_index.tsv", Path(download_dirs[1])/"su_id_male/line_index.tsv", ] dfs = [] dfs.append(pd.read_csv(filenames[0], sep='\t4?\t', names=["path", "sentence"])) dfs.append(pd.read_csv(filenames[1], sep='\t\t', names=["path", "sentence"])) for i, dir in enumerate(data_dirs): dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1) df = pd.concat(dfs) # df = df.sample(frac=1, random_state=1).reset_index(drop=True) dataset = Dataset.from_pandas(df) dataset = dataset.remove_columns('__index_level_0__') return dataset.train_test_split(test_size=0.1, seed=1) dataset = load_dataset_sundanese() test_dataset = dataset['test'] wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”_\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the audio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 6.19 % ## Training [OpenSLR High quality TTS data for Sundanese](https://openslr.org/44/) was used for training. The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb) and to [evaluate it](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb)
calbert/indic-bert
a4b89055473ad90117d438b4614dcebcb9ce6911
2021-10-28T02:17:32.000Z
[ "pytorch", "albert", "feature-extraction", "transformers" ]
feature-extraction
false
calbert
null
calbert/indic-bert
2
null
transformers
23,758
Entry not found
cambridgeltl/mirrorwic-roberta-base
3ff6d724303b5e5f7d16adb2ea44b6ad99fe9fcb
2021-10-25T19:27:09.000Z
[ "pytorch", "roberta", "feature-extraction", "transformers" ]
feature-extraction
false
cambridgeltl
null
cambridgeltl/mirrorwic-roberta-base
2
null
transformers
23,759
Entry not found
camille/bert-base-pruned-voc-esw0.1-40000-en-fr-cased
b681787db2d547861cf621392817be03c9bbb9a9
2021-05-19T13:49:02.000Z
[ "pytorch", "jax", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
camille
null
camille/bert-base-pruned-voc-esw0.1-40000-en-fr-cased
2
null
transformers
23,760
Entry not found
camille/bert-base-pruned-voc-esw0.3-40000-en-de-cased
a5ea87bcdce6f4fe00f4551ea8a05b78e5c1d7f6
2021-05-19T13:49:57.000Z
[ "pytorch", "jax", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
camille
null
camille/bert-base-pruned-voc-esw0.3-40000-en-de-cased
2
null
transformers
23,761
Entry not found
camille/bert-base-pruned-voc-esw0.3-40000-en-fr-cased
9943df7dc4df83e659f27ad3db73e32a0ba25911
2021-05-19T13:51:33.000Z
[ "pytorch", "jax", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
camille
null
camille/bert-base-pruned-voc-esw0.3-40000-en-fr-cased
2
null
transformers
23,762
Entry not found
cammy/bart-large-cnn-finetuned-weaksup-1000
570ae9fe39bab349b361a07dbf444dfbec39cb2d
2022-02-22T06:34:42.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
text2text-generation
false
cammy
null
cammy/bart-large-cnn-finetuned-weaksup-1000
2
null
transformers
23,763
--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-large-cnn-finetuned-weaksup-1000 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-large-cnn-finetuned-weaksup-1000 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6325 - Rouge1: 26.1954 - Rouge2: 10.7128 - Rougel: 19.3873 - Rougelsum: 22.785 - Gen Len: 66.85 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.3896 | 1.0 | 1000 | 1.6325 | 26.1954 | 10.7128 | 19.3873 | 22.785 | 66.85 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2 - Datasets 1.18.3 - Tokenizers 0.11.0
carlosejimenez/vokenization-bert-small-k-10-v1-epoch0039
847457a6639b82abfa646b19f3c2b1d4c3fc2994
2021-12-06T00:00:45.000Z
[ "pytorch", "bert", "transformers" ]
null
false
carlosejimenez
null
carlosejimenez/vokenization-bert-small-k-10-v1-epoch0039
2
null
transformers
23,764
Entry not found
carlosejimenez/vokenization-bert-small-v1-epoch0039
9d4af17ebb91687831d6805476c63e5d409718e9
2021-12-04T22:40:08.000Z
[ "pytorch", "bert", "transformers" ]
null
false
carlosejimenez
null
carlosejimenez/vokenization-bert-small-v1-epoch0039
2
null
transformers
23,765
Entry not found
carlosejimenez/wiki103_bert_small_non_visual_only_e27
e034d188f2bf31f8511e3c30a3b6f97654822b58
2021-12-14T17:05:35.000Z
[ "pytorch", "bert", "transformers" ]
null
false
carlosejimenez
null
carlosejimenez/wiki103_bert_small_non_visual_only_e27
2
null
transformers
23,766
Entry not found
cartyparty/DialoGPT-small-iteration1
d4e83828658050fe7194b40c37c63835fe78ef20
2021-08-30T18:29:03.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
cartyparty
null
cartyparty/DialoGPT-small-iteration1
2
null
transformers
23,767
--- tags: - conversational --- # Iteration 1
castorini/dkrr-dpr-nq-retriever
7052adf67b403b0625f0360f3f2f46e7b7abae34
2022-02-13T17:46:38.000Z
[ "pytorch", "bert", "feature-extraction", "arxiv:2012.04584", "transformers" ]
feature-extraction
false
castorini
null
castorini/dkrr-dpr-nq-retriever
2
null
transformers
23,768
This model is converted from the original DKRR [repo](https://github.com/facebookresearch/FiD) and ported into Pyserini: ``` @misc{izacard2020distilling, title={Distilling Knowledge from Reader to Retriever for Question Answering}, author={Gautier Izacard and Edouard Grave}, year={2020}, eprint={2012.04584}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
cd-dvd/testmodel2
9d208029d6d8479f34e6048b6a68654bc9fe8f91
2022-01-27T19:45:14.000Z
[ "pytorch", "gpt_neo", "text-generation", "transformers", "Text Generation" ]
text-generation
false
cd-dvd
null
cd-dvd/testmodel2
2
null
transformers
23,769
--- tags: - Text Generation --- # GIMPLEARN knows modeltest2 # To generate conversation use input such as Human: What should I do?\nAI:
cestwc/bart-base-concise-baseline
daf2c38e470f67bc12f4b1ab65b6fb5cfdac0e85
2022-01-06T10:37:51.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
cestwc
null
cestwc/bart-base-concise-baseline
2
null
transformers
23,770
Entry not found
cfinley/punct_restore_fr
a874b865e945131d35c151b7e8b3a779d38a1da4
2021-06-27T19:03:56.000Z
[ "pytorch", "camembert", "token-classification", "transformers", "generated_from_trainer", "license:mit", "autotrain_compatible" ]
token-classification
false
cfinley
null
cfinley/punct_restore_fr
2
1
transformers
23,771
--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model_index: - name: punct_restore_fr results: - task: name: Token Classification type: token-classification metric: name: Accuracy type: accuracy value: 0.991500810518732 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # punct_restore_fr This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on a raw, French opensubtitles dataset. It achieves the following results on the evaluation set: - Loss: 0.0301 - Precision: 0.9601 - Recall: 0.9527 - F1: 0.9564 - Accuracy: 0.9915 ## Model description Classifies tokens based on beginning of French sentences (B-SENT) and everything else (O). ## Intended uses & limitations This model aims to help punctuation restoration on French YouTube auto-generated subtitles. In doing so, one can measure more in a corpus such as words per sentence, grammar structures per sentence, etc. ## Training and evaluation data 1 million Open Subtitles (French) sentences. 80%/10%/10% training/validation/test split. The sentences: - were lower-cased - had end punctuation (.?!) removed - were of length between 7 and 70 words - had beginning word of sentence tagged with B-SENT. - All other words marked with O. Token/tag pairs batched together in groups of 64. This helps show variety of positions for B-SENT and O tags. This also keeps training examples from just being one sentence. Otherwise, this leads to having the first word and only the first word in a sequence being labeled B-SENT. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.8.1 - Pytorch 1.9.0+cu102 - Datasets 1.8.0 - Tokenizers 0.10.3
cgou/fin_RoBERTa-v1-finetuned-squad
77ad8d601915ce5a7dea5b538827e1cbfae103ef
2021-12-14T21:36:06.000Z
[ "pytorch", "tensorboard", "roberta", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
cgou
null
cgou/fin_RoBERTa-v1-finetuned-squad
2
null
transformers
23,772
Entry not found
chaitanya97/german_trained
73a78d380a463d8ff8f765f38a1aa974cf0e3ef8
2021-10-26T12:37:19.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
chaitanya97
null
chaitanya97/german_trained
2
null
transformers
23,773
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: german_trained results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # german_trained This model is a fine-tuned version of [flozi00/wav2vec-xlsr-german](https://huggingface.co/flozi00/wav2vec-xlsr-german) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9367 - Wer: 1.0 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 12.0352 | 5.0 | 5 | 12.6165 | 1.0 | | 4.0249 | 10.0 | 10 | 6.6453 | 1.0 | | 2.6661 | 15.0 | 15 | 5.7873 | 1.0 | | 2.4123 | 20.0 | 20 | 4.3250 | 1.0 | | 1.9481 | 25.0 | 25 | 3.9899 | 1.0 | | 1.7533 | 30.0 | 30 | 3.9367 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu102 - Datasets 1.13.3 - Tokenizers 0.10.3
chandank/bart-base-finetuned-kaggglenews-baseline-final
efa8e2706e717f89f1d64df2a54739ac0173ac2d
2021-12-05T18:45:24.000Z
[ "pytorch", "tensorboard", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
chandank
null
chandank/bart-base-finetuned-kaggglenews-baseline-final
2
null
transformers
23,774
--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-base-finetuned-kaggglenews-baseline-final results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-finetuned-kaggglenews-baseline-final This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6942 - Rouge1: 28.581 - Rouge2: 16.3417 - Rougel: 24.1277 - Rougelsum: 25.9797 - Gen Len: 20.0 ## 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: 0.0002 - 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 495 | 1.7514 | 27.911 | 15.7038 | 23.6466 | 25.2111 | 20.0 | | 2.0585 | 2.0 | 990 | 1.6655 | 28.7581 | 16.4875 | 24.2669 | 26.1676 | 20.0 | | 1.4173 | 3.0 | 1485 | 1.6942 | 28.581 | 16.3417 | 24.1277 | 25.9797 | 20.0 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
chandank/bart-base-finetuned-kaggglenews-batch8
1fe651ad0cc3f73590483f86bcb7e6180fc762c8
2021-12-02T09:16:30.000Z
[ "pytorch", "tensorboard", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
chandank
null
chandank/bart-base-finetuned-kaggglenews-batch8
2
null
transformers
23,775
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-base-finetuned-kaggglenews-batch8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-finetuned-kaggglenews-batch8 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:---------:|:-------:| | No log | 1.0 | 495 | 1.6409 | 27.9647 | 15.4352 | 23.611 | 25.107 | 20.0 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
chandank/bart-base-finetuned-kaggglenews-fact-corrector-I
015191523cccb8f3e8b895c4ab850f23ae8f8564
2021-12-05T20:45:53.000Z
[ "pytorch", "tensorboard", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
chandank
null
chandank/bart-base-finetuned-kaggglenews-fact-corrector-I
2
null
transformers
23,776
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-base-finetuned-kaggglenews-fact-corrector-I results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-finetuned-kaggglenews-fact-corrector-I This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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 | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 432 | 1.5483 | 28.9811 | 16.5711 | 24.7826 | 26.4132 | 20.0 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
chandank/bart-base-finetuned-kagglenews-entityfiltering
dabdfecb4cf49a59cc694b38066696942953d961
2021-10-27T01:06:10.000Z
[ "pytorch", "tensorboard", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
chandank
null
chandank/bart-base-finetuned-kagglenews-entityfiltering
2
null
transformers
23,777
--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-base-finetuned-kagglenews-entityfiltering results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-finetuned-kagglenews-entityfiltering This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5703 - Rouge1: 28.2719 - Rouge2: 15.6883 - Rougel: 24.0674 - Rougelsum: 25.616 - Gen Len: 20.0 ## 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: 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.9187 | 1.0 | 863 | 1.5703 | 28.2719 | 15.6883 | 24.0674 | 25.616 | 20.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu102 - Datasets 1.14.0 - Tokenizers 0.10.3
charsiu/en_w2v2_ctc_libris_and_cv
70f5061463f2927a27236d7e9d309cf0fd5282b3
2021-10-03T04:59:47.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
charsiu
null
charsiu/en_w2v2_ctc_libris_and_cv
2
1
transformers
23,778
Entry not found
charsiu/en_w2v2_tiny_fc_10ms
8272e6ec07582696d212c2b15bdd271c92ae64ee
2021-12-17T02:18:12.000Z
[ "pytorch", "wav2vec2", "transformers" ]
null
false
charsiu
null
charsiu/en_w2v2_tiny_fc_10ms
2
2
transformers
23,779
Entry not found
charsiu/zh_xlsr_fc_20ms
292fb78943f6e9390bb60c77df19b59a95c7ae0b
2021-12-15T18:53:10.000Z
[ "pytorch", "wav2vec2", "transformers" ]
null
false
charsiu
null
charsiu/zh_xlsr_fc_20ms
2
null
transformers
23,780
Entry not found
chicaaago/coomaa_sensei
0ac40933a5462a3f0cbd19d5328a1048082ebad5
2021-11-12T20:53:32.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
chicaaago
null
chicaaago/coomaa_sensei
2
null
transformers
23,781
Entry not found
chinhon/distilgpt2-sgnews
f574b255e0cfa7d7de905074c20913110f446167
2021-10-28T14:12:13.000Z
[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-generation
false
chinhon
null
chinhon/distilgpt2-sgnews
2
null
transformers
23,782
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-sgnews results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-sgnews This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1516 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 3.3558 | 1.0 | 23769 | 3.2316 | | 3.2558 | 2.0 | 47538 | 3.1683 | | 3.2321 | 3.0 | 71307 | 3.1516 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
chip/DialoGPT-small-chizuru
d79ea54ca63b874a581d9fc7f24b738fabfa6147
2021-09-12T07:00:54.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
chip
null
chip/DialoGPT-small-chizuru
2
null
transformers
23,783
--- tags: - conversational --- Chizuru Ichinose DialoGPT Model.
chmanoj/xls-r-1B-te
09da099798a421404b3a7790982a37c7fdc53865
2022-03-24T11:53:32.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "te", "dataset:openslr", "dataset:SLR66", "transformers", "openslr_SLR66", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
chmanoj
null
chmanoj/xls-r-1B-te
2
null
transformers
23,784
--- language: - te license: apache-2.0 tags: - automatic-speech-recognition - openslr_SLR66 - generated_from_trainer - robust-speech-event - hf-asr-leaderboard datasets: - openslr - SLR66 metrics: - wer model-index: - name: xls-r-1B-te results: - task: type: automatic-speech-recognition name: Speech Recognition dataset: type: openslr name: Open SLR args: SLR66 metrics: - type: wer value: 20.624 name: Test WER - type: cer value: 3.979 name: Test CER - type: wer value: 26.14777618364419 name: Test WER (without LM) - type: cer value: 4.932543184970369 name: Test CER (without LM) --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the OPENSLR_SLR66 - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.3119 - Wer: 0.2613 ### Evaluation metrics | Metric | Split | Decode with LM | Value | |:------:|:------:|:--------------:|:---------:| | WER | Train | No | 5.36 | | CER | Train | No | 1.11 | | WER | Test | No | 26.14 | | CER | Test | No | 4.93 | | WER | Train | Yes | 5.04 | | CER | Train | Yes | 1.07 | | WER | Test | Yes | 20.69 | | CER | Test | Yes | 3.986 | ## 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: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 150.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 2.9038 | 4.8 | 500 | 3.0125 | 1.0 | | 1.3777 | 9.61 | 1000 | 0.8681 | 0.8753 | | 1.1436 | 14.42 | 1500 | 0.6256 | 0.7961 | | 1.0997 | 19.23 | 2000 | 0.5244 | 0.6875 | | 1.0363 | 24.04 | 2500 | 0.4585 | 0.6276 | | 0.7996 | 28.84 | 3000 | 0.4072 | 0.5295 | | 0.825 | 33.65 | 3500 | 0.3590 | 0.5222 | | 0.8018 | 38.46 | 4000 | 0.3678 | 0.4671 | | 0.7545 | 43.27 | 4500 | 0.3474 | 0.3962 | | 0.7375 | 48.08 | 5000 | 0.3224 | 0.3869 | | 0.6198 | 52.88 | 5500 | 0.3233 | 0.3630 | | 0.6608 | 57.69 | 6000 | 0.3029 | 0.3308 | | 0.645 | 62.5 | 6500 | 0.3195 | 0.3722 | | 0.5249 | 67.31 | 7000 | 0.3004 | 0.3202 | | 0.4875 | 72.11 | 7500 | 0.2826 | 0.2992 | | 0.5171 | 76.92 | 8000 | 0.2962 | 0.2976 | | 0.4974 | 81.73 | 8500 | 0.2990 | 0.2933 | | 0.4387 | 86.54 | 9000 | 0.2834 | 0.2755 | | 0.4511 | 91.34 | 9500 | 0.2886 | 0.2787 | | 0.4112 | 96.15 | 10000 | 0.3093 | 0.2976 | | 0.4064 | 100.96 | 10500 | 0.3123 | 0.2863 | | 0.4047 | 105.77 | 11000 | 0.2968 | 0.2719 | | 0.3519 | 110.57 | 11500 | 0.3106 | 0.2832 | | 0.3719 | 115.38 | 12000 | 0.3030 | 0.2737 | | 0.3669 | 120.19 | 12500 | 0.2964 | 0.2714 | | 0.3386 | 125.0 | 13000 | 0.3101 | 0.2714 | | 0.3137 | 129.8 | 13500 | 0.3063 | 0.2710 | | 0.3008 | 134.61 | 14000 | 0.3082 | 0.2617 | | 0.301 | 139.42 | 14500 | 0.3121 | 0.2628 | | 0.3291 | 144.23 | 15000 | 0.3105 | 0.2612 | | 0.3133 | 149.04 | 15500 | 0.3114 | 0.2624 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0
chmanoj/xls-r-300m-sv
d62004c679185147fcbf705031c4f7e02d76a96c
2022-01-26T00:01:07.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "sv-SE", "dataset:common_voice", "transformers", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
chmanoj
null
chmanoj/xls-r-300m-sv
2
null
transformers
23,785
--- language: - sv-SE license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer datasets: - common_voice model-index: - name: '' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 0.8004 - Wer: 0.7139 ## 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: 7.5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.6683 | 1.45 | 500 | 1.7698 | 1.0041 | | 1.9548 | 2.91 | 1000 | 1.0890 | 0.8602 | | 1.9568 | 4.36 | 1500 | 1.0878 | 0.8680 | | 1.9497 | 5.81 | 2000 | 1.1501 | 0.8838 | | 1.8453 | 7.27 | 2500 | 1.0452 | 0.8418 | | 1.6952 | 8.72 | 3000 | 0.9153 | 0.7823 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu113 - Datasets 1.18.1.dev0 - Tokenizers 0.10.3
chmanoj/xls-r-300m-ta
fcd1725bd313658bf7746c960e78fbdcfacca62a
2022-01-29T10:51:55.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
chmanoj
null
chmanoj/xls-r-300m-ta
2
null
transformers
23,786
Entry not found
chmanoj/xls-r-demo-test
2255388f9298cbd07a56cae9e89bddf3f0b57468
2022-01-25T19:44:24.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "ab", "dataset:common_voice", "transformers", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "model-index" ]
automatic-speech-recognition
false
chmanoj
null
chmanoj/xls-r-demo-test
2
null
transformers
23,787
--- language: - ab tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer datasets: - common_voice model-index: - name: '' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset. It achieves the following results on the evaluation set: - Loss: 156.8786 - Wer: 1.3460 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu113 - Datasets 1.18.1.dev0 - Tokenizers 0.10.3
christophalt/test-model
a70320f1b36b0f2f362b86a51dda598ef2b653d5
2021-11-19T09:03:01.000Z
[ "pytorch", "transformers" ]
null
false
christophalt
null
christophalt/test-model
2
null
transformers
23,788
Entry not found
cimm-kzn/endr-bert
69ed91b1dfd0a46cead8f70ec05a9aaab2d64f94
2020-12-11T21:35:42.000Z
[ "pytorch", "ru", "en", "arxiv:2004.03659", "transformers" ]
null
false
cimm-kzn
null
cimm-kzn/endr-bert
2
null
transformers
23,789
--- language: - ru - en --- ## EnDR-BERT EnDR-BERT - Multilingual, Cased, which pretrained on the english collection of consumer comments on drug administration from [2]. Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization and all the parameters are the same as in Multi-BERT. Training details are described in our paper. \ link: https://yadi.sk/d/-PTn0xhk1PqvgQ ## Citing & Authors If you find this repository helpful, feel free to cite our publication: [1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020. preprint: https://arxiv.org/abs/2004.03659 ``` @article{10.1093/bioinformatics/btaa675, author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey}, title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}", journal = {Bioinformatics}, year = {2020}, month = {07}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btaa675}, url = {https://doi.org/10.1093/bioinformatics/btaa675}, note = {btaa675}, eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf}, } ``` [2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189. [link to paper](https://www.researchgate.net/profile/Elena_Tutubalina/publication/323751823_Using_semantic_analysis_of_texts_for_the_identification_of_drugs_with_similar_therapeutic_effects/links/5bf7cfc3299bf1a0202cbc1f/Using-semantic-analysis-of-texts-for-the-identification-of-drugs-with-similar-therapeutic-effects.pdf) ``` @article{tutubalina2017using, title={Using semantic analysis of texts for the identification of drugs with similar therapeutic effects}, author={Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE}, journal={Russian Chemical Bulletin}, volume={66}, number={11}, pages={2180--2189}, year={2017}, publisher={Springer} } ```
ck46/camembert-base
78e0e20263bf80aa2a35201134eb2ccc60fb4122
2021-11-07T00:01:45.000Z
[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
ck46
null
ck46/camembert-base
2
null
transformers
23,790
Entry not found
ck46/t5-base-qg-prefix
5f489e2a5c7715791156dc8ff0216f50667624f4
2021-12-24T14:32:30.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
ck46
null
ck46/t5-base-qg-prefix
2
null
transformers
23,791
Entry not found
ck46/t5-base-squad-qa-qg
11d03e07b88d510d9dcdbbcc2497d9d032792e61
2021-12-24T15:02:45.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
ck46
null
ck46/t5-base-squad-qa-qg
2
null
transformers
23,792
Entry not found
cl-nagoya/defsent-bert-base-uncased-max
a962345843e4f744bb6614c226bb471d19792038
2021-08-05T05:38:53.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
cl-nagoya
null
cl-nagoya/defsent-bert-base-uncased-max
2
null
transformers
23,793
Entry not found
cl-nagoya/defsent-bert-base-uncased-mean
b6d0e0ef9c59461a4a2a2d5b8d76666897ef1aad
2021-08-05T05:35:05.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
cl-nagoya
null
cl-nagoya/defsent-bert-base-uncased-mean
2
null
transformers
23,794
Entry not found
cl-nagoya/defsent-bert-large-uncased-max
b43bd33acb1c94a3648fa401ce660fb06172826d
2021-08-05T05:47:35.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
cl-nagoya
null
cl-nagoya/defsent-bert-large-uncased-max
2
null
transformers
23,795
Entry not found
cl-nagoya/defsent-roberta-base-mean
c2026661dbab8291b5ef9a234ea9e0297398e492
2021-08-05T05:47:46.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
cl-nagoya
null
cl-nagoya/defsent-roberta-base-mean
2
null
transformers
23,796
Entry not found
cl-nagoya/defsent-roberta-large-max
76691980e30dbc591180b8dadfa20f4938b3d5d6
2021-08-05T05:49:11.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
cl-nagoya
null
cl-nagoya/defsent-roberta-large-max
2
null
transformers
23,797
Entry not found
cl-nagoya/defsent-roberta-large-mean
d17bca04b03a9b2f1c6cd275060d20f846e736de
2021-08-05T05:49:00.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
cl-nagoya
null
cl-nagoya/defsent-roberta-large-mean
2
null
transformers
23,798
Entry not found
clairesb/kindness_bot_repo
0dd4f5cdfea4b1f07e0987d7cda42f004cd7f01e
2021-10-25T04:33:25.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
clairesb
null
clairesb/kindness_bot_repo
2
null
transformers
23,799
--- tags: - conversational --- # Affirmation Bot