davanstrien/pytorchmodelmetadata · Datasets at Hugging Face

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clisi2000/distilbert-base-uncased-finetuned-clinc	51bb574b837b4c9abe9995c92f6b7267ba6f2f33	2022-03-25T06:23:40.000Z	[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:clinc_oos", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	clisi2000	null	clisi2000/distilbert-base-uncased-finetuned-clinc	7	null	transformers	14,300	--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9158064516129032 --- <!-- 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-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7796 - Accuracy: 0.9158 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.2883 \| 1.0 \| 318 \| 3.2778 \| 0.7390 \| \| 2.6185 \| 2.0 \| 636 \| 1.8740 \| 0.8232 \| \| 1.5423 \| 3.0 \| 954 \| 1.1579 \| 0.8890 \| \| 1.0131 \| 4.0 \| 1272 \| 0.8629 \| 0.9077 \| \| 0.7964 \| 5.0 \| 1590 \| 0.7796 \| 0.9158 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.2+cpu - Datasets 1.18.4 - Tokenizers 0.10.3
sanchit-gandhi/wav2vec2-2-rnd-regularisation	12b2319967b12a4540b5bd05a40af34b85f2134d	2022-03-26T06:45:45.000Z	[ "pytorch", "tensorboard", "speech-encoder-decoder", "automatic-speech-recognition", "dataset:librispeech_asr", "transformers", "generated_from_trainer", "model-index" ]	automatic-speech-recognition	false	sanchit-gandhi	null	sanchit-gandhi/wav2vec2-2-rnd-regularisation	7	null	transformers	14,301	--- tags: - generated_from_trainer datasets: - librispeech_asr 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 was trained from scratch on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 0.6977 - Wer: 0.1231 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - 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: 1000 - num_epochs: 25.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 6.1467 \| 1.68 \| 1500 \| 6.0558 \| 1.3243 \| \| 5.4388 \| 3.36 \| 3000 \| 5.4711 \| 1.5604 \| \| 3.3434 \| 5.04 \| 4500 \| 3.4808 \| 0.7461 \| \| 1.5259 \| 6.73 \| 6000 \| 2.1931 \| 0.3430 \| \| 1.4285 \| 8.41 \| 7500 \| 1.5883 \| 0.2784 \| \| 1.0687 \| 10.09 \| 9000 \| 1.2481 \| 0.2069 \| \| 0.6425 \| 11.77 \| 10500 \| 1.0507 \| 0.1758 \| \| 0.7147 \| 13.45 \| 12000 \| 0.9397 \| 0.1584 \| \| 0.5083 \| 15.13 \| 13500 \| 0.8452 \| 0.1453 \| \| 0.4287 \| 16.82 \| 15000 \| 0.7915 \| 0.1388 \| \| 0.3499 \| 18.5 \| 16500 \| 0.7477 \| 0.1315 \| \| 0.3733 \| 20.18 \| 18000 \| 0.7307 \| 0.1287 \| \| 0.2609 \| 21.86 \| 19500 \| 0.7061 \| 0.1263 \| \| 0.2602 \| 23.54 \| 21000 \| 0.6977 \| 0.1231 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0
ScandinavianMrT/gpt2_ONION_prefinetune_3.0	a2168168102ccbb4ac61ec2252476808bc4b64ae	2022-03-23T15:54:46.000Z	[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers" ]	text-generation	false	ScandinavianMrT	null	ScandinavianMrT/gpt2_ONION_prefinetune_3.0	7	null	transformers	14,302	Entry not found
shahrukhx01/gbert-hasoc-german-2019	f183c4cd25c5e6a0d89f0550b2fe7c15b03d4975	2022-03-23T18:18:56.000Z	[ "pytorch", "bert", "text-classification", "de", "transformers", "hate-speech-classification" ]	text-classification	false	shahrukhx01	null	shahrukhx01/gbert-hasoc-german-2019	7	null	transformers	14,303	--- language: "de" tags: - hate-speech-classification widget: - text: "Das ist der absolute Gipfel! Lächerliche 2,5 Jahre Haft für einen extremst sadistischen Mord. Ich fasse es nicht. Das sitzt der Killer auf der linken Arschbacke ab und lacht sich dabei kaputt. Unsere Justiz ist nur noch zum Kotzen." - text: "Das ist der absolute Gipfel! Lächerliche 2,5 Jahre Haft für einen extremst sadistischen Mord. Ich fasse es nicht. Das sitzt der Killer auf der linken Arschbacke ab und lacht sich dabei kaputt. Unsere Justiz ist nur noch zum Kotzen." --- # Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shahrukhx01/gbert-hasoc-german-2019") model = AutoModelForSequenceClassification.from_pretrained("shahrukhx01/gbert-hasoc-german-2019") ``` # Dataset ```bibtext @inproceedings{10.1145/3368567.3368584, author = {Mandl, Thomas and Modha, Sandip and Majumder, Prasenjit and Patel, Daksh and Dave, Mohana and Mandlia, Chintak and Patel, Aditya}, title = {Overview of the HASOC Track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages}, year = {2019}, isbn = {9781450377508}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3368567.3368584}, doi = {10.1145/3368567.3368584}, abstract = {The identification of Hate Speech in Social Media is of great importance and receives much attention in the text classification community. There is a huge demand for research for languages other than English. The HASOC track intends to stimulate development in Hate Speech for Hindi, German and English. Three datasets were developed from Twitter and Facebook and made available. Binary classification and more fine-grained subclasses were offered in 3 subtasks. For all subtasks, 321 experiments were submitted. The approaches used most often were LSTM networks processing word embedding input. The performance of the best system for identification of Hate Speech for English, Hindi, and German was a Marco-F1 score of 0.78, 0.81 and 0.61, respectively.}, booktitle = {Proceedings of the 11th Forum for Information Retrieval Evaluation}, pages = {14–17}, numpages = {4}, keywords = {Text Classification, Hate Speech, Evaluation, Deep Learning}, location = {Kolkata, India}, series = {FIRE '19} } ``` --- license: mit ---
tiennvcs/distilbert-base-uncased-finetuned-ner	d3b6e52068d367d457a2040e8a992e9ab9a142cc	2022-03-24T07:29:26.000Z	[ "pytorch", "distilbert", "token-classification", "dataset:conll2003", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	token-classification	false	tiennvcs	null	tiennvcs/distilbert-base-uncased-finetuned-ner	7	null	transformers	14,304	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9264836138175376 - name: Recall type: recall value: 0.9361226087929299 - name: F1 type: f1 value: 0.9312781703856213 - name: Accuracy type: accuracy value: 0.9836529143565221 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0616 - Precision: 0.9265 - Recall: 0.9361 - F1: 0.9313 - Accuracy: 0.9837 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2437 \| 1.0 \| 878 \| 0.0745 \| 0.9144 \| 0.9173 \| 0.9158 \| 0.9799 \| \| 0.0518 \| 2.0 \| 1756 \| 0.0621 \| 0.9177 \| 0.9353 \| 0.9264 \| 0.9826 \| \| 0.03 \| 3.0 \| 2634 \| 0.0616 \| 0.9265 \| 0.9361 \| 0.9313 \| 0.9837 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6
ScandinavianMrT/gpt2_prefinetune_SARC_2.0	348938280764c18065b8b55b1e4d54defdf6417a	2022-03-28T08:36:28.000Z	[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers" ]	text-generation	false	ScandinavianMrT	null	ScandinavianMrT/gpt2_prefinetune_SARC_2.0	7	null	transformers	14,305	Entry not found
Flag/joebiden	713e0686015661fdaa02a16631f0cd75375e63a9	2022-03-25T22:10:02.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	Flag	null	Flag/joebiden	7	null	transformers	14,306	Entry not found
SergeyKamenshchikov/nsi_tuned	d47d412b02ef7dd44fb70776a62ab9614a219393	2022-03-27T13:58:13.000Z	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	false	SergeyKamenshchikov	null	SergeyKamenshchikov/nsi_tuned	7	null	transformers	14,307	Entry not found
mrm8488/t5-base-iterater	309fe4132155899ee2a228bddfbcd1e645555114	2022-03-28T11:00:41.000Z	[ "pytorch", "tensorboard", "t5", "text2text-generation", "en", "dataset:wanyu/IteraTeR_full_sent", "transformers", "generated_from_trainer", "IteraTeR", "license:apache-2.0", "model-index", "autotrain_compatible" ]	text2text-generation	false	mrm8488	null	mrm8488/t5-base-iterater	7	1	transformers	14,308	--- license: apache-2.0 language: - en datasets: - wanyu/IteraTeR_full_sent tags: - generated_from_trainer - IteraTeR widget: - text: "<clarity> Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay for the packet has encountered." model-index: - name: t5-base-iterater results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # T5 (base) fine-tuned on IteraTeR This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an [IteraTeR](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset. It achieves the following results on the evaluation set: - Loss: 0.2580 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.3286 \| 0.09 \| 2000 \| 0.3010 \| \| 0.3194 \| 0.18 \| 4000 \| 0.2872 \| \| 0.3208 \| 0.27 \| 6000 \| 0.2792 \| \| 0.3091 \| 0.36 \| 8000 \| 0.2731 \| \| 0.3164 \| 0.45 \| 10000 \| 0.2678 \| \| 0.2941 \| 0.54 \| 12000 \| 0.2682 \| \| 0.2981 \| 0.63 \| 14000 \| 0.2696 \| \| 0.2975 \| 0.72 \| 16000 \| 0.2643 \| \| 0.3109 \| 0.81 \| 18000 \| 0.2624 \| \| 0.2965 \| 0.9 \| 20000 \| 0.2648 \| \| 0.3053 \| 0.99 \| 22000 \| 0.2627 \| \| 0.2779 \| 1.08 \| 24000 \| 0.2632 \| \| 0.2692 \| 1.17 \| 26000 \| 0.2608 \| \| 0.2755 \| 1.26 \| 28000 \| 0.2600 \| \| 0.2771 \| 1.35 \| 30000 \| 0.2584 \| \| 0.2774 \| 1.44 \| 32000 \| 0.2609 \| \| 0.2976 \| 1.53 \| 34000 \| 0.2593 \| \| 0.2646 \| 1.62 \| 36000 \| 0.2616 \| \| 0.2705 \| 1.71 \| 38000 \| 0.2574 \| \| 0.2714 \| 1.8 \| 40000 \| 0.2577 \| \| 0.2857 \| 1.9 \| 42000 \| 0.2576 \| \| 0.2832 \| 1.99 \| 44000 \| 0.2580 \| ### How to use ```py from transformers import T5ForConditionalGeneration, T5TokenizerFast MODEL_CKPT = 'mrm8488/t5-base-iterater' tokenizer = T5TokenizerFast.from_pretrained(MODEL_CKPT) model = T5ForConditionalGeneration.from_pretrained(MODEL_CKPT) def predict(intent, text): input_text = f"<{intent}> {text}" features = tokenizer([input_text], return_tensors='pt') output = model.generate(input_ids=features['input_ids'], attention_mask=features['attention_mask'], max_length=128, num_beams=8) return tokenizer.decode(output[0], skip_special_tokens=True) text = "Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay for the packet has encountered." intent = "clarity" predict(intent, text) # Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay the packet has encountered. ``` ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
hackathon-pln-es/es_text_neutralizer	6b9055aad684a42b14248badd06ad9d2ec7603aa	2022-04-01T12:38:43.000Z	[ "pytorch", "t5", "text2text-generation", "es", "dataset:hackathon-pln-es/neutral-es", "transformers", "Text2Text Generation", "Inclusive Language", "Text Neutralization", "license:apache-2.0", "model-index", "autotrain_compatible" ]	text2text-generation	false	hackathon-pln-es	null	hackathon-pln-es/es_text_neutralizer	7	5	transformers	14,309	--- language: - es license: apache-2.0 tags: - Text2Text Generation - Inclusive Language - Text Neutralization - pytorch datasets: - hackathon-pln-es/neutral-es metrics: - sacrebleu model-index: - name: es_text_neutralizer results: - task: type: Text2Text Generation name: Neutralization of texts in Spanish dataset: type: hackathon-pln-es/neutral-es name: neutral-es metrics: - type: sacrebleu value: 0.96 name: sacrebleu # Optional. Example: Test WER - type: bertscore # Required. Example: wer value: 0.98 name: BertScoreF1 # Optional. Example: Test WER - type: DiffBleu # Required. Example: wer value: 0.35 name: DiffBleu # Optional. Example: Test WER --- ## Model objective Spanish is a beautiful language and it has many ways of referring to people, neutralizing the genders and using some of the resources inside the language. One would say Todas las personas asistentes instead of Todos los asistentes and it would end in a more inclusive way for talking about people. The purpose of this collaboratively trained model is to create a solution that reinforces the UN objective of the gender equality. Given any input, our model will generate a gender neutral sentence, correcting any non-inclusive expressions or words. It's a straightforward and fast solution that creates a positive impact in the contemporary social panorama. <p align="center"> <img src="https://upload.wikimedia.org/wikipedia/commons/2/29/Gender_equality_symbol_%28clipart%29.png" width="250"/> </p> By using gender inclusive models we can help reducing gender bias in a language corpus by, for instance, adding data augmentation and creating different examples ## Training and evaluation data The data used for the model training has been created form a compilation of sources, obtained from a series of guidelines and manuals issued by Spanish Ministry of Health, Social Services and Equality in the matter of the usage of non-sexist language, stipulated in this linked [document:](https://www.inmujeres.gob.es/servRecursos/formacion/GuiasLengNoSexista/docs/Guiaslenguajenosexista_.pdf): ### Compiled sources [Guía para un discurso igualitario en la universidad de alicante](https://ieg.ua.es/es/documentos/normativasobreigualdad/guia-para-un-discurso-igualitario-en-la-ua.pdf) [Guía UC de Comunicación en Igualdad](<https://web.unican.es/unidades/igualdad/SiteAssets/igualdad/comunicacion-en-igualdad/guia%20comunicacion%20igualdad%20(web).pdf>) [Buenas prácticas para el tratamiento del lenguaje en igualdad](https://e-archivo.uc3m.es/handle/10016/22811) [Guía del lenguaje no sexista de la Universidad de Castilla-La Mancha](https://unidadigualdad.ugr.es/page/guiialenguajeuniversitarionosexista_universidaddecastillalamancha/!) [Guía de Lenguaje Para el Ámbito Educativo](https://www.educacionyfp.gob.es/va/dam/jcr:8ce318fd-c8ff-4ad2-97b4-7318c27d1682/guialenguajeambitoeducativo.pdf) [Guía para un uso igualitario y no sexista del lenguaje y dela imagen en la Universidad de Jaén](https://www.ujaen.es/servicios/uigualdad/sites/servicio_uigualdad/files/uploads/Guia_lenguaje_no_sexista.pdf) [Guía de uso no sexista del vocabulario español](https://www.um.es/documents/2187255/2187763/guia-leng-no-sexista.pdf/d5b22eb9-b2e4-4f4b-82aa-8a129cdc83e3) [Guía para el uso no sexista de la lengua castellana y de imágnes en la UPV/EHV](https://www.ehu.eus/documents/1734204/1884196/Guia_uso_no_sexista_EHU.pdf) [Guía de lenguaje no sexista UNED](http://portal.uned.es/pls/portal/docs/PAGE/UNED_MAIN/LAUNIVERSIDAD/VICERRECTORADOS/GERENCIA/OFICINA_IGUALDAD/CONCEPTOS%20BASICOS/GUIA_LENGUAJE.PDF) [COMUNICACIÓN AMBIENTAL CON PERSPECTIVA DE GÉNERO](https://cima.cantabria.es/documents/5710649/5729124/COMUNICACI%C3%93N+AMBIENTAL+CON+PERSPECTIVA+DE+G%C3%89NERO.pdf/ccc18730-53e3-35b9-731e-b4c43339254b) [Recomendaciones para la utilización de lenguaje no sexista](https://www.csic.es/sites/default/files/guia_para_un_uso_no_sexista_de_la_lengua_adoptada_por_csic2.pdf) [Estudio sobre lenguaje y contenido sexista en la Web](https://www.mujeresenred.net/IMG/pdf/Estudio_paginas_web_T-incluye_ok.pdf) [Nombra.en.red. En femenino y en masculino](https://www.inmujeres.gob.es/areasTematicas/educacion/publicaciones/serieLenguaje/docs/Nombra_en_red.pdf) ## Model specs This model is a fine-tuned version of [spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on the data described below. It achieves the following results on the evaluation set: - 'eval_bleu': 93.8347, - 'eval_f1': 0.9904, ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-04 - train_batch_size: 32 - seed: 42 - num_epochs: 10 - weight_decay: 0,01 ## Metrics For training, we used both Blue (sacrebleu implementation in HF) and BertScore. The first one, a standard in Machine Translation processes, has been added for ensuring robustness of the newly generated data, while the second one is kept for keeping the expected semantic similarity. However, given the actual use case, we expect generated segments to be very close to input segments and to label segments in training. As an example, we can take the following: inputSegment = 'De acuerdo con las informaciones anteriores , las alumnas se han quejado de la actitud de los profesores en los exámenes finales. Los representantes estudiantiles son los alumnos Juanju y Javi.' expectedOutput (label) = 'De acuerdo con las informaciones anteriores, el alumnado se ha quejado de la actitud del profesorado en los exámenes finales. Los representantes estudiantiles son los alumnos Juanju y Javi.' actualOutput = 'De acuerdo con las informaciones anteriores, el alumnado se ha quejado de la actitud del profesorado en los exámenes finales. Los representantes estudiantiles son el alumnado Juanju y Javi.' As you can see, segments are pretty similar. So, instead of measuring Bleu or BertScore here, we propose an alternate metric that would be DiffBleu: $$DiffBleu = BLEU(actualOutput - inputSegment, labels - inputSegment)$$ Where the minuses as in set notation. This way, we also evaluate DiffBleu after the model has been trained. ## Team Members - Fernando Velasco [(fermaat)](https://huggingface.co/fermaat) - Cibeles Redondo [(CibelesR)](https://huggingface.co/CibelesR) - Juan Julian Cea [(Juanju)](https://huggingface.co/Juanju) - Magdalena Kujalowicz [(MacadellaCosta)](https://huggingface.co/MacadellaCosta) - Javier Blasco [(javiblasco)](https://huggingface.co/javiblasco) Enjoy!
DrishtiSharma/wav2vec2-base-finetuned-sentiment-mesd-v9	4c98d2b04b8fe342151f614800e7abc4fb63304c	2022-03-29T00:52:52.000Z	[ "pytorch", "tensorboard", "wav2vec2", "audio-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	audio-classification	false	DrishtiSharma	null	DrishtiSharma/wav2vec2-base-finetuned-sentiment-mesd-v9	7	null	transformers	14,310	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-sentiment-mesd-v9 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-finetuned-sentiment-mesd-v9 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: 0.3500 - Accuracy: 0.9154 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 40 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.86 \| 3 \| 1.7825 \| 0.1846 \| \| 1.9553 \| 1.86 \| 6 \| 1.7212 \| 0.4308 \| \| 1.9553 \| 2.86 \| 9 \| 1.6164 \| 0.3769 \| \| 2.002 \| 3.86 \| 12 \| 1.4904 \| 0.3769 \| \| 1.6191 \| 4.86 \| 15 \| 1.4426 \| 0.4385 \| \| 1.6191 \| 5.86 \| 18 \| 1.3516 \| 0.5231 \| \| 1.6209 \| 6.86 \| 21 \| 1.2176 \| 0.5538 \| \| 1.6209 \| 7.86 \| 24 \| 1.1683 \| 0.5692 \| \| 1.371 \| 8.86 \| 27 \| 1.0885 \| 0.5923 \| \| 1.1568 \| 9.86 \| 30 \| 1.0152 \| 0.6385 \| \| 1.1568 \| 10.86 \| 33 \| 0.9289 \| 0.6385 \| \| 1.1023 \| 11.86 \| 36 \| 0.9141 \| 0.6308 \| \| 1.1023 \| 12.86 \| 39 \| 0.8526 \| 0.6462 \| \| 0.9448 \| 13.86 \| 42 \| 0.8420 \| 0.6769 \| \| 0.7972 \| 14.86 \| 45 \| 0.7976 \| 0.6692 \| \| 0.7972 \| 15.86 \| 48 \| 0.8192 \| 0.7308 \| \| 0.7793 \| 16.86 \| 51 \| 0.7108 \| 0.7615 \| \| 0.7793 \| 17.86 \| 54 \| 0.6712 \| 0.7769 \| \| 0.6468 \| 18.86 \| 57 \| 0.6684 \| 0.7923 \| \| 0.5083 \| 19.86 \| 60 \| 0.6922 \| 0.7385 \| \| 0.5083 \| 20.86 \| 63 \| 0.6148 \| 0.7923 \| \| 0.4988 \| 21.86 \| 66 \| 0.5846 \| 0.7923 \| \| 0.4988 \| 22.86 \| 69 \| 0.6050 \| 0.8154 \| \| 0.4123 \| 23.86 \| 72 \| 0.5506 \| 0.7846 \| \| 0.3511 \| 24.86 \| 75 \| 0.6095 \| 0.7846 \| \| 0.3511 \| 25.86 \| 78 \| 0.5916 \| 0.8154 \| \| 0.3268 \| 26.86 \| 81 \| 0.5912 \| 0.8077 \| \| 0.3268 \| 27.86 \| 84 \| 0.5142 \| 0.8538 \| \| 0.3036 \| 28.86 \| 87 \| 0.5492 \| 0.8077 \| \| 0.3066 \| 29.86 \| 90 \| 0.6007 \| 0.8231 \| \| 0.3066 \| 30.86 \| 93 \| 0.5748 \| 0.8231 \| \| 0.2538 \| 31.86 \| 96 \| 0.6027 \| 0.7692 \| \| 0.2538 \| 32.86 \| 99 \| 0.6979 \| 0.7462 \| \| 0.2281 \| 33.86 \| 102 \| 0.7002 \| 0.7615 \| \| 0.2183 \| 34.86 \| 105 \| 0.6650 \| 0.7769 \| \| 0.2183 \| 35.86 \| 108 \| 0.5192 \| 0.8462 \| \| 0.2202 \| 36.86 \| 111 \| 0.5389 \| 0.8308 \| \| 0.2202 \| 37.86 \| 114 \| 0.5050 \| 0.8385 \| \| 0.1906 \| 38.86 \| 117 \| 0.5722 \| 0.7769 \| \| 0.154 \| 39.86 \| 120 \| 0.5239 \| 0.8308 \| \| 0.154 \| 40.86 \| 123 \| 0.4448 \| 0.8615 \| \| 0.1474 \| 41.86 \| 126 \| 0.4623 \| 0.8615 \| \| 0.1474 \| 42.86 \| 129 \| 0.4282 \| 0.8615 \| \| 0.1345 \| 43.86 \| 132 \| 0.5087 \| 0.8615 \| \| 0.1567 \| 44.86 \| 135 \| 0.4859 \| 0.8385 \| \| 0.1567 \| 45.86 \| 138 \| 0.6603 \| 0.8077 \| \| 0.1731 \| 46.86 \| 141 \| 0.5379 \| 0.8385 \| \| 0.1731 \| 47.86 \| 144 \| 0.8666 \| 0.7538 \| \| 0.1606 \| 48.86 \| 147 \| 0.7518 \| 0.8 \| \| 0.1484 \| 49.86 \| 150 \| 0.5986 \| 0.8385 \| \| 0.1484 \| 50.86 \| 153 \| 0.6368 \| 0.8231 \| \| 0.2256 \| 51.86 \| 156 \| 0.4639 \| 0.8692 \| \| 0.2256 \| 52.86 \| 159 \| 0.5533 \| 0.8462 \| \| 0.1178 \| 53.86 \| 162 \| 0.5038 \| 0.8615 \| \| 0.0815 \| 54.86 \| 165 \| 0.5052 \| 0.8692 \| \| 0.0815 \| 55.86 \| 168 \| 0.4337 \| 0.8846 \| \| 0.0998 \| 56.86 \| 171 \| 0.4422 \| 0.8769 \| \| 0.0998 \| 57.86 \| 174 \| 0.4317 \| 0.8692 \| \| 0.0855 \| 58.86 \| 177 \| 0.4025 \| 0.8923 \| \| 0.0962 \| 59.86 \| 180 \| 0.4605 \| 0.8769 \| \| 0.0962 \| 60.86 \| 183 \| 0.4356 \| 0.8769 \| \| 0.0763 \| 61.86 \| 186 \| 0.4614 \| 0.8769 \| \| 0.0763 \| 62.86 \| 189 \| 0.4382 \| 0.8846 \| \| 0.0902 \| 63.86 \| 192 \| 0.4701 \| 0.8692 \| \| 0.0654 \| 64.86 \| 195 \| 0.4922 \| 0.8692 \| \| 0.0654 \| 65.86 \| 198 \| 0.5413 \| 0.8538 \| \| 0.0651 \| 66.86 \| 201 \| 0.5759 \| 0.8615 \| \| 0.0651 \| 67.86 \| 204 \| 0.4238 \| 0.9 \| \| 0.0822 \| 68.86 \| 207 \| 0.3500 \| 0.9154 \| \| 0.0625 \| 69.86 \| 210 \| 0.3878 \| 0.8923 \| \| 0.0625 \| 70.86 \| 213 \| 0.4952 \| 0.8615 \| \| 0.0548 \| 71.86 \| 216 \| 0.4544 \| 0.8615 \| \| 0.0548 \| 72.86 \| 219 \| 0.5497 \| 0.8769 \| \| 0.054 \| 73.86 \| 222 \| 0.4434 \| 0.8846 \| \| 0.0543 \| 74.86 \| 225 \| 0.4732 \| 0.8769 \| \| 0.0543 \| 75.86 \| 228 \| 0.4425 \| 0.8923 \| \| 0.0881 \| 76.86 \| 231 \| 0.4788 \| 0.8769 \| \| 0.0881 \| 77.86 \| 234 \| 0.5448 \| 0.8769 \| \| 0.061 \| 78.86 \| 237 \| 0.4221 \| 0.9077 \| \| 0.0567 \| 79.86 \| 240 \| 0.4404 \| 0.8769 \| \| 0.0567 \| 80.86 \| 243 \| 0.4099 \| 0.9 \| \| 0.052 \| 81.86 \| 246 \| 0.5259 \| 0.8769 \| \| 0.052 \| 82.86 \| 249 \| 0.5874 \| 0.8692 \| \| 0.0444 \| 83.86 \| 252 \| 0.5555 \| 0.8846 \| \| 0.0332 \| 84.86 \| 255 \| 0.5156 \| 0.8615 \| \| 0.0332 \| 85.86 \| 258 \| 0.4564 \| 0.8615 \| \| 0.0449 \| 86.86 \| 261 \| 0.4826 \| 0.8692 \| \| 0.0449 \| 87.86 \| 264 \| 0.4726 \| 0.8615 \| \| 0.0385 \| 88.86 \| 267 \| 0.4206 \| 0.8846 \| \| 0.0356 \| 89.86 \| 270 \| 0.4050 \| 0.8769 \| \| 0.0356 \| 90.86 \| 273 \| 0.4161 \| 0.8923 \| \| 0.0391 \| 91.86 \| 276 \| 0.4100 \| 0.9077 \| \| 0.0391 \| 92.86 \| 279 \| 0.4047 \| 0.9 \| \| 0.0249 \| 93.86 \| 282 \| 0.4044 \| 0.9 \| \| 0.0399 \| 94.86 \| 285 \| 0.3968 \| 0.8846 \| \| 0.0399 \| 95.86 \| 288 \| 0.3802 \| 0.9 \| \| 0.031 \| 96.86 \| 291 \| 0.3689 \| 0.9 \| \| 0.031 \| 97.86 \| 294 \| 0.3616 \| 0.9077 \| \| 0.036 \| 98.86 \| 297 \| 0.3584 \| 0.9077 \| \| 0.0386 \| 99.86 \| 300 \| 0.3574 \| 0.9077 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Wende/bert-finetuned-ner-accelerate	dc42358d1015ece64958fdf3af450fffbad0022d	2022-03-29T12:25:52.000Z	[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	false	Wende	null	Wende/bert-finetuned-ner-accelerate	7	null	transformers	14,311	Entry not found
anjandash/JavaBERT-small	af38e1dbef6b22553e0898e0114cc0548f183f33	2022-03-30T11:52:00.000Z	[ "pytorch", "tf", "bert", "text-classification", "java", "dataset:anjandash/java-8m-methods-v1", "transformers", "license:mit" ]	text-classification	false	anjandash	null	anjandash/JavaBERT-small	7	null	transformers	14,312	--- language: - java license: mit datasets: - anjandash/java-8m-methods-v1 ---
Finnish-NLP/t5-mini-nl8-finnish	858db87c24cfec96771b3a3beeea9348c07deee4	2022-07-12T13:14:12.000Z	[ "pytorch", "jax", "tensorboard", "t5", "text2text-generation", "fi", "dataset:Finnish-NLP/mc4_fi_cleaned", "dataset:wikipedia", "arxiv:1910.10683", "arxiv:2002.05202", "arxiv:2109.10686", "transformers", "finnish", "t5x", "seq2seq", "license:apache-2.0", "autotrain_compatible" ]	text2text-generation	false	Finnish-NLP	null	Finnish-NLP/t5-mini-nl8-finnish	7	null	transformers	14,313	--- language: - fi license: apache-2.0 tags: - finnish - t5 - t5x - seq2seq datasets: - Finnish-NLP/mc4_fi_cleaned - wikipedia inference: false --- # T5-mini-nl8 for Finnish Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in [this paper](https://arxiv.org/abs/1910.10683) and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer). Note: The Hugging Face inference widget is deactivated because this model needs a text-to-text fine-tuning on a specific downstream task to be useful in practice. As an example of a fine-tuned Finnish T5 model, you can check [Finnish-NLP/t5-small-nl24-casing-punctuation-correction](https://huggingface.co/Finnish-NLP/t5-small-nl24-casing-punctuation-correction) which has been fine-tuned to correct missing casing and punctuation for Finnish text. ## Model description T5 is an encoder-decoder model and treats all NLP problems in a text-to-text format. Finnish T5 is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and outputs from those texts. More precisely, it was pretrained with the span-based masked language modeling (MLM) objective. Spans of the input sequence are masked by so-called sentinel tokens (a.k.a unique mask tokens) and the output sequence is formed as a concatenation of the same sentinel tokens and the real masked tokens. This way, the model learns an inner representation of the Finnish language. This model used the [T5 v1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) improvements compared to the original T5 model during the pretraining: - GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202) - Dropout was turned off in pretraining (quality win). Dropout should be re-enabled during fine-tuning - Pretrained on span-based masked language modeling (MLM) objective only without mixing in the downstream tasks - No parameter sharing between embedding and classifier layer This model also used the "efficient" T5 architecture findings presented in [this paper](https://arxiv.org/abs/2109.10686). In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count. To be more precise, model depth is defined as the number of transformer blocks that are stacked sequentially. This model uses the [t5-efficient-mini-nl8](https://huggingface.co/google/t5-efficient-mini-nl8) architecture's layer depth which means both the encoder and the decoder have 8 transformer layers compared to the original T5 "mini" model's architecture of 4 transformer layers. In total, this model has 72 million parameters. ## Intended uses & limitations This model was only pretrained in a self-supervised way excluding any supervised training. Therefore, this model has to be fine-tuned before it is usable on a downstream task, like text classification, unlike the Google's original T5 model. Note: You most likely need to fine-tune these T5 models without mixed precision so fine-tune them with full fp32 precision. You can also find more fine-tuning tips from [here](https://discuss.huggingface.co/t/t5-finetuning-tips), for example. ### How to use Here is how to use this model in PyTorch: ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("Finnish-NLP/t5-mini-nl8-finnish") model = T5ForConditionalGeneration.from_pretrained("Finnish-NLP/t5-mini-nl8-finnish") ``` and in TensorFlow: ```python from transformers import T5Tokenizer, TFT5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("Finnish-NLP/t5-mini-nl8-finnish") model = T5ForConditionalGeneration.from_pretrained("Finnish-NLP/t5-mini-nl8-finnish", from_pt=True) ``` ### Limitations and bias The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model. ## Training data This Finnish T5 model was pretrained on the combination of six datasets: - [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo). - [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset - [Yle Finnish News Archive 2011-2018](http://urn.fi/urn:nbn:fi:lb-2017070501) - [Yle Finnish News Archive 2019-2020](http://urn.fi/urn:nbn:fi:lb-2021050401) - [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001) - [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803) Raw datasets were automatically cleaned to filter out bad quality and non-Finnish examples. Also, a [perplexity](https://huggingface.co/course/chapter7/3#perplexity-for-language-models) score was calculated for all texts with a KenLM model which was trained with very clean Finnish texts only. This perplexity score can then be used to determine how "clean" Finnish language the text contains. Lastly, all datasets were concatenated and the top 90% perplexity score was used as a filtering threshold to filter out the worst quality 10% of texts. Together these cleaned datasets were around 76GB of text. ## Training procedure ### Preprocessing The texts are tokenized using WordPiece and a vocabulary size of 32000. The inputs and the outputs are sequences of 512 consecutive tokens. Texts are not lower cased so this model is case-sensitive: it makes a difference between finnish and Finnish. ### Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 500K steps with a batch size of 256 (in total 66B tokens). The optimizer used was a AdaFactor with learning rate warmup for 10K steps with a constant learning rate of 1e-2, and then an inverse square root decay (exponential decay) of the learning rate after. Training code was from the Google's Jax/Flax based [t5x framework](https://github.com/google-research/t5x) and also some t5x task definitions were adapted from [Per's t5x work](https://huggingface.co/pere). ## Evaluation results Evaluation was done by fine-tuning the model on a downstream text classification task with two different labeled Finnish datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Classification fine-tuning was done with a sequence length of 128 tokens. When fine-tuned on those datasets, this model (the second row of the table) achieves the following accuracy results compared to our other T5 models and their parameter counts: \| \| Model parameters \| Yle News accuracy \| Eduskunta accuracy \| \|-------------------------------------------------------\|------------------\|---------------------\|----------------------\| \|Finnish-NLP/t5-tiny-nl6-finnish \| 31 million \|92.80 \|69.07 \| \|Finnish-NLP/t5-mini-nl8-finnish \| 72 million \|93.89 \|71.43 \| \|Finnish-NLP/t5-small-nl24-finnish \| 260 million \|94.68 \|74.90 \| \|Finnish-NLP/byt5-base-finnish \| 582 million \|92.33 \|73.13 \| \|Finnish-NLP/t5-base-nl36-finnish \| 814 million \|94.40 \|75.97 \| \|Finnish-NLP/t5-large-nl36-finnish \| 1425 million \|TBA \|TBA \| Fine-tuning Google's multilingual mT5 models on the same datasets we can clearly see that our monolingual Finnish T5 models achieve much better results on Finnish text classification: \| \| Model parameters \| Yle News accuracy \| Eduskunta accuracy \| \|-------------------------------------------------------\|------------------\|---------------------\|----------------------\| \|google/mt5-small \| 301 million \|91.51 \|64.10 \| \|google/mt5-base \| 583 million \|92.71 \|68.40 \| ## Acknowledgements This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/). ## Team Members - Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/) - Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/) Feel free to contact us for more details 🤗
huggingtweets/youtube	5c0ce7c76c62a6fbcc59278d2d4d714bc0fc1570	2022-03-31T14:06:33.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/youtube	7	null	transformers	14,314	--- language: en thumbnail: http://www.huggingtweets.com/youtube/1648735587597/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1427292844612595720/RC1YSvuT_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">YouTube</div> <div style="text-align: center; font-size: 14px;">@youtube</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from YouTube. \| Data \| YouTube \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 23 \| \| Short tweets \| 104 \| \| Tweets kept \| 3123 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2dx34obn/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @youtube's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/p527w5q3) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/p527w5q3/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/youtube') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
blacktree/distilbert-base-uncased-finetuned-cola	e7f06da23210b070466e7d2b9cfca769b903c9fe	2022-04-01T09:00:33.000Z	[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	blacktree	null	blacktree/distilbert-base-uncased-finetuned-cola	7	null	transformers	14,315	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5285676961321106 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4883 - Matthews Correlation: 0.5286 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5269 \| 1.0 \| 535 \| 0.5197 \| 0.4187 \| \| 0.3477 \| 2.0 \| 1070 \| 0.4883 \| 0.5286 \| \| 0.2333 \| 3.0 \| 1605 \| 0.6530 \| 0.5079 \| \| 0.17 \| 4.0 \| 2140 \| 0.7567 \| 0.5272 \| \| 0.1271 \| 5.0 \| 2675 \| 0.8887 \| 0.5259 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.12.0
plasticfruits/gpt2-finetuned-how-to-qa	e01e38294f2234582eb52a4b00c6c6598bf99121	2022-05-03T15:32:40.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "license:mit" ]	text-generation	false	plasticfruits	null	plasticfruits/gpt2-finetuned-how-to-qa	7	null	transformers	14,316	--- language: en license: mit --- # HowTo QA with GPT-2 base GPT-2 English language model fine-tuned with ±2.000 entries from WikiHow. You can try it here: https://how-to-generator.herokuapp.com/ Input prompt should follow the following format: `\n<\|startoftext\|>[WP] How to {text} \n[RESPONSE]` Example: `\n<\|startoftext\|>[WP] How to create a universe \n[RESPONSE]`
vicl/canine-s-finetuned-stsb	41640b64739856165ea13e65c4a2aed13fdd6109	2022-04-01T23:25:04.000Z	[ "pytorch", "tensorboard", "canine", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	vicl	null	vicl/canine-s-finetuned-stsb	7	null	transformers	14,317	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: canine-s-finetuned-stsb results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.8397182061195433 --- <!-- 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. --> # canine-s-finetuned-stsb This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7223 - Pearson: 0.8397 - Spearmanr: 0.8397 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Pearson \| Spearmanr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:---------:\| \| No log \| 1.0 \| 360 \| 0.7938 \| 0.8083 \| 0.8077 \| \| 1.278 \| 2.0 \| 720 \| 0.7349 \| 0.8322 \| 0.8305 \| \| 0.6765 \| 3.0 \| 1080 \| 0.7075 \| 0.8374 \| 0.8366 \| \| 0.6765 \| 4.0 \| 1440 \| 0.7586 \| 0.8360 \| 0.8376 \| \| 0.4629 \| 5.0 \| 1800 \| 0.7223 \| 0.8397 \| 0.8397 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
facebook/data2vec-audio-large-10m	2c971412b1e8382f2b0b213b984626b5ae398f45	2022-04-18T16:23:58.000Z	[ "pytorch", "data2vec-audio", "automatic-speech-recognition", "en", "dataset:librispeech_asr", "arxiv:2202.03555", "transformers", "speech", "license:apache-2.0" ]	automatic-speech-recognition	false	facebook	null	facebook/data2vec-audio-large-10m	7	null	transformers	14,318	--- language: en datasets: - librispeech_asr tags: - speech license: apache-2.0 --- # Data2Vec-Audio-Large-10m [Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/) The large model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. [Paper](https://arxiv.org/abs/2202.03555) Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli Abstract While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches. The original model can be found under https://github.com/pytorch/fairseq/tree/main/examples/data2vec . # Pre-Training method ![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/data2vec.png) For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555). # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Data2VecForCTC from datasets import load_dataset import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-large-10m") model = Data2VecForCTC.from_pretrained("facebook/data2vec-audio-large-10m") # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # tokenize input_values = processor(ds[0]["audio"]["array"],, return_tensors="pt", padding="longest").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) ```
hackathon-pln-es/roberta-base-biomedical-es-squad2-es	3c77e2941e7773dc5f2cbee39ffeb6503e9d598e	2022-04-03T14:51:38.000Z	[ "pytorch", "roberta", "question-answering", "es", "dataset:squad_es", "dataset:hackathon-pln-es/biomed_squad_es_v2", "transformers", "autotrain_compatible" ]	question-answering	false	hackathon-pln-es	null	hackathon-pln-es/roberta-base-biomedical-es-squad2-es	7	null	transformers	14,319	--- language: es datasets: - squad_es - hackathon-pln-es/biomed_squad_es_v2 metrics: - "f1" --- # roberta-base-biomedical-es for QA This model was trained as part of the "Extractive QA Biomedicine" project developed during the 2022 [Hackathon](https://somosnlp.org/hackathon) organized by SOMOS NLP. ## Motivation Recent research has made available Spanish Language Models trained on Biomedical corpus. This project explores the use of these new models to generate extractive Question Answering models for Biomedicine, and compares their effectiveness with general masked language models. The models trained during the [Hackathon](https://somosnlp.org/hackathon) were: [hackathon-pln-es/roberta-base-bne-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-bne-squad2-es) [hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es) [hackathon-pln-es/roberta-base-biomedical-es-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-es-squad2-es) [hackathon-pln-es/biomedtra-small-es-squad2-es](https://huggingface.co/hackathon-pln-es/biomedtra-small-es-squad2-es) ## Description This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-es) on the [squad_es (v2)](https://huggingface.co/datasets/squad_es) training dataset. ## Hyperparameters The hyperparameters were chosen based on those used in [PlanTL-GOB-ES/roberta-base-bne-sqac](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-sqac), a spanish-based QA model trained on a dataset with SQUAD v1 fromat. ``` --num_train_epochs 2 --learning_rate 3e-5 --weight_decay 0.01 --max_seq_length 386 --doc_stride 128 ``` ## Performance Evaluated on the [hackathon-pln-es/biomed_squad_es_v2](https://huggingface.co/datasets/hackathon-pln-es/biomed_squad_es_v2) dev set. \|Model \|Base Model Domain\|exact \|f1 \|HasAns_exact\|HasAns_f1\|NoAns_exact\|NoAns_f1\| \|--------------------------------------------------------------\|-----------------\|-------\|-------\|------------\|---------\|-----------\|--------\| \|hackathon-pln-es/roberta-base-bne-squad2-es \|General \|67.6341\|75.6988\|53.7367 \|70.0526 \|81.2174 \|81.2174 \| \|hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es\|Biomedical \|66.8426\|75.2346\|53.0249 \|70.0031 \|80.3478 \|80.3478 \| \|hackathon-pln-es/roberta-base-biomedical-es-squad2-es \|Biomedical \|67.6341\|74.5612\|47.6868 \|61.7012 \|87.1304 \| 87.1304\| \|hackathon-pln-es/biomedtra-small-es-squad2-es \|Biomedical \|34.4767\|44.3294\|45.3737 \|65.307 \|23.8261 \|23.8261 \| ## Team Santiago Maximo: [smaximo](https://huggingface.co/smaximo)
benjamin/roberta-large-wechsel-ukrainian	a40f97ad5dd4b638a51e0a3c124211eb4581f78d	2022-07-13T23:43:31.000Z	[ "pytorch", "roberta", "fill-mask", "uk", "transformers", "license:mit", "autotrain_compatible" ]	fill-mask	false	benjamin	null	benjamin/roberta-large-wechsel-ukrainian	7	null	transformers	14,320	--- license: mit language: uk --- # roberta-large-wechsel-ukrainian [`roberta-base`](https://huggingface.co/roberta-base) transferred to Ukrainian using the method from the NAACL2022 paper [WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models](https://aclanthology.org/2022.naacl-main.293/). # Evaluation Evaluation was done on [lang-uk's ner-uk project](https://github.com/lang-uk/ner-uk), the Ukrainian portion of [WikiANN](https://huggingface.co/datasets/wikiann) and the [Ukrainian IU corpus from the Universal Dependencies project](https://github.com/UniversalDependencies/UD_Ukrainian-IU). Evaluation results are the mean of 5 runs with different seeds. __Validation Results__ \| \| lang-uk NER (Micro F1) \| WikiANN (Micro F1) \| UD Ukrainian IU POS (Accuracy) \| \|:-------------------------------------------------\|:-------------------------\|:-------------\|:-------------------------\| \| roberta-base-wechsel-ukrainian \| 88.06 (0.50) \| 92.96 (0.08) \| 98.70 (0.05) \| \| roberta-large-wechsel-ukrainian \| __89.27 (0.53)__ \| __93.22 (0.15)__ \| __98.86 (0.03)__ \| \| \| roberta-base-scratch-ukrainian* \| 85.49 (0.88) \| 91.91 (0.08) \| 98.49 (0.04) \| \| roberta-large-scratch-ukrainian* \| 86.54 (0.70) \| 92.39 (0.16) \| 98.65 (0.09) \| \| \| dbmdz/electra-base-ukrainian-cased-discriminator \| 87.49 (0.52) \| 93.20 (0.16) \| 98.60 (0.03) \| \| xlm-roberta-base \| 86.68 (0.44) \| 92.41 (0.13) \| 98.53 (0.02) \| \| xlm-roberta-large \| 86.64 (1.61) \| 93.01 (0.13) \| 98.71 (0.04) \| __Test Results__ \| \| lang-uk NER (Micro F1) \| WikiANN (Micro F1) \| UD Ukrainian IU POS (Accuracy) \| \|:-------------------------------------------------\|:-------------------------\|:-------------\|:-------------------------\| \| roberta-base-wechsel-ukrainian \| 90.81 (1.51) \| 92.98 (0.12) \| 98.57 (0.03) \| \| roberta-large-wechsel-ukrainian \| __91.24 (1.16)__ \| __93.22 (0.17)__ \| __98.74 (0.06)__ \| \| \| roberta-base-scratch-ukrainian* \| 89.57 (1.01) \| 92.05 (0.09) \| 98.31 (0.08) \| \| roberta-large-scratch-ukrainian* \| 89.96 (0.89) \| 92.49 (0.15) \| 98.52 (0.04) \| \| \| dbmdz/electra-base-ukrainian-cased-discriminator \| 90.43 (1.29) \| 92.99 (0.11) \| 98.59 (0.06) \| \| xlm-roberta-base \| 90.86 (0.81) \| 92.27 (0.09) \| 98.45 (0.07) \| \| xlm-roberta-large \| 90.16 (2.98) \| 92.92 (0.19) \| 98.71 (0.04) \| \trained using the same exact training setup as the wechsel-\ models, but without parameter transfer from WECHSEL. # License MIT
anton-l/xtreme_s_xlsr_300m_fleurs_langid_test	7f8e824dae623a78b32228013193850694adf810	2022-04-04T10:59:40.000Z	[ "pytorch", "tensorboard", "wav2vec2", "audio-classification", "transformers" ]	audio-classification	false	anton-l	null	anton-l/xtreme_s_xlsr_300m_fleurs_langid_test	7	null	transformers	14,321	Entry not found
efederici/cross-encoder-bert-base-stsb	083a417f617f9eb04389d91953ce1d404879e65e	2022-04-04T17:09:02.000Z	[ "pytorch", "bert", "text-classification", "it", "dataset:stsb_multi_mt", "transformers", "cross-encoder", "sentence-similarity" ]	text-classification	false	efederici	null	efederici/cross-encoder-bert-base-stsb	7	null	transformers	14,322	--- pipeline_tag: text-classification language: - it datasets: - stsb_multi_mt tags: - cross-encoder - sentence-similarity - transformers --- # Cross-Encoder This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. <p align="center"> <img src="https://upload.wikimedia.org/wikipedia/commons/f/f6/Edouard_Vuillard%2C_1920c_-_Sunlit_Interior.jpg" width="400"> </br> Edouard Vuillard, Sunlit Interior </p> ## Training Data This model was trained on [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/it/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. ## Usage and Performance ```python from sentence_transformers import CrossEncoder model = CrossEncoder('efederici/cross-encoder-umberto-stsb') scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')]) ``` The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
dapang/distilbert-base-uncased-finetuned-truthful	3991bb8e5539e6b77e7d0990d8d4da760a273e1b	2022-04-05T07:23:56.000Z	[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	dapang	null	dapang/distilbert-base-uncased-finetuned-truthful	7	null	transformers	14,323	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-truthful 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-truthful This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4660 - Accuracy: 0.87 - F1: 0.8697 ## 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: 9.910294163459086e-05 - train_batch_size: 400 - eval_batch_size: 400 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 5 \| 0.6509 \| 0.59 \| 0.5780 \| \| No log \| 2.0 \| 10 \| 0.4950 \| 0.77 \| 0.7701 \| \| No log \| 3.0 \| 15 \| 0.4787 \| 0.81 \| 0.8099 \| \| No log \| 4.0 \| 20 \| 0.4936 \| 0.81 \| 0.8096 \| \| No log \| 5.0 \| 25 \| 0.4443 \| 0.82 \| 0.82 \| \| No log \| 6.0 \| 30 \| 0.4547 \| 0.85 \| 0.8497 \| \| No log \| 7.0 \| 35 \| 0.4268 \| 0.85 \| 0.8500 \| \| No log \| 8.0 \| 40 \| 0.4790 \| 0.87 \| 0.8697 \| \| No log \| 9.0 \| 45 \| 0.4660 \| 0.87 \| 0.8697 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.1 - Datasets 2.0.0 - Tokenizers 0.11.0
btjiong/robbert-twitter-sentiment	7597fd9000648604dd95084acd2e730c18834e92	2022-04-06T17:18:23.000Z	[ "pytorch", "roberta", "text-classification", "dataset:dutch_social", "transformers", "generated_from_trainer", "license:mit", "model-index" ]	text-classification	false	btjiong	null	btjiong/robbert-twitter-sentiment	7	null	transformers	14,324	--- license: mit tags: - generated_from_trainer datasets: - dutch_social metrics: - accuracy - f1 - precision - recall model-index: - name: robbert-twitter-sentiment results: - task: name: Text Classification type: text-classification dataset: name: dutch_social type: dutch_social args: dutch_social metrics: - name: Accuracy type: accuracy value: 0.749 - name: F1 type: f1 value: 0.7491844724992662 - name: Precision type: precision value: 0.7493911755249737 - name: Recall type: recall value: 0.749 --- <!-- 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. --> # robbert-twitter-sentiment This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset. It achieves the following results on the evaluation set: - Loss: 0.6818 - Accuracy: 0.749 - F1: 0.7492 - Precision: 0.7494 - Recall: 0.749 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.7485 \| 1.0 \| 188 \| 0.7670 \| 0.692 \| 0.6915 \| 0.6920 \| 0.692 \| \| 0.5202 \| 2.0 \| 376 \| 0.6818 \| 0.749 \| 0.7492 \| 0.7494 \| 0.749 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cpu - Datasets 2.0.0 - Tokenizers 0.12.0
afbudiman/distilled-indobert-classification	1ef8177a1003700f67e937987b1cc16e5c44337f	2022-04-08T09:32:57.000Z	[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:indonlu", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	afbudiman	null	afbudiman/distilled-indobert-classification	7	null	transformers	14,325	--- license: apache-2.0 tags: - generated_from_trainer datasets: - indonlu metrics: - accuracy - f1 model-index: - name: distilled-indobert-classification results: - task: name: Text Classification type: text-classification dataset: name: indonlu type: indonlu args: smsa metrics: - name: Accuracy type: accuracy value: 0.9015873015873016 - name: F1 type: f1 value: 0.9014926755197933 --- <!-- 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. --> # distilled-indobert-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.6015 - Accuracy: 0.9016 - F1: 0.9015 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.0427 \| 1.0 \| 688 \| 0.6306 \| 0.8683 \| 0.8684 \| \| 0.5332 \| 2.0 \| 1376 \| 0.5621 \| 0.8794 \| 0.8779 \| \| 0.3021 \| 3.0 \| 2064 \| 0.6785 \| 0.8905 \| 0.8896 \| \| 0.1851 \| 4.0 \| 2752 \| 0.6085 \| 0.8968 \| 0.8959 \| \| 0.1152 \| 5.0 \| 3440 \| 0.6015 \| 0.9016 \| 0.9015 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Damith/AraELECTRA-discriminator-SOQAL	7f792fca12659b8b040d7ad82650d83fadc486fd	2022-04-08T10:40:38.000Z	[ "pytorch", "electra", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	false	Damith	null	Damith/AraELECTRA-discriminator-SOQAL	7	null	transformers	14,326	Entry not found
nikhedward/bart-large-cnn-finetuned-multi-news1	f0dc0138e6249547ea8b52f07e26cbd689ff4567	2022-04-09T04:51:07.000Z	[ "pytorch", "tensorboard", "bart", "text2text-generation", "dataset:multi_news", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]	text2text-generation	false	nikhedward	null	nikhedward/bart-large-cnn-finetuned-multi-news1	7	null	transformers	14,327	--- license: mit tags: - generated_from_trainer datasets: - multi_news metrics: - rouge model-index: - name: bart-large-cnn-finetuned-multi-news1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: multi_news type: multi_news args: default metrics: - name: Rouge1 type: rouge value: 42.1215 --- <!-- 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-multi-news1 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the multi_news dataset. It achieves the following results on the evaluation set: - Loss: 2.0858 - Rouge1: 42.1215 - Rouge2: 14.9986 - Rougel: 23.4737 - Rougelsum: 36.4212 - Gen Len: 133.703 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 2.1984 \| 1.0 \| 750 \| 2.0858 \| 42.1215 \| 14.9986 \| 23.4737 \| 36.4212 \| 133.703 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
aleksavega/t5-efficient-base-finetuned-1.2	e9e8adcdd412e00bdc3cf824b14c4dc711086594	2022-04-11T12:04:08.000Z	[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "summarization", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	summarization	false	aleksavega	null	aleksavega/t5-efficient-base-finetuned-1.2	7	null	transformers	14,328	--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: t5-efficient-base-finetuned-1.2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-efficient-base-finetuned-1.2 This model is a fine-tuned version of [google/t5-efficient-base](https://huggingface.co/google/t5-efficient-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5294 - Rouge1: 62.691 - Rouge2: 55.9731 - Rougel: 60.9097 - Rougelsum: 61.4393 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 4662 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\| \| 2.2424 \| 1.0 \| 1217 \| 1.7042 \| 34.2215 \| 24.2754 \| 31.7289 \| 32.4237 \| \| 1.7716 \| 2.0 \| 2434 \| 1.6184 \| 43.4774 \| 34.0476 \| 41.3691 \| 41.9132 \| \| 1.6324 \| 3.0 \| 3651 \| 1.5811 \| 49.1441 \| 40.7935 \| 47.0077 \| 47.6388 \| \| 1.5226 \| 4.0 \| 4868 \| 1.5243 \| 54.4769 \| 46.3387 \| 52.3289 \| 52.9555 \| \| 1.4121 \| 5.0 \| 6085 \| 1.5040 \| 56.8792 \| 49.1963 \| 54.7327 \| 55.2805 \| \| 1.331 \| 6.0 \| 7302 \| 1.4930 \| 58.6896 \| 51.1683 \| 56.7096 \| 57.3605 \| \| 1.2677 \| 7.0 \| 8519 \| 1.4785 \| 59.9285 \| 52.4631 \| 57.8575 \| 58.4203 \| \| 1.2175 \| 8.0 \| 9736 \| 1.4839 \| 60.0299 \| 52.8806 \| 58.0099 \| 58.6348 \| \| 1.1782 \| 9.0 \| 10953 \| 1.4908 \| 61.247 \| 54.0887 \| 59.2175 \| 59.7658 \| \| 1.1442 \| 10.0 \| 12170 \| 1.4882 \| 61.9895 \| 54.9455 \| 60.0728 \| 60.5786 \| \| 1.1118 \| 11.0 \| 13387 \| 1.5061 \| 62.1077 \| 55.1276 \| 60.2218 \| 60.7475 \| \| 1.081 \| 12.0 \| 14604 \| 1.5078 \| 61.6083 \| 54.6805 \| 59.7912 \| 60.2489 \| \| 1.0668 \| 13.0 \| 15821 \| 1.5200 \| 62.3075 \| 55.5201 \| 60.5192 \| 60.9557 \| \| 1.0488 \| 14.0 \| 17038 \| 1.5344 \| 62.5144 \| 55.6332 \| 60.6845 \| 61.1715 \| \| 1.0324 \| 15.0 \| 18255 \| 1.5313 \| 62.7697 \| 56.0313 \| 60.9298 \| 61.4739 \| \| 1.0302 \| 16.0 \| 19472 \| 1.5294 \| 62.691 \| 55.9731 \| 60.9097 \| 61.4393 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.6
optimum/MiniLMv2-L12-H384-distilled-finetuned-clinc	cf662b985fc43f786b78909f506b09d8c723be15	2022-04-11T11:21:21.000Z	[ "pytorch", "roberta", "text-classification", "dataset:clinc_oos", "transformers", "generated_from_trainer", "model-index" ]	text-classification	false	optimum	null	optimum/MiniLMv2-L12-H384-distilled-finetuned-clinc	7	null	transformers	14,329	--- tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.94 --- <!-- 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. --> # MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.3479 - Accuracy: 0.94 ## 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: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 60 \| 0.8171 \| 0.2490 \| \| No log \| 2.0 \| 120 \| 0.7039 \| 0.6568 \| \| No log \| 3.0 \| 180 \| 0.6067 \| 0.7932 \| \| 0.7269 \| 4.0 \| 240 \| 0.5270 \| 0.8674 \| \| 0.7269 \| 5.0 \| 300 \| 0.4659 \| 0.9010 \| \| 0.7269 \| 6.0 \| 360 \| 0.4201 \| 0.9194 \| \| 0.7269 \| 7.0 \| 420 \| 0.3867 \| 0.9352 \| \| 0.4426 \| 8.0 \| 480 \| 0.3649 \| 0.9352 \| \| 0.4426 \| 9.0 \| 540 \| 0.3520 \| 0.9403 \| \| 0.4426 \| 10.0 \| 600 \| 0.3479 \| 0.94 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3
NlpHUST/Condenser-phobert-base	acdb842569b37097f91335db0f2fdfd491982a5b	2022-04-12T14:30:53.000Z	[ "pytorch", "tf", "roberta", "fill-mask", "arxiv:2104.08253", "arxiv:2108.05540", "transformers", "autotrain_compatible" ]	fill-mask	false	NlpHUST	null	NlpHUST/Condenser-phobert-base	7	null	transformers	14,330	# Condenser for Vietnamese Transformer architectures for dense retrieval pre-training on vietnamese dataset. Details can be found in our papers, [Condenser: a Pre-training Architecture for Dense Retrieval](https://arxiv.org/abs/2104.08253) and [Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval ](https://arxiv.org/abs/2108.05540). For example, to load Condenser weights, ``` from transformers import AutoModel model = AutoModel.from_pretrained('NlpHUST/Condenser-phobert-base') ```
Auruncus/gpt-j-6b-8bit-ml	3a5c3a146436446547bf6a56b9581e9305b8fffd	2022-04-18T14:47:20.000Z	[ "pytorch", "gptj", "text-generation", "transformers" ]	text-generation	false	Auruncus	null	Auruncus/gpt-j-6b-8bit-ml	7	null	transformers	14,331	Entry not found
lewtun/sagemaker-distilbert-emotion-1	2d79e5aa0394bd73597d62333792f46508e9ab31	2022-04-12T19:23:45.000Z	[ "pytorch", "distilbert", "text-classification", "dataset:emotion", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	lewtun	null	lewtun/sagemaker-distilbert-emotion-1	7	null	transformers	14,332	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: sagemaker-distilbert-emotion-1 results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9325 --- <!-- 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. --> # sagemaker-distilbert-emotion-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1651 - Accuracy: 0.9325 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 64 - 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 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.966 \| 1.0 \| 500 \| 0.2497 \| 0.921 \| \| 0.1913 \| 2.0 \| 1000 \| 0.1651 \| 0.9325 \| \| 0.1037 \| 3.0 \| 1500 \| 0.1501 \| 0.9285 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.15.1 - Tokenizers 0.10.3
Helsinki-NLP/opus-mt-tc-big-cel-en	54c6c217cbc72642cea7911a55f73efc14f650a8	2022-06-01T12:59:34.000Z	[ "pytorch", "marian", "text2text-generation", "br", "cel", "cy", "en", "ga", "transformers", "translation", "opus-mt-tc", "license:cc-by-4.0", "model-index", "autotrain_compatible" ]	translation	false	Helsinki-NLP	null	Helsinki-NLP/opus-mt-tc-big-cel-en	7	1	transformers	14,333	--- language: - br - cel - cy - en - ga tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-cel-en results: - task: name: Translation cym-eng type: translation args: cym-eng dataset: name: flores101-devtest type: flores_101 args: cym eng devtest metrics: - name: BLEU type: bleu value: 50.2 - task: name: Translation gle-eng type: translation args: gle-eng dataset: name: flores101-devtest type: flores_101 args: gle eng devtest metrics: - name: BLEU type: bleu value: 37.4 - task: name: Translation bre-eng type: translation args: bre-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: bre-eng metrics: - name: BLEU type: bleu value: 36.1 - task: name: Translation cym-eng type: translation args: cym-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: cym-eng metrics: - name: BLEU type: bleu value: 53.6 - task: name: Translation gle-eng type: translation args: gle-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: gle-eng metrics: - name: BLEU type: bleu value: 57.7 --- # opus-mt-tc-big-cel-en Neural machine translation model for translating from Celtic languages (cel) to English (en). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Model info * Release: 2022-03-13 * source language(s): bre cym gle * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/cel-eng/opusTCv20210807+bt_transformer-big_2022-03-13.zip) * more information released models: [OPUS-MT cel-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cel-eng/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "A-du emaoc’h?", "Ta'n ushtey glen." ] model_name = "pytorch-models/opus-mt-tc-big-cel-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(*tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Is that you? # Ta'n ushtey glen. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-cel-en") print(pipe("A-du emaoc’h?")) # expected output: Is that you? ``` ## Benchmarks test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cel-eng/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cel-eng/opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) \| langpair \| testset \| chr-F \| BLEU \| #sent \| #words \| \|----------\|---------\|-------\|-------\|-------\|--------\| \| bre-eng \| tatoeba-test-v2021-08-07 \| 0.53712 \| 36.1 \| 383 \| 2065 \| \| cym-eng \| tatoeba-test-v2021-08-07 \| 0.69239 \| 53.6 \| 818 \| 5563 \| \| gle-eng \| tatoeba-test-v2021-08-07 \| 0.72087 \| 57.7 \| 1913 \| 11190 \| \| cym-eng \| flores101-devtest \| 0.71379 \| 50.2 \| 1012 \| 24721 \| \| gle-eng \| flores101-devtest \| 0.63946 \| 37.4 \| 1012 \| 24721 \| ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 3405783 * port time: Wed Apr 13 18:36:25 EEST 2022 * port machine: LM0-400-22516.local
cj-mills/distilbert-base-uncased-finetuned-clinc	028b8f56cb944e1c7e1b8f4f6265c5beeddef127	2022-04-14T07:21:55.000Z	[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:clinc_oos", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	cj-mills	null	cj-mills/distilbert-base-uncased-finetuned-clinc	7	null	transformers	14,334	--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9161290322580645 --- <!-- 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-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7796 - Accuracy: 0.9161 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.2938 \| 1.0 \| 318 \| 3.2905 \| 0.7410 \| \| 2.6346 \| 2.0 \| 636 \| 1.8833 \| 0.8326 \| \| 1.5554 \| 3.0 \| 954 \| 1.1650 \| 0.8926 \| \| 1.0189 \| 4.0 \| 1272 \| 0.8636 \| 0.9110 \| \| 0.8028 \| 5.0 \| 1590 \| 0.7796 \| 0.9161 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.12.1
cj-mills/distilbert-base-uncased-distilled-clinc	418e51c3027813c933d35683c0fd88bac69e7b44	2022-04-14T07:56:35.000Z	[ "pytorch", "distilbert", "text-classification", "dataset:clinc_oos", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	cj-mills	null	cj-mills/distilbert-base-uncased-distilled-clinc	7	null	transformers	14,335	--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9467741935483871 --- <!-- 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-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.2525 - Accuracy: 0.9468 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.2246 \| 1.0 \| 318 \| 3.1584 \| 0.7545 \| \| 2.4033 \| 2.0 \| 636 \| 1.5656 \| 0.8652 \| \| 1.1684 \| 3.0 \| 954 \| 0.7795 \| 0.9161 \| \| 0.5693 \| 4.0 \| 1272 \| 0.4653 \| 0.9329 \| \| 0.3042 \| 5.0 \| 1590 \| 0.3412 \| 0.9406 \| \| 0.1794 \| 6.0 \| 1908 \| 0.2912 \| 0.9403 \| \| 0.1184 \| 7.0 \| 2226 \| 0.2654 \| 0.9461 \| \| 0.0873 \| 8.0 \| 2544 \| 0.2557 \| 0.9439 \| \| 0.0719 \| 9.0 \| 2862 \| 0.2549 \| 0.9465 \| \| 0.0646 \| 10.0 \| 3180 \| 0.2525 \| 0.9468 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.12.1
profoz/toxic-distilbert	87e01ec6b7f4b42ee83bd4a40a546eb748c51f7f	2022-04-15T14:17:52.000Z	[ "pytorch", "distilbert", "text-classification", "transformers" ]	text-classification	false	profoz	null	profoz/toxic-distilbert	7	null	transformers	14,336	Entry not found
xma/gptj-small-train-test	d2afd33621948135ef4e4b35d796166af9a77236	2022-04-15T18:42:37.000Z	[ "pytorch", "distilbert", "text-classification", "transformers", "license:ecl-2.0" ]	text-classification	false	xma	null	xma/gptj-small-train-test	7	null	transformers	14,337	--- license: ecl-2.0 ---
haohaoxuexi/distilbert-base-uncased-finetuned-emotion	29c8abe4d785db5acf90569306ad4f19e8c996a8	2022-04-16T06:03:18.000Z	[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:emotion", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	haohaoxuexi	null	haohaoxuexi/distilbert-base-uncased-finetuned-emotion	7	null	transformers	14,338	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.923 - name: F1 type: f1 value: 0.9233263918743045 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2239 - Accuracy: 0.923 - F1: 0.9233 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8359 \| 1.0 \| 250 \| 0.3198 \| 0.9085 \| 0.9057 \| \| 0.2491 \| 2.0 \| 500 \| 0.2239 \| 0.923 \| 0.9233 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
Raychanan/bert-bert-cased-first512-Conflict	6e2f1160ba67545e51556f1a9fb19e977cef374a	2022-04-16T18:39:25.000Z	[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	Raychanan	null	Raychanan/bert-bert-cased-first512-Conflict	7	null	transformers	14,339	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 - accuracy - precision - recall model-index: - name: bert-bert-cased-first512-Conflict 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-bert-cased-first512-Conflict `conv_text = '\n'.join([utt.text for utt in conv.get_chronological_utterance_list()])` This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6932 - F1: 0.6667 - Accuracy: 0.5 - Precision: 0.5 - Recall: 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Accuracy \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:--------:\|:---------:\|:------:\| \| 0.7098 \| 1.0 \| 685 \| 0.6945 \| 0.0 \| 0.5 \| 0.0 \| 0.0 \| \| 0.7046 \| 2.0 \| 1370 \| 0.6997 \| 0.6667 \| 0.5 \| 0.5 \| 1.0 \| \| 0.7013 \| 3.0 \| 2055 \| 0.6949 \| 0.6667 \| 0.5 \| 0.5 \| 1.0 \| \| 0.7027 \| 4.0 \| 2740 \| 0.6931 \| 0.6667 \| 0.5 \| 0.5 \| 1.0 \| \| 0.702 \| 5.0 \| 3425 \| 0.6932 \| 0.6667 \| 0.5 \| 0.5 \| 1.0 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
kalex/bert-finetuned-ner	4848b10f2633ef330fb3ee756b543a11ead674a3	2022-04-17T03:43:25.000Z	[ "pytorch", "tensorboard", "bert", "token-classification", "dataset:ncbi_disease", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	token-classification	false	kalex	null	kalex/bert-finetuned-ner	7	null	transformers	14,340	--- license: apache-2.0 tags: - generated_from_trainer datasets: - ncbi_disease model-index: - name: bert-finetuned-ner 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-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ncbi_disease dataset. It achieves the following results on the evaluation set: - Loss: 0.0591 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1127 \| 1.0 \| 680 \| 0.0593 \| \| 0.0442 \| 2.0 \| 1360 \| 0.0557 \| \| 0.0181 \| 3.0 \| 2040 \| 0.0591 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
joniponi/communication-classifier	75a52990c1865945494bd8f56c0b296c2fcd5f0c	2022-04-18T02:09:32.000Z	[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	joniponi	null	joniponi/communication-classifier	7	null	transformers	14,341	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: communication-classifier 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. --> # communication-classifier This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1249 - eval_accuracy: 0.9644 - eval_f1: 0.9644 - eval_runtime: 2.6719 - eval_samples_per_second: 126.126 - eval_steps_per_second: 8.234 - epoch: 3.0 - step: 255 ## 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: 20 ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
crcb/imp_hatred	e46fd2d7391e241eaac00583096c500a43540edb	2022-04-18T14:11:43.000Z	[ "pytorch", "roberta", "text-classification", "en", "dataset:crcb/autotrain-data-imp_hs", "transformers", "autotrain", "co2_eq_emissions" ]	text-classification	false	crcb	null	crcb/imp_hatred	7	null	transformers	14,342	--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - crcb/autotrain-data-imp_hs co2_eq_emissions: 15.91710539314839 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 753423062 - CO2 Emissions (in grams): 15.91710539314839 ## Validation Metrics - Loss: 0.5205655694007874 - Accuracy: 0.7746741154562383 - Macro F1: 0.5796696218586866 - Micro F1: 0.7746741154562382 - Weighted F1: 0.7602379277947592 - Macro Precision: 0.6976905233970596 - Micro Precision: 0.7746741154562383 - Weighted Precision: 0.7628815999440115 - Macro Recall: 0.557144871405371 - Micro Recall: 0.7746741154562383 - Weighted Recall: 0.7746741154562383 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/crcb/autotrain-imp_hs-753423062 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-imp_hs-753423062", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-imp_hs-753423062", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
ucabqfe/bigBird_PER_bio	8c76b8bb81af12511f0b2b83b37a105db83f86fb	2022-04-18T18:18:00.000Z	[ "pytorch", "big_bird", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	false	ucabqfe	null	ucabqfe/bigBird_PER_bio	7	null	transformers	14,343	Entry not found
ndavid/binary-qa-bert	8e361a60b738221f155ce67ad0d251879c2a9b81	2022-04-18T23:41:36.000Z	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	false	ndavid	null	ndavid/binary-qa-bert	7	null	transformers	14,344	Entry not found
afbudiman/distilled-optimized-indobert-classification	d72b74ea900a96c36a3abf752a939a5980fa8c17	2022-04-19T16:02:44.000Z	[ "pytorch", "distilbert", "text-classification", "dataset:indonlu", "transformers", "generated_from_trainer", "model-index" ]	text-classification	false	afbudiman	null	afbudiman/distilled-optimized-indobert-classification	7	null	transformers	14,345	--- tags: - generated_from_trainer datasets: - indonlu metrics: - accuracy - f1 model-index: - name: distilled-optimized-indobert-classification results: - task: name: Text Classification type: text-classification dataset: name: indonlu type: indonlu args: smsa metrics: - name: Accuracy type: accuracy value: 0.9 - name: F1 type: f1 value: 0.8994069293432798 --- <!-- 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. --> # distilled-optimized-indobert-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.7397 - Accuracy: 0.9 - F1: 0.8994 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.315104717136378e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.128 \| 1.0 \| 688 \| 0.8535 \| 0.8913 \| 0.8917 \| \| 0.1475 \| 2.0 \| 1376 \| 0.9171 \| 0.8913 \| 0.8913 \| \| 0.0997 \| 3.0 \| 2064 \| 0.7799 \| 0.8960 \| 0.8951 \| \| 0.0791 \| 4.0 \| 2752 \| 0.7179 \| 0.9032 \| 0.9023 \| \| 0.0577 \| 5.0 \| 3440 \| 0.6908 \| 0.9063 \| 0.9055 \| \| 0.0406 \| 6.0 \| 4128 \| 0.7613 \| 0.8992 \| 0.8986 \| \| 0.0275 \| 7.0 \| 4816 \| 0.7502 \| 0.8992 \| 0.8989 \| \| 0.023 \| 8.0 \| 5504 \| 0.7408 \| 0.8976 \| 0.8969 \| \| 0.0169 \| 9.0 \| 6192 \| 0.7397 \| 0.9 \| 0.8994 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
demoversion/bert-fa-base-uncased-haddad-wikinli	f218add6ab043db4b762f2744c11c5e7a440ae78	2022-04-21T18:17:34.000Z	[ "pytorch", "bert", "text-classification", "fa", "transformers", "license:apache-2.0" ]	text-classification	false	demoversion	null	demoversion/bert-fa-base-uncased-haddad-wikinli	7	1	transformers	14,346	--- language: fa license: apache-2.0 --- This repository is created with the aim to provide better models for NLI in persian, with the transparent codes for training I hope you guys find it inspiring and build better model in the future. for more details about the task and methods used for training check the [medium post](https://haddadhesam.medium.com/) and notebooks. # Dataset The dataset used for training is Wiki D/Similar dataset (wiki-d-similar.zip), obtained from [Sentence Transformers](https://github.com/m3hrdadfi/sentence-transformers) repository. # Model The proposed model is published at HuggingFace Hub with the name of ``demoversion/bert-fa-base-uncased-haddad-wikinli``. You can download and use the model from [HuggingFace Website](https://huggingface.co/demoversion/bert-fa-base-uncased-haddad-wikinli) or directly in transformers library like this: from transformers import pipeline model = pipeline("zero-shot-classification", model="demoversion/bert-fa-base-uncased-haddad-wikinli") labels = ["ورزشی", "سیاسی", "علمی", "فرهنگی"] template_str = "این یک متن {} است." str_sentence = "مرحله مقدماتی جام جهانی حاشیه‌های زیادی داشت." model(str_sentence, labels, hypothesis_template=template_str) The result of this code snippet is: Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation. {'labels': ['فرهنگی', 'علمی', 'سیاسی', 'ورزشی'], 'scores': [0.25921085476875305, 0.25713297724723816, 0.24884170293807983, 0.23481446504592896], 'sequence': 'مرحله مقدماتی جام جهانی حاشیه\u200cهای زیادی داشت.'} Yep, the right label (highest score) without training. # Results The result comparing to the original model published for this dataset is available in the table bellow. \|Model\|dev_accuracy\| dev_f1\|test_accuracy\|test_f1\| \|--\|--\|--\|--\|--\| \|[m3hrdadfi/bert-fa-base-uncased-wikinli](https://huggingface.co/m3hrdadfi/bert-fa-base-uncased-wikinli)\|77.88\|77.57\|76.64\|75.99\| \|[demoversion/bert-fa-base-uncased-haddad-wikinli](https://huggingface.co/demoversion/bert-fa-base-uncased-haddad-wikinli)\|78.62\|79.74\|77.04\|78.56\| # Notebooks Notebooks used for training and evaluation are available below. [Training ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DemoVersion/persian-nli-trainer/blob/main/notebooks/training.ipynb) [Evaluation ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DemoVersion/persian-nli-trainer/blob/main/notebooks/evaluation.ipynb)
tuhailong/cross_encoder_roberta-wwm-ext_v1	3ac66951c2ca373cc7081624c721515e8b39f6b4	2022-04-20T02:41:23.000Z	[ "pytorch", "bert", "text-classification", "zh", "dataset:dialogue", "transformers", "cross-encoder" ]	text-classification	false	tuhailong	null	tuhailong/cross_encoder_roberta-wwm-ext_v1	7	null	transformers	14,347	--- language: zh tags: - cross-encoder datasets: - dialogue --- # Data train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs. ## Model model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder, pretrained model is hfl/chinese-roberta-wwm-ext. This model structure is as same as [tuhailong/cross_encoder_roberta-wwm-ext_v0](https://huggingface.co/tuhailong/cross_encoder_roberta-wwm-ext_v0)，the difference is changing the order of input sentences and put them in train dataset, the performance is better in my dataset. ### Usage ```python >>> from sentence_transformers.cross_encoder import CrossEncoder >>> model = CrossEncoder(model_save_path, device="cuda", max_length=64) >>> sentences = ["今天天气不错", "今天心情不错"] >>> score = model.predict([sentences]) >>> print(score[0]) ``` #### Code train code from https://github.com/TTurn/cross-encoder
tuhailong/cross_encoder_roberta-wwm-ext_v2	cb0ca1424c3cd02fd1be9a147e959e9b64f0fd98	2022-04-20T02:41:07.000Z	[ "pytorch", "bert", "text-classification", "zh", "dataset:dialogue", "transformers", "cross-encoder" ]	text-classification	false	tuhailong	null	tuhailong/cross_encoder_roberta-wwm-ext_v2	7	null	transformers	14,348	--- language: zh tags: - cross-encoder datasets: - dialogue --- # Data train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs. ## Model model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder, pretrained model is hfl/chinese-roberta-wwm-ext. This model structure is as same as [tuhailong/cross_encoder_roberta-wwm-ext_v1](https://huggingface.co/tuhailong/cross_encoder_roberta-wwm-ext_v1)，the difference is changing the epoch from 5 to 1, the performance is better in my dataset. ### Usage ```python >>> from sentence_transformers.cross_encoder import CrossEncoder >>> model = CrossEncoder(model_save_path, device="cuda", max_length=64) >>> sentences = ["今天天气不错", "今天心情不错"] >>> score = model.predict([sentences]) >>> print(score[0]) ``` #### Code train code from https://github.com/TTurn/cross-encoder
James-kc-min/AGT_Roberta	3abd7ddab489817b17c184e714cf6765af1d01eb	2022-04-20T09:39:04.000Z	[ "pytorch", "roberta", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	James-kc-min	null	James-kc-min/AGT_Roberta	7	null	transformers	14,349	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: AGT_Roberta 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. --> # AGT_Roberta This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.0 - Tokenizers 0.12.1
brad1141/Longformer_v5	4b6147fac5cb8316dd03ae9895b5e4fa9b1eff58	2022-04-20T19:13:09.000Z	[ "pytorch", "tensorboard", "longformer", "token-classification", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]	token-classification	false	brad1141	null	brad1141/Longformer_v5	7	null	transformers	14,350	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: Longformer_v5 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. --> # Longformer_v5 This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7919 - Precision: 0.8516 - Recall: 0.8678 - F1: 0.6520 - Accuracy: 0.8259 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.7744 \| 1.0 \| 1012 \| 0.5785 \| 0.8375 \| 0.8501 \| 0.5798 \| 0.8098 \| \| 0.5211 \| 2.0 \| 2024 \| 0.5415 \| 0.8434 \| 0.8801 \| 0.6251 \| 0.8282 \| \| 0.3996 \| 3.0 \| 3036 \| 0.5565 \| 0.8500 \| 0.8766 \| 0.6303 \| 0.8274 \| \| 0.2964 \| 4.0 \| 4048 \| 0.6017 \| 0.8617 \| 0.8546 \| 0.6415 \| 0.8240 \| \| 0.2187 \| 5.0 \| 5060 \| 0.6660 \| 0.8485 \| 0.8718 \| 0.6431 \| 0.8271 \| \| 0.1603 \| 6.0 \| 6072 \| 0.7235 \| 0.8493 \| 0.8759 \| 0.6544 \| 0.8290 \| \| 0.1208 \| 7.0 \| 7084 \| 0.7919 \| 0.8516 \| 0.8678 \| 0.6520 \| 0.8259 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
AntoineB/roberta-tiny-imdb	395a16062c8898922e544bcc4c8f8d9bc369ad4a	2022-04-21T11:45:11.000Z	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	false	AntoineB	null	AntoineB/roberta-tiny-imdb	7	null	transformers	14,351	Entry not found
QuickRead/reward_model_wandb_dynamic_bs_1_idx	a74e679c73030b491b52e331088dca9068bf8139	2022-04-22T10:24:39.000Z	[ "pytorch", "pegasus", "feature-extraction", "transformers" ]	feature-extraction	false	QuickRead	null	QuickRead/reward_model_wandb_dynamic_bs_1_idx	7	null	transformers	14,352	Entry not found
Saisam/gpt-neo-math-small	b748555bc45c89784c29e36cbd952118f035c375	2022-04-22T01:13:57.000Z	[ "pytorch", "gpt_neo", "text-generation", "transformers", "license:apache-2.0" ]	text-generation	false	Saisam	null	Saisam/gpt-neo-math-small	7	null	transformers	14,353	--- license: apache-2.0 --- # GPT-NEO-Model for Lean Tactics In the project, we used an HuggingFace GPT-NEO small model and fine-tuned the tactic dataset. The Input should be of the form ``` <GOAL> Goal <PROOFSTEP> ``` The model can easily be accessed using the following code. ``` from transformers import GPT2Tokenizer, GPTNeoForCausalLM import torch tokenizer = GPT2Tokenizer.from_pretrained("Saisam/gpt-neo-math-small") model = GPTNeoForCausalLM.from_pretrained("Saisam/gpt-neo-math-small") ``` Worked along with Xihao Xhang and Moya Zhu
niuca/DeepDebug	e6bc8bb8a64393e4b6c2a363aaf71c684c65106f	2022-04-22T07:10:27.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	niuca	null	niuca/DeepDebug	7	null	transformers	14,354	Entry not found
abdouaziiz/wav2vec2-WOLOF-2.6K-base	dfe30081849ef2b46421488c532fdb577c12586e	2022-04-22T07:17:17.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	abdouaziiz	null	abdouaziiz/wav2vec2-WOLOF-2.6K-base	7	null	transformers	14,355	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wolof 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. --> # wolof This model is a fine-tuned version of [LeBenchmark/wav2vec2-FR-2.6K-base](https://huggingface.co/LeBenchmark/wav2vec2-FR-2.6K-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2816 - Wer: 0.3897 ## 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: 20 - 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: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 2.9468 \| 1.67 \| 1500 \| 0.7036 \| 0.6418 \| \| 0.5506 \| 3.33 \| 3000 \| 0.4129 \| 0.5018 \| \| 0.3817 \| 5.0 \| 4500 \| 0.3414 \| 0.4519 \| \| 0.2885 \| 6.67 \| 6000 \| 0.3181 \| 0.4305 \| \| 0.2275 \| 8.33 \| 7500 \| 0.2920 \| 0.4011 \| \| 0.1852 \| 10.0 \| 9000 \| 0.2816 \| 0.3897 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu102 - Datasets 2.1.0 - Tokenizers 0.12.1
buidung2004/wav2vec-vietnamese-number-digits-finetune	6546a582457cc73c9ecbfeca4554b33ea284fae7	2022-05-03T14:16:38.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]	automatic-speech-recognition	false	buidung2004	null	buidung2004/wav2vec-vietnamese-number-digits-finetune	7	null	transformers	14,356	Entry not found
dapang/distilroberta-base-mic-sym	1b5e930b847f04de70a13a5dfc5603c77e476d37	2022-04-23T03:53:15.000Z	[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	dapang	null	dapang/distilroberta-base-mic-sym	7	null	transformers	14,357	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilroberta-base-mic-sym 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. --> # distilroberta-base-mic-sym This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0023 - Accuracy: 0.9997 - F1: 0.9997 ## 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: 2.740146306575944e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 188 \| 0.0049 \| 0.9990 \| 0.9990 \| \| No log \| 2.0 \| 376 \| 0.0023 \| 0.9997 \| 0.9997 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.12.0.dev20220422+cu116 - Datasets 2.1.0 - Tokenizers 0.12.1
allenai/aspire-biencoder-biomed-scib	76e5d1c6f0af4d30d3b4340d6cd1affebaec44c0	2022-04-24T19:38:56.000Z	[ "pytorch", "bert", "feature-extraction", "arxiv:2111.08366", "transformers", "license:apache-2.0" ]	feature-extraction	false	allenai	null	allenai/aspire-biencoder-biomed-scib	7	null	transformers	14,358	--- license: apache-2.0 --- ## Overview Model included in a paper for modeling fine grained similarity between documents: Title: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity" Authors: Sheshera Mysore, Arman Cohan, Tom Hope Paper: https://arxiv.org/abs/2111.08366 Github: https://github.com/allenai/aspire Note: In the context of the paper, this model is referred to as `Specter-CoCite_Scib` and represents a baseline bi-encoder for scientific document similarity. This model is similar in architecture to the [`allenai/specter`](https://github.com/allenai/specter) model but is trained on co-citation data instead of citation data. ## Model Card ### Model description This model is a BERT bi-encoder model trained for similarity of title-abstract pairs in biomedical scientific papers. The model is initialized with the SciBert model. This model inputs the title and abstract of a paper and represents it with a single vector obtained by a scalar mix of the CLS token at every layer of the SciBert encoder. These scalar mix parameters can be important for performance in some datasets. Importantly, these scalar mix weights are not included as part of this HF model, if you wish to use these parameters please download the full model at: [`aspire-biencoder-biomed-scib-full.zip`](https://drive.google.com/file/d/1X6S5qwaKUlI3N3RDQSG-tJCzMBWAnqxP/view?usp=sharing). ### Training data The model is trained on pairs of co-cited papers in a contrastive learning setup. The model is trained on 1.2 million biomedical paper pairs. In training the model negative examples for the contrastive loss are obtained as random in-batch negatives. Co-citations are obtained from the full text of papers, for example - the papers in brackets below are all co-cited and each pairs title and abstracts would be used as a training pair: > The idea of distant supervision has been proposed and used widely in Relation Extraction (Mintz et al., 2009; Riedel et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012) , where the source of labels is an external knowledge base. ### Training procedure The model was trained with the Adam Optimizer and a learning rate of 2e-5 with 1000 warm-up steps followed by linear decay of the learning rate. The model training convergence is checked with the loss on a held out dev set consisting of co-cited paper pairs. ### Intended uses & limitations This model is trained for document similarity tasks in biomedical scientific text using a single vector per document. Here, the documents are the title and abstract of a paper. With appropriate fine-tuning the model can also be used for other tasks such as classification. Since the training data comes primarily from biomedicine, performance on other domains may be poorer. ### How to use Follow instructions for use detailed on the model github repo: https://github.com/allenai/aspire#specter-cocite ### Variable and metrics This model is evaluated on information retrieval datasets with document level queries. Here we report performance on RELISH (biomedical/English), and TRECCOVID (biomedical/English). These are detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). These datasets represent a abstract level retrieval task, where given a query scientific abstract the task requires the retrieval of relevant candidate abstracts. We rank documents by the L2 distance between the query and candidate documents. ### Evaluation results The released model `aspire-biencoder-biomed-scib` (and `aspire-biencoder-biomed-scib-full`) is compared against `allenai/specter`. `aspire-biencoder-biomed-scib-full`<sup></sup> is the performance reported in our paper by averaging over 3 re-runs of the model. The released models `aspire-biencoder-biomed-scib` and `aspire-biencoder-biomed-scib-full` are the single best run among the 3 re-runs. \| \| TRECCOVID \| TRECCOVID \| RELISH \| RELISH \| \|-------------------------------------------:\|:---------:\|:-------:\|:------:\|:-------:\| \| \| MAP \| NDCG%20 \| MAP \| NDCG%20 \| \| `specter` \| 28.24 \| 59.28 \| 60.62\| 77.20 \| \| `aspire-biencoder-biomed-scib-full`<sup></sup> \| 30.60 \| 62.07 \| 61.43\| 78.01 \| \| `aspire-biencoder-biomed-scib` \| 30.74 \| 60.16 \| 61.52\| 78.07 \| \| `aspire-biencoder-biomed-scib-full` \| 31.45 \| 63.15 \| 61.34\| 77.89 \| Alternative models: Besides the above models consider these alternative models also released in the Aspire paper: [`aspire-biencoder-compsci-spec`](https://huggingface.co/allenai/aspire-biencoder-compsci-spec): If you wanted to run on computer science papers. [`aspire-biencoder-biomed-spec`](https://huggingface.co/allenai/aspire-biencoder-biomed-spec): This is an alternative bi-encoder model identical to the above model, except that it is initialized with `allenai/specter` instead of SciBert. This usually under-performs the model released here.
rdchambers/bert-finetuned-ner	4b81e5bb92b94b5b9d9c73f5db67fbcf175b6695	2022-05-05T20:34:58.000Z	[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	token-classification	false	rdchambers	null	rdchambers/bert-finetuned-ner	7	null	transformers	14,359	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner 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-finetuned-ner 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: 0.0176 - Precision: 0.8418 - Recall: 0.8095 - F1: 0.8253 - Accuracy: 0.9937 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 48 \| 0.0268 \| 0.7280 \| 0.7829 \| 0.7544 \| 0.9908 \| \| No log \| 2.0 \| 96 \| 0.0194 \| 0.8295 \| 0.8050 \| 0.8171 \| 0.9934 \| \| No log \| 3.0 \| 144 \| 0.0176 \| 0.8418 \| 0.8095 \| 0.8253 \| 0.9937 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
Reproducibility/naacl22_causalDistilBERT_instance_1	6b6e342cc4145642d683ccb0c92e4cbd9fe7c5be	2022-04-23T19:50:56.000Z	[ "pytorch", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	Reproducibility	null	Reproducibility/naacl22_causalDistilBERT_instance_1	7	null	transformers	14,360	Entry not found
avacaondata/maria-exist22-task1	fd5a13a4f89a2f55cbd6fd1ced095886183fb6f0	2022-04-23T23:32:32.000Z	[ "pytorch", "roberta", "text-classification", "transformers" ]	text-classification	false	avacaondata	null	avacaondata/maria-exist22-task1	7	null	transformers	14,361	Entry not found
Ghost1/bert-finetuned-ner-accelerate	3e082f9026d76d2bb1185a8433bd1a44a1396a0d	2022-04-25T10:34:12.000Z	[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	false	Ghost1	null	Ghost1/bert-finetuned-ner-accelerate	7	null	transformers	14,362	Entry not found
xfbai/AMRBART-base	9c3b31e5c1bfedec71f595bb4f7f1a9ccfca07ed	2022-04-26T06:12:47.000Z	[ "pytorch", "bart", "text2text-generation", "en", "arxiv:2203.07836", "transformers", "AMRBART", "license:mit", "autotrain_compatible" ]	text2text-generation	false	xfbai	null	xfbai/AMRBART-base	7	null	transformers	14,363	--- language: en tags: - AMRBART license: mit --- ## AMRBART (base-sized model) AMRBART model is continually pre-trained on the English text and AMR Graphs based on the BART model. It was introduced in the paper: [Graph Pre-training for AMR Parsing and Generation](https://arxiv.org/pdf/2203.07836.pdf) by bai et al. in ACL 2022 and first released in [this repository](https://github.com/muyeby/AMRBART). ## Model description AMRBART follows the BART model which uses a transformer encoder-encoder architecture. AMRBART is pre-trained with 6 tasks: + learning to reconstruct the text based on the corrupted text. + learning to reconstruct AMR graphs based on the corrupted AMR graph. + learning to reconstruct the text based on the corrupted text and its corresponding AMR graph. + learning to reconstruct an AMR graph based on the corrupted AMR graph and its corresponding text. + learning to reconstruct the text based on the corrupted text and its corresponding corrupted AMR graph. + learning to reconstruct an AMR graph based on the corrupted AMR graph and its corresponding corrupted text. AMRBART is particularly effective when fine-tuned for AMR parsing and AMR-to-text generation tasks. ## Training data The AMRBART model is pre-trained on [AMR3.0](https://catalog.ldc.upenn.edu/LDC2020T02), a dataset consisting of 55,635 training instances and [English Gigaword](https://catalog.ldc.upenn.edu/LDC2003T05) (we randomly sampled 200,000 sentences). ## Intended uses & limitations You can use the raw model for either AMR encoding or AMR parsing, but it's mostly intended to be fine-tuned on a downstream task. ## How to use Here is how to initialize this model in PyTorch: ```python from transformers import BartForConditionalGeneration model = BartForConditionalGeneration.from_pretrained("xfbai/AMRBART-base") ``` Please refer to [this repository](https://github.com/muyeby/AMRBART) for tokenizer initialization and data preprocessing. ## BibTeX entry and citation info Please cite this paper if you find this model helpful ```bibtex @inproceedings{bai-etal-2022-graph, title = "Graph Pre-training for {AMR} Parsing and Generation", author = "Bai, Xuefeng and Chen, Yulong and Zhang, Yue", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Online", publisher = "Association for Computational Linguistics", url = "todo", doi = "todo", pages = "todo" } ```
Nithiwat/fake-news-debunker	8f22a53ce662277bb13bf361cacbafc14a0055cb	2022-04-26T13:53:36.000Z	[ "pytorch", "roberta", "text-classification", "en", "dataset:Fake and real news datasets by CLÉMENT BISAILLON", "transformers", "autotrain", "co2_eq_emissions" ]	text-classification	false	Nithiwat	null	Nithiwat/fake-news-debunker	7	1	transformers	14,364	--- tags: autotrain language: en widget: - text: "Bill Gates wants to use mass Covid-19 vaccination campaign to implant microchips to track people" datasets: - Fake and real news datasets by CLÉMENT BISAILLON co2_eq_emissions: 4.415122243239347 --- # Model Trained Using AutoTrain - Problem: Fake News Classification - Problem type: Binary Classification - Model ID: 785124234 - CO2 Emissions (in grams): 4.415122243239347 ## Validation Metrics - Loss: 0.00012586714001372457 - Accuracy: 0.9998886538247411 - Precision: 1.0 - Recall: 0.9997665732959851 - AUC: 0.9999999999999999 - F1: 0.999883273024396 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Nithiwat/autotrain-fake-news-classifier-785124234 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("Nithiwat/autotrain-fake-news-classifier-785124234", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("Nithiwat/autotrain-fake-news-classifier-785124234", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
drsis/pegasus-samsum-tb	6fb60d780e037f6618fcc0b6ff48cff123c306b4	2022-04-26T02:18:42.000Z	[ "pytorch", "pegasus", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	drsis	null	drsis/pegasus-samsum-tb	7	null	transformers	14,365	Entry not found
anablasi/financial_model	d916472c95596b95d033ec69e11b93b122dcaf45	2022-05-10T16:32:38.000Z	[ "pytorch", "roberta", "fill-mask", "transformers", "generated_from_trainer", "license:cc-by-4.0", "model-index", "autotrain_compatible" ]	fill-mask	false	anablasi	null	anablasi/financial_model	7	null	transformers	14,366	--- license: cc-by-4.0 tags: - generated_from_trainer model-index: - name: model2 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. --> # model2 This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Training results ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
sangjeedondrub/tibetan-roberta-base	1c2458923edf160701295b9b8bc6195fa7e4c9aa	2022-05-05T02:18:22.000Z	[ "pytorch", "roberta", "fill-mask", "bo", "transformers", "tibetan", "pretrained language model", "license:mit", "autotrain_compatible" ]	fill-mask	false	sangjeedondrub	null	sangjeedondrub/tibetan-roberta-base	7	0	transformers	14,367	--- language: - bo tags: - tibetan - pretrained language model - roberta widget: - text: "རྫོགས་པའི་ <mask>" - text: "ཆོས་ཀྱི་<mask>་བ" - text: "གངས་རིའི་ <mask>" - text: "བོད་ཀྱི་སྨན་<mask>" license: "mit" --- # Demo in a `fill-mask` task ``` from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline model_name = 'sangjeedondrub/tibetan-roberta-base' model = AutoModelForMaskedLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) fill_mask_pipe = pipeline( "fill-mask", model=model, tokenizer=tokenizer ) samples = """རིན་ <mask> ཆོས་ཀྱི་ <mask> རྫོགས་པའི་ <mask> གངས་རིའི་ <mask> མེ་ལོང་ <mask> བདེན་པའི་ <mask> 'འབྱུང་ <mask>""".splitlines() for idx, sample in enumerate(samples, start=1): outputs = fill_mask_pipe(sample) print(idx, sample) for output in outputs: print(output) ``` # Output ``` 1 རིན་ <mask> {'score': 0.943362832069397, 'token': 459, 'token_str': 'ཐང', 'sequence': 'རིན་ཐང'} {'score': 0.025716140866279602, 'token': 282, 'token_str': 'པ', 'sequence': 'རིན་པ'} {'score': 0.004410382825881243, 'token': 596, 'token_str': 'འཕར', 'sequence': 'རིན་འཕར'} {'score': 0.003161463886499405, 'token': 561, 'token_str': 'ཅང', 'sequence': 'རིན་ཅང'} {'score': 0.0025683969724923372, 'token': 360, 'token_str': 'གནས', 'sequence': 'རིན་གནས'} 2 ཆོས་ཀྱི་ <mask> {'score': 0.08558642119169235, 'token': 476, 'token_str': 'དཔལ', 'sequence': 'ཆོས་ཀྱི་དཔལ'} {'score': 0.0616581067442894, 'token': 323, 'token_str': 'ལས', 'sequence': 'ཆོས་ཀྱི་ལས'} {'score': 0.04617622494697571, 'token': 568, 'token_str': 'ཉམས', 'sequence': 'ཆོས་ཀྱི་ཉམས'} {'score': 0.042447883635759354, 'token': 467, 'token_str': 'དབང', 'sequence': 'ཆོས་ཀྱི་དབང'} {'score': 0.0358237698674202, 'token': 768, 'token_str': 'དད', 'sequence': 'ཆོས་ཀྱི་དད'} 3 རྫོགས་པའི་ <mask> {'score': 0.06635843217372894, 'token': 323, 'token_str': 'ལས', 'sequence': 'རྫོགས་པའི་ལས'} {'score': 0.06410858780145645, 'token': 360, 'token_str': 'གནས', 'sequence': 'རྫོགས་པའི་གནས'} {'score': 0.0570441335439682, 'token': 573, 'token_str': 'གཏམ', 'sequence': 'རྫོགས་པའི་གཏམ'} {'score': 0.05679900944232941, 'token': 397, 'token_str': 'ལམ', 'sequence': 'རྫོགས་པའི་ལམ'} {'score': 0.05157950520515442, 'token': 543, 'token_str': 'མཚན', 'sequence': 'རྫོགས་པའི་མཚན'} 4 གངས་རིའི་ <mask> {'score': 0.21429458260536194, 'token': 971, 'token_str': 'འདབས', 'sequence': 'གངས་རིའི་འདབས'} {'score': 0.05296638607978821, 'token': 360, 'token_str': 'གནས', 'sequence': 'གངས་རིའི་གནས'} {'score': 0.04839177057147026, 'token': 712, 'token_str': 'གངས', 'sequence': 'གངས་རིའི་གངས'} {'score': 0.04389436915516853, 'token': 984, 'token_str': 'འདབ', 'sequence': 'གངས་རིའི་འདབ'} {'score': 0.04158150777220726, 'token': 274, 'token_str': 'ན', 'sequence': 'གངས་རིའི་ན'} 5 མེ་ལོང་ <mask> {'score': 0.19395706057548523, 'token': 323, 'token_str': 'ལས', 'sequence': 'མེ་ལོང་ལས'} {'score': 0.12707622349262238, 'token': 293, 'token_str': 'དང', 'sequence': 'མེ་ལོང་དང'} {'score': 0.08089829981327057, 'token': 280, 'token_str': 'མ', 'sequence': 'མེ་ལོང་མ'} {'score': 0.06481984257698059, 'token': 279, 'token_str': 'ལ', 'sequence': 'མེ་ལོང་ལ'} {'score': 0.0577043853700161, 'token': 362, 'token_str': 'ནང', 'sequence': 'མེ་ལོང་ནང'} 6 བདེན་པའི་ <mask> {'score': 0.12633271515369415, 'token': 573, 'token_str': 'གཏམ', 'sequence': 'བདེན་པའི་གཏམ'} {'score': 0.0909079909324646, 'token': 360, 'token_str': 'གནས', 'sequence': 'བདེན་པའི་གནས'} {'score': 0.08624855428934097, 'token': 397, 'token_str': 'ལམ', 'sequence': 'བདེན་པའི་ལམ'} {'score': 0.07476165890693665, 'token': 362, 'token_str': 'ནང', 'sequence': 'བདེན་པའི་ནང'} {'score': 0.06319335103034973, 'token': 323, 'token_str': 'ལས', 'sequence': 'བདེན་པའི་ལས'} 7 'འབྱུང་ <mask> {'score': 0.8271735906600952, 'token': 360, 'token_str': 'གནས', 'sequence': "'འབྱུང་གནས"} {'score': 0.10802919417619705, 'token': 270, 'token_str': 'བ', 'sequence': "'འབྱུང་བ"} {'score': 0.021947095170617104, 'token': 503, 'token_str': 'ཁམས', 'sequence': "'འབྱུང་ཁམས"} {'score': 0.006081813480705023, 'token': 484, 'token_str': 'རབས', 'sequence': "'འབྱུང་རབས"} {'score': 0.002384472405537963, 'token': 293, 'token_str': 'དང', 'sequence': "'འབྱུང་དང"} ``` # About This model is trained and released by Sangjee Dondrub [sangjeedondrub at live dot com], the mere purpose of conducting these experiments is to improve my familiarity with Transformers APIs.
jenspt/bert_classification_27_04	3f855f9f59c22d02d91b8f737efe1a8e521b6b29	2022-04-27T14:10:02.000Z	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	false	jenspt	null	jenspt/bert_classification_27_04	7	null	transformers	14,368	Entry not found
NeuML/t5-small-bashsql	4e9bed94c0454354aa4fb2db142ba62d413d0fde	2022-04-28T13:12:43.000Z	[ "pytorch", "t5", "text2text-generation", "en", "transformers", "license:apache-2.0", "autotrain_compatible" ]	text2text-generation	false	NeuML	null	NeuML/t5-small-bashsql	7	null	transformers	14,369	--- language: en widget: - text: "translate Bash to SQL: find -name \"feel good story\" -mtime -1" example_title: Last day - text: "translate Bash to SQL: find -name \"show me sports stories\" -mtime -1 -team \"Red Sox\"" example_title: Last day with filter - text: "translate Bash to SQL: find -name \"breaking news\" -summary" example_title: Summary - text: "translate Bash to SQL: find -name \"breaking news\" -translate fr" example_title: Translate to French inference: parameters: max_length: 512 license: apache-2.0 --- # T5-small finedtuned to generate txtai SQL [T5 small](https://huggingface.co/t5-small) fine-tuned to generate [txtai](https://github.com/neuml/txtai) SQL. This model takes [Bash](https://en.wikipedia.org/wiki/Bash_(Unix_shell)) like commands and builds txtai-compatible SQL statements. ``` find -name "feel good story" -mtime -1 find -name "show me sports stories" -mtime -1 -team \"Red Sox\" find -name "breaking news" -summary find -name "breaking news" -translate fr ``` ## Custom query syntax This model is an example of creating a custom query syntax that can be translated into SQL txtai can understand. Any query syntax can be created. This one supports Bash-like commands but a similar strategy can be deployed to support other languages. Natural language can be translated to functions, query clauses, column selection and more. See [t5-small-txtsql](https://huggingface.co/NeuML/t5-small-txtsql) for a model that translates natural language statements into txtai SQL. ## Model training This model was trained using scripts that can be [found here](https://github.com/neuml/txtai/tree/master/models/bashsql). Steps to train: ```bash python generate.py bashsql.csv python train.py bashsql.csv t5-small-bashsql ```
classla/wav2vec2-large-slavic-parlaspeech-hr	6bd500f77d2a9f3b49a79102d4db388041be59c7	2022-05-18T13:58:26.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "hr", "dataset:parlaspeech-hr", "transformers", "audio", "parlaspeech" ]	automatic-speech-recognition	false	classla	null	classla/wav2vec2-large-slavic-parlaspeech-hr	7	1	transformers	14,370	--- language: hr datasets: - parlaspeech-hr tags: - audio - automatic-speech-recognition - parlaspeech widget: - example_title: example 1 src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/1800.m4a - example_title: example 2 src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020578b.flac.wav - example_title: example 3 src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020570a.flac.wav --- # wav2vec2-large-slavic-parlaspeech-hr This model for Croatian ASR is based on the [facebook/wav2vec2-large-slavic-voxpopuli-v2 model](https://huggingface.co/facebook/wav2vec2-large-slavic-voxpopuli-v2) and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset [ParlaSpeech-HR v1.0](http://hdl.handle.net/11356/1494). If you use this model, please cite the following paper: Nikola Ljubešić, Danijel Koržinek, Peter Rupnik, Ivo-Pavao Jazbec. ParlaSpeech-HR -- a freely available ASR dataset for Croatian bootstrapped from the ParlaMint corpus. Accepted at ParlaCLARIN@LREC. ## Metrics Evaluation is performed on the dev and test portions of the [ParlaSpeech-HR v1.0](http://hdl.handle.net/11356/1494) dataset. \|split\|CER\|WER\| \|---\|---\|---\| \|dev\|0.0311\|0.0921\| \|test\|0.0222\|0.0679\| ## Usage in `transformers` ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC import soundfile as sf import torch import os device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # load model and tokenizer processor = Wav2Vec2Processor.from_pretrained( "classla/wav2vec2-large-slavic-parlaspeech-hr") model = Wav2Vec2ForCTC.from_pretrained("classla/wav2vec2-large-slavic-parlaspeech-hr") # download the example wav files: os.system("wget https://huggingface.co/classla/wav2vec2-large-slavic-parlaspeech-hr/raw/main/00020570a.flac.wav") # read the wav file speech, sample_rate = sf.read("00020570a.flac.wav") input_values = processor(speech, sampling_rate=sample_rate, return_tensors="pt").input_values.to(device) # remove the raw wav file os.system("rm 00020570a.flac.wav") # retrieve logits logits = model.to(device)(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.decode(predicted_ids[0]).lower() # transcription: 'veliki broj poslovnih subjekata posluje sa minusom velik dio' ``` ## Training hyperparameters In fine-tuning, the following arguments were used: \| arg \| value \| \|-------------------------------\|-------\| \| `per_device_train_batch_size` \| 16 \| \| `gradient_accumulation_steps` \| 4 \| \| `num_train_epochs` \| 8 \| \| `learning_rate` \| 3e-4 \| \| `warmup_steps` \| 500 \|
efederici/cross-encoder-distilbert-it	30142b713ba540668032bd736435536022468203	2022-05-03T13:14:47.000Z	[ "pytorch", "distilbert", "text-classification", "it", "transformers", "cross-encoder", "sentence-similarity", "license:apache-2.0" ]	text-classification	false	efederici	null	efederici/cross-encoder-distilbert-it	7	null	transformers	14,371	--- pipeline_tag: text-classification license: apache-2.0 language: - it tags: - cross-encoder - sentence-similarity - transformers --- # Cross-Encoder The model can be used for Information Retrieval: given a query, encode the query will all possible passages. Then sort the passages in a decreasing order. <p align="center"> <img src="https://www.exibart.com/repository/media/2020/07/bridget-riley-cool-edge.jpg" width="400"> </br> Bridget Riley, COOL EDGE </p> ## Training Data This model was trained on a custom biomedical ranking dataset. ## Usage and Performance ```python from sentence_transformers import CrossEncoder model = CrossEncoder('efederici/cross-encoder-distilbert-it') scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')]) ``` The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
lilykaw/distilbert-base-uncased-finetuned-stsb	0f0f322c369e9a4edcf95bc841b60b4da5c1d0ca	2022-04-28T22:46:26.000Z	[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	lilykaw	null	lilykaw/distilbert-base-uncased-finetuned-stsb	7	null	transformers	14,372	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: distilbert-base-uncased-finetuned-stsb results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.8651841336703003 --- <!-- 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-stsb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5634 - Pearson: 0.8680 - Spearmanr: 0.8652 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Pearson \| Spearmanr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:---------:\| \| No log \| 1.0 \| 360 \| 0.6646 \| 0.8516 \| 0.8494 \| \| 1.0238 \| 2.0 \| 720 \| 0.5617 \| 0.8666 \| 0.8637 \| \| 0.3952 \| 3.0 \| 1080 \| 0.6533 \| 0.8649 \| 0.8646 \| \| 0.3952 \| 4.0 \| 1440 \| 0.5889 \| 0.8651 \| 0.8625 \| \| 0.2488 \| 5.0 \| 1800 \| 0.5634 \| 0.8680 \| 0.8652 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
thusken/nb-bert-base-target-group	bec6a71230e50ff6df31a195e1fd78da0af14dde	2022-05-06T12:24:27.000Z	[ "pytorch", "bert", "text-classification", "transformers", "generated_from_trainer", "license:cc-by-4.0", "model-index" ]	text-classification	false	thusken	null	thusken/nb-bert-base-target-group	7	null	transformers	14,373	--- license: cc-by-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: nb-bert-base-target-group 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. --> # nb-bert-base-target-group This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2820 - Accuracy: 0.8822 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 64 - 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: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2779 \| 1.0 \| 2032 \| 0.2820 \| 0.8822 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.12.1
chiragasarpota/scotus-bert	d93025e5c0fe0810f4f30bd5a1a9d5725916eee6	2022-04-29T16:36:17.000Z	[ "pytorch", "bert", "text-classification", "transformers", "license:apache-2.0" ]	text-classification	false	chiragasarpota	null	chiragasarpota/scotus-bert	7	null	transformers	14,374	--- license: apache-2.0 ---
omar47/wav2vec2-large-xls-r-300m-urdu	1b18aac1552bac1987a72c719267f2e59c38cbb4	2022-05-16T15:20:18.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	omar47	null	omar47/wav2vec2-large-xls-r-300m-urdu	7	null	transformers	14,375	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xls-r-300m-urdu 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-urdu This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m). It achieves the following results on the evaluation set: - Loss: 0.5285 - Wer: 0.1702 ## 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: 35 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 16.9618 \| 0.74 \| 32 \| 15.0745 \| 1.0 \| \| 9.1928 \| 1.49 \| 64 \| 5.9361 \| 1.0 \| \| 4.9307 \| 2.23 \| 96 \| 4.2924 \| 1.0 \| \| 3.8917 \| 2.98 \| 128 \| 3.5873 \| 1.0 \| \| 3.3867 \| 3.72 \| 160 \| 3.2594 \| 1.0 \| \| 3.2107 \| 4.47 \| 192 \| 3.1718 \| 1.0 \| \| 3.1395 \| 5.21 \| 224 \| 3.1281 \| 1.0 \| \| 3.115 \| 5.95 \| 256 \| 3.1238 \| 1.0 \| \| 3.0801 \| 6.7 \| 288 \| 3.0674 \| 1.0 \| \| 2.9725 \| 7.44 \| 320 \| 2.8277 \| 1.0 \| \| 2.4159 \| 8.19 \| 352 \| 1.7186 \| 0.9036 \| \| 1.3377 \| 8.93 \| 384 \| 1.0271 \| 0.6433 \| \| 0.8591 \| 9.67 \| 416 \| 0.8087 \| 0.5441 \| \| 0.726 \| 10.42 \| 448 \| 0.7263 \| 0.4634 \| \| 0.6242 \| 11.16 \| 480 \| 0.6783 \| 0.4156 \| \| 0.5417 \| 11.91 \| 512 \| 0.6611 \| 0.4305 \| \| 0.4784 \| 12.65 \| 544 \| 0.6300 \| 0.3926 \| \| 0.4198 \| 13.4 \| 576 \| 0.5646 \| 0.3499 \| \| 0.3798 \| 14.14 \| 608 \| 0.5919 \| 0.3229 \| \| 0.3356 \| 14.88 \| 640 \| 0.5715 \| 0.3369 \| \| 0.2954 \| 15.63 \| 672 \| 0.5325 \| 0.2728 \| \| 0.264 \| 16.37 \| 704 \| 0.5535 \| 0.2689 \| \| 0.2535 \| 17.12 \| 736 \| 0.5467 \| 0.2366 \| \| 0.2277 \| 17.86 \| 768 \| 0.5219 \| 0.2345 \| \| 0.2141 \| 18.6 \| 800 \| 0.5314 \| 0.2487 \| \| 0.2036 \| 19.35 \| 832 \| 0.5382 \| 0.2236 \| \| 0.2021 \| 20.09 \| 864 \| 0.5038 \| 0.1922 \| \| 0.1676 \| 20.84 \| 896 \| 0.5238 \| 0.2033 \| \| 0.1544 \| 21.58 \| 928 \| 0.5069 \| 0.1866 \| \| 0.1512 \| 22.33 \| 960 \| 0.5045 \| 0.1965 \| \| 0.1512 \| 23.07 \| 992 \| 0.5167 \| 0.1862 \| \| 0.1399 \| 23.81 \| 1024 \| 0.5236 \| 0.1840 \| \| 0.1291 \| 24.56 \| 1056 \| 0.5234 \| 0.1957 \| \| 0.1274 \| 25.3 \| 1088 \| 0.5348 \| 0.1943 \| \| 0.127 \| 26.05 \| 1120 \| 0.4978 \| 0.1719 \| \| 0.1105 \| 26.79 \| 1152 \| 0.5067 \| 0.1767 \| \| 0.1069 \| 27.53 \| 1184 \| 0.5150 \| 0.1758 \| \| 0.1058 \| 28.28 \| 1216 \| 0.5218 \| 0.1844 \| \| 0.0999 \| 29.02 \| 1248 \| 0.5375 \| 0.1852 \| \| 0.0964 \| 29.77 \| 1280 \| 0.5373 \| 0.1843 \| \| 0.0971 \| 30.51 \| 1312 \| 0.5190 \| 0.1776 \| \| 0.0906 \| 31.26 \| 1344 \| 0.5217 \| 0.1747 \| \| 0.0909 \| 32.0 \| 1376 \| 0.5204 \| 0.1778 \| \| 0.0784 \| 32.74 \| 1408 \| 0.5336 \| 0.1756 \| \| 0.0823 \| 33.49 \| 1440 \| 0.5281 \| 0.1699 \| \| 0.0834 \| 34.23 \| 1472 \| 0.5292 \| 0.1700 \| \| 0.0827 \| 34.98 \| 1504 \| 0.5285 \| 0.1702 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
TehranNLP-org/bert-large-hateXplain	e9344baa4889877e63918285b4f55f1e24f5b3d9	2022-05-03T17:01:45.000Z	[ "pytorch", "bert", "text-classification", "en", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	TehranNLP-org	null	TehranNLP-org/bert-large-hateXplain	7	null	transformers	14,376	--- language: - en license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: SEED0042 results: - task: name: Text Classification type: text-classification dataset: name: HATEXPLAIN type: '' args: hatexplain metrics: - name: Accuracy type: accuracy value: 0.40790842872008326 --- <!-- 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. --> # SEED0042 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the HATEXPLAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.7731 - Accuracy: 0.4079 - Accuracy 0: 0.8027 - Accuracy 1: 0.1869 - Accuracy 2: 0.2956 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: not_parallel - gradient_accumulation_steps: 32 - 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: 150 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Accuracy 0 \| Accuracy 1 \| Accuracy 2 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:----------:\|:----------:\| \| No log \| 1.0 \| 480 \| 0.8029 \| 0.4235 \| 0.7589 \| 0.0461 \| 0.5985 \| \| No log \| 2.0 \| 960 \| 0.7574 \| 0.4011 \| 0.7470 \| 0.1831 \| 0.3376 \| \| No log \| 3.0 \| 1440 \| 0.7731 \| 0.4079 \| 0.8027 \| 0.1869 \| 0.2956 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu113 - Datasets 2.1.0 - Tokenizers 0.11.6
cuzeverynameistaken/wav2vec2-base-timit-demo-colab0	fe329a0253f47c7f0e7868d381459f6d3814ea67	2022-05-01T08:59:37.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	cuzeverynameistaken	null	cuzeverynameistaken/wav2vec2-base-timit-demo-colab0	7	null	transformers	14,377	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab0 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-colab0 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: 0.6960 - Wer: 0.5694 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 5.3196 \| 13.89 \| 500 \| 3.1225 \| 1.0 \| \| 1.2756 \| 27.78 \| 1000 \| 0.6960 \| 0.5694 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
pszemraj/mGPT-Peter-2E	7663e8d936855df0c4b9b26a06a46d7b6d54672b	2022-05-18T17:49:12.000Z	[ "pytorch", "gpt2", "text-generation", "dataset:mc4", "dataset:Wikipedia", "transformers", "multilingual", "PyTorch", "Transformers", "gpt3", "Deepspeed", "Megatron", "mGPT", "license:apache-2.0" ]	text-generation	false	pszemraj	null	pszemraj/mGPT-Peter-2E	7	null	transformers	14,378	--- license: apache-2.0 pipeline_tag: text-generation tags: - multilingual - PyTorch - Transformers - gpt3 - gpt2 - Deepspeed - Megatron - mGPT datasets: - mc4 - Wikipedia widget: - text: "Ich weiß, dass du müde bist, aber können wir heute Abend noch einen Spaziergang machen? peter szemraj: ich" example_title: "walk - Deutsch" - text: "peter szemraj: 我喜欢穿很酷的衣服" example_title: "fashion - Chinese" - text: "Wat zei je over mijn moeder? peter szemraj: ik" example_title: "🚎 - Dutch" - text: "Zagadka: Człowiekowi, który przebywał na dworze w deszczu bez parasola czy kapelusza, nie zmoczył się ani jeden włos na głowie. Dlaczego? peter szemraj: czy to" example_title: "brain teaser - Polish" - text: "Minha amiga diz que conhece todas as línguas, mas não fala nenhuma delas... o que há de errado com ela? peter szemraj: eu" example_title: "language - Portuguese" - text: "se potesse vivere ovunque, dove sarebbe? peter szemraj: io" example_title: "dream living place - Italian" - text: "Can you take me for dinner somewhere nice this time? peter szemraj:" example_title: "dinner" - text: "What really makes you angry? peter szemraj:" example_title: "pet peeve" - text: "Jak nazwać aligatora, który właśnie przeszedł operację usunięcia lewego ramienia?peter szemraj: ja" example_title: "alligator - Polish" - text: "Warum sind Transformers für die Sprachmodellierung wichtig? peter szemraj: es ist" example_title: "Transformers - German" - text: "как написать хорошие подсказки для языковых моделей? peter szemraj: сначала вам нужно" example_title: "prompt tutorial - Russian" - text: "Pewien mężczyzna wpycha swój samochód do hotelu i mówi właścicielowi, że jest bankrutem. Dlaczego? peter szemraj: może" example_title: "brain teaser - Polish 2" - text: "Zagadka: Mówię bez ust i słyszę bez uszu. Nie mam ciała, ale ożywiam się wraz z wiatrem. Czym jestem? peter szemraj: czy to" example_title: "brain teaser - Polish 3" - text: "Què t'agrada fer per divertir-te? peter szemraj: m'agrada" example_title: "hobbies - Catalan" - text: "为什么你总是那么累？peter szemraj: 呃，我想" example_title: "tired - Chinese" inference: parameters: min_length: 2 max_length: 64 do_sample: True top_k: 10 top_p: 0.9 temperature: 0.65 repetition_penalty: 3.5 no_repeat_ngram_size: 3 length_penalty: 0.4 pad_token: 1 --- # mGPT: fine-tune on message data - 2E - This model is a fine-tuned version of [sberbank-ai/mGPT](https://huggingface.co/sberbank-ai/mGPT) on 80k messages. This builds on the minimum-working-example checkpoint [here](https://huggingface.co/pszemraj/mGPT-Peter-mwe). - 2E = 2 epochs ## Model description - testing if fine-tuned personality data bleeds over to other languages without being trained in them explicitly Interesting findings thus far: - Passing a generic word after the `<name-identifier>` that is in a non-English language helps ensure the model responds in the question language (see: any example). - Model generations (in general) remain semantically consistent, even if the generations switch from `<language>`to English in the middle of the generated text. This demonstrates some sort of "universal concept understanding" ### Usage in python Install the transformers library if you don't have it: ``` pip install -U transformers ``` load the model into a pipeline object: ``` from transformers import pipeline import torch device = 'cuda' if torch.cuda.is_available() else 'cpu' my_chatbot = pipeline('text-generation', 'pszemraj/mGPT-Peter-2E', device=0 if device == 'cuda' else -1, ) ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 1 (in addition to all training on prior checkpoints) ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
dineshmane/bert-finetuned-mrpc	2a3780ca3d2b5a6d0c83fca7066214ab3147c0aa	2022-05-01T17:55:58.000Z	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	false	dineshmane	null	dineshmane/bert-finetuned-mrpc	7	null	transformers	14,379	Entry not found
Ghani-25/SummFinFR	18cf0d7f07f8fffdb0cd5df889da4efd87318a34	2022-05-02T13:15:08.000Z	[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	Ghani-25	null	Ghani-25/SummFinFR	7	null	transformers	14,380	Entry not found
ali2066/DistilBERTFINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False	17e7f04b5fad299a01915670cfafba1682d0f3f0	2022-05-02T13:33:27.000Z	[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers", "generated_from_trainer", "model-index" ]	text-classification	false	ali2066	null	ali2066/DistilBERTFINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False	7	null	transformers	14,381	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: DistilBERTFINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False 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. --> # DistilBERTFINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7680 - Precision: 0.9838 - Recall: 0.6632 - F1: 0.7923 - Accuracy: 0.6624 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 130 \| 0.2980 \| 0.9315 \| 0.9533 \| 0.9423 \| 0.9081 \| \| No log \| 2.0 \| 260 \| 0.2053 \| 0.9537 \| 0.9626 \| 0.9581 \| 0.9338 \| \| No log \| 3.0 \| 390 \| 0.1873 \| 0.9464 \| 0.9907 \| 0.9680 \| 0.9485 \| \| 0.3064 \| 4.0 \| 520 \| 0.1811 \| 0.9585 \| 0.9720 \| 0.9652 \| 0.9449 \| \| 0.3064 \| 5.0 \| 650 \| 0.1887 \| 0.9587 \| 0.9766 \| 0.9676 \| 0.9485 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False	c9dfd66b1bea7eaf4edc170d8deae57807a18d21	2022-05-02T18:27:20.000Z	[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	ali2066	null	ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False	7	null	transformers	14,382	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: DistilBERT_FINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False 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_FINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0703 - Precision: 0.9667 - Recall: 0.0505 - F1: 0.0961 - Accuracy: 0.0766 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 95 \| 0.5442 \| 0.6667 \| 0.1132 \| 0.1935 \| 0.75 \| \| No log \| 2.0 \| 190 \| 0.5316 \| 0.5385 \| 0.1321 \| 0.2121 \| 0.74 \| \| No log \| 3.0 \| 285 \| 0.5384 \| 0.4615 \| 0.2264 \| 0.3038 \| 0.725 \| \| No log \| 4.0 \| 380 \| 0.5503 \| 0.4286 \| 0.2264 \| 0.2963 \| 0.715 \| \| No log \| 5.0 \| 475 \| 0.5529 \| 0.4286 \| 0.2264 \| 0.2963 \| 0.715 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False	c3b8e2538407cc275c431bbaead1ef9a5039c455	2022-05-02T18:36:09.000Z	[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	ali2066	null	ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False	7	null	transformers	14,383	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False 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_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0699 - Precision: 0.9942 - Recall: 0.9773 - F1: 0.9857 - Accuracy: 0.9725 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 479 \| 0.4036 \| 0.8333 \| 0.9326 \| 0.8802 \| 0.8054 \| \| 0.5047 \| 2.0 \| 958 \| 0.3749 \| 0.8635 \| 0.9339 \| 0.8973 \| 0.8361 \| \| 0.3336 \| 3.0 \| 1437 \| 0.3789 \| 0.8862 \| 0.9184 \| 0.9020 \| 0.8471 \| \| 0.2644 \| 4.0 \| 1916 \| 0.4024 \| 0.8762 \| 0.9171 \| 0.8962 \| 0.8371 \| \| 0.2233 \| 5.0 \| 2395 \| 0.4195 \| 0.8784 \| 0.9171 \| 0.8973 \| 0.8391 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
laituan245/molt5-base-caption2smiles	c7689836acd99876a7255256505e378110140714	2022-05-03T18:08:45.000Z	[ "pytorch", "t5", "text2text-generation", "arxiv:2204.11817", "transformers", "license:apache-2.0", "autotrain_compatible" ]	text2text-generation	false	laituan245	null	laituan245/molt5-base-caption2smiles	7	null	transformers	14,384	--- license: apache-2.0 --- This model can be used to generate a SMILES string from an input caption. ## Example Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-base-caption2smiles", model_max_length=512) model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-base-caption2smiles') input_text = 'The molecule is a monomethoxybenzene that is 2-methoxyphenol substituted by a hydroxymethyl group at position 4. It has a role as a plant metabolite. It is a member of guaiacols and a member of benzyl alcohols.' input_ids = tokenizer(input_text, return_tensors="pt").input_ids outputs = model.generate(input_ids, num_beams=5, max_length=512) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # The model will generate "COC1=C(C=CC(=C1)CCCO)O". The ground-truth is "COC1=C(C=CC(=C1)CO)O". ``` ## Paper For more information, please take a look at our paper. Paper: [Translation between Molecules and Natural Language](https://arxiv.org/abs/2204.11817) Authors: Carl Edwards\, Tuan Lai\, Kevin Ros, Garrett Honke, Heng Ji
tartuNLP/EstBERT_NER_v2	fc9726c7b6ef3b876b3a826fa471e9120c19c4c3	2022-05-06T06:27:43.000Z	[ "pytorch", "bert", "token-classification", "et", "transformers", "license:cc-by-4.0", "autotrain_compatible" ]	token-classification	false	tartuNLP	null	tartuNLP/EstBERT_NER_v2	7	null	transformers	14,385	--- language: et license: cc-by-4.0 widget: - text: "Eesti President on Alar Karis." --- # Estonian NER model based on EstBERT This model is a fine-tuned version of [tartuNLP/EstBERT](https://huggingface.co/tartuNLP/EstBERT) on the Estonian NER dataset. The model was trained by tartuNLP, the NLP research group at the institute of Computer Science at the University of Tartu. It achieves the following results on the test set: - Loss: 0.3565 - Precision: 0.7612 - Recall: 0.7744 - F1: 0.7678 - Accuracy: 0.9672 The entity-level results are as follows: \| \| Precision \| Recall \| F1 \| Number \| \|---------\| --------- \| ------- \| ------- \| ------- \| \| DATE \| 0.7278 \| 0.7258 \| 0.7268 \| 372 \| \| EVENT \| 0.3721 \| 0.5714 \| 0.4507 \| 28 \| \| GPE \| 0.8679 \| 0.8369 \| 0.8521 \| 840 \| \| LOC \| 0.6545 \| 0.4832 \| 0.5560 \| 149 \| \| MONEY \| 0.6625 \| 0.6023 \| 0.6310 \| 88 \| \| ORG \| 0.6761 \| 0.7267 \| 0.7005 \| 589 \| \| PER \| 0.8255 \| 0.9068 \| 0.8642 \| 751 \| \| PERCENT \| 1.0 \| 0.9589 \| 0.9790 \| 73 \| \| PROD \| 0.6030 \| 0.5430 \| 0.5714 \| 221 \| \| TIME \| 0.5682 \| 0.5556 \| 0.5618 \| 45 \| \| TITLE \| 0.7 \| 0.8063 \| 0.7494 \| 191 \| ## How to use You can use this model with Transformers pipeline for NER. Post-processing of results may be necessary as the model occasionally tags subword tokens as entities. ``` from transformers import BertTokenizer, BertForTokenClassification from transformers import pipeline tokenizer = BertTokenizer.from_pretrained('tartuNLP/EstBERT_NER') bertner = BertForTokenClassification.from_pretrained('tartuNLP/EstBERT_NER') nlp = pipeline("ner", model=bertner, tokenizer=tokenizer) text = "Kaia Kanepi (WTA 57.) langes USA-s Charlestonis toimuval WTA 500 kategooria tenniseturniiril konkurentsist kaheksandikfinaalis, kaotades poolatarile Magda Linette'ile (WTA 64.) 3 : 6, 6 : 4, 2 : 6." ner_results = new_nlp(text) tokens=tokenizer(text) tokens=tokenizer.convert_ids_to_tokens(tokens['input_ids']) print(f'tokens: {tokens}') print(f'NER model:{ner_results}') ``` ``` tokens: ['[CLS]', 'kai', '##a', 'kanepi', '(', 'w', '##ta', '57', '.', ')', 'langes', 'usa', '-', 's', 'cha', '##rl', '##est', '##onis', 'toimuval', 'w', '##ta', '500', 'kategooria', 'tennise', '##turniiril', 'konkurentsist', 'kaheksandik', '##finaalis', ',', 'kaotades', 'poola', '##tari', '##le', 'ma', '##gda', 'line', '##tte', "'", 'ile', '(', 'w', '##ta', '64', '.', ')', '3', ':', '6', ',', '6', ':', '4', ',', '2', ':', '6', '.', '[SEP]'] ``` ``` NER model: [{'entity': 'B-PER', 'score': 0.99999887, 'index': 1, 'word': 'kai', 'start': None, 'end': None}, {'entity': 'B-PER', 'score': 0.97371966, 'index': 2, 'word': '##a', 'start': None, 'end': None}, {'entity': 'I-PER', 'score': 0.99999815, 'index': 3, 'word': 'kanepi', 'start': None, 'end': None}, {'entity': 'B-ORG', 'score': 0.63085276, 'index': 5, 'word': 'w', 'start': None, 'end': None}, {'entity': 'B-GPE', 'score': 0.99999934, 'index': 11, 'word': 'usa', 'start': None, 'end': None}, {'entity': 'B-GPE', 'score': 0.9999685, 'index': 14, 'word': 'cha', 'start': None, 'end': None}, {'entity': 'I-GPE', 'score': 0.8875574, 'index': 15, 'word': '##rl', 'start': None, 'end': None}, {'entity': 'I-GPE', 'score': 0.9996168, 'index': 16, 'word': '##est', 'start': None, 'end': None}, {'entity': 'I-GPE', 'score': 0.9992657, 'index': 17, 'word': '##onis', 'start': None, 'end': None}, {'entity': 'B-EVENT', 'score': 0.99999064, 'index': 19, 'word': 'w', 'start': None, 'end': None}, {'entity': 'I-EVENT', 'score': 0.9772493, 'index': 20, 'word': '##ta', 'start': None, 'end': None}, {'entity': 'I-EVENT', 'score': 0.99999076, 'index': 21, 'word': '500', 'start': None, 'end': None}, {'entity': 'I-EVENT', 'score': 0.99955636, 'index': 22, 'word': 'kategooria', 'start': None, 'end': None}, {'entity': 'B-TITLE', 'score': 0.8771319, 'index': 30, 'word': 'poola', 'start': None, 'end': None}, {'entity': 'B-PER', 'score': 0.99999785, 'index': 33, 'word': 'ma', 'start': None, 'end': None}, {'entity': 'B-PER', 'score': 0.9998398, 'index': 34, 'word': '##gda', 'start': None, 'end': None}, {'entity': 'I-PER', 'score': 0.9999987, 'index': 35, 'word': 'line', 'start': None, 'end': None}, {'entity': 'I-PER', 'score': 0.9999976, 'index': 36, 'word': '##tte', 'start': None, 'end': None}, {'entity': 'I-PER', 'score': 0.99999285, 'index': 37, 'word': "'", 'start': None, 'end': None}, {'entity': 'I-PER', 'score': 0.9999794, 'index': 38, 'word': 'ile', 'start': None, 'end': None}, {'entity': 'B-ORG', 'score': 0.7664479, 'index': 40, 'word': 'w', 'start': None, 'end': None}] ``` ## Intended uses & limitations This model can be used to find named entities from Estonian texts. The model is free to use for anyone. TartuNLP does not guarantee that the model is useful for anyone or anything. TartuNLP is not responsible for any results it generates. ## Training and evaluation data The model was trained on two Estonian NER datasets: - [The Reannotated Estonian NER corpus](https://metashare.ut.ee/repository/browse/reannotated-estonian-ner-corpus/bd43f1f614a511eca6e4fa163e9d45477d086613d2894fd5af79bf13e3f13594/) - [The New Estonian NER corpus](https://metashare.ut.ee/repository/browse/new-estonian-ner-corpus/98b6706c963c11eba6e4fa163e9d45470bcd0533b6994c93ab8b8c628516ffed/) Both datasets have been annotated with the same annotation scheme. For training this model, the datasets were joined. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 1024 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 - lr_scheduler_type: polynomial - max num_epochs: 150 - early stopping limit: 20 - early stopping tol: 0.0001 - mixed_precision_training: Native AMP ### Training results The final model was saved after epoch 53 (shown in bold) where the overall F1 was the highest on the development set. \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| Date Precision \| Date Recall \| Date F1 \| Date Number \| Event Precision \| Event Recall \| Event F1 \| Event Number \| Gpe Precision \| Gpe Recall \| Gpe F1 \| Gpe Number \| Loc Precision \| Loc Recall \| Loc F1 \| Loc Number \| Money Precision \| Money Recall \| Money F1 \| Money Number \| Org Precision \| Org Recall \| Org F1 \| Org Number \| Per Precision \| Per Recall \| Per F1 \| Per Number \| Percent Precision \| Percent Recall \| Percent F1 \| Percent Number \| Prod Precision \| Prod Recall \| Prod F1 \| Prod Number \| Time Precision \| Time Recall \| Time F1 \| Time Number \| Title Precision \| Title Recall \| Title F1 \| Title Number \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|:--------------:\|:-----------:\|:-------:\|:-----------:\|:---------------:\|:------------:\|:--------:\|:------------:\|:-------------:\|:----------:\|:------:\|:----------:\|:-------------:\|:----------:\|:------:\|:----------:\|:---------------:\|:------------:\|:--------:\|:------------:\|:-------------:\|:----------:\|:------:\|:----------:\|:-------------:\|:----------:\|:------:\|:----------:\|:-----------------:\|:--------------:\|:----------:\|:--------------:\|:--------------:\|:-----------:\|:-------:\|:-----------:\|:--------------:\|:-----------:\|:-------:\|:-----------:\|:---------------:\|:------------:\|:--------:\|:------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 0.3252 \| 1 \| 1061 \| 0.1628 \| 0.6835 \| 0.6083 \| 0.6437 \| 0.9526 \| 0.5910 \| 0.6022 \| 0.5965 \| 372 \| 0.0 \| 0.0 \| 0.0 \| 28 \| 0.8073 \| 0.7631 \| 0.7846 \| 840 \| 0.1389 \| 0.0336 \| 0.0541 \| 149 \| 0.4217 \| 0.3977 \| 0.4094 \| 88 \| 0.5381 \| 0.5280 \| 0.5330 \| 589 \| 0.7917 \| 0.8655 \| 0.8270 \| 751 \| 0.6471 \| 0.3014 \| 0.4112 \| 73 \| 0.2581 \| 0.0724 \| 0.1131 \| 221 \| 0.1429 \| 0.0889 \| 0.1096 \| 45 \| 0.7805 \| 0.6702 \| 0.7211 \| 191 \| 0.6835 \| 0.6083 \| 0.6437 \| 0.9526 \| \| 0.1513 \| 2 \| 2122 \| 0.1332 \| 0.6906 \| 0.7329 \| 0.7111 \| 0.9615 \| 0.6185 \| 0.7366 \| 0.6724 \| 372 \| 0.0857 \| 0.1071 \| 0.0952 \| 28 \| 0.7874 \| 0.8595 \| 0.8219 \| 840 \| 0.4767 \| 0.2752 \| 0.3489 \| 149 \| 0.6848 \| 0.7159 \| 0.7000 \| 88 \| 0.6158 \| 0.6231 \| 0.6194 \| 589 \| 0.7770 \| 0.9001 \| 0.8341 \| 751 \| 0.9565 \| 0.9041 \| 0.9296 \| 73 \| 0.5 \| 0.3620 \| 0.4199 \| 221 \| 0.3571 \| 0.3333 \| 0.3448 \| 45 \| 0.6033 \| 0.7644 \| 0.6744 \| 191 \| 0.6906 \| 0.7329 \| 0.7111 \| 0.9615 \| \| 0.1131 \| 3 \| 3183 \| 0.1281 \| 0.7224 \| 0.7338 \| 0.7280 \| 0.9638 \| 0.7054 \| 0.7339 \| 0.7194 \| 372 \| 0.1053 \| 0.1429 \| 0.1212 \| 28 \| 0.8013 \| 0.85 \| 0.8250 \| 840 \| 0.5476 \| 0.3087 \| 0.3948 \| 149 \| 0.6386 \| 0.6023 \| 0.6199 \| 88 \| 0.6371 \| 0.6469 \| 0.6420 \| 589 \| 0.8235 \| 0.8762 \| 0.8490 \| 751 \| 0.9859 \| 0.9589 \| 0.9722 \| 73 \| 0.5148 \| 0.3937 \| 0.4462 \| 221 \| 0.5116 \| 0.4889 \| 0.5 \| 45 \| 0.6245 \| 0.7749 \| 0.6916 \| 191 \| 0.7224 \| 0.7338 \| 0.7280 \| 0.9638 \| \| 0.0884 \| 4 \| 4244 \| 0.1354 \| 0.7283 \| 0.7386 \| 0.7334 \| 0.9639 \| 0.6785 \| 0.6694 \| 0.6739 \| 372 \| 0.1795 \| 0.25 \| 0.2090 \| 28 \| 0.8231 \| 0.8310 \| 0.8270 \| 840 \| 0.6020 \| 0.3960 \| 0.4777 \| 149 \| 0.6092 \| 0.6023 \| 0.6057 \| 88 \| 0.6473 \| 0.7012 \| 0.6732 \| 589 \| 0.8351 \| 0.8628 \| 0.8487 \| 751 \| 1.0 \| 0.9726 \| 0.9861 \| 73 \| 0.5899 \| 0.4751 \| 0.5263 \| 221 \| 0.4524 \| 0.4222 \| 0.4368 \| 45 \| 0.6 \| 0.7853 \| 0.6803 \| 191 \| 0.7283 \| 0.7386 \| 0.7334 \| 0.9639 \| \| 0.0685 \| 5 \| 5305 \| 0.1383 \| 0.7224 \| 0.7696 \| 0.7453 \| 0.9644 \| 0.6635 \| 0.7473 \| 0.7029 \| 372 \| 0.26 \| 0.4643 \| 0.3333 \| 28 \| 0.8259 \| 0.8357 \| 0.8308 \| 840 \| 0.5913 \| 0.4564 \| 0.5152 \| 149 \| 0.6437 \| 0.6364 \| 0.64 \| 88 \| 0.6540 \| 0.7284 \| 0.6892 \| 589 \| 0.8070 \| 0.8961 \| 0.8492 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.5693 \| 0.5204 \| 0.5437 \| 221 \| 0.5192 \| 0.6 \| 0.5567 \| 45 \| 0.6320 \| 0.7644 \| 0.6919 \| 191 \| 0.7224 \| 0.7696 \| 0.7453 \| 0.9644 \| \| 0.0532 \| 6 \| 6366 \| 0.1493 \| 0.7099 \| 0.7613 \| 0.7347 \| 0.9631 \| 0.6727 \| 0.6962 \| 0.6843 \| 372 \| 0.2308 \| 0.5357 \| 0.3226 \| 28 \| 0.8242 \| 0.8262 \| 0.8252 \| 840 \| 0.5877 \| 0.4497 \| 0.5095 \| 149 \| 0.6410 \| 0.5682 \| 0.6024 \| 88 \| 0.6232 \| 0.7470 \| 0.6795 \| 589 \| 0.8087 \| 0.8895 \| 0.8472 \| 751 \| 0.9672 \| 0.8082 \| 0.8806 \| 73 \| 0.5107 \| 0.5385 \| 0.5242 \| 221 \| 0.6190 \| 0.5778 \| 0.5977 \| 45 \| 0.6371 \| 0.7906 \| 0.7056 \| 191 \| 0.7099 \| 0.7613 \| 0.7347 \| 0.9631 \| \| 0.0403 \| 7 \| 7427 \| 0.1592 \| 0.7239 \| 0.7592 \| 0.7411 \| 0.9642 \| 0.6923 \| 0.7016 \| 0.6969 \| 372 \| 0.2857 \| 0.5714 \| 0.3810 \| 28 \| 0.8272 \| 0.8262 \| 0.8267 \| 840 \| 0.5752 \| 0.4362 \| 0.4962 \| 149 \| 0.6265 \| 0.5909 \| 0.6082 \| 88 \| 0.6402 \| 0.6978 \| 0.6677 \| 589 \| 0.8404 \| 0.8762 \| 0.8579 \| 751 \| 0.9859 \| 0.9589 \| 0.9722 \| 73 \| 0.5257 \| 0.6018 \| 0.5612 \| 221 \| 0.5870 \| 0.6 \| 0.5934 \| 45 \| 0.6235 \| 0.8063 \| 0.7032 \| 191 \| 0.7239 \| 0.7592 \| 0.7411 \| 0.9642 \| \| 0.0304 \| 8 \| 8488 \| 0.1738 \| 0.7301 \| 0.7484 \| 0.7392 \| 0.9644 \| 0.6866 \| 0.6774 \| 0.6820 \| 372 \| 0.3409 \| 0.5357 \| 0.4167 \| 28 \| 0.8393 \| 0.8083 \| 0.8235 \| 840 \| 0.5882 \| 0.4698 \| 0.5224 \| 149 \| 0.6429 \| 0.6136 \| 0.6279 \| 88 \| 0.6608 \| 0.6978 \| 0.6788 \| 589 \| 0.8268 \| 0.8708 \| 0.8482 \| 751 \| 0.9595 \| 0.9726 \| 0.9660 \| 73 \| 0.5351 \| 0.5520 \| 0.5434 \| 221 \| 0.5208 \| 0.5556 \| 0.5376 \| 45 \| 0.6204 \| 0.7958 \| 0.6972 \| 191 \| 0.7301 \| 0.7484 \| 0.7392 \| 0.9644 \| \| 0.0234 \| 9 \| 9549 \| 0.1860 \| 0.7248 \| 0.7625 \| 0.7432 \| 0.9641 \| 0.6947 \| 0.7097 \| 0.7021 \| 372 \| 0.2963 \| 0.5714 \| 0.3902 \| 28 \| 0.8317 \| 0.8298 \| 0.8308 \| 840 \| 0.5913 \| 0.4564 \| 0.5152 \| 149 \| 0.6118 \| 0.5909 \| 0.6012 \| 88 \| 0.6361 \| 0.7063 \| 0.6693 \| 589 \| 0.8410 \| 0.8735 \| 0.8570 \| 751 \| 0.9859 \| 0.9589 \| 0.9722 \| 73 \| 0.5212 \| 0.6109 \| 0.5625 \| 221 \| 0.5417 \| 0.5778 \| 0.5591 \| 45 \| 0.6414 \| 0.7958 \| 0.7103 \| 191 \| 0.7248 \| 0.7625 \| 0.7432 \| 0.9641 \| \| 0.0178 \| 10 \| 10610 \| 0.2037 \| 0.7434 \| 0.7383 \| 0.7408 \| 0.9640 \| 0.7159 \| 0.6774 \| 0.6961 \| 372 \| 0.2857 \| 0.4286 \| 0.3429 \| 28 \| 0.8333 \| 0.8333 \| 0.8333 \| 840 \| 0.6262 \| 0.4497 \| 0.5234 \| 149 \| 0.6324 \| 0.4886 \| 0.5513 \| 88 \| 0.6568 \| 0.6757 \| 0.6661 \| 589 \| 0.8291 \| 0.8722 \| 0.8501 \| 751 \| 1.0 \| 0.8219 \| 0.9023 \| 73 \| 0.5672 \| 0.5158 \| 0.5403 \| 221 \| 0.5 \| 0.5333 \| 0.5161 \| 45 \| 0.6952 \| 0.7644 \| 0.7282 \| 191 \| 0.7434 \| 0.7383 \| 0.7408 \| 0.9640 \| \| 0.0147 \| 11 \| 11671 \| 0.2114 \| 0.7440 \| 0.7233 \| 0.7335 \| 0.9643 \| 0.7009 \| 0.6613 \| 0.6805 \| 372 \| 0.3030 \| 0.3571 \| 0.3279 \| 28 \| 0.8352 \| 0.8024 \| 0.8185 \| 840 \| 0.6238 \| 0.4228 \| 0.504 \| 149 \| 0.65 \| 0.5909 \| 0.6190 \| 88 \| 0.6436 \| 0.6469 \| 0.6452 \| 589 \| 0.8407 \| 0.8575 \| 0.8490 \| 751 \| 0.9315 \| 0.9315 \| 0.9315 \| 73 \| 0.5812 \| 0.5023 \| 0.5388 \| 221 \| 0.5476 \| 0.5111 \| 0.5287 \| 45 \| 0.6835 \| 0.7801 \| 0.7286 \| 191 \| 0.7440 \| 0.7233 \| 0.7335 \| 0.9643 \| \| 0.0118 \| 12 \| 12732 \| 0.2218 \| 0.7331 \| 0.7532 \| 0.7430 \| 0.9649 \| 0.7119 \| 0.6909 \| 0.7012 \| 372 \| 0.3488 \| 0.5357 \| 0.4225 \| 28 \| 0.8325 \| 0.8405 \| 0.8365 \| 840 \| 0.5303 \| 0.4698 \| 0.4982 \| 149 \| 0.65 \| 0.5909 \| 0.6190 \| 88 \| 0.6690 \| 0.6587 \| 0.6638 \| 589 \| 0.8178 \| 0.8908 \| 0.8528 \| 751 \| 0.9677 \| 0.8219 \| 0.8889 \| 73 \| 0.5408 \| 0.5701 \| 0.5551 \| 221 \| 0.5102 \| 0.5556 \| 0.5319 \| 45 \| 0.6567 \| 0.8010 \| 0.7217 \| 191 \| 0.7331 \| 0.7532 \| 0.7430 \| 0.9649 \| \| 0.0093 \| 13 \| 13793 \| 0.2283 \| 0.7495 \| 0.7359 \| 0.7427 \| 0.9644 \| 0.7163 \| 0.6989 \| 0.7075 \| 372 \| 0.3810 \| 0.5714 \| 0.4571 \| 28 \| 0.8612 \| 0.7905 \| 0.8243 \| 840 \| 0.6111 \| 0.4430 \| 0.5136 \| 149 \| 0.6145 \| 0.5795 \| 0.5965 \| 88 \| 0.6775 \| 0.6740 \| 0.6757 \| 589 \| 0.8346 \| 0.8802 \| 0.8568 \| 751 \| 0.9710 \| 0.9178 \| 0.9437 \| 73 \| 0.5619 \| 0.5339 \| 0.5476 \| 221 \| 0.4 \| 0.4889 \| 0.4400 \| 45 \| 0.6812 \| 0.7382 \| 0.7085 \| 191 \| 0.7495 \| 0.7359 \| 0.7427 \| 0.9644 \| \| 0.0079 \| 14 \| 14854 \| 0.2383 \| 0.7371 \| 0.7490 \| 0.7430 \| 0.9647 \| 0.6727 \| 0.7016 \| 0.6868 \| 372 \| 0.3261 \| 0.5357 \| 0.4054 \| 28 \| 0.8453 \| 0.8 \| 0.8220 \| 840 \| 0.5963 \| 0.4362 \| 0.5039 \| 149 \| 0.625 \| 0.5682 \| 0.5952 \| 88 \| 0.6634 \| 0.6927 \| 0.6777 \| 589 \| 0.8433 \| 0.8815 \| 0.8620 \| 751 \| 0.9853 \| 0.9178 \| 0.9504 \| 73 \| 0.5427 \| 0.5747 \| 0.5582 \| 221 \| 0.5814 \| 0.5556 \| 0.5682 \| 45 \| 0.6513 \| 0.8115 \| 0.7226 \| 191 \| 0.7371 \| 0.7490 \| 0.7430 \| 0.9647 \| \| 0.0068 \| 15 \| 15915 \| 0.2511 \| 0.7255 \| 0.7359 \| 0.7306 \| 0.9639 \| 0.6826 \| 0.6532 \| 0.6676 \| 372 \| 0.3590 \| 0.5 \| 0.4179 \| 28 \| 0.8295 \| 0.8167 \| 0.8230 \| 840 \| 0.5263 \| 0.4698 \| 0.4965 \| 149 \| 0.6575 \| 0.5455 \| 0.5963 \| 88 \| 0.6549 \| 0.6604 \| 0.6577 \| 589 \| 0.8242 \| 0.8802 \| 0.8513 \| 751 \| 0.9833 \| 0.8082 \| 0.8872 \| 73 \| 0.5398 \| 0.5520 \| 0.5459 \| 221 \| 0.36 \| 0.4 \| 0.3789 \| 45 \| 0.6511 \| 0.8010 \| 0.7183 \| 191 \| 0.7255 \| 0.7359 \| 0.7306 \| 0.9639 \| \| 0.0061 \| 16 \| 16976 \| 0.2497 \| 0.7253 \| 0.7690 \| 0.7465 \| 0.9648 \| 0.6824 \| 0.6989 \| 0.6906 \| 372 \| 0.3333 \| 0.5357 \| 0.4110 \| 28 \| 0.8473 \| 0.8321 \| 0.8396 \| 840 \| 0.4583 \| 0.5168 \| 0.4858 \| 149 \| 0.6494 \| 0.5682 \| 0.6061 \| 88 \| 0.6556 \| 0.7368 \| 0.6938 \| 589 \| 0.8382 \| 0.8828 \| 0.8599 \| 751 \| 0.9841 \| 0.8493 \| 0.9118 \| 73 \| 0.5341 \| 0.6380 \| 0.5814 \| 221 \| 0.5 \| 0.5333 \| 0.5161 \| 45 \| 0.6622 \| 0.7801 \| 0.7163 \| 191 \| 0.7253 \| 0.7690 \| 0.7465 \| 0.9648 \| \| 0.0054 \| 17 \| 18037 \| 0.2554 \| 0.7323 \| 0.7625 \| 0.7471 \| 0.9650 \| 0.6870 \| 0.6962 \| 0.6916 \| 372 \| 0.3421 \| 0.4643 \| 0.3939 \| 28 \| 0.8463 \| 0.8262 \| 0.8361 \| 840 \| 0.5902 \| 0.4832 \| 0.5314 \| 149 \| 0.6753 \| 0.5909 \| 0.6303 \| 88 \| 0.6640 \| 0.7148 \| 0.6885 \| 589 \| 0.8317 \| 0.8948 \| 0.8621 \| 751 \| 0.9437 \| 0.9178 \| 0.9306 \| 73 \| 0.5210 \| 0.5611 \| 0.5403 \| 221 \| 0.5 \| 0.5111 \| 0.5055 \| 45 \| 0.6102 \| 0.8115 \| 0.6966 \| 191 \| 0.7323 \| 0.7625 \| 0.7471 \| 0.9650 \| \| 0.005 \| 18 \| 19098 \| 0.2601 \| 0.7273 \| 0.7747 \| 0.7503 \| 0.9654 \| 0.6970 \| 0.7608 \| 0.7275 \| 372 \| 0.2830 \| 0.5357 \| 0.3704 \| 28 \| 0.8320 \| 0.8488 \| 0.8403 \| 840 \| 0.5841 \| 0.4430 \| 0.5038 \| 149 \| 0.6477 \| 0.6477 \| 0.6477 \| 88 \| 0.6378 \| 0.6995 \| 0.6672 \| 589 \| 0.8501 \| 0.8908 \| 0.8700 \| 751 \| 0.9722 \| 0.9589 \| 0.9655 \| 73 \| 0.5323 \| 0.5973 \| 0.5629 \| 221 \| 0.4444 \| 0.4444 \| 0.4444 \| 45 \| 0.624 \| 0.8168 \| 0.7075 \| 191 \| 0.7273 \| 0.7747 \| 0.7503 \| 0.9654 \| \| 0.0044 \| 19 \| 20159 \| 0.2602 \| 0.7369 \| 0.7616 \| 0.7490 \| 0.9656 \| 0.7124 \| 0.7124 \| 0.7124 \| 372 \| 0.3415 \| 0.5 \| 0.4058 \| 28 \| 0.8239 \| 0.8631 \| 0.8430 \| 840 \| 0.6355 \| 0.4564 \| 0.5313 \| 149 \| 0.6667 \| 0.6136 \| 0.6391 \| 88 \| 0.6517 \| 0.6638 \| 0.6577 \| 589 \| 0.8405 \| 0.8842 \| 0.8618 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.5144 \| 0.5656 \| 0.5388 \| 221 \| 0.5217 \| 0.5333 \| 0.5275 \| 45 \| 0.6550 \| 0.7853 \| 0.7143 \| 191 \| 0.7369 \| 0.7616 \| 0.7490 \| 0.9656 \| \| 0.004 \| 20 \| 21220 \| 0.2677 \| 0.7347 \| 0.7702 \| 0.7520 \| 0.9658 \| 0.7374 \| 0.7097 \| 0.7233 \| 372 \| 0.2857 \| 0.4286 \| 0.3429 \| 28 \| 0.8466 \| 0.8345 \| 0.8405 \| 840 \| 0.6050 \| 0.4832 \| 0.5373 \| 149 \| 0.6667 \| 0.6136 \| 0.6391 \| 88 \| 0.6593 \| 0.7131 \| 0.6852 \| 589 \| 0.8240 \| 0.8975 \| 0.8591 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.4981 \| 0.5837 \| 0.5375 \| 221 \| 0.5102 \| 0.5556 \| 0.5319 \| 45 \| 0.6371 \| 0.8272 \| 0.7198 \| 191 \| 0.7347 \| 0.7702 \| 0.7520 \| 0.9658 \| \| 0.0034 \| 21 \| 22281 \| 0.2743 \| 0.7386 \| 0.7717 \| 0.7548 \| 0.9657 \| 0.6984 \| 0.7097 \| 0.704 \| 372 \| 0.3784 \| 0.5 \| 0.4308 \| 28 \| 0.8475 \| 0.8333 \| 0.8403 \| 840 \| 0.6333 \| 0.5101 \| 0.5651 \| 149 \| 0.6190 \| 0.5909 \| 0.6047 \| 88 \| 0.6512 \| 0.7385 \| 0.6921 \| 589 \| 0.8428 \| 0.8921 \| 0.8668 \| 751 \| 0.9846 \| 0.8767 \| 0.9275 \| 73 \| 0.5513 \| 0.5837 \| 0.5670 \| 221 \| 0.5106 \| 0.5333 \| 0.5217 \| 45 \| 0.6379 \| 0.8115 \| 0.7143 \| 191 \| 0.7386 \| 0.7717 \| 0.7548 \| 0.9657 \| \| 0.0033 \| 22 \| 23342 \| 0.2788 \| 0.7418 \| 0.7520 \| 0.7469 \| 0.9652 \| 0.7143 \| 0.6989 \| 0.7065 \| 372 \| 0.3182 \| 0.5 \| 0.3889 \| 28 \| 0.8367 \| 0.8298 \| 0.8332 \| 840 \| 0.6168 \| 0.4430 \| 0.5156 \| 149 \| 0.6235 \| 0.6023 \| 0.6127 \| 88 \| 0.6758 \| 0.6689 \| 0.6724 \| 589 \| 0.8327 \| 0.8815 \| 0.8564 \| 751 \| 0.9714 \| 0.9315 \| 0.9510 \| 73 \| 0.5458 \| 0.5928 \| 0.5683 \| 221 \| 0.4783 \| 0.4889 \| 0.4835 \| 45 \| 0.6637 \| 0.7853 \| 0.7194 \| 191 \| 0.7418 \| 0.7520 \| 0.7469 \| 0.9652 \| \| 0.0033 \| 23 \| 24403 \| 0.2831 \| 0.7342 \| 0.7535 \| 0.7437 \| 0.9650 \| 0.6981 \| 0.6962 \| 0.6972 \| 372 \| 0.3784 \| 0.5 \| 0.4308 \| 28 \| 0.8499 \| 0.8024 \| 0.8255 \| 840 \| 0.5034 \| 0.4966 \| 0.5 \| 149 \| 0.6067 \| 0.6136 \| 0.6102 \| 88 \| 0.6581 \| 0.6961 \| 0.6766 \| 589 \| 0.8350 \| 0.8961 \| 0.8645 \| 751 \| 0.9714 \| 0.9315 \| 0.9510 \| 73 \| 0.5424 \| 0.5792 \| 0.5602 \| 221 \| 0.3774 \| 0.4444 \| 0.4082 \| 45 \| 0.7048 \| 0.7749 \| 0.7382 \| 191 \| 0.7342 \| 0.7535 \| 0.7437 \| 0.9650 \| \| 0.0029 \| 24 \| 25464 \| 0.2931 \| 0.7544 \| 0.7380 \| 0.7461 \| 0.9648 \| 0.7365 \| 0.6989 \| 0.7172 \| 372 \| 0.3590 \| 0.5 \| 0.4179 \| 28 \| 0.8535 \| 0.7976 \| 0.8246 \| 840 \| 0.5849 \| 0.4161 \| 0.4863 \| 149 \| 0.6622 \| 0.5568 \| 0.6049 \| 88 \| 0.6672 \| 0.6706 \| 0.6689 \| 589 \| 0.8474 \| 0.8802 \| 0.8635 \| 751 \| 0.9701 \| 0.8904 \| 0.9286 \| 73 \| 0.5550 \| 0.5475 \| 0.5513 \| 221 \| 0.4889 \| 0.4889 \| 0.4889 \| 45 \| 0.7023 \| 0.7906 \| 0.7438 \| 191 \| 0.7544 \| 0.7380 \| 0.7461 \| 0.9648 \| \| 0.0028 \| 25 \| 26525 \| 0.2899 \| 0.7489 \| 0.7574 \| 0.7531 \| 0.9654 \| 0.7021 \| 0.7097 \| 0.7059 \| 372 \| 0.3902 \| 0.5714 \| 0.4638 \| 28 \| 0.8635 \| 0.8131 \| 0.8375 \| 840 \| 0.6182 \| 0.4564 \| 0.5251 \| 149 \| 0.6471 \| 0.625 \| 0.6358 \| 88 \| 0.6613 \| 0.6995 \| 0.6799 \| 589 \| 0.8454 \| 0.9028 \| 0.8731 \| 751 \| 0.9583 \| 0.9452 \| 0.9517 \| 73 \| 0.5681 \| 0.5475 \| 0.5576 \| 221 \| 0.4222 \| 0.4222 \| 0.4222 \| 45 \| 0.6608 \| 0.7853 \| 0.7177 \| 191 \| 0.7489 \| 0.7574 \| 0.7531 \| 0.9654 \| \| 0.0023 \| 26 \| 27586 \| 0.2922 \| 0.7413 \| 0.7532 \| 0.7472 \| 0.9649 \| 0.6897 \| 0.6989 \| 0.6943 \| 372 \| 0.35 \| 0.5 \| 0.4118 \| 28 \| 0.85 \| 0.8298 \| 0.8398 \| 840 \| 0.6161 \| 0.4631 \| 0.5287 \| 149 \| 0.6486 \| 0.5455 \| 0.5926 \| 88 \| 0.6486 \| 0.6927 \| 0.6700 \| 589 \| 0.8457 \| 0.8828 \| 0.8638 \| 751 \| 0.9853 \| 0.9178 \| 0.9504 \| 73 \| 0.5636 \| 0.5611 \| 0.5624 \| 221 \| 0.3958 \| 0.4222 \| 0.4086 \| 45 \| 0.6638 \| 0.7958 \| 0.7238 \| 191 \| 0.7413 \| 0.7532 \| 0.7472 \| 0.9649 \| \| 0.0021 \| 27 \| 28647 \| 0.2967 \| 0.7514 \| 0.7568 \| 0.7541 \| 0.9656 \| 0.7081 \| 0.7043 \| 0.7062 \| 372 \| 0.3659 \| 0.5357 \| 0.4348 \| 28 \| 0.8547 \| 0.8190 \| 0.8365 \| 840 \| 0.5641 \| 0.4430 \| 0.4962 \| 149 \| 0.6582 \| 0.5909 \| 0.6228 \| 88 \| 0.6677 \| 0.7097 \| 0.6881 \| 589 \| 0.8459 \| 0.8842 \| 0.8646 \| 751 \| 0.9710 \| 0.9178 \| 0.9437 \| 73 \| 0.5806 \| 0.5701 \| 0.5753 \| 221 \| 0.4898 \| 0.5333 \| 0.5106 \| 45 \| 0.7089 \| 0.7906 \| 0.7475 \| 191 \| 0.7514 \| 0.7568 \| 0.7541 \| 0.9656 \| \| 0.0025 \| 28 \| 29708 \| 0.2957 \| 0.7335 \| 0.7622 \| 0.7475 \| 0.9651 \| 0.7060 \| 0.7231 \| 0.7145 \| 372 \| 0.3077 \| 0.4286 \| 0.3582 \| 28 \| 0.8459 \| 0.8429 \| 0.8444 \| 840 \| 0.5069 \| 0.4899 \| 0.4983 \| 149 \| 0.6438 \| 0.5341 \| 0.5839 \| 88 \| 0.6838 \| 0.7012 \| 0.6924 \| 589 \| 0.8413 \| 0.8895 \| 0.8647 \| 751 \| 0.9552 \| 0.8767 \| 0.9143 \| 73 \| 0.4901 \| 0.5611 \| 0.5232 \| 221 \| 0.3818 \| 0.4667 \| 0.42 \| 45 \| 0.6580 \| 0.7958 \| 0.7204 \| 191 \| 0.7335 \| 0.7622 \| 0.7475 \| 0.9651 \| \| 0.0023 \| 29 \| 30769 \| 0.3049 \| 0.7455 \| 0.7544 \| 0.7499 \| 0.9654 \| 0.6997 \| 0.7392 \| 0.7190 \| 372 \| 0.3182 \| 0.5 \| 0.3889 \| 28 \| 0.8483 \| 0.8119 \| 0.8297 \| 840 \| 0.5630 \| 0.5101 \| 0.5352 \| 149 \| 0.6579 \| 0.5682 \| 0.6098 \| 88 \| 0.6791 \| 0.7114 \| 0.6949 \| 589 \| 0.8583 \| 0.8628 \| 0.8606 \| 751 \| 0.9853 \| 0.9178 \| 0.9504 \| 73 \| 0.5234 \| 0.5566 \| 0.5395 \| 221 \| 0.4565 \| 0.4667 \| 0.4615 \| 45 \| 0.7009 \| 0.7853 \| 0.7407 \| 191 \| 0.7455 \| 0.7544 \| 0.7499 \| 0.9654 \| \| 0.0018 \| 30 \| 31830 \| 0.3042 \| 0.7415 \| 0.7679 \| 0.7544 \| 0.9654 \| 0.6935 \| 0.7419 \| 0.7169 \| 372 \| 0.3333 \| 0.5 \| 0.4 \| 28 \| 0.8563 \| 0.8226 \| 0.8391 \| 840 \| 0.5878 \| 0.5168 \| 0.55 \| 149 \| 0.6582 \| 0.5909 \| 0.6228 \| 88 \| 0.6677 \| 0.7470 \| 0.7051 \| 589 \| 0.8544 \| 0.8828 \| 0.8684 \| 751 \| 0.9710 \| 0.9178 \| 0.9437 \| 73 \| 0.5300 \| 0.5204 \| 0.5251 \| 221 \| 0.4375 \| 0.4667 \| 0.4516 \| 45 \| 0.6417 \| 0.8063 \| 0.7146 \| 191 \| 0.7415 \| 0.7679 \| 0.7544 \| 0.9654 \| \| 0.0017 \| 31 \| 32891 \| 0.3071 \| 0.7540 \| 0.7481 \| 0.7510 \| 0.9660 \| 0.7083 \| 0.7312 \| 0.7196 \| 372 \| 0.4054 \| 0.5357 \| 0.4615 \| 28 \| 0.8552 \| 0.8226 \| 0.8386 \| 840 \| 0.6311 \| 0.4362 \| 0.5159 \| 149 \| 0.6220 \| 0.5795 \| 0.6 \| 88 \| 0.6734 \| 0.6757 \| 0.6746 \| 589 \| 0.8626 \| 0.8775 \| 0.8700 \| 751 \| 0.9855 \| 0.9315 \| 0.9577 \| 73 \| 0.5307 \| 0.5475 \| 0.5390 \| 221 \| 0.3830 \| 0.4 \| 0.3913 \| 45 \| 0.7019 \| 0.7644 \| 0.7318 \| 191 \| 0.7540 \| 0.7481 \| 0.7510 \| 0.9660 \| \| 0.0018 \| 32 \| 33952 \| 0.3190 \| 0.7499 \| 0.7553 \| 0.7526 \| 0.9656 \| 0.7182 \| 0.7124 \| 0.7152 \| 372 \| 0.3333 \| 0.5357 \| 0.4110 \| 28 \| 0.8586 \| 0.7952 \| 0.8257 \| 840 \| 0.6116 \| 0.4966 \| 0.5481 \| 149 \| 0.6463 \| 0.6023 \| 0.6235 \| 88 \| 0.6805 \| 0.6978 \| 0.6890 \| 589 \| 0.8360 \| 0.8895 \| 0.8619 \| 751 \| 0.9855 \| 0.9315 \| 0.9577 \| 73 \| 0.5633 \| 0.5837 \| 0.5733 \| 221 \| 0.5106 \| 0.5333 \| 0.5217 \| 45 \| 0.6711 \| 0.8010 \| 0.7303 \| 191 \| 0.7499 \| 0.7553 \| 0.7526 \| 0.9656 \| \| 0.0018 \| 33 \| 35013 \| 0.3094 \| 0.7460 \| 0.7774 \| 0.7614 \| 0.9665 \| 0.7147 \| 0.7473 \| 0.7306 \| 372 \| 0.3659 \| 0.5357 \| 0.4348 \| 28 \| 0.8556 \| 0.8393 \| 0.8474 \| 840 \| 0.6273 \| 0.4631 \| 0.5328 \| 149 \| 0.6506 \| 0.6136 \| 0.6316 \| 88 \| 0.6787 \| 0.7351 \| 0.7058 \| 589 \| 0.8344 \| 0.8988 \| 0.8654 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.5702 \| 0.6063 \| 0.5877 \| 221 \| 0.3036 \| 0.3778 \| 0.3366 \| 45 \| 0.6567 \| 0.8010 \| 0.7217 \| 191 \| 0.7460 \| 0.7774 \| 0.7614 \| 0.9665 \| \| 0.0015 \| 34 \| 36074 \| 0.3091 \| 0.7441 \| 0.7759 \| 0.7597 \| 0.9665 \| 0.7113 \| 0.7285 \| 0.7198 \| 372 \| 0.3404 \| 0.5714 \| 0.4267 \| 28 \| 0.8266 \| 0.8512 \| 0.8387 \| 840 \| 0.5405 \| 0.5369 \| 0.5387 \| 149 \| 0.6707 \| 0.625 \| 0.6471 \| 88 \| 0.6856 \| 0.7182 \| 0.7015 \| 589 \| 0.8517 \| 0.8868 \| 0.8689 \| 751 \| 1.0 \| 0.9452 \| 0.9718 \| 73 \| 0.5752 \| 0.5882 \| 0.5817 \| 221 \| 0.3878 \| 0.4222 \| 0.4043 \| 45 \| 0.6830 \| 0.8010 \| 0.7373 \| 191 \| 0.7441 \| 0.7759 \| 0.7597 \| 0.9665 \| \| 0.0015 \| 35 \| 37135 \| 0.3185 \| 0.7487 \| 0.7619 \| 0.7552 \| 0.9660 \| 0.6982 \| 0.7339 \| 0.7156 \| 372 \| 0.3415 \| 0.5 \| 0.4058 \| 28 \| 0.8685 \| 0.8179 \| 0.8424 \| 840 \| 0.5504 \| 0.4765 \| 0.5108 \| 149 \| 0.6353 \| 0.6136 \| 0.6243 \| 88 \| 0.6636 \| 0.7267 \| 0.6937 \| 589 \| 0.8654 \| 0.8815 \| 0.8734 \| 751 \| 1.0 \| 0.9315 \| 0.9645 \| 73 \| 0.55 \| 0.5475 \| 0.5488 \| 221 \| 0.3673 \| 0.4 \| 0.3830 \| 45 \| 0.6937 \| 0.8063 \| 0.7458 \| 191 \| 0.7487 \| 0.7619 \| 0.7552 \| 0.9660 \| \| 0.0015 \| 36 \| 38196 \| 0.3203 \| 0.7438 \| 0.7649 \| 0.7542 \| 0.9660 \| 0.6961 \| 0.7204 \| 0.7081 \| 372 \| 0.3659 \| 0.5357 \| 0.4348 \| 28 \| 0.8617 \| 0.8381 \| 0.8497 \| 840 \| 0.5203 \| 0.5168 \| 0.5185 \| 149 \| 0.6667 \| 0.5909 \| 0.6265 \| 88 \| 0.6710 \| 0.7063 \| 0.6882 \| 589 \| 0.8495 \| 0.8868 \| 0.8678 \| 751 \| 0.9710 \| 0.9178 \| 0.9437 \| 73 \| 0.5561 \| 0.5385 \| 0.5471 \| 221 \| 0.42 \| 0.4667 \| 0.4421 \| 45 \| 0.6568 \| 0.8115 \| 0.7260 \| 191 \| 0.7438 \| 0.7649 \| 0.7542 \| 0.9660 \| \| 0.0013 \| 37 \| 39257 \| 0.3298 \| 0.7315 \| 0.7732 \| 0.7518 \| 0.9656 \| 0.6915 \| 0.7231 \| 0.7070 \| 372 \| 0.3333 \| 0.5714 \| 0.4211 \| 28 \| 0.8654 \| 0.8190 \| 0.8416 \| 840 \| 0.4793 \| 0.5436 \| 0.5094 \| 149 \| 0.6582 \| 0.5909 \| 0.6228 \| 88 \| 0.6656 \| 0.7267 \| 0.6948 \| 589 \| 0.8289 \| 0.9028 \| 0.8642 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.5574 \| 0.5928 \| 0.5746 \| 221 \| 0.4043 \| 0.4222 \| 0.4130 \| 45 \| 0.6408 \| 0.8220 \| 0.7202 \| 191 \| 0.7315 \| 0.7732 \| 0.7518 \| 0.9656 \| \| 0.0012 \| 38 \| 40318 \| 0.3311 \| 0.7533 \| 0.7610 \| 0.7571 \| 0.9664 \| 0.7060 \| 0.7231 \| 0.7145 \| 372 \| 0.3571 \| 0.5357 \| 0.4286 \| 28 \| 0.8613 \| 0.8357 \| 0.8483 \| 840 \| 0.6339 \| 0.4765 \| 0.5441 \| 149 \| 0.6543 \| 0.6023 \| 0.6272 \| 88 \| 0.6528 \| 0.7182 \| 0.6839 \| 589 \| 0.8424 \| 0.8828 \| 0.8622 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.6031 \| 0.5294 \| 0.5639 \| 221 \| 0.4130 \| 0.4222 \| 0.4176 \| 45 \| 0.7122 \| 0.7644 \| 0.7374 \| 191 \| 0.7533 \| 0.7610 \| 0.7571 \| 0.9664 \| \| 0.0012 \| 39 \| 41379 \| 0.3328 \| 0.7444 \| 0.7553 \| 0.7498 \| 0.9657 \| 0.6818 \| 0.7258 \| 0.7031 \| 372 \| 0.3478 \| 0.5714 \| 0.4324 \| 28 \| 0.8561 \| 0.8143 \| 0.8347 \| 840 \| 0.6055 \| 0.4430 \| 0.5116 \| 149 \| 0.6582 \| 0.5909 \| 0.6228 \| 88 \| 0.6715 \| 0.7046 \| 0.6877 \| 589 \| 0.8461 \| 0.8708 \| 0.8583 \| 751 \| 0.9706 \| 0.9041 \| 0.9362 \| 73 \| 0.5665 \| 0.5973 \| 0.5815 \| 221 \| 0.4082 \| 0.4444 \| 0.4255 \| 45 \| 0.6770 \| 0.8010 \| 0.7338 \| 191 \| 0.7444 \| 0.7553 \| 0.7498 \| 0.9657 \| \| 0.0014 \| 40 \| 42440 \| 0.3415 \| 0.7421 \| 0.7437 \| 0.7429 \| 0.9641 \| 0.6931 \| 0.7043 \| 0.6987 \| 372 \| 0.3488 \| 0.5357 \| 0.4225 \| 28 \| 0.8422 \| 0.8262 \| 0.8341 \| 840 \| 0.6190 \| 0.4362 \| 0.5118 \| 149 \| 0.6622 \| 0.5568 \| 0.6049 \| 88 \| 0.6888 \| 0.6350 \| 0.6608 \| 589 \| 0.8175 \| 0.8828 \| 0.8489 \| 751 \| 1.0 \| 0.9178 \| 0.9571 \| 73 \| 0.5584 \| 0.5837 \| 0.5708 \| 221 \| 0.4043 \| 0.4222 \| 0.4130 \| 45 \| 0.6580 \| 0.7958 \| 0.7204 \| 191 \| 0.7421 \| 0.7437 \| 0.7429 \| 0.9641 \| \| 0.0013 \| 41 \| 43501 \| 0.3401 \| 0.7501 \| 0.7487 \| 0.7494 \| 0.9651 \| 0.6915 \| 0.7231 \| 0.7070 \| 372 \| 0.3421 \| 0.4643 \| 0.3939 \| 28 \| 0.8545 \| 0.8179 \| 0.8358 \| 840 \| 0.6346 \| 0.4430 \| 0.5217 \| 149 \| 0.6812 \| 0.5341 \| 0.5987 \| 88 \| 0.6728 \| 0.6808 \| 0.6768 \| 589 \| 0.8380 \| 0.8748 \| 0.8560 \| 751 \| 0.9710 \| 0.9178 \| 0.9437 \| 73 \| 0.5860 \| 0.5701 \| 0.5780 \| 221 \| 0.4423 \| 0.5111 \| 0.4742 \| 45 \| 0.6787 \| 0.7853 \| 0.7282 \| 191 \| 0.7501 \| 0.7487 \| 0.7494 \| 0.9651 \| \| 0.0011 \| 42 \| 44562 \| 0.3468 \| 0.7426 \| 0.7687 \| 0.7554 \| 0.9650 \| 0.6965 \| 0.7527 \| 0.7235 \| 372 \| 0.3488 \| 0.5357 \| 0.4225 \| 28 \| 0.8667 \| 0.8202 \| 0.8428 \| 840 \| 0.6408 \| 0.4430 \| 0.5238 \| 149 \| 0.6709 \| 0.6023 \| 0.6347 \| 88 \| 0.6902 \| 0.7148 \| 0.7023 \| 589 \| 0.8404 \| 0.8975 \| 0.8680 \| 751 \| 0.9444 \| 0.9315 \| 0.9379 \| 73 \| 0.5191 \| 0.6154 \| 0.5631 \| 221 \| 0.3469 \| 0.3778 \| 0.3617 \| 45 \| 0.6210 \| 0.8063 \| 0.7016 \| 191 \| 0.7426 \| 0.7687 \| 0.7554 \| 0.9650 \| \| 0.0015 \| 43 \| 45623 \| 0.3440 \| 0.7566 \| 0.7422 \| 0.7493 \| 0.9648 \| 0.6937 \| 0.7366 \| 0.7145 \| 372 \| 0.3846 \| 0.5357 \| 0.4478 \| 28 \| 0.8608 \| 0.8095 \| 0.8344 \| 840 \| 0.6082 \| 0.3960 \| 0.4797 \| 149 \| 0.7 \| 0.5568 \| 0.6203 \| 88 \| 0.6766 \| 0.6570 \| 0.6667 \| 589 \| 0.8317 \| 0.8881 \| 0.8590 \| 751 \| 0.9701 \| 0.8904 \| 0.9286 \| 73 \| 0.6224 \| 0.5520 \| 0.5851 \| 221 \| 0.3913 \| 0.4 \| 0.3956 \| 45 \| 0.7081 \| 0.7749 \| 0.74 \| 191 \| 0.7566 \| 0.7422 \| 0.7493 \| 0.9648 \| \| 0.0011 \| 44 \| 46684 \| 0.3354 \| 0.7565 \| 0.7640 \| 0.7602 \| 0.9664 \| 0.7062 \| 0.7366 \| 0.7211 \| 372 \| 0.3659 \| 0.5357 \| 0.4348 \| 28 \| 0.8483 \| 0.8452 \| 0.8468 \| 840 \| 0.6095 \| 0.4295 \| 0.5039 \| 149 \| 0.6883 \| 0.6023 \| 0.6424 \| 88 \| 0.6880 \| 0.6740 \| 0.6810 \| 589 \| 0.8517 \| 0.8948 \| 0.8727 \| 751 \| 0.9710 \| 0.9178 \| 0.9437 \| 73 \| 0.6238 \| 0.5928 \| 0.6079 \| 221 \| 0.3830 \| 0.4 \| 0.3913 \| 45 \| 0.65 \| 0.8168 \| 0.7239 \| 191 \| 0.7565 \| 0.7640 \| 0.7602 \| 0.9664 \| \| 0.0011 \| 45 \| 47745 \| 0.3347 \| 0.7485 \| 0.7622 \| 0.7553 \| 0.9655 \| 0.7088 \| 0.7392 \| 0.7237 \| 372 \| 0.3636 \| 0.5714 \| 0.4444 \| 28 \| 0.8603 \| 0.8286 \| 0.8441 \| 840 \| 0.5882 \| 0.4698 \| 0.5224 \| 149 \| 0.6023 \| 0.6023 \| 0.6023 \| 88 \| 0.6770 \| 0.6689 \| 0.6729 \| 589 \| 0.8417 \| 0.8921 \| 0.8662 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.6037 \| 0.5928 \| 0.5982 \| 221 \| 0.4583 \| 0.4889 \| 0.4731 \| 45 \| 0.6275 \| 0.8115 \| 0.7078 \| 191 \| 0.7485 \| 0.7622 \| 0.7553 \| 0.9655 \| \| 0.0011 \| 46 \| 48806 \| 0.3421 \| 0.7481 \| 0.7640 \| 0.7559 \| 0.9657 \| 0.7261 \| 0.7339 \| 0.7299 \| 372 \| 0.3171 \| 0.4643 \| 0.3768 \| 28 \| 0.8570 \| 0.8202 \| 0.8382 \| 840 \| 0.5691 \| 0.4698 \| 0.5147 \| 149 \| 0.6429 \| 0.6136 \| 0.6279 \| 88 \| 0.6769 \| 0.7114 \| 0.6937 \| 589 \| 0.8311 \| 0.8908 \| 0.8599 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.5714 \| 0.5611 \| 0.5662 \| 221 \| 0.5 \| 0.5556 \| 0.5263 \| 45 \| 0.6638 \| 0.7958 \| 0.7238 \| 191 \| 0.7481 \| 0.7640 \| 0.7559 \| 0.9657 \| \| 0.0009 \| 47 \| 49867 \| 0.3487 \| 0.7496 \| 0.7604 \| 0.7550 \| 0.9656 \| 0.7158 \| 0.7043 \| 0.7100 \| 372 \| 0.3409 \| 0.5357 \| 0.4167 \| 28 \| 0.86 \| 0.8190 \| 0.8390 \| 840 \| 0.5496 \| 0.4832 \| 0.5143 \| 149 \| 0.7162 \| 0.6023 \| 0.6543 \| 88 \| 0.6745 \| 0.7284 \| 0.7004 \| 589 \| 0.8346 \| 0.8802 \| 0.8568 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.5566 \| 0.5339 \| 0.5450 \| 221 \| 0.5349 \| 0.5111 \| 0.5227 \| 45 \| 0.6828 \| 0.8115 \| 0.7416 \| 191 \| 0.7496 \| 0.7604 \| 0.7550 \| 0.9656 \| \| 0.0009 \| 48 \| 50928 \| 0.3470 \| 0.7414 \| 0.7649 \| 0.7529 \| 0.9651 \| 0.7092 \| 0.7473 \| 0.7277 \| 372 \| 0.3333 \| 0.5357 \| 0.4110 \| 28 \| 0.8541 \| 0.8226 \| 0.8381 \| 840 \| 0.5847 \| 0.4631 \| 0.5169 \| 149 \| 0.6835 \| 0.6136 \| 0.6467 \| 88 \| 0.6801 \| 0.7148 \| 0.6970 \| 589 \| 0.8319 \| 0.8895 \| 0.8597 \| 751 \| 0.9571 \| 0.9178 \| 0.9371 \| 73 \| 0.5307 \| 0.5475 \| 0.5390 \| 221 \| 0.4583 \| 0.4889 \| 0.4731 \| 45 \| 0.6364 \| 0.8063 \| 0.7113 \| 191 \| 0.7414 \| 0.7649 \| 0.7529 \| 0.9651 \| \| 0.0011 \| 49 \| 51989 \| 0.3389 \| 0.7435 \| 0.7664 \| 0.7547 \| 0.9659 \| 0.6957 \| 0.7312 \| 0.7130 \| 372 \| 0.3590 \| 0.5 \| 0.4179 \| 28 \| 0.8561 \| 0.8286 \| 0.8421 \| 840 \| 0.6636 \| 0.4899 \| 0.5637 \| 149 \| 0.6136 \| 0.6136 \| 0.6136 \| 88 \| 0.6732 \| 0.6995 \| 0.6861 \| 589 \| 0.8251 \| 0.8921 \| 0.8573 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.5746 \| 0.5928 \| 0.5835 \| 221 \| 0.4348 \| 0.4444 \| 0.4396 \| 45 \| 0.6390 \| 0.8063 \| 0.7130 \| 191 \| 0.7435 \| 0.7664 \| 0.7547 \| 0.9659 \| \| 0.0009 \| 50 \| 53050 \| 0.3557 \| 0.7490 \| 0.7640 \| 0.7564 \| 0.9659 \| 0.6948 \| 0.6855 \| 0.6901 \| 372 \| 0.3947 \| 0.5357 \| 0.4545 \| 28 \| 0.8584 \| 0.8298 \| 0.8438 \| 840 \| 0.6455 \| 0.4765 \| 0.5483 \| 149 \| 0.6933 \| 0.5909 \| 0.6380 \| 88 \| 0.6745 \| 0.7317 \| 0.7020 \| 589 \| 0.8296 \| 0.8948 \| 0.8610 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.6082 \| 0.5339 \| 0.5687 \| 221 \| 0.4043 \| 0.4222 \| 0.4130 \| 45 \| 0.6270 \| 0.8272 \| 0.7133 \| 191 \| 0.7490 \| 0.7640 \| 0.7564 \| 0.9659 \| \| 0.0008 \| 51 \| 54111 \| 0.3492 \| 0.7516 \| 0.7601 \| 0.7558 \| 0.9662 \| 0.7104 \| 0.6989 \| 0.7046 \| 372 \| 0.3714 \| 0.4643 \| 0.4127 \| 28 \| 0.8545 \| 0.8321 \| 0.8432 \| 840 \| 0.6496 \| 0.5101 \| 0.5714 \| 149 \| 0.625 \| 0.5682 \| 0.5952 \| 88 \| 0.6722 \| 0.6893 \| 0.6806 \| 589 \| 0.8413 \| 0.8895 \| 0.8647 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.5611 \| 0.5611 \| 0.5611 \| 221 \| 0.4792 \| 0.5111 \| 0.4946 \| 45 \| 0.6724 \| 0.8168 \| 0.7376 \| 191 \| 0.7516 \| 0.7601 \| 0.7558 \| 0.9662 \| \| 0.0008 \| 52 \| 55172 \| 0.3432 \| 0.7526 \| 0.7625 \| 0.7575 \| 0.9661 \| 0.7044 \| 0.7366 \| 0.7201 \| 372 \| 0.3571 \| 0.5357 \| 0.4286 \| 28 \| 0.8610 \| 0.8262 \| 0.8433 \| 840 \| 0.6140 \| 0.4698 \| 0.5323 \| 149 \| 0.6667 \| 0.5909 \| 0.6265 \| 88 \| 0.6766 \| 0.6927 \| 0.6846 \| 589 \| 0.8403 \| 0.8895 \| 0.8642 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.5849 \| 0.5611 \| 0.5727 \| 221 \| 0.46 \| 0.5111 \| 0.4842 \| 45 \| 0.6681 \| 0.8115 \| 0.7329 \| 191 \| 0.7526 \| 0.7625 \| 0.7575 \| 0.9661 \| \| 0.0006 \| 53 \| 56233 \| 0.3565 \| 0.7615 \| 0.7747 \| 0.7681 \| 0.9672 \| 0.7305 \| 0.7285 \| 0.7295 \| 372 \| 0.3721 \| 0.5714 \| 0.4507 \| 28 \| 0.8679 \| 0.8369 \| 0.8521 \| 840 \| 0.6545 \| 0.4832 \| 0.5560 \| 149 \| 0.6625 \| 0.6023 \| 0.6310 \| 88 \| 0.6761 \| 0.7267 \| 0.7005 \| 589 \| 0.8255 \| 0.9068 \| 0.8642 \| 751 \| 1.0 \| 0.9589 \| 0.9790 \| 73 \| 0.6030 \| 0.5430 \| 0.5714 \| 221 \| 0.5682 \| 0.5556 \| 0.5618 \| 45 \| 0.7 \| 0.8063 \| 0.7494 \| 191 \| 0.7615 \| 0.7747 \| 0.7681 \| 0.9672 \| \| 0.0008 \| 54 \| 57294 \| 0.3480 \| 0.7590 \| 0.7631 \| 0.7610 \| 0.9668 \| 0.7452 \| 0.7312 \| 0.7381 \| 372 \| 0.3409 \| 0.5357 \| 0.4167 \| 28 \| 0.8589 \| 0.8190 \| 0.8385 \| 840 \| 0.5935 \| 0.4899 \| 0.5368 \| 149 \| 0.7027 \| 0.5909 \| 0.6420 \| 88 \| 0.6924 \| 0.6842 \| 0.6883 \| 589 \| 0.8432 \| 0.8948 \| 0.8682 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.5856 \| 0.5882 \| 0.5869 \| 221 \| 0.5102 \| 0.5556 \| 0.5319 \| 45 \| 0.6513 \| 0.8115 \| 0.7226 \| 191 \| 0.7590 \| 0.7631 \| 0.7610 \| 0.9668 \| \| 0.0008 \| 55 \| 58355 \| 0.3568 \| 0.7601 \| 0.7622 \| 0.7612 \| 0.9663 \| 0.7228 \| 0.7151 \| 0.7189 \| 372 \| 0.3571 \| 0.5357 \| 0.4286 \| 28 \| 0.8429 \| 0.8429 \| 0.8429 \| 840 \| 0.6634 \| 0.4497 \| 0.536 \| 149 \| 0.7 \| 0.5568 \| 0.6203 \| 88 \| 0.6828 \| 0.7165 \| 0.6993 \| 589 \| 0.8655 \| 0.8828 \| 0.8741 \| 751 \| 0.9853 \| 0.9178 \| 0.9504 \| 73 \| 0.5909 \| 0.5294 \| 0.5585 \| 221 \| 0.5106 \| 0.5333 \| 0.5217 \| 45 \| 0.6429 \| 0.8010 \| 0.7133 \| 191 \| 0.7601 \| 0.7622 \| 0.7612 \| 0.9663 \| \| 0.0009 \| 56 \| 59416 \| 0.3498 \| 0.7542 \| 0.7580 \| 0.7561 \| 0.9661 \| 0.7178 \| 0.7043 \| 0.7110 \| 372 \| 0.3409 \| 0.5357 \| 0.4167 \| 28 \| 0.8379 \| 0.8429 \| 0.8404 \| 840 \| 0.6634 \| 0.4497 \| 0.536 \| 149 \| 0.6322 \| 0.625 \| 0.6286 \| 88 \| 0.6895 \| 0.6825 \| 0.6860 \| 589 \| 0.8513 \| 0.8842 \| 0.8674 \| 751 \| 0.9577 \| 0.9315 \| 0.9444 \| 73 \| 0.5613 \| 0.5385 \| 0.5497 \| 221 \| 0.5111 \| 0.5111 \| 0.5111 \| 45 \| 0.6667 \| 0.8063 \| 0.7299 \| 191 \| 0.7542 \| 0.7580 \| 0.7561 \| 0.9661 \| \| 0.0007 \| 57 \| 60477 \| 0.3486 \| 0.7479 \| 0.7711 \| 0.7593 \| 0.9663 \| 0.7143 \| 0.7392 \| 0.7266 \| 372 \| 0.3571 \| 0.5357 \| 0.4286 \| 28 \| 0.8417 \| 0.8417 \| 0.8417 \| 840 \| 0.5923 \| 0.5168 \| 0.5520 \| 149 \| 0.6667 \| 0.6136 \| 0.6391 \| 88 \| 0.6720 \| 0.7165 \| 0.6935 \| 589 \| 0.8562 \| 0.8802 \| 0.8680 \| 751 \| 0.9714 \| 0.9315 \| 0.9510 \| 73 \| 0.5670 \| 0.5747 \| 0.5708 \| 221 \| 0.4583 \| 0.4889 \| 0.4731 \| 45 \| 0.6623 \| 0.8010 \| 0.7251 \| 191 \| 0.7479 \| 0.7711 \| 0.7593 \| 0.9663 \| \| 0.0007 \| 58 \| 61538 \| 0.3497 \| 0.7539 \| 0.7744 \| 0.7640 \| 0.9667 \| 0.7143 \| 0.7392 \| 0.7266 \| 372 \| 0.3659 \| 0.5357 \| 0.4348 \| 28 \| 0.8449 \| 0.8429 \| 0.8439 \| 840 \| 0.6429 \| 0.4832 \| 0.5517 \| 149 \| 0.6667 \| 0.5909 \| 0.6265 \| 88 \| 0.6708 \| 0.7267 \| 0.6976 \| 589 \| 0.8499 \| 0.8975 \| 0.8731 \| 751 \| 0.9714 \| 0.9315 \| 0.9510 \| 73 \| 0.6108 \| 0.5611 \| 0.5849 \| 221 \| 0.5 \| 0.4889 \| 0.4944 \| 45 \| 0.6525 \| 0.8063 \| 0.7213 \| 191 \| 0.7539 \| 0.7744 \| 0.7640 \| 0.9667 \| \| 0.0008 \| 59 \| 62599 \| 0.3581 \| 0.7474 \| 0.7762 \| 0.7615 \| 0.9662 \| 0.7183 \| 0.7473 \| 0.7325 \| 372 \| 0.3409 \| 0.5357 \| 0.4167 \| 28 \| 0.8439 \| 0.8429 \| 0.8434 \| 840 \| 0.5467 \| 0.5503 \| 0.5485 \| 149 \| 0.6709 \| 0.6023 \| 0.6347 \| 88 \| 0.6693 \| 0.7250 \| 0.6960 \| 589 \| 0.8454 \| 0.8881 \| 0.8662 \| 751 \| 0.9714 \| 0.9315 \| 0.9510 \| 73 \| 0.5961 \| 0.5475 \| 0.5708 \| 221 \| 0.5 \| 0.5333 \| 0.5161 \| 45 \| 0.6769 \| 0.8115 \| 0.7381 \| 191 \| 0.7474 \| 0.7762 \| 0.7615 \| 0.9662 \| \| 0.0007 \| 60 \| 63660 \| 0.3636 \| 0.7494 \| 0.7676 \| 0.7584 \| 0.9662 \| 0.7016 \| 0.7204 \| 0.7109 \| 372 \| 0.3488 \| 0.5357 \| 0.4225 \| 28 \| 0.8489 \| 0.8357 \| 0.8422 \| 840 \| 0.6 \| 0.4832 \| 0.5353 \| 149 \| 0.6538 \| 0.5795 \| 0.6145 \| 88 \| 0.6828 \| 0.7199 \| 0.7008 \| 589 \| 0.8476 \| 0.8815 \| 0.8642 \| 751 \| 0.9714 \| 0.9315 \| 0.9510 \| 73 \| 0.5579 \| 0.5882 \| 0.5727 \| 221 \| 0.4762 \| 0.4444 \| 0.4598 \| 45 \| 0.6797 \| 0.8220 \| 0.7441 \| 191 \| 0.7494 \| 0.7676 \| 0.7584 \| 0.9662 \| \| 0.0008 \| 61 \| 64721 \| 0.3646 \| 0.7538 \| 0.7574 \| 0.7556 \| 0.9660 \| 0.6854 \| 0.7204 \| 0.7025 \| 372 \| 0.3659 \| 0.5357 \| 0.4348 \| 28 \| 0.8573 \| 0.8369 \| 0.8470 \| 840 \| 0.6306 \| 0.4698 \| 0.5385 \| 149 \| 0.6667 \| 0.5909 \| 0.6265 \| 88 \| 0.6896 \| 0.6978 \| 0.6937 \| 589 \| 0.8495 \| 0.8722 \| 0.8607 \| 751 \| 0.9714 \| 0.9315 \| 0.9510 \| 73 \| 0.5728 \| 0.5520 \| 0.5622 \| 221 \| 0.375 \| 0.4 \| 0.3871 \| 45 \| 0.6830 \| 0.8010 \| 0.7373 \| 191 \| 0.7538 \| 0.7574 \| 0.7556 \| 0.9660 \| \| 0.0006 \| 62 \| 65782 \| 0.3697 \| 0.7510 \| 0.7460 \| 0.7485 \| 0.9651 \| 0.6885 \| 0.7070 \| 0.6976 \| 372 \| 0.4286 \| 0.5357 \| 0.4762 \| 28 \| 0.8663 \| 0.7869 \| 0.8247 \| 840 \| 0.5902 \| 0.4832 \| 0.5314 \| 149 \| 0.6757 \| 0.5682 \| 0.6173 \| 88 \| 0.6667 \| 0.6927 \| 0.6794 \| 589 \| 0.8432 \| 0.8948 \| 0.8682 \| 751 \| 0.9851 \| 0.9041 \| 0.9429 \| 73 \| 0.5829 \| 0.5566 \| 0.5694 \| 221 \| 0.3673 \| 0.4 \| 0.3830 \| 45 \| 0.6995 \| 0.7801 \| 0.7376 \| 191 \| 0.7510 \| 0.7460 \| 0.7485 \| 0.9651 \| \| 0.0006 \| 63 \| 66843 \| 0.3661 \| 0.7504 \| 0.7502 \| 0.7503 \| 0.9655 \| 0.6909 \| 0.6909 \| 0.6909 \| 372 \| 0.4286 \| 0.5357 \| 0.4762 \| 28 \| 0.8571 \| 0.8143 \| 0.8352 \| 840 \| 0.5814 \| 0.5034 \| 0.5396 \| 149 \| 0.6582 \| 0.5909 \| 0.6228 \| 88 \| 0.7013 \| 0.6655 \| 0.6829 \| 589 \| 0.8348 \| 0.8948 \| 0.8638 \| 751 \| 0.9571 \| 0.9178 \| 0.9371 \| 73 \| 0.5570 \| 0.5747 \| 0.5657 \| 221 \| 0.3830 \| 0.4 \| 0.3913 \| 45 \| 0.6786 \| 0.7958 \| 0.7325 \| 191 \| 0.7504 \| 0.7502 \| 0.7503 \| 0.9655 \| \| 0.0006 \| 64 \| 67904 \| 0.3711 \| 0.7404 \| 0.7628 \| 0.7514 \| 0.9656 \| 0.6911 \| 0.7097 \| 0.7003 \| 372 \| 0.3784 \| 0.5 \| 0.4308 \| 28 \| 0.8455 \| 0.8405 \| 0.8430 \| 840 \| 0.6 \| 0.5034 \| 0.5474 \| 149 \| 0.65 \| 0.5909 \| 0.6190 \| 88 \| 0.6667 \| 0.7029 \| 0.6843 \| 589 \| 0.8350 \| 0.8961 \| 0.8645 \| 751 \| 0.9714 \| 0.9315 \| 0.9510 \| 73 \| 0.5673 \| 0.5339 \| 0.5501 \| 221 \| 0.2917 \| 0.3111 \| 0.3011 \| 45 \| 0.6568 \| 0.8115 \| 0.7260 \| 191 \| 0.7404 \| 0.7628 \| 0.7514 \| 0.9656 \| \| 0.0007 \| 65 \| 68965 \| 0.3672 \| 0.7377 \| 0.7696 \| 0.7533 \| 0.9661 \| 0.7005 \| 0.7419 \| 0.7206 \| 372 \| 0.3333 \| 0.5357 \| 0.4110 \| 28 \| 0.8433 \| 0.8393 \| 0.8413 \| 840 \| 0.5839 \| 0.5369 \| 0.5594 \| 149 \| 0.6506 \| 0.6136 \| 0.6316 \| 88 \| 0.6840 \| 0.7131 \| 0.6983 \| 589 \| 0.8412 \| 0.8815 \| 0.8609 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.5427 \| 0.5747 \| 0.5582 \| 221 \| 0.3019 \| 0.3556 \| 0.3265 \| 45 \| 0.6360 \| 0.7958 \| 0.7070 \| 191 \| 0.7377 \| 0.7696 \| 0.7533 \| 0.9661 \| \| 0.0005 \| 66 \| 70026 \| 0.3768 \| 0.7496 \| 0.7520 \| 0.7508 \| 0.9657 \| 0.6903 \| 0.7070 \| 0.6985 \| 372 \| 0.3415 \| 0.5 \| 0.4058 \| 28 \| 0.8454 \| 0.8333 \| 0.8393 \| 840 \| 0.6372 \| 0.4832 \| 0.5496 \| 149 \| 0.6795 \| 0.6023 \| 0.6386 \| 88 \| 0.6914 \| 0.6655 \| 0.6782 \| 589 \| 0.8483 \| 0.8788 \| 0.8633 \| 751 \| 0.9577 \| 0.9315 \| 0.9444 \| 73 \| 0.5714 \| 0.5792 \| 0.5753 \| 221 \| 0.3 \| 0.3333 \| 0.3158 \| 45 \| 0.6696 \| 0.7958 \| 0.7273 \| 191 \| 0.7496 \| 0.7520 \| 0.7508 \| 0.9657 \| \| 0.0007 \| 67 \| 71087 \| 0.3682 \| 0.7461 \| 0.7664 \| 0.7561 \| 0.9656 \| 0.7094 \| 0.7285 \| 0.7188 \| 372 \| 0.3409 \| 0.5357 \| 0.4167 \| 28 \| 0.8563 \| 0.8369 \| 0.8465 \| 840 \| 0.6290 \| 0.5235 \| 0.5714 \| 149 \| 0.6974 \| 0.6023 \| 0.6463 \| 88 \| 0.6935 \| 0.6876 \| 0.6905 \| 589 \| 0.8363 \| 0.8842 \| 0.8595 \| 751 \| 0.9437 \| 0.9178 \| 0.9306 \| 73 \| 0.5175 \| 0.6018 \| 0.5565 \| 221 \| 0.4694 \| 0.5111 \| 0.4894 \| 45 \| 0.6483 \| 0.8010 \| 0.7166 \| 191 \| 0.7461 \| 0.7664 \| 0.7561 \| 0.9656 \| \| 0.0005 \| 68 \| 72148 \| 0.3815 \| 0.7590 \| 0.7416 \| 0.7502 \| 0.9654 \| 0.7092 \| 0.7016 \| 0.7054 \| 372 \| 0.4054 \| 0.5357 \| 0.4615 \| 28 \| 0.8489 \| 0.8095 \| 0.8288 \| 840 \| 0.6796 \| 0.4698 \| 0.5556 \| 149 \| 0.6456 \| 0.5795 \| 0.6108 \| 88 \| 0.6801 \| 0.6570 \| 0.6684 \| 589 \| 0.8476 \| 0.8815 \| 0.8642 \| 751 \| 0.9571 \| 0.9178 \| 0.9371 \| 73 \| 0.615 \| 0.5566 \| 0.5843 \| 221 \| 0.4348 \| 0.4444 \| 0.4396 \| 45 \| 0.6759 \| 0.7644 \| 0.7174 \| 191 \| 0.7590 \| 0.7416 \| 0.7502 \| 0.9654 \| \| 0.0006 \| 69 \| 73209 \| 0.3919 \| 0.7494 \| 0.7487 \| 0.7491 \| 0.9650 \| 0.6888 \| 0.6962 \| 0.6925 \| 372 \| 0.3590 \| 0.5 \| 0.4179 \| 28 \| 0.8416 \| 0.8095 \| 0.8252 \| 840 \| 0.5865 \| 0.5235 \| 0.5532 \| 149 \| 0.6901 \| 0.5568 \| 0.6164 \| 88 \| 0.6950 \| 0.6808 \| 0.6878 \| 589 \| 0.8490 \| 0.8908 \| 0.8694 \| 751 \| 1.0 \| 0.9041 \| 0.9496 \| 73 \| 0.5662 \| 0.5611 \| 0.5636 \| 221 \| 0.3265 \| 0.3556 \| 0.3404 \| 45 \| 0.6881 \| 0.7853 \| 0.7335 \| 191 \| 0.7494 \| 0.7487 \| 0.7491 \| 0.9650 \| \| 0.0006 \| 70 \| 74270 \| 0.3704 \| 0.7587 \| 0.7619 \| 0.7603 \| 0.9666 \| 0.6891 \| 0.7151 \| 0.7018 \| 372 \| 0.3947 \| 0.5357 \| 0.4545 \| 28 \| 0.8376 \| 0.8536 \| 0.8455 \| 840 \| 0.6697 \| 0.4899 \| 0.5659 \| 149 \| 0.6420 \| 0.5909 \| 0.6154 \| 88 \| 0.7018 \| 0.6791 \| 0.6903 \| 589 \| 0.8491 \| 0.8842 \| 0.8663 \| 751 \| 0.9857 \| 0.9452 \| 0.9650 \| 73 \| 0.6219 \| 0.5656 \| 0.5924 \| 221 \| 0.3913 \| 0.4 \| 0.3956 \| 45 \| 0.6802 \| 0.7906 \| 0.7312 \| 191 \| 0.7587 \| 0.7619 \| 0.7603 \| 0.9666 \| \| 0.0005 \| 71 \| 75331 \| 0.3841 \| 0.7501 \| 0.7634 \| 0.7567 \| 0.9659 \| 0.7005 \| 0.6855 \| 0.6929 \| 372 \| 0.4054 \| 0.5357 \| 0.4615 \| 28 \| 0.8531 \| 0.8298 \| 0.8413 \| 840 \| 0.6293 \| 0.4899 \| 0.5509 \| 149 \| 0.6410 \| 0.5682 \| 0.6024 \| 88 \| 0.6774 \| 0.7165 \| 0.6964 \| 589 \| 0.8264 \| 0.9001 \| 0.8617 \| 751 \| 0.9706 \| 0.9041 \| 0.9362 \| 73 \| 0.5882 \| 0.5882 \| 0.5882 \| 221 \| 0.4545 \| 0.4444 \| 0.4494 \| 45 \| 0.6864 \| 0.7906 \| 0.7348 \| 191 \| 0.7501 \| 0.7634 \| 0.7567 \| 0.9659 \| \| 0.0005 \| 72 \| 76392 \| 0.3830 \| 0.7605 \| 0.7496 \| 0.7550 \| 0.9655 \| 0.7036 \| 0.6828 \| 0.6930 \| 372 \| 0.3824 \| 0.4643 \| 0.4194 \| 28 \| 0.8618 \| 0.8238 \| 0.8424 \| 840 \| 0.6542 \| 0.4698 \| 0.5469 \| 149 \| 0.6582 \| 0.5909 \| 0.6228 \| 88 \| 0.6935 \| 0.6723 \| 0.6828 \| 589 \| 0.8476 \| 0.8815 \| 0.8642 \| 751 \| 0.9577 \| 0.9315 \| 0.9444 \| 73 \| 0.5830 \| 0.5882 \| 0.5856 \| 221 \| 0.4043 \| 0.4222 \| 0.4130 \| 45 \| 0.6892 \| 0.8010 \| 0.7409 \| 191 \| 0.7605 \| 0.7496 \| 0.7550 \| 0.9655 \| \| 0.0006 \| 73 \| 77453 \| 0.3839 \| 0.7611 \| 0.7547 \| 0.7579 \| 0.9661 \| 0.712 \| 0.7177 \| 0.7149 \| 372 \| 0.3429 \| 0.4286 \| 0.3810 \| 28 \| 0.8494 \| 0.8393 \| 0.8443 \| 840 \| 0.6542 \| 0.4698 \| 0.5469 \| 149 \| 0.6538 \| 0.5795 \| 0.6145 \| 88 \| 0.6877 \| 0.6655 \| 0.6764 \| 589 \| 0.8428 \| 0.8921 \| 0.8668 \| 751 \| 0.9710 \| 0.9178 \| 0.9437 \| 73 \| 0.6257 \| 0.5294 \| 0.5735 \| 221 \| 0.4468 \| 0.4667 \| 0.4565 \| 45 \| 0.6814 \| 0.8063 \| 0.7386 \| 191 \| 0.7611 \| 0.7547 \| 0.7579 \| 0.9661 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0
pietrolesci/t5v1_1-base-mnli	1cc56642ced2f861390ae57d00dcd0cd703a204b	2022-05-03T14:53:23.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	pietrolesci	null	pietrolesci/t5v1_1-base-mnli	7	null	transformers	14,386	## Overview T5-Base v1.1 model trained to generate hypotheses given a premise and a label. Below the settings used to train it ```yaml Experiment configurations ├── datasets │ └── mnli_train: │ dataset_name: multi_nli │ dataset_config_name: null │ cache_dir: null │ input_fields: │ - premise │ - hypothesis │ target_field: label │ train_subset_names: null │ val_subset_names: validation_matched │ test_subset_names: none │ train_val_split: null │ limit_train_samples: null │ limit_val_samples: null │ limit_test_samples: null │ sampling_kwargs: │ sampling_strategy: random │ seed: 42 │ replace: false │ align_labels_with_mapping: null │ avoid_consistency_check: false │ predict_label_mapping: null │ mnli: │ dataset_name: multi_nli │ dataset_config_name: null │ cache_dir: null │ input_fields: │ - premise │ - hypothesis │ target_field: label │ train_subset_names: none │ val_subset_names: none │ test_subset_names: validation_mismatched │ train_val_split: null │ limit_train_samples: null │ limit_val_samples: null │ limit_test_samples: null │ sampling_kwargs: │ sampling_strategy: random │ seed: 42 │ replace: false │ align_labels_with_mapping: null │ avoid_consistency_check: false │ predict_label_mapping: null │ ├── data │ └── _target_: src.task.nli.data.NLIGenerationData.from_config │ main_dataset_name: null │ use_additional_as_test: null │ dataloader: │ batch_size: 64 │ eval_batch_size: 100 │ num_workers: 16 │ pin_memory: true │ drop_last: false │ persistent_workers: false │ shuffle: true │ seed_dataloader: 42 │ replacement: false │ processing: │ preprocessing_num_workers: 16 │ preprocessing_batch_size: 1000 │ load_from_cache_file: true │ padding: longest │ truncation: longest_first │ max_source_length: 128 │ max_target_length: 128 │ template: 'premise: $premise $label hypothesis: ' │ tokenizer: │ _target_: transformers.AutoTokenizer.from_pretrained │ pretrained_model_name_or_path: google/t5-v1_1-base │ use_fast: true │ ├── task │ └── optimizer: │ name: Adafactor │ lr: 0.001 │ weight_decay: 0.0 │ no_decay: │ - bias │ - LayerNorm.weight │ decay_rate: -0.8 │ clip_threshold: 1.0 │ relative_step: false │ scale_parameter: false │ warmup_init: false │ scheduler: │ name: constant_schedule │ model: │ model_name_or_path: google/t5-v1_1-base │ checkpoint_path: null │ freeze: false │ seed_init_weight: 42 │ _target_: src.task.nli.NLIGenerationTask.from_config │ generation: │ max_length: 128 │ min_length: 3 │ do_sample: true │ early_stopping: false │ num_beams: 1 │ temperature: 1.0 │ top_k: 50 │ top_p: 0.95 │ repetition_penalty: null │ length_penalty: null │ no_repeat_ngram_size: null │ encoder_no_repeat_ngram_size: null │ num_return_sequences: 1 │ max_time: null │ max_new_tokens: null │ decoder_start_token_id: null │ use_cache: null │ num_beam_groups: null │ diversity_penalty: null │ ├── trainer │ └── _target_: pytorch_lightning.Trainer │ callbacks: │ lr_monitor: │ _target_: pytorch_lightning.callbacks.LearningRateMonitor │ logging_interval: step │ log_momentum: false │ model_checkpoint: │ _target_: pytorch_lightning.callbacks.ModelCheckpoint │ dirpath: ./checkpoints/ │ filename: nli_generator_mnli-epoch={epoch:02d}-val_loss={val/aggregated_loss:.2f} │ monitor: val/aggregated_loss │ mode: min │ verbose: false │ save_last: true │ save_top_k: 1 │ auto_insert_metric_name: false │ save_on_train_epoch_end: false │ rich_model_summary: │ _target_: pytorch_lightning.callbacks.RichModelSummary │ max_depth: 1 │ log_grad_norm: │ _target_: src.core.callbacks.LogGradNorm │ norm_type: 2 │ group_separator: / │ only_total: true │ on_step: true │ on_epoch: false │ prog_bar: true │ log_generated_text: │ _target_: src.core.callbacks.GenerateAndLogText │ dirpath: ./generated_text │ type: generated_text │ pop_keys_after_logging: true │ on_train: false │ on_validation: false │ on_test: true │ log_to_wandb: true │ wandb_log_dataset_sizes: │ _target_: src.core.callbacks.WandbLogDatasetSizes │ logger: │ wandb: │ _target_: pytorch_lightning.loggers.WandbLogger │ project: nli_debiasing │ entity: team_brushino │ name: nli_generator_mnli │ save_dir: ./ │ offline: false │ log_model: false │ group: mnli │ job_type: generator │ tags: │ - nli_generator_mnli │ - seed=42 │ - seed_dataloader=42 │ notes: nli_generator_mnli_time=02-24-53 │ enable_checkpointing: true │ enable_progress_bar: true │ enable_model_summary: true │ gradient_clip_val: 0.0 │ gradient_clip_algorithm: null │ accelerator: gpu │ devices: auto │ gpus: null │ auto_select_gpus: true │ accumulate_grad_batches: 1 │ max_epochs: 3 │ min_epochs: 1 │ max_steps: -1 │ min_steps: null │ max_time: null │ num_sanity_val_steps: 2 │ overfit_batches: 0.0 │ fast_dev_run: false │ limit_train_batches: 1.0 │ limit_val_batches: 1.0 │ limit_test_batches: 1.0 │ profiler: null │ detect_anomaly: false │ deterministic: false │ check_val_every_n_epoch: 1 │ val_check_interval: 0.1 │ log_every_n_steps: 10 │ move_metrics_to_cpu: false │ └── training └── run_val_before_fit: false run_val_after_fit: false run_test_before_fit: false run_test_after_fit: true lr: 0.001 seed: 42 show_batch: false batch_size: 64 eval_batch_size: 100 num_workers: 16 pin_memory: true drop_last: false persistent_workers: false shuffle: true seed_dataloader: 42 ignore_warnings: true experiment_name: nli_generator_mnli ```
laituan245/molt5-small-caption2smiles	5c4d1d5b1d819185a8e43d77cec8e9ebf2ae5853	2022-05-03T18:08:09.000Z	[ "pytorch", "t5", "text2text-generation", "arxiv:2204.11817", "transformers", "license:apache-2.0", "autotrain_compatible" ]	text2text-generation	false	laituan245	null	laituan245/molt5-small-caption2smiles	7	null	transformers	14,387	--- license: apache-2.0 --- This model can be used to generate a SMILES string from an input caption. ## Example Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-small-caption2smiles", model_max_length=512) model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-small-caption2smiles') input_text = 'The molecule is a monomethoxybenzene that is 2-methoxyphenol substituted by a hydroxymethyl group at position 4. It has a role as a plant metabolite. It is a member of guaiacols and a member of benzyl alcohols.' input_ids = tokenizer(input_text, return_tensors="pt").input_ids outputs = model.generate(input_ids, num_beams=5, max_length=512) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # The model will generate "COC1=C(C=CC(=C1)CCCO)O". The ground-truth is "COC1=C(C=CC(=C1)CO)O". ``` ## Paper For more information, please take a look at our paper. Paper: [Translation between Molecules and Natural Language](https://arxiv.org/abs/2204.11817) Authors: Carl Edwards\, Tuan Lai\, Kevin Ros, Garrett Honke, Heng Ji
praf-choub/bart-mofe-rl-xsum	9d07a8534f28cd87b72cc7d303786f658a986dde	2022-06-14T04:52:41.000Z	[ "pytorch", "bart", "text2text-generation", "en", "dataset:xsum", "arxiv:2110.07166", "transformers", "summarization", "license:bsd-3-clause", "autotrain_compatible" ]	summarization	false	praf-choub	null	praf-choub/bart-mofe-rl-xsum	7	null	transformers	14,388	--- language: en tags: - summarization license: bsd-3-clause datasets: - xsum --- Citation ``` @article{DBLP:journals/corr/abs-2110-07166, author = {Prafulla Kumar Choubey and Jesse Vig and Wenhao Liu and Nazneen Fatema Rajani}, title = {MoFE: Mixture of Factual Experts for Controlling Hallucinations in Abstractive Summarization}, journal = {CoRR}, volume = {abs/2110.07166}, year = {2021}, url = {https://arxiv.org/abs/2110.07166}, eprinttype = {arXiv}, eprint = {2110.07166}, timestamp = {Fri, 22 Oct 2021 13:33:09 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2110-07166.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
mrm8488/data2vec-text-base-finetuned-sst2	56b5cba6cf53835f194601f285e874875cc76419	2022-05-03T21:52:23.000Z	[ "pytorch", "tensorboard", "data2vec-text", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:mit", "model-index" ]	text-classification	false	mrm8488	null	mrm8488/data2vec-text-base-finetuned-sst2	7	null	transformers	14,389	--- license: mit tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: data2vec-text-base-finetuned-sst2 results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.9231651376146789 --- <!-- 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. --> # data2vec-text-base-finetuned-sst2 This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/facebook/data2vec-text-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3600 - Accuracy: 0.9232 ## 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: 1.1519343408010398e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.2865 \| 1.0 \| 4210 \| 0.2662 \| 0.9128 \| \| 0.2256 \| 2.0 \| 8420 \| 0.3698 \| 0.9002 \| \| 0.1676 \| 3.0 \| 12630 \| 0.3107 \| 0.9186 \| \| 0.1481 \| 4.0 \| 16840 \| 0.3425 \| 0.9186 \| \| 0.1429 \| 5.0 \| 21050 \| 0.3600 \| 0.9232 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
chrisvinsen/xlsr-wav2vec2-base-commonvoice-demo-colab-4	faf75890ac2d670314715047e7dc7a73a837814d	2022-05-04T00:35:19.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]	automatic-speech-recognition	false	chrisvinsen	null	chrisvinsen/xlsr-wav2vec2-base-commonvoice-demo-colab-4	7	null	transformers	14,390	Entry not found
eastmountaincode/generate	3f37fe0bd39e6e5a46528ea9ed5e786468b20519	2022-05-03T21:03:45.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	eastmountaincode	null	eastmountaincode/generate	7	null	transformers	14,391	Entry not found
Lauler/sentiment-classifier	8430d75ec3fe42eeaed6b7918b7c1b87a2a1a693	2022-05-03T23:28:00.000Z	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	false	Lauler	null	Lauler/sentiment-classifier	7	null	transformers	14,392	## Sentiment classifier Sentiment classifier for Swedish trained on ScandiSent dataset.
ml4pubmed/BioM-BERT-PubMed-PMC-Large_pub_section	5b4ca9ab5528d34cbaffbc5b693f9f99782d4068	2022-05-04T00:50:46.000Z	[ "pytorch", "electra", "text-classification", "en", "dataset:pubmed", "transformers" ]	text-classification	false	ml4pubmed	null	ml4pubmed/BioM-BERT-PubMed-PMC-Large_pub_section	7	null	transformers	14,393	--- language: - en datasets: - pubmed metrics: - f1 pipeline_tag: text-classification widget: - text: "Many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti-inflammatory actions." example_title: "BACKGROUND example" - text: "A total of 192 MI patients and 140 control persons were included." example_title: "METHODS example" - text: "MI patients had 18 % higher plasma levels of MAp44 (IQR 11-25 %) as compared to the healthy control group (p < 0. 001.)" example_title: "RESULTS example" - text: "The finding that a brief CB group intervention delivered by real-world providers significantly reduced MDD onset relative to both brochure control and bibliotherapy is very encouraging, although effects on continuous outcome measures were small or nonsignificant and approximately half the magnitude of those found in efficacy research, potentially because the present sample reported lower initial depression." example_title: "CONCLUSIONS example" - text: "In order to understand and update the prevalence of myopia in Taiwan, a nationwide survey was performed in 1995." example_title: "OBJECTIVE example" --- # BioM-BERT-PubMed-PMC-Large_pub_section - original model file name: textclassifer_BioM-BERT-PubMed-PMC-Large_pubmed_20k - This is a fine-tuned checkpoint of `sultan/BioM-BERT-PubMed-PMC-Large` for document section text classification - possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS, ## metadata ### training_metrics - date_run: Apr-23-2022_t-04 - huggingface_tag: sultan/BioM-BERT-PubMed-PMC-Large ### training_parameters - date_run: Apr-23-2022_t-04 - huggingface_tag: sultan/BioM-BERT-PubMed-PMC-Large
IsekaiMeta/dapprf4	59dc342176101e0194c043f8dfec8ced902a6413	2022-05-04T02:53:09.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	IsekaiMeta	null	IsekaiMeta/dapprf4	7	null	transformers	14,394	--- tags: - conversational --- #dapprf4
learningdude/wav2vec2-base-sound2	f86577c2acc89b4a19caa010f2d2a0ab0fcd88a7	2022-05-05T04:34:26.000Z	[ "pytorch", "tensorboard", "wav2vec2", "audio-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	audio-classification	false	learningdude	null	learningdude/wav2vec2-base-sound2	7	null	transformers	14,395	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-base-sound2 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-sound2 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5012 - Accuracy: 0.5357 ## 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: 9e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 1 \| 2.0762 \| 0.0714 \| \| No log \| 2.0 \| 2 \| 2.0638 \| 0.1429 \| \| No log \| 3.0 \| 3 \| 2.0387 \| 0.2143 \| \| No log \| 4.0 \| 4 \| 2.0124 \| 0.2143 \| \| No log \| 5.0 \| 5 \| 1.9864 \| 0.2143 \| \| No log \| 6.0 \| 6 \| 1.9609 \| 0.2143 \| \| No log \| 7.0 \| 7 \| 1.9235 \| 0.2143 \| \| No log \| 8.0 \| 8 \| 1.9379 \| 0.2143 \| \| No log \| 9.0 \| 9 \| 1.8627 \| 0.2857 \| \| 1.9713 \| 10.0 \| 10 \| 1.8277 \| 0.3214 \| \| 1.9713 \| 11.0 \| 11 \| 1.7765 \| 0.3571 \| \| 1.9713 \| 12.0 \| 12 \| 1.7204 \| 0.5 \| \| 1.9713 \| 13.0 \| 13 \| 1.6956 \| 0.5 \| \| 1.9713 \| 14.0 \| 14 \| 1.6602 \| 0.5357 \| \| 1.9713 \| 15.0 \| 15 \| 1.6277 \| 0.5714 \| \| 1.9713 \| 16.0 \| 16 \| 1.6053 \| 0.5 \| \| 1.9713 \| 17.0 \| 17 \| 1.5825 \| 0.5 \| \| 1.9713 \| 18.0 \| 18 \| 1.5656 \| 0.4286 \| \| 1.9713 \| 19.0 \| 19 \| 1.5616 \| 0.4643 \| \| 1.6334 \| 20.0 \| 20 \| 1.5613 \| 0.4286 \| \| 1.6334 \| 21.0 \| 21 \| 1.5419 \| 0.5 \| \| 1.6334 \| 22.0 \| 22 \| 1.5166 \| 0.5357 \| \| 1.6334 \| 23.0 \| 23 \| 1.5088 \| 0.5 \| \| 1.6334 \| 24.0 \| 24 \| 1.5052 \| 0.5 \| \| 1.6334 \| 25.0 \| 25 \| 1.5012 \| 0.5357 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 1.14.0 - Tokenizers 0.12.1
eastmountaincode/duneGenerationNoUser	4dec2e4acd76e80845b0f656ab09e61625f7a923	2022-05-05T20:42:32.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	eastmountaincode	null	eastmountaincode/duneGenerationNoUser	7	null	transformers	14,396	Entry not found
xingqiang/macbert-zh-address-match-finetuned	6e19b5ee0da301ef3b37ecb5b32f5873430c2087	2022-05-06T08:48:00.000Z	[ "pytorch", "bert", "feature-extraction", "transformers" ]	feature-extraction	false	xingqiang	null	xingqiang/macbert-zh-address-match-finetuned	7	null	transformers	14,397	Entry not found
DioLiu/distilbert-base-uncased-finetuned-sst2-with-unfamiliar-words	33a9dbc7c1595f282267e4b401cd16253331d7ec	2022-05-06T07:34:54.000Z	[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	false	DioLiu	null	DioLiu/distilbert-base-uncased-finetuned-sst2-with-unfamiliar-words	7	null	transformers	14,398	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-sst2-with-unfamiliar-words 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-sst2-with-unfamiliar-words This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0870 - Accuracy: 0.9866 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2917 \| 1.0 \| 975 \| 0.0703 \| 0.9778 \| \| 0.063 \| 2.0 \| 1950 \| 0.0815 \| 0.9821 \| \| 0.0233 \| 3.0 \| 2925 \| 0.0680 \| 0.9866 \| \| 0.0134 \| 4.0 \| 3900 \| 0.0817 \| 0.9866 \| \| 0.0054 \| 5.0 \| 4875 \| 0.0870 \| 0.9866 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
EAST/autotrain-maysix-828926405	78db205d25e32617202947fd8827a0111b39f1a1	2022-05-06T07:13:15.000Z	[ "pytorch", "bert", "text-classification", "zh", "dataset:EAST/autotrain-data-maysix", "transformers", "autotrain", "co2_eq_emissions" ]	text-classification	false	EAST	null	EAST/autotrain-maysix-828926405	7	null	transformers	14,399	--- tags: autotrain language: zh widget: - text: "I love AutoTrain 🤗" datasets: - EAST/autotrain-data-maysix co2_eq_emissions: 0.00258669198292644 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 828926405 - CO2 Emissions (in grams): 0.00258669198292644 ## Validation Metrics - Loss: 0.1797131597995758 - Accuracy: 0.9318181818181818 - Precision: 0.9047619047619048 - Recall: 0.95 - AUC: 0.9875 - F1: 0.9268292682926829 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/EAST/autotrain-maysix-828926405 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("EAST/autotrain-maysix-828926405", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("EAST/autotrain-maysix-828926405", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

clisi2000/distilbert-base-uncased-finetuned-clinc

51bb574b837b4c9abe9995c92f6b7267ba6f2f33

2022-03-25T06:23:40.000Z

[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:clinc_oos", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

clisi2000

null

clisi2000/distilbert-base-uncased-finetuned-clinc

7

null

transformers

14,300

--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9158064516129032 ---  # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7796 - Accuracy: 0.9158 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2883 | 1.0 | 318 | 3.2778 | 0.7390 | | 2.6185 | 2.0 | 636 | 1.8740 | 0.8232 | | 1.5423 | 3.0 | 954 | 1.1579 | 0.8890 | | 1.0131 | 4.0 | 1272 | 0.8629 | 0.9077 | | 0.7964 | 5.0 | 1590 | 0.7796 | 0.9158 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.2+cpu - Datasets 1.18.4 - Tokenizers 0.10.3

sanchit-gandhi/wav2vec2-2-rnd-regularisation

12b2319967b12a4540b5bd05a40af34b85f2134d

2022-03-26T06:45:45.000Z

[ "pytorch", "tensorboard", "speech-encoder-decoder", "automatic-speech-recognition", "dataset:librispeech_asr", "transformers", "generated_from_trainer", "model-index" ]

automatic-speech-recognition

false

sanchit-gandhi

null

sanchit-gandhi/wav2vec2-2-rnd-regularisation

7

null

transformers

14,301

--- tags: - generated_from_trainer datasets: - librispeech_asr model-index: - name: '' results: [] ---  # This model was trained from scratch on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 0.6977 - Wer: 0.1231 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - 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: 1000 - num_epochs: 25.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 6.1467 | 1.68 | 1500 | 6.0558 | 1.3243 | | 5.4388 | 3.36 | 3000 | 5.4711 | 1.5604 | | 3.3434 | 5.04 | 4500 | 3.4808 | 0.7461 | | 1.5259 | 6.73 | 6000 | 2.1931 | 0.3430 | | 1.4285 | 8.41 | 7500 | 1.5883 | 0.2784 | | 1.0687 | 10.09 | 9000 | 1.2481 | 0.2069 | | 0.6425 | 11.77 | 10500 | 1.0507 | 0.1758 | | 0.7147 | 13.45 | 12000 | 0.9397 | 0.1584 | | 0.5083 | 15.13 | 13500 | 0.8452 | 0.1453 | | 0.4287 | 16.82 | 15000 | 0.7915 | 0.1388 | | 0.3499 | 18.5 | 16500 | 0.7477 | 0.1315 | | 0.3733 | 20.18 | 18000 | 0.7307 | 0.1287 | | 0.2609 | 21.86 | 19500 | 0.7061 | 0.1263 | | 0.2602 | 23.54 | 21000 | 0.6977 | 0.1231 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0

ScandinavianMrT/gpt2_ONION_prefinetune_3.0

a2168168102ccbb4ac61ec2252476808bc4b64ae

2022-03-23T15:54:46.000Z

[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers" ]

text-generation

false

ScandinavianMrT

null

ScandinavianMrT/gpt2_ONION_prefinetune_3.0

7

null

transformers

14,302

Entry not found

shahrukhx01/gbert-hasoc-german-2019

f183c4cd25c5e6a0d89f0550b2fe7c15b03d4975

2022-03-23T18:18:56.000Z

[ "pytorch", "bert", "text-classification", "de", "transformers", "hate-speech-classification" ]

text-classification

false

shahrukhx01

null

shahrukhx01/gbert-hasoc-german-2019

7

null

transformers

14,303

--- language: "de" tags: - hate-speech-classification widget: - text: "Das ist der absolute Gipfel! Lächerliche 2,5 Jahre Haft für einen extremst sadistischen Mord. Ich fasse es nicht. Das sitzt der Killer auf der linken Arschbacke ab und lacht sich dabei kaputt. Unsere Justiz ist nur noch zum Kotzen." - text: "Das ist der absolute Gipfel! Lächerliche 2,5 Jahre Haft für einen extremst sadistischen Mord. Ich fasse es nicht. Das sitzt der Killer auf der linken Arschbacke ab und lacht sich dabei kaputt. Unsere Justiz ist nur noch zum Kotzen." --- # Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shahrukhx01/gbert-hasoc-german-2019") model = AutoModelForSequenceClassification.from_pretrained("shahrukhx01/gbert-hasoc-german-2019") ``` # Dataset ```bibtext @inproceedings{10.1145/3368567.3368584, author = {Mandl, Thomas and Modha, Sandip and Majumder, Prasenjit and Patel, Daksh and Dave, Mohana and Mandlia, Chintak and Patel, Aditya}, title = {Overview of the HASOC Track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages}, year = {2019}, isbn = {9781450377508}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3368567.3368584}, doi = {10.1145/3368567.3368584}, abstract = {The identification of Hate Speech in Social Media is of great importance and receives much attention in the text classification community. There is a huge demand for research for languages other than English. The HASOC track intends to stimulate development in Hate Speech for Hindi, German and English. Three datasets were developed from Twitter and Facebook and made available. Binary classification and more fine-grained subclasses were offered in 3 subtasks. For all subtasks, 321 experiments were submitted. The approaches used most often were LSTM networks processing word embedding input. The performance of the best system for identification of Hate Speech for English, Hindi, and German was a Marco-F1 score of 0.78, 0.81 and 0.61, respectively.}, booktitle = {Proceedings of the 11th Forum for Information Retrieval Evaluation}, pages = {14–17}, numpages = {4}, keywords = {Text Classification, Hate Speech, Evaluation, Deep Learning}, location = {Kolkata, India}, series = {FIRE '19} } ``` --- license: mit ---

tiennvcs/distilbert-base-uncased-finetuned-ner

d3b6e52068d367d457a2040e8a992e9ab9a142cc

2022-03-24T07:29:26.000Z

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

token-classification

false

tiennvcs

null

tiennvcs/distilbert-base-uncased-finetuned-ner

7

null

transformers

14,304

--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9264836138175376 - name: Recall type: recall value: 0.9361226087929299 - name: F1 type: f1 value: 0.9312781703856213 - name: Accuracy type: accuracy value: 0.9836529143565221 ---  # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0616 - Precision: 0.9265 - Recall: 0.9361 - F1: 0.9313 - Accuracy: 0.9837 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2437 | 1.0 | 878 | 0.0745 | 0.9144 | 0.9173 | 0.9158 | 0.9799 | | 0.0518 | 2.0 | 1756 | 0.0621 | 0.9177 | 0.9353 | 0.9264 | 0.9826 | | 0.03 | 3.0 | 2634 | 0.0616 | 0.9265 | 0.9361 | 0.9313 | 0.9837 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6

ScandinavianMrT/gpt2_prefinetune_SARC_2.0

348938280764c18065b8b55b1e4d54defdf6417a

2022-03-28T08:36:28.000Z

[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers" ]

text-generation

false

ScandinavianMrT

null

ScandinavianMrT/gpt2_prefinetune_SARC_2.0

7

null

transformers

14,305

Entry not found

Flag/joebiden

713e0686015661fdaa02a16631f0cd75375e63a9

2022-03-25T22:10:02.000Z

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

false

Flag

null

Flag/joebiden

7

null

transformers

14,306

Entry not found

SergeyKamenshchikov/nsi_tuned

d47d412b02ef7dd44fb70776a62ab9614a219393

2022-03-27T13:58:13.000Z

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

false

SergeyKamenshchikov

null

SergeyKamenshchikov/nsi_tuned

7

null

transformers

14,307

Entry not found

mrm8488/t5-base-iterater

309fe4132155899ee2a228bddfbcd1e645555114

2022-03-28T11:00:41.000Z

[ "pytorch", "tensorboard", "t5", "text2text-generation", "en", "dataset:wanyu/IteraTeR_full_sent", "transformers", "generated_from_trainer", "IteraTeR", "license:apache-2.0", "model-index", "autotrain_compatible" ]

text2text-generation

false

mrm8488

null

mrm8488/t5-base-iterater

7

1

transformers

14,308

--- license: apache-2.0 language: - en datasets: - wanyu/IteraTeR_full_sent tags: - generated_from_trainer - IteraTeR widget: - text: "<clarity> Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay for the packet has encountered." model-index: - name: t5-base-iterater results: [] ---  # T5 (base) fine-tuned on IteraTeR This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an [IteraTeR](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset. It achieves the following results on the evaluation set: - Loss: 0.2580 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.3286 | 0.09 | 2000 | 0.3010 | | 0.3194 | 0.18 | 4000 | 0.2872 | | 0.3208 | 0.27 | 6000 | 0.2792 | | 0.3091 | 0.36 | 8000 | 0.2731 | | 0.3164 | 0.45 | 10000 | 0.2678 | | 0.2941 | 0.54 | 12000 | 0.2682 | | 0.2981 | 0.63 | 14000 | 0.2696 | | 0.2975 | 0.72 | 16000 | 0.2643 | | 0.3109 | 0.81 | 18000 | 0.2624 | | 0.2965 | 0.9 | 20000 | 0.2648 | | 0.3053 | 0.99 | 22000 | 0.2627 | | 0.2779 | 1.08 | 24000 | 0.2632 | | 0.2692 | 1.17 | 26000 | 0.2608 | | 0.2755 | 1.26 | 28000 | 0.2600 | | 0.2771 | 1.35 | 30000 | 0.2584 | | 0.2774 | 1.44 | 32000 | 0.2609 | | 0.2976 | 1.53 | 34000 | 0.2593 | | 0.2646 | 1.62 | 36000 | 0.2616 | | 0.2705 | 1.71 | 38000 | 0.2574 | | 0.2714 | 1.8 | 40000 | 0.2577 | | 0.2857 | 1.9 | 42000 | 0.2576 | | 0.2832 | 1.99 | 44000 | 0.2580 | ### How to use ```py from transformers import T5ForConditionalGeneration, T5TokenizerFast MODEL_CKPT = 'mrm8488/t5-base-iterater' tokenizer = T5TokenizerFast.from_pretrained(MODEL_CKPT) model = T5ForConditionalGeneration.from_pretrained(MODEL_CKPT) def predict(intent, text): input_text = f"<{intent}> {text}" features = tokenizer([input_text], return_tensors='pt') output = model.generate(input_ids=features['input_ids'], attention_mask=features['attention_mask'], max_length=128, num_beams=8) return tokenizer.decode(output[0], skip_special_tokens=True) text = "Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay for the packet has encountered." intent = "clarity" predict(intent, text) # Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay the packet has encountered. ``` ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

hackathon-pln-es/es_text_neutralizer

6b9055aad684a42b14248badd06ad9d2ec7603aa

2022-04-01T12:38:43.000Z

[ "pytorch", "t5", "text2text-generation", "es", "dataset:hackathon-pln-es/neutral-es", "transformers", "Text2Text Generation", "Inclusive Language", "Text Neutralization", "license:apache-2.0", "model-index", "autotrain_compatible" ]

text2text-generation

false

hackathon-pln-es

null

hackathon-pln-es/es_text_neutralizer

7

5

transformers

14,309

--- language: - es license: apache-2.0 tags: - Text2Text Generation - Inclusive Language - Text Neutralization - pytorch datasets: - hackathon-pln-es/neutral-es metrics: - sacrebleu model-index: - name: es_text_neutralizer results: - task: type: Text2Text Generation name: Neutralization of texts in Spanish dataset: type: hackathon-pln-es/neutral-es name: neutral-es metrics: - type: sacrebleu value: 0.96 name: sacrebleu # Optional. Example: Test WER - type: bertscore # Required. Example: wer value: 0.98 name: BertScoreF1 # Optional. Example: Test WER - type: DiffBleu # Required. Example: wer value: 0.35 name: DiffBleu # Optional. Example: Test WER --- ## Model objective Spanish is a beautiful language and it has many ways of referring to people, neutralizing the genders and using some of the resources inside the language. One would say *Todas las personas asistentes* instead of *Todos los asistentes* and it would end in a more inclusive way for talking about people. The purpose of this collaboratively trained model is to create a solution that reinforces the UN objective of the gender equality. Given any input, our model will generate a gender neutral sentence, correcting any non-inclusive expressions or words. It's a straightforward and fast solution that creates a positive impact in the contemporary social panorama. <p align="center"> <img src="https://upload.wikimedia.org/wikipedia/commons/2/29/Gender_equality_symbol_%28clipart%29.png" width="250"/> </p> By using gender inclusive models we can help reducing gender bias in a language corpus by, for instance, adding data augmentation and creating different examples ## Training and evaluation data The data used for the model training has been created form a compilation of sources, obtained from a series of guidelines and manuals issued by Spanish Ministry of Health, Social Services and Equality in the matter of the usage of non-sexist language, stipulated in this linked [document:](https://www.inmujeres.gob.es/servRecursos/formacion/GuiasLengNoSexista/docs/Guiaslenguajenosexista_.pdf): ### Compiled sources [Guía para un discurso igualitario en la universidad de alicante](https://ieg.ua.es/es/documentos/normativasobreigualdad/guia-para-un-discurso-igualitario-en-la-ua.pdf) [Guía UC de Comunicación en Igualdad](<https://web.unican.es/unidades/igualdad/SiteAssets/igualdad/comunicacion-en-igualdad/guia%20comunicacion%20igualdad%20(web).pdf>) [Buenas prácticas para el tratamiento del lenguaje en igualdad](https://e-archivo.uc3m.es/handle/10016/22811) [Guía del lenguaje no sexista de la Universidad de Castilla-La Mancha](https://unidadigualdad.ugr.es/page/guiialenguajeuniversitarionosexista_universidaddecastillalamancha/!) [Guía de Lenguaje Para el Ámbito Educativo](https://www.educacionyfp.gob.es/va/dam/jcr:8ce318fd-c8ff-4ad2-97b4-7318c27d1682/guialenguajeambitoeducativo.pdf) [Guía para un uso igualitario y no sexista del lenguaje y dela imagen en la Universidad de Jaén](https://www.ujaen.es/servicios/uigualdad/sites/servicio_uigualdad/files/uploads/Guia_lenguaje_no_sexista.pdf) [Guía de uso no sexista del vocabulario español](https://www.um.es/documents/2187255/2187763/guia-leng-no-sexista.pdf/d5b22eb9-b2e4-4f4b-82aa-8a129cdc83e3) [Guía para el uso no sexista de la lengua castellana y de imágnes en la UPV/EHV](https://www.ehu.eus/documents/1734204/1884196/Guia_uso_no_sexista_EHU.pdf) [Guía de lenguaje no sexista UNED](http://portal.uned.es/pls/portal/docs/PAGE/UNED_MAIN/LAUNIVERSIDAD/VICERRECTORADOS/GERENCIA/OFICINA_IGUALDAD/CONCEPTOS%20BASICOS/GUIA_LENGUAJE.PDF) [COMUNICACIÓN AMBIENTAL CON PERSPECTIVA DE GÉNERO](https://cima.cantabria.es/documents/5710649/5729124/COMUNICACI%C3%93N+AMBIENTAL+CON+PERSPECTIVA+DE+G%C3%89NERO.pdf/ccc18730-53e3-35b9-731e-b4c43339254b) [Recomendaciones para la utilización de lenguaje no sexista](https://www.csic.es/sites/default/files/guia_para_un_uso_no_sexista_de_la_lengua_adoptada_por_csic2.pdf) [Estudio sobre lenguaje y contenido sexista en la Web](https://www.mujeresenred.net/IMG/pdf/Estudio_paginas_web_T-incluye_ok.pdf) [Nombra.en.red. En femenino y en masculino](https://www.inmujeres.gob.es/areasTematicas/educacion/publicaciones/serieLenguaje/docs/Nombra_en_red.pdf) ## Model specs This model is a fine-tuned version of [spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on the data described below. It achieves the following results on the evaluation set: - 'eval_bleu': 93.8347, - 'eval_f1': 0.9904, ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-04 - train_batch_size: 32 - seed: 42 - num_epochs: 10 - weight_decay: 0,01 ## Metrics For training, we used both Blue (sacrebleu implementation in HF) and BertScore. The first one, a standard in Machine Translation processes, has been added for ensuring robustness of the newly generated data, while the second one is kept for keeping the expected semantic similarity. However, given the actual use case, we expect generated segments to be very close to input segments and to label segments in training. As an example, we can take the following: inputSegment = 'De acuerdo con las informaciones anteriores , las alumnas se han quejado de la actitud de los profesores en los exámenes finales. Los representantes estudiantiles son los alumnos Juanju y Javi.' expectedOutput (label) = 'De acuerdo con las informaciones anteriores, el alumnado se ha quejado de la actitud del profesorado en los exámenes finales. Los representantes estudiantiles son los alumnos Juanju y Javi.' actualOutput = 'De acuerdo con las informaciones anteriores, el alumnado se ha quejado de la actitud del profesorado en los exámenes finales. Los representantes estudiantiles son el alumnado Juanju y Javi.' As you can see, segments are pretty similar. So, instead of measuring Bleu or BertScore here, we propose an alternate metric that would be DiffBleu: $$DiffBleu = BLEU(actualOutput - inputSegment, labels - inputSegment)$$ Where the minuses as in set notation. This way, we also evaluate DiffBleu after the model has been trained. ## Team Members - Fernando Velasco [(fermaat)](https://huggingface.co/fermaat) - Cibeles Redondo [(CibelesR)](https://huggingface.co/CibelesR) - Juan Julian Cea [(Juanju)](https://huggingface.co/Juanju) - Magdalena Kujalowicz [(MacadellaCosta)](https://huggingface.co/MacadellaCosta) - Javier Blasco [(javiblasco)](https://huggingface.co/javiblasco) Enjoy!

DrishtiSharma/wav2vec2-base-finetuned-sentiment-mesd-v9

4c98d2b04b8fe342151f614800e7abc4fb63304c

2022-03-29T00:52:52.000Z

[ "pytorch", "tensorboard", "wav2vec2", "audio-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

audio-classification

false

DrishtiSharma

null

DrishtiSharma/wav2vec2-base-finetuned-sentiment-mesd-v9

7

null

transformers

14,310

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-sentiment-mesd-v9 results: [] ---  # wav2vec2-base-finetuned-sentiment-mesd-v9 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: 0.3500 - Accuracy: 0.9154 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 40 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.86 | 3 | 1.7825 | 0.1846 | | 1.9553 | 1.86 | 6 | 1.7212 | 0.4308 | | 1.9553 | 2.86 | 9 | 1.6164 | 0.3769 | | 2.002 | 3.86 | 12 | 1.4904 | 0.3769 | | 1.6191 | 4.86 | 15 | 1.4426 | 0.4385 | | 1.6191 | 5.86 | 18 | 1.3516 | 0.5231 | | 1.6209 | 6.86 | 21 | 1.2176 | 0.5538 | | 1.6209 | 7.86 | 24 | 1.1683 | 0.5692 | | 1.371 | 8.86 | 27 | 1.0885 | 0.5923 | | 1.1568 | 9.86 | 30 | 1.0152 | 0.6385 | | 1.1568 | 10.86 | 33 | 0.9289 | 0.6385 | | 1.1023 | 11.86 | 36 | 0.9141 | 0.6308 | | 1.1023 | 12.86 | 39 | 0.8526 | 0.6462 | | 0.9448 | 13.86 | 42 | 0.8420 | 0.6769 | | 0.7972 | 14.86 | 45 | 0.7976 | 0.6692 | | 0.7972 | 15.86 | 48 | 0.8192 | 0.7308 | | 0.7793 | 16.86 | 51 | 0.7108 | 0.7615 | | 0.7793 | 17.86 | 54 | 0.6712 | 0.7769 | | 0.6468 | 18.86 | 57 | 0.6684 | 0.7923 | | 0.5083 | 19.86 | 60 | 0.6922 | 0.7385 | | 0.5083 | 20.86 | 63 | 0.6148 | 0.7923 | | 0.4988 | 21.86 | 66 | 0.5846 | 0.7923 | | 0.4988 | 22.86 | 69 | 0.6050 | 0.8154 | | 0.4123 | 23.86 | 72 | 0.5506 | 0.7846 | | 0.3511 | 24.86 | 75 | 0.6095 | 0.7846 | | 0.3511 | 25.86 | 78 | 0.5916 | 0.8154 | | 0.3268 | 26.86 | 81 | 0.5912 | 0.8077 | | 0.3268 | 27.86 | 84 | 0.5142 | 0.8538 | | 0.3036 | 28.86 | 87 | 0.5492 | 0.8077 | | 0.3066 | 29.86 | 90 | 0.6007 | 0.8231 | | 0.3066 | 30.86 | 93 | 0.5748 | 0.8231 | | 0.2538 | 31.86 | 96 | 0.6027 | 0.7692 | | 0.2538 | 32.86 | 99 | 0.6979 | 0.7462 | | 0.2281 | 33.86 | 102 | 0.7002 | 0.7615 | | 0.2183 | 34.86 | 105 | 0.6650 | 0.7769 | | 0.2183 | 35.86 | 108 | 0.5192 | 0.8462 | | 0.2202 | 36.86 | 111 | 0.5389 | 0.8308 | | 0.2202 | 37.86 | 114 | 0.5050 | 0.8385 | | 0.1906 | 38.86 | 117 | 0.5722 | 0.7769 | | 0.154 | 39.86 | 120 | 0.5239 | 0.8308 | | 0.154 | 40.86 | 123 | 0.4448 | 0.8615 | | 0.1474 | 41.86 | 126 | 0.4623 | 0.8615 | | 0.1474 | 42.86 | 129 | 0.4282 | 0.8615 | | 0.1345 | 43.86 | 132 | 0.5087 | 0.8615 | | 0.1567 | 44.86 | 135 | 0.4859 | 0.8385 | | 0.1567 | 45.86 | 138 | 0.6603 | 0.8077 | | 0.1731 | 46.86 | 141 | 0.5379 | 0.8385 | | 0.1731 | 47.86 | 144 | 0.8666 | 0.7538 | | 0.1606 | 48.86 | 147 | 0.7518 | 0.8 | | 0.1484 | 49.86 | 150 | 0.5986 | 0.8385 | | 0.1484 | 50.86 | 153 | 0.6368 | 0.8231 | | 0.2256 | 51.86 | 156 | 0.4639 | 0.8692 | | 0.2256 | 52.86 | 159 | 0.5533 | 0.8462 | | 0.1178 | 53.86 | 162 | 0.5038 | 0.8615 | | 0.0815 | 54.86 | 165 | 0.5052 | 0.8692 | | 0.0815 | 55.86 | 168 | 0.4337 | 0.8846 | | 0.0998 | 56.86 | 171 | 0.4422 | 0.8769 | | 0.0998 | 57.86 | 174 | 0.4317 | 0.8692 | | 0.0855 | 58.86 | 177 | 0.4025 | 0.8923 | | 0.0962 | 59.86 | 180 | 0.4605 | 0.8769 | | 0.0962 | 60.86 | 183 | 0.4356 | 0.8769 | | 0.0763 | 61.86 | 186 | 0.4614 | 0.8769 | | 0.0763 | 62.86 | 189 | 0.4382 | 0.8846 | | 0.0902 | 63.86 | 192 | 0.4701 | 0.8692 | | 0.0654 | 64.86 | 195 | 0.4922 | 0.8692 | | 0.0654 | 65.86 | 198 | 0.5413 | 0.8538 | | 0.0651 | 66.86 | 201 | 0.5759 | 0.8615 | | 0.0651 | 67.86 | 204 | 0.4238 | 0.9 | | 0.0822 | 68.86 | 207 | 0.3500 | 0.9154 | | 0.0625 | 69.86 | 210 | 0.3878 | 0.8923 | | 0.0625 | 70.86 | 213 | 0.4952 | 0.8615 | | 0.0548 | 71.86 | 216 | 0.4544 | 0.8615 | | 0.0548 | 72.86 | 219 | 0.5497 | 0.8769 | | 0.054 | 73.86 | 222 | 0.4434 | 0.8846 | | 0.0543 | 74.86 | 225 | 0.4732 | 0.8769 | | 0.0543 | 75.86 | 228 | 0.4425 | 0.8923 | | 0.0881 | 76.86 | 231 | 0.4788 | 0.8769 | | 0.0881 | 77.86 | 234 | 0.5448 | 0.8769 | | 0.061 | 78.86 | 237 | 0.4221 | 0.9077 | | 0.0567 | 79.86 | 240 | 0.4404 | 0.8769 | | 0.0567 | 80.86 | 243 | 0.4099 | 0.9 | | 0.052 | 81.86 | 246 | 0.5259 | 0.8769 | | 0.052 | 82.86 | 249 | 0.5874 | 0.8692 | | 0.0444 | 83.86 | 252 | 0.5555 | 0.8846 | | 0.0332 | 84.86 | 255 | 0.5156 | 0.8615 | | 0.0332 | 85.86 | 258 | 0.4564 | 0.8615 | | 0.0449 | 86.86 | 261 | 0.4826 | 0.8692 | | 0.0449 | 87.86 | 264 | 0.4726 | 0.8615 | | 0.0385 | 88.86 | 267 | 0.4206 | 0.8846 | | 0.0356 | 89.86 | 270 | 0.4050 | 0.8769 | | 0.0356 | 90.86 | 273 | 0.4161 | 0.8923 | | 0.0391 | 91.86 | 276 | 0.4100 | 0.9077 | | 0.0391 | 92.86 | 279 | 0.4047 | 0.9 | | 0.0249 | 93.86 | 282 | 0.4044 | 0.9 | | 0.0399 | 94.86 | 285 | 0.3968 | 0.8846 | | 0.0399 | 95.86 | 288 | 0.3802 | 0.9 | | 0.031 | 96.86 | 291 | 0.3689 | 0.9 | | 0.031 | 97.86 | 294 | 0.3616 | 0.9077 | | 0.036 | 98.86 | 297 | 0.3584 | 0.9077 | | 0.0386 | 99.86 | 300 | 0.3574 | 0.9077 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

Wende/bert-finetuned-ner-accelerate

dc42358d1015ece64958fdf3af450fffbad0022d

2022-03-29T12:25:52.000Z

[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

false

Wende

null

Wende/bert-finetuned-ner-accelerate

7

null

transformers

14,311

Entry not found

anjandash/JavaBERT-small

af38e1dbef6b22553e0898e0114cc0548f183f33

2022-03-30T11:52:00.000Z

[ "pytorch", "tf", "bert", "text-classification", "java", "dataset:anjandash/java-8m-methods-v1", "transformers", "license:mit" ]

text-classification

false

anjandash

null

anjandash/JavaBERT-small

7

null

transformers

14,312

--- language: - java license: mit datasets: - anjandash/java-8m-methods-v1 ---

Finnish-NLP/t5-mini-nl8-finnish

858db87c24cfec96771b3a3beeea9348c07deee4

2022-07-12T13:14:12.000Z

[ "pytorch", "jax", "tensorboard", "t5", "text2text-generation", "fi", "dataset:Finnish-NLP/mc4_fi_cleaned", "dataset:wikipedia", "arxiv:1910.10683", "arxiv:2002.05202", "arxiv:2109.10686", "transformers", "finnish", "t5x", "seq2seq", "license:apache-2.0", "autotrain_compatible" ]

text2text-generation

false

Finnish-NLP

null

Finnish-NLP/t5-mini-nl8-finnish

7

null

transformers

14,313

--- language: - fi license: apache-2.0 tags: - finnish - t5 - t5x - seq2seq datasets: - Finnish-NLP/mc4_fi_cleaned - wikipedia inference: false --- # T5-mini-nl8 for Finnish Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in [this paper](https://arxiv.org/abs/1910.10683) and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer). **Note:** The Hugging Face inference widget is deactivated because this model needs a text-to-text fine-tuning on a specific downstream task to be useful in practice. As an example of a fine-tuned Finnish T5 model, you can check [Finnish-NLP/t5-small-nl24-casing-punctuation-correction](https://huggingface.co/Finnish-NLP/t5-small-nl24-casing-punctuation-correction) which has been fine-tuned to correct missing casing and punctuation for Finnish text. ## Model description T5 is an encoder-decoder model and treats all NLP problems in a text-to-text format. Finnish T5 is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and outputs from those texts. More precisely, it was pretrained with the span-based masked language modeling (MLM) objective. Spans of the input sequence are masked by so-called sentinel tokens (a.k.a unique mask tokens) and the output sequence is formed as a concatenation of the same sentinel tokens and the real masked tokens. This way, the model learns an inner representation of the Finnish language. This model used the [T5 v1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) improvements compared to the original T5 model during the pretraining: - GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202) - Dropout was turned off in pretraining (quality win). Dropout should be re-enabled during fine-tuning - Pretrained on span-based masked language modeling (MLM) objective only without mixing in the downstream tasks - No parameter sharing between embedding and classifier layer This model also used the "efficient" T5 architecture findings presented in [this paper](https://arxiv.org/abs/2109.10686). In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count. To be more precise, model depth is defined as the number of transformer blocks that are stacked sequentially. This model uses the [t5-efficient-mini-nl8](https://huggingface.co/google/t5-efficient-mini-nl8) architecture's layer depth which means both the encoder and the decoder have 8 transformer layers compared to the original T5 "mini" model's architecture of 4 transformer layers. In total, this model has 72 million parameters. ## Intended uses & limitations This model was only pretrained in a self-supervised way excluding any supervised training. Therefore, this model has to be fine-tuned before it is usable on a downstream task, like text classification, unlike the Google's original T5 model. **Note:** You most likely need to fine-tune these T5 models without mixed precision so fine-tune them with full fp32 precision. You can also find more fine-tuning tips from [here](https://discuss.huggingface.co/t/t5-finetuning-tips), for example. ### How to use Here is how to use this model in PyTorch: ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("Finnish-NLP/t5-mini-nl8-finnish") model = T5ForConditionalGeneration.from_pretrained("Finnish-NLP/t5-mini-nl8-finnish") ``` and in TensorFlow: ```python from transformers import T5Tokenizer, TFT5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("Finnish-NLP/t5-mini-nl8-finnish") model = T5ForConditionalGeneration.from_pretrained("Finnish-NLP/t5-mini-nl8-finnish", from_pt=True) ``` ### Limitations and bias The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model. ## Training data This Finnish T5 model was pretrained on the combination of six datasets: - [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo). - [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset - [Yle Finnish News Archive 2011-2018](http://urn.fi/urn:nbn:fi:lb-2017070501) - [Yle Finnish News Archive 2019-2020](http://urn.fi/urn:nbn:fi:lb-2021050401) - [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001) - [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803) Raw datasets were automatically cleaned to filter out bad quality and non-Finnish examples. Also, a [perplexity](https://huggingface.co/course/chapter7/3#perplexity-for-language-models) score was calculated for all texts with a KenLM model which was trained with very clean Finnish texts only. This perplexity score can then be used to determine how "clean" Finnish language the text contains. Lastly, all datasets were concatenated and the top 90% perplexity score was used as a filtering threshold to filter out the worst quality 10% of texts. Together these cleaned datasets were around 76GB of text. ## Training procedure ### Preprocessing The texts are tokenized using WordPiece and a vocabulary size of 32000. The inputs and the outputs are sequences of 512 consecutive tokens. Texts are not lower cased so this model is case-sensitive: it makes a difference between finnish and Finnish. ### Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 500K steps with a batch size of 256 (in total 66B tokens). The optimizer used was a AdaFactor with learning rate warmup for 10K steps with a constant learning rate of 1e-2, and then an inverse square root decay (exponential decay) of the learning rate after. Training code was from the Google's Jax/Flax based [t5x framework](https://github.com/google-research/t5x) and also some t5x task definitions were adapted from [Per's t5x work](https://huggingface.co/pere). ## Evaluation results Evaluation was done by fine-tuning the model on a downstream text classification task with two different labeled Finnish datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Classification fine-tuning was done with a sequence length of 128 tokens. When fine-tuned on those datasets, this model (the second row of the table) achieves the following accuracy results compared to our other T5 models and their parameter counts: | | Model parameters | Yle News accuracy | Eduskunta accuracy | |-------------------------------------------------------|------------------|---------------------|----------------------| |Finnish-NLP/t5-tiny-nl6-finnish | 31 million |92.80 |69.07 | |Finnish-NLP/t5-mini-nl8-finnish | 72 million |93.89 |71.43 | |Finnish-NLP/t5-small-nl24-finnish | 260 million |**94.68** |74.90 | |Finnish-NLP/byt5-base-finnish | 582 million |92.33 |73.13 | |Finnish-NLP/t5-base-nl36-finnish | 814 million |94.40 |**75.97** | |Finnish-NLP/t5-large-nl36-finnish | 1425 million |TBA |TBA | Fine-tuning Google's multilingual mT5 models on the same datasets we can clearly see that our monolingual Finnish T5 models achieve much better results on Finnish text classification: | | Model parameters | Yle News accuracy | Eduskunta accuracy | |-------------------------------------------------------|------------------|---------------------|----------------------| |google/mt5-small | 301 million |91.51 |64.10 | |google/mt5-base | 583 million |92.71 |68.40 | ## Acknowledgements This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/). ## Team Members - Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/) - Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/) Feel free to contact us for more details 🤗

huggingtweets/youtube

5c0ce7c76c62a6fbcc59278d2d4d714bc0fc1570

2022-03-31T14:06:33.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/youtube

7

null

transformers

14,314

--- language: en thumbnail: http://www.huggingtweets.com/youtube/1648735587597/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1427292844612595720/RC1YSvuT_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">YouTube</div> <div style="text-align: center; font-size: 14px;">@youtube</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from YouTube. | Data | YouTube | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 23 | | Short tweets | 104 | | Tweets kept | 3123 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2dx34obn/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @youtube's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/p527w5q3) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/p527w5q3/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/youtube') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

blacktree/distilbert-base-uncased-finetuned-cola

e7f06da23210b070466e7d2b9cfca769b903c9fe

2022-04-01T09:00:33.000Z

[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

blacktree

null

blacktree/distilbert-base-uncased-finetuned-cola

7

null

transformers

14,315

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5285676961321106 ---  # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4883 - Matthews Correlation: 0.5286 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5269 | 1.0 | 535 | 0.5197 | 0.4187 | | 0.3477 | 2.0 | 1070 | 0.4883 | 0.5286 | | 0.2333 | 3.0 | 1605 | 0.6530 | 0.5079 | | 0.17 | 4.0 | 2140 | 0.7567 | 0.5272 | | 0.1271 | 5.0 | 2675 | 0.8887 | 0.5259 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.12.0

plasticfruits/gpt2-finetuned-how-to-qa

e01e38294f2234582eb52a4b00c6c6598bf99121

2022-05-03T15:32:40.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "license:mit" ]

text-generation

false

plasticfruits

null

plasticfruits/gpt2-finetuned-how-to-qa

7

null

transformers

14,316

--- language: en license: mit --- # HowTo QA with GPT-2 base GPT-2 English language model fine-tuned with ±2.000 entries from WikiHow. You can try it here: https://how-to-generator.herokuapp.com/ Input prompt should follow the following format: `\n<|startoftext|>[WP] How to {text} \n[RESPONSE]` Example: `\n<|startoftext|>[WP] How to create a universe \n[RESPONSE]`

vicl/canine-s-finetuned-stsb

41640b64739856165ea13e65c4a2aed13fdd6109

2022-04-01T23:25:04.000Z

[ "pytorch", "tensorboard", "canine", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

vicl

null

vicl/canine-s-finetuned-stsb

7

null

transformers

14,317

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: canine-s-finetuned-stsb results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.8397182061195433 ---  # canine-s-finetuned-stsb This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7223 - Pearson: 0.8397 - Spearmanr: 0.8397 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | No log | 1.0 | 360 | 0.7938 | 0.8083 | 0.8077 | | 1.278 | 2.0 | 720 | 0.7349 | 0.8322 | 0.8305 | | 0.6765 | 3.0 | 1080 | 0.7075 | 0.8374 | 0.8366 | | 0.6765 | 4.0 | 1440 | 0.7586 | 0.8360 | 0.8376 | | 0.4629 | 5.0 | 1800 | 0.7223 | 0.8397 | 0.8397 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

facebook/data2vec-audio-large-10m

2c971412b1e8382f2b0b213b984626b5ae398f45

2022-04-18T16:23:58.000Z

[ "pytorch", "data2vec-audio", "automatic-speech-recognition", "en", "dataset:librispeech_asr", "arxiv:2202.03555", "transformers", "speech", "license:apache-2.0" ]

automatic-speech-recognition

false

facebook

null

facebook/data2vec-audio-large-10m

7

null

transformers

14,318

--- language: en datasets: - librispeech_asr tags: - speech license: apache-2.0 --- # Data2Vec-Audio-Large-10m [Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/) The large model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. [Paper](https://arxiv.org/abs/2202.03555) Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli **Abstract** While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches. The original model can be found under https://github.com/pytorch/fairseq/tree/main/examples/data2vec . # Pre-Training method ![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/data2vec.png) For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555). # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Data2VecForCTC from datasets import load_dataset import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-large-10m") model = Data2VecForCTC.from_pretrained("facebook/data2vec-audio-large-10m") # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # tokenize input_values = processor(ds[0]["audio"]["array"],, return_tensors="pt", padding="longest").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) ```

hackathon-pln-es/roberta-base-biomedical-es-squad2-es

3c77e2941e7773dc5f2cbee39ffeb6503e9d598e

2022-04-03T14:51:38.000Z

[ "pytorch", "roberta", "question-answering", "es", "dataset:squad_es", "dataset:hackathon-pln-es/biomed_squad_es_v2", "transformers", "autotrain_compatible" ]

question-answering

false

hackathon-pln-es

null

hackathon-pln-es/roberta-base-biomedical-es-squad2-es

7

null

transformers

14,319

--- language: es datasets: - squad_es - hackathon-pln-es/biomed_squad_es_v2 metrics: - "f1" --- # roberta-base-biomedical-es for QA This model was trained as part of the "Extractive QA Biomedicine" project developed during the 2022 [Hackathon](https://somosnlp.org/hackathon) organized by SOMOS NLP. ## Motivation Recent research has made available Spanish Language Models trained on Biomedical corpus. This project explores the use of these new models to generate extractive Question Answering models for Biomedicine, and compares their effectiveness with general masked language models. The models trained during the [Hackathon](https://somosnlp.org/hackathon) were: [hackathon-pln-es/roberta-base-bne-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-bne-squad2-es) [hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es) [hackathon-pln-es/roberta-base-biomedical-es-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-es-squad2-es) [hackathon-pln-es/biomedtra-small-es-squad2-es](https://huggingface.co/hackathon-pln-es/biomedtra-small-es-squad2-es) ## Description This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-es) on the [squad_es (v2)](https://huggingface.co/datasets/squad_es) training dataset. ## Hyperparameters The hyperparameters were chosen based on those used in [PlanTL-GOB-ES/roberta-base-bne-sqac](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-sqac), a spanish-based QA model trained on a dataset with SQUAD v1 fromat. ``` --num_train_epochs 2 --learning_rate 3e-5 --weight_decay 0.01 --max_seq_length 386 --doc_stride 128 ``` ## Performance Evaluated on the [hackathon-pln-es/biomed_squad_es_v2](https://huggingface.co/datasets/hackathon-pln-es/biomed_squad_es_v2) dev set. |Model |Base Model Domain|exact |f1 |HasAns_exact|HasAns_f1|NoAns_exact|NoAns_f1| |--------------------------------------------------------------|-----------------|-------|-------|------------|---------|-----------|--------| |hackathon-pln-es/roberta-base-bne-squad2-es |General |67.6341|75.6988|53.7367 |70.0526 |81.2174 |81.2174 | |hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es|Biomedical |66.8426|75.2346|53.0249 |70.0031 |80.3478 |80.3478 | |hackathon-pln-es/roberta-base-biomedical-es-squad2-es |Biomedical |67.6341|74.5612|47.6868 |61.7012 |87.1304 | 87.1304| |hackathon-pln-es/biomedtra-small-es-squad2-es |Biomedical |34.4767|44.3294|45.3737 |65.307 |23.8261 |23.8261 | ## Team Santiago Maximo: [smaximo](https://huggingface.co/smaximo)

benjamin/roberta-large-wechsel-ukrainian

a40f97ad5dd4b638a51e0a3c124211eb4581f78d

2022-07-13T23:43:31.000Z

[ "pytorch", "roberta", "fill-mask", "uk", "transformers", "license:mit", "autotrain_compatible" ]

fill-mask

false

benjamin

null

benjamin/roberta-large-wechsel-ukrainian

7

null

transformers

14,320

--- license: mit language: uk --- # roberta-large-wechsel-ukrainian [`roberta-base`](https://huggingface.co/roberta-base) transferred to Ukrainian using the method from the NAACL2022 paper [WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models](https://aclanthology.org/2022.naacl-main.293/). # Evaluation Evaluation was done on [lang-uk's ner-uk project](https://github.com/lang-uk/ner-uk), the Ukrainian portion of [WikiANN](https://huggingface.co/datasets/wikiann) and the [Ukrainian IU corpus from the Universal Dependencies project](https://github.com/UniversalDependencies/UD_Ukrainian-IU). Evaluation results are the mean of 5 runs with different seeds. __Validation Results__ | | lang-uk NER (Micro F1) | WikiANN (Micro F1) | UD Ukrainian IU POS (Accuracy) | |:-------------------------------------------------|:-------------------------|:-------------|:-------------------------| | roberta-base-wechsel-ukrainian | 88.06 (0.50) | 92.96 (0.08) | 98.70 (0.05) | | roberta-large-wechsel-ukrainian | __89.27 (0.53)__ | __93.22 (0.15)__ | __98.86 (0.03)__ | | | roberta-base-scratch-ukrainian* | 85.49 (0.88) | 91.91 (0.08) | 98.49 (0.04) | | roberta-large-scratch-ukrainian* | 86.54 (0.70) | 92.39 (0.16) | 98.65 (0.09) | | | dbmdz/electra-base-ukrainian-cased-discriminator | 87.49 (0.52) | 93.20 (0.16) | 98.60 (0.03) | | xlm-roberta-base | 86.68 (0.44) | 92.41 (0.13) | 98.53 (0.02) | | xlm-roberta-large | 86.64 (1.61) | 93.01 (0.13) | 98.71 (0.04) | __Test Results__ | | lang-uk NER (Micro F1) | WikiANN (Micro F1) | UD Ukrainian IU POS (Accuracy) | |:-------------------------------------------------|:-------------------------|:-------------|:-------------------------| | roberta-base-wechsel-ukrainian | 90.81 (1.51) | 92.98 (0.12) | 98.57 (0.03) | | roberta-large-wechsel-ukrainian | __91.24 (1.16)__ | __93.22 (0.17)__ | __98.74 (0.06)__ | | | roberta-base-scratch-ukrainian* | 89.57 (1.01) | 92.05 (0.09) | 98.31 (0.08) | | roberta-large-scratch-ukrainian* | 89.96 (0.89) | 92.49 (0.15) | 98.52 (0.04) | | | dbmdz/electra-base-ukrainian-cased-discriminator | 90.43 (1.29) | 92.99 (0.11) | 98.59 (0.06) | | xlm-roberta-base | 90.86 (0.81) | 92.27 (0.09) | 98.45 (0.07) | | xlm-roberta-large | 90.16 (2.98) | 92.92 (0.19) | 98.71 (0.04) | \*trained using the same exact training setup as the wechsel-\* models, but without parameter transfer from WECHSEL. # License MIT

anton-l/xtreme_s_xlsr_300m_fleurs_langid_test

7f8e824dae623a78b32228013193850694adf810

2022-04-04T10:59:40.000Z

[ "pytorch", "tensorboard", "wav2vec2", "audio-classification", "transformers" ]

audio-classification

false

anton-l

null

anton-l/xtreme_s_xlsr_300m_fleurs_langid_test

7

null

transformers

14,321

Entry not found

efederici/cross-encoder-bert-base-stsb

083a417f617f9eb04389d91953ce1d404879e65e

2022-04-04T17:09:02.000Z

[ "pytorch", "bert", "text-classification", "it", "dataset:stsb_multi_mt", "transformers", "cross-encoder", "sentence-similarity" ]

text-classification

false

efederici

null

efederici/cross-encoder-bert-base-stsb

7

null

transformers

14,322

--- pipeline_tag: text-classification language: - it datasets: - stsb_multi_mt tags: - cross-encoder - sentence-similarity - transformers --- # Cross-Encoder This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. <p align="center"> <img src="https://upload.wikimedia.org/wikipedia/commons/f/f6/Edouard_Vuillard%2C_1920c_-_Sunlit_Interior.jpg" width="400"> </br> Edouard Vuillard, Sunlit Interior </p> ## Training Data This model was trained on [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/it/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. ## Usage and Performance ```python from sentence_transformers import CrossEncoder model = CrossEncoder('efederici/cross-encoder-umberto-stsb') scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')]) ``` The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.

dapang/distilbert-base-uncased-finetuned-truthful

3991bb8e5539e6b77e7d0990d8d4da760a273e1b

2022-04-05T07:23:56.000Z

[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

dapang

null

dapang/distilbert-base-uncased-finetuned-truthful

7

null

transformers

14,323

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-truthful results: [] ---  # distilbert-base-uncased-finetuned-truthful This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4660 - Accuracy: 0.87 - F1: 0.8697 ## 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: 9.910294163459086e-05 - train_batch_size: 400 - eval_batch_size: 400 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 5 | 0.6509 | 0.59 | 0.5780 | | No log | 2.0 | 10 | 0.4950 | 0.77 | 0.7701 | | No log | 3.0 | 15 | 0.4787 | 0.81 | 0.8099 | | No log | 4.0 | 20 | 0.4936 | 0.81 | 0.8096 | | No log | 5.0 | 25 | 0.4443 | 0.82 | 0.82 | | No log | 6.0 | 30 | 0.4547 | 0.85 | 0.8497 | | No log | 7.0 | 35 | 0.4268 | 0.85 | 0.8500 | | No log | 8.0 | 40 | 0.4790 | 0.87 | 0.8697 | | No log | 9.0 | 45 | 0.4660 | 0.87 | 0.8697 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.1 - Datasets 2.0.0 - Tokenizers 0.11.0

btjiong/robbert-twitter-sentiment

7597fd9000648604dd95084acd2e730c18834e92

2022-04-06T17:18:23.000Z

[ "pytorch", "roberta", "text-classification", "dataset:dutch_social", "transformers", "generated_from_trainer", "license:mit", "model-index" ]

text-classification

false

btjiong

null

btjiong/robbert-twitter-sentiment

7

null

transformers

14,324

--- license: mit tags: - generated_from_trainer datasets: - dutch_social metrics: - accuracy - f1 - precision - recall model-index: - name: robbert-twitter-sentiment results: - task: name: Text Classification type: text-classification dataset: name: dutch_social type: dutch_social args: dutch_social metrics: - name: Accuracy type: accuracy value: 0.749 - name: F1 type: f1 value: 0.7491844724992662 - name: Precision type: precision value: 0.7493911755249737 - name: Recall type: recall value: 0.749 ---  # robbert-twitter-sentiment This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset. It achieves the following results on the evaluation set: - Loss: 0.6818 - Accuracy: 0.749 - F1: 0.7492 - Precision: 0.7494 - Recall: 0.749 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.7485 | 1.0 | 188 | 0.7670 | 0.692 | 0.6915 | 0.6920 | 0.692 | | 0.5202 | 2.0 | 376 | 0.6818 | 0.749 | 0.7492 | 0.7494 | 0.749 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cpu - Datasets 2.0.0 - Tokenizers 0.12.0

afbudiman/distilled-indobert-classification

1ef8177a1003700f67e937987b1cc16e5c44337f

2022-04-08T09:32:57.000Z

[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:indonlu", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

afbudiman

null

afbudiman/distilled-indobert-classification

7

null

transformers

14,325

--- license: apache-2.0 tags: - generated_from_trainer datasets: - indonlu metrics: - accuracy - f1 model-index: - name: distilled-indobert-classification results: - task: name: Text Classification type: text-classification dataset: name: indonlu type: indonlu args: smsa metrics: - name: Accuracy type: accuracy value: 0.9015873015873016 - name: F1 type: f1 value: 0.9014926755197933 ---  # distilled-indobert-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.6015 - Accuracy: 0.9016 - F1: 0.9015 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.0427 | 1.0 | 688 | 0.6306 | 0.8683 | 0.8684 | | 0.5332 | 2.0 | 1376 | 0.5621 | 0.8794 | 0.8779 | | 0.3021 | 3.0 | 2064 | 0.6785 | 0.8905 | 0.8896 | | 0.1851 | 4.0 | 2752 | 0.6085 | 0.8968 | 0.8959 | | 0.1152 | 5.0 | 3440 | 0.6015 | 0.9016 | 0.9015 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

Damith/AraELECTRA-discriminator-SOQAL

7f792fca12659b8b040d7ad82650d83fadc486fd

2022-04-08T10:40:38.000Z

[ "pytorch", "electra", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

false

Damith

null

Damith/AraELECTRA-discriminator-SOQAL

7

null

transformers

14,326

Entry not found

nikhedward/bart-large-cnn-finetuned-multi-news1

f0dc0138e6249547ea8b52f07e26cbd689ff4567

2022-04-09T04:51:07.000Z

[ "pytorch", "tensorboard", "bart", "text2text-generation", "dataset:multi_news", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]

text2text-generation

false

nikhedward

null

nikhedward/bart-large-cnn-finetuned-multi-news1

7

null

transformers

14,327

--- license: mit tags: - generated_from_trainer datasets: - multi_news metrics: - rouge model-index: - name: bart-large-cnn-finetuned-multi-news1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: multi_news type: multi_news args: default metrics: - name: Rouge1 type: rouge value: 42.1215 ---  # bart-large-cnn-finetuned-multi-news1 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the multi_news dataset. It achieves the following results on the evaluation set: - Loss: 2.0858 - Rouge1: 42.1215 - Rouge2: 14.9986 - Rougel: 23.4737 - Rougelsum: 36.4212 - Gen Len: 133.703 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - 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 | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.1984 | 1.0 | 750 | 2.0858 | 42.1215 | 14.9986 | 23.4737 | 36.4212 | 133.703 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

aleksavega/t5-efficient-base-finetuned-1.2

e9e8adcdd412e00bdc3cf824b14c4dc711086594

2022-04-11T12:04:08.000Z

[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "summarization", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

summarization

false

aleksavega

null

aleksavega/t5-efficient-base-finetuned-1.2

7

null

transformers

14,328

--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: t5-efficient-base-finetuned-1.2 results: [] ---  # t5-efficient-base-finetuned-1.2 This model is a fine-tuned version of [google/t5-efficient-base](https://huggingface.co/google/t5-efficient-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5294 - Rouge1: 62.691 - Rouge2: 55.9731 - Rougel: 60.9097 - Rougelsum: 61.4393 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 4662 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 2.2424 | 1.0 | 1217 | 1.7042 | 34.2215 | 24.2754 | 31.7289 | 32.4237 | | 1.7716 | 2.0 | 2434 | 1.6184 | 43.4774 | 34.0476 | 41.3691 | 41.9132 | | 1.6324 | 3.0 | 3651 | 1.5811 | 49.1441 | 40.7935 | 47.0077 | 47.6388 | | 1.5226 | 4.0 | 4868 | 1.5243 | 54.4769 | 46.3387 | 52.3289 | 52.9555 | | 1.4121 | 5.0 | 6085 | 1.5040 | 56.8792 | 49.1963 | 54.7327 | 55.2805 | | 1.331 | 6.0 | 7302 | 1.4930 | 58.6896 | 51.1683 | 56.7096 | 57.3605 | | 1.2677 | 7.0 | 8519 | 1.4785 | 59.9285 | 52.4631 | 57.8575 | 58.4203 | | 1.2175 | 8.0 | 9736 | 1.4839 | 60.0299 | 52.8806 | 58.0099 | 58.6348 | | 1.1782 | 9.0 | 10953 | 1.4908 | 61.247 | 54.0887 | 59.2175 | 59.7658 | | 1.1442 | 10.0 | 12170 | 1.4882 | 61.9895 | 54.9455 | 60.0728 | 60.5786 | | 1.1118 | 11.0 | 13387 | 1.5061 | 62.1077 | 55.1276 | 60.2218 | 60.7475 | | 1.081 | 12.0 | 14604 | 1.5078 | 61.6083 | 54.6805 | 59.7912 | 60.2489 | | 1.0668 | 13.0 | 15821 | 1.5200 | 62.3075 | 55.5201 | 60.5192 | 60.9557 | | 1.0488 | 14.0 | 17038 | 1.5344 | 62.5144 | 55.6332 | 60.6845 | 61.1715 | | 1.0324 | 15.0 | 18255 | 1.5313 | 62.7697 | 56.0313 | 60.9298 | 61.4739 | | 1.0302 | 16.0 | 19472 | 1.5294 | 62.691 | 55.9731 | 60.9097 | 61.4393 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.6

optimum/MiniLMv2-L12-H384-distilled-finetuned-clinc

cf662b985fc43f786b78909f506b09d8c723be15

2022-04-11T11:21:21.000Z

[ "pytorch", "roberta", "text-classification", "dataset:clinc_oos", "transformers", "generated_from_trainer", "model-index" ]

text-classification

false

optimum

null

optimum/MiniLMv2-L12-H384-distilled-finetuned-clinc

7

null

transformers

14,329

--- tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.94 ---  # MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.3479 - Accuracy: 0.94 ## 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: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 60 | 0.8171 | 0.2490 | | No log | 2.0 | 120 | 0.7039 | 0.6568 | | No log | 3.0 | 180 | 0.6067 | 0.7932 | | 0.7269 | 4.0 | 240 | 0.5270 | 0.8674 | | 0.7269 | 5.0 | 300 | 0.4659 | 0.9010 | | 0.7269 | 6.0 | 360 | 0.4201 | 0.9194 | | 0.7269 | 7.0 | 420 | 0.3867 | 0.9352 | | 0.4426 | 8.0 | 480 | 0.3649 | 0.9352 | | 0.4426 | 9.0 | 540 | 0.3520 | 0.9403 | | 0.4426 | 10.0 | 600 | 0.3479 | 0.94 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3

NlpHUST/Condenser-phobert-base

acdb842569b37097f91335db0f2fdfd491982a5b

2022-04-12T14:30:53.000Z

[ "pytorch", "tf", "roberta", "fill-mask", "arxiv:2104.08253", "arxiv:2108.05540", "transformers", "autotrain_compatible" ]

fill-mask

false

NlpHUST

null

NlpHUST/Condenser-phobert-base

7

null

transformers

14,330

# Condenser for Vietnamese Transformer architectures for dense retrieval pre-training on vietnamese dataset. Details can be found in our papers, [Condenser: a Pre-training Architecture for Dense Retrieval](https://arxiv.org/abs/2104.08253) and [Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval ](https://arxiv.org/abs/2108.05540). For example, to load Condenser weights, ``` from transformers import AutoModel model = AutoModel.from_pretrained('NlpHUST/Condenser-phobert-base') ```

Auruncus/gpt-j-6b-8bit-ml

3a5c3a146436446547bf6a56b9581e9305b8fffd

2022-04-18T14:47:20.000Z

[ "pytorch", "gptj", "text-generation", "transformers" ]

text-generation

false

Auruncus

null

Auruncus/gpt-j-6b-8bit-ml

7

null

transformers

14,331

Entry not found

lewtun/sagemaker-distilbert-emotion-1

2d79e5aa0394bd73597d62333792f46508e9ab31

2022-04-12T19:23:45.000Z

[ "pytorch", "distilbert", "text-classification", "dataset:emotion", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

lewtun

null

lewtun/sagemaker-distilbert-emotion-1

7

null

transformers

14,332

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: sagemaker-distilbert-emotion-1 results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9325 ---  # sagemaker-distilbert-emotion-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1651 - Accuracy: 0.9325 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 64 - 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.966 | 1.0 | 500 | 0.2497 | 0.921 | | 0.1913 | 2.0 | 1000 | 0.1651 | 0.9325 | | 0.1037 | 3.0 | 1500 | 0.1501 | 0.9285 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.15.1 - Tokenizers 0.10.3

Helsinki-NLP/opus-mt-tc-big-cel-en

54c6c217cbc72642cea7911a55f73efc14f650a8

2022-06-01T12:59:34.000Z

[ "pytorch", "marian", "text2text-generation", "br", "cel", "cy", "en", "ga", "transformers", "translation", "opus-mt-tc", "license:cc-by-4.0", "model-index", "autotrain_compatible" ]

translation

false

Helsinki-NLP

null

Helsinki-NLP/opus-mt-tc-big-cel-en

7

1

transformers

14,333

--- language: - br - cel - cy - en - ga tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-cel-en results: - task: name: Translation cym-eng type: translation args: cym-eng dataset: name: flores101-devtest type: flores_101 args: cym eng devtest metrics: - name: BLEU type: bleu value: 50.2 - task: name: Translation gle-eng type: translation args: gle-eng dataset: name: flores101-devtest type: flores_101 args: gle eng devtest metrics: - name: BLEU type: bleu value: 37.4 - task: name: Translation bre-eng type: translation args: bre-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: bre-eng metrics: - name: BLEU type: bleu value: 36.1 - task: name: Translation cym-eng type: translation args: cym-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: cym-eng metrics: - name: BLEU type: bleu value: 53.6 - task: name: Translation gle-eng type: translation args: gle-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: gle-eng metrics: - name: BLEU type: bleu value: 57.7 --- # opus-mt-tc-big-cel-en Neural machine translation model for translating from Celtic languages (cel) to English (en). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Model info * Release: 2022-03-13 * source language(s): bre cym gle * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/cel-eng/opusTCv20210807+bt_transformer-big_2022-03-13.zip) * more information released models: [OPUS-MT cel-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cel-eng/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "A-du emaoc’h?", "Ta'n ushtey glen." ] model_name = "pytorch-models/opus-mt-tc-big-cel-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Is that you? # Ta'n ushtey glen. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-cel-en") print(pipe("A-du emaoc’h?")) # expected output: Is that you? ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cel-eng/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cel-eng/opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | bre-eng | tatoeba-test-v2021-08-07 | 0.53712 | 36.1 | 383 | 2065 | | cym-eng | tatoeba-test-v2021-08-07 | 0.69239 | 53.6 | 818 | 5563 | | gle-eng | tatoeba-test-v2021-08-07 | 0.72087 | 57.7 | 1913 | 11190 | | cym-eng | flores101-devtest | 0.71379 | 50.2 | 1012 | 24721 | | gle-eng | flores101-devtest | 0.63946 | 37.4 | 1012 | 24721 | ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 3405783 * port time: Wed Apr 13 18:36:25 EEST 2022 * port machine: LM0-400-22516.local

cj-mills/distilbert-base-uncased-finetuned-clinc

028b8f56cb944e1c7e1b8f4f6265c5beeddef127

2022-04-14T07:21:55.000Z

[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:clinc_oos", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

cj-mills

null

cj-mills/distilbert-base-uncased-finetuned-clinc

7

null

transformers

14,334

--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9161290322580645 ---  # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7796 - Accuracy: 0.9161 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2938 | 1.0 | 318 | 3.2905 | 0.7410 | | 2.6346 | 2.0 | 636 | 1.8833 | 0.8326 | | 1.5554 | 3.0 | 954 | 1.1650 | 0.8926 | | 1.0189 | 4.0 | 1272 | 0.8636 | 0.9110 | | 0.8028 | 5.0 | 1590 | 0.7796 | 0.9161 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.12.1

cj-mills/distilbert-base-uncased-distilled-clinc

418e51c3027813c933d35683c0fd88bac69e7b44

2022-04-14T07:56:35.000Z

[ "pytorch", "distilbert", "text-classification", "dataset:clinc_oos", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

cj-mills

null

cj-mills/distilbert-base-uncased-distilled-clinc

7

null

transformers

14,335

--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9467741935483871 ---  # distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.2525 - Accuracy: 0.9468 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2246 | 1.0 | 318 | 3.1584 | 0.7545 | | 2.4033 | 2.0 | 636 | 1.5656 | 0.8652 | | 1.1684 | 3.0 | 954 | 0.7795 | 0.9161 | | 0.5693 | 4.0 | 1272 | 0.4653 | 0.9329 | | 0.3042 | 5.0 | 1590 | 0.3412 | 0.9406 | | 0.1794 | 6.0 | 1908 | 0.2912 | 0.9403 | | 0.1184 | 7.0 | 2226 | 0.2654 | 0.9461 | | 0.0873 | 8.0 | 2544 | 0.2557 | 0.9439 | | 0.0719 | 9.0 | 2862 | 0.2549 | 0.9465 | | 0.0646 | 10.0 | 3180 | 0.2525 | 0.9468 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.12.1

profoz/toxic-distilbert

87e01ec6b7f4b42ee83bd4a40a546eb748c51f7f

2022-04-15T14:17:52.000Z

[ "pytorch", "distilbert", "text-classification", "transformers" ]

text-classification

false

profoz

null

profoz/toxic-distilbert

7

null

transformers

14,336

Entry not found

xma/gptj-small-train-test

d2afd33621948135ef4e4b35d796166af9a77236

2022-04-15T18:42:37.000Z

[ "pytorch", "distilbert", "text-classification", "transformers", "license:ecl-2.0" ]

text-classification

false

xma

null

xma/gptj-small-train-test

7

null

transformers

14,337

--- license: ecl-2.0 ---

haohaoxuexi/distilbert-base-uncased-finetuned-emotion

29c8abe4d785db5acf90569306ad4f19e8c996a8

2022-04-16T06:03:18.000Z

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

text-classification

false

haohaoxuexi

null

haohaoxuexi/distilbert-base-uncased-finetuned-emotion

7

null

transformers

14,338

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.923 - name: F1 type: f1 value: 0.9233263918743045 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2239 - Accuracy: 0.923 - F1: 0.9233 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8359 | 1.0 | 250 | 0.3198 | 0.9085 | 0.9057 | | 0.2491 | 2.0 | 500 | 0.2239 | 0.923 | 0.9233 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

Raychanan/bert-bert-cased-first512-Conflict

6e2f1160ba67545e51556f1a9fb19e977cef374a

2022-04-16T18:39:25.000Z

[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

Raychanan

null

Raychanan/bert-bert-cased-first512-Conflict

7

null

transformers

14,339

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 - accuracy - precision - recall model-index: - name: bert-bert-cased-first512-Conflict results: [] ---  # bert-bert-cased-first512-Conflict `conv_text = '\n'.join([utt.text for utt in conv.get_chronological_utterance_list()])` This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6932 - F1: 0.6667 - Accuracy: 0.5 - Precision: 0.5 - Recall: 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:| | 0.7098 | 1.0 | 685 | 0.6945 | 0.0 | 0.5 | 0.0 | 0.0 | | 0.7046 | 2.0 | 1370 | 0.6997 | 0.6667 | 0.5 | 0.5 | 1.0 | | 0.7013 | 3.0 | 2055 | 0.6949 | 0.6667 | 0.5 | 0.5 | 1.0 | | 0.7027 | 4.0 | 2740 | 0.6931 | 0.6667 | 0.5 | 0.5 | 1.0 | | 0.702 | 5.0 | 3425 | 0.6932 | 0.6667 | 0.5 | 0.5 | 1.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1

kalex/bert-finetuned-ner

4848b10f2633ef330fb3ee756b543a11ead674a3

2022-04-17T03:43:25.000Z

[ "pytorch", "tensorboard", "bert", "token-classification", "dataset:ncbi_disease", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

token-classification

false

kalex

null

kalex/bert-finetuned-ner

7

null

transformers

14,340

--- license: apache-2.0 tags: - generated_from_trainer datasets: - ncbi_disease model-index: - name: bert-finetuned-ner results: [] ---  # bert-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ncbi_disease dataset. It achieves the following results on the evaluation set: - Loss: 0.0591 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1127 | 1.0 | 680 | 0.0593 | | 0.0442 | 2.0 | 1360 | 0.0557 | | 0.0181 | 3.0 | 2040 | 0.0591 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1

joniponi/communication-classifier

75a52990c1865945494bd8f56c0b296c2fcd5f0c

2022-04-18T02:09:32.000Z

[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

joniponi

null

joniponi/communication-classifier

7

null

transformers

14,341

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: communication-classifier results: [] ---  # communication-classifier This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1249 - eval_accuracy: 0.9644 - eval_f1: 0.9644 - eval_runtime: 2.6719 - eval_samples_per_second: 126.126 - eval_steps_per_second: 8.234 - epoch: 3.0 - step: 255 ## 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: 20 ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1

crcb/imp_hatred

e46fd2d7391e241eaac00583096c500a43540edb

2022-04-18T14:11:43.000Z

[ "pytorch", "roberta", "text-classification", "en", "dataset:crcb/autotrain-data-imp_hs", "transformers", "autotrain", "co2_eq_emissions" ]

text-classification

false

crcb

null

crcb/imp_hatred

7

null

transformers

14,342

--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - crcb/autotrain-data-imp_hs co2_eq_emissions: 15.91710539314839 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 753423062 - CO2 Emissions (in grams): 15.91710539314839 ## Validation Metrics - Loss: 0.5205655694007874 - Accuracy: 0.7746741154562383 - Macro F1: 0.5796696218586866 - Micro F1: 0.7746741154562382 - Weighted F1: 0.7602379277947592 - Macro Precision: 0.6976905233970596 - Micro Precision: 0.7746741154562383 - Weighted Precision: 0.7628815999440115 - Macro Recall: 0.557144871405371 - Micro Recall: 0.7746741154562383 - Weighted Recall: 0.7746741154562383 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/crcb/autotrain-imp_hs-753423062 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-imp_hs-753423062", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-imp_hs-753423062", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

ucabqfe/bigBird_PER_bio

8c76b8bb81af12511f0b2b83b37a105db83f86fb

2022-04-18T18:18:00.000Z

[ "pytorch", "big_bird", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

false

ucabqfe

null

ucabqfe/bigBird_PER_bio

7

null

transformers

14,343

Entry not found

ndavid/binary-qa-bert

8e361a60b738221f155ce67ad0d251879c2a9b81

2022-04-18T23:41:36.000Z

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

false

ndavid

null

ndavid/binary-qa-bert

7

null

transformers

14,344

Entry not found

afbudiman/distilled-optimized-indobert-classification

d72b74ea900a96c36a3abf752a939a5980fa8c17

2022-04-19T16:02:44.000Z

[ "pytorch", "distilbert", "text-classification", "dataset:indonlu", "transformers", "generated_from_trainer", "model-index" ]

text-classification

false

afbudiman

null

afbudiman/distilled-optimized-indobert-classification

7

null

transformers

14,345

--- tags: - generated_from_trainer datasets: - indonlu metrics: - accuracy - f1 model-index: - name: distilled-optimized-indobert-classification results: - task: name: Text Classification type: text-classification dataset: name: indonlu type: indonlu args: smsa metrics: - name: Accuracy type: accuracy value: 0.9 - name: F1 type: f1 value: 0.8994069293432798 ---  # distilled-optimized-indobert-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.7397 - Accuracy: 0.9 - F1: 0.8994 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.315104717136378e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.128 | 1.0 | 688 | 0.8535 | 0.8913 | 0.8917 | | 0.1475 | 2.0 | 1376 | 0.9171 | 0.8913 | 0.8913 | | 0.0997 | 3.0 | 2064 | 0.7799 | 0.8960 | 0.8951 | | 0.0791 | 4.0 | 2752 | 0.7179 | 0.9032 | 0.9023 | | 0.0577 | 5.0 | 3440 | 0.6908 | 0.9063 | 0.9055 | | 0.0406 | 6.0 | 4128 | 0.7613 | 0.8992 | 0.8986 | | 0.0275 | 7.0 | 4816 | 0.7502 | 0.8992 | 0.8989 | | 0.023 | 8.0 | 5504 | 0.7408 | 0.8976 | 0.8969 | | 0.0169 | 9.0 | 6192 | 0.7397 | 0.9 | 0.8994 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1

demoversion/bert-fa-base-uncased-haddad-wikinli

f218add6ab043db4b762f2744c11c5e7a440ae78

2022-04-21T18:17:34.000Z

[ "pytorch", "bert", "text-classification", "fa", "transformers", "license:apache-2.0" ]

text-classification

false

demoversion

null

demoversion/bert-fa-base-uncased-haddad-wikinli

7

1

transformers

14,346

--- language: fa license: apache-2.0 --- This repository is created with the aim to provide better models for NLI in persian, with the transparent codes for training I hope you guys find it inspiring and build better model in the future. for more details about the task and methods used for training check the [medium post](https://haddadhesam.medium.com/) and notebooks. # Dataset The dataset used for training is Wiki D/Similar dataset (wiki-d-similar.zip), obtained from [Sentence Transformers](https://github.com/m3hrdadfi/sentence-transformers) repository. # Model The proposed model is published at HuggingFace Hub with the name of ``demoversion/bert-fa-base-uncased-haddad-wikinli``. You can download and use the model from [HuggingFace Website](https://huggingface.co/demoversion/bert-fa-base-uncased-haddad-wikinli) or directly in transformers library like this: from transformers import pipeline model = pipeline("zero-shot-classification", model="demoversion/bert-fa-base-uncased-haddad-wikinli") labels = ["ورزشی", "سیاسی", "علمی", "فرهنگی"] template_str = "این یک متن {} است." str_sentence = "مرحله مقدماتی جام جهانی حاشیه‌های زیادی داشت." model(str_sentence, labels, hypothesis_template=template_str) The result of this code snippet is: Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation. {'labels': ['فرهنگی', 'علمی', 'سیاسی', 'ورزشی'], 'scores': [0.25921085476875305, 0.25713297724723816, 0.24884170293807983, 0.23481446504592896], 'sequence': 'مرحله مقدماتی جام جهانی حاشیه\u200cهای زیادی داشت.'} Yep, the right label (highest score) without training. # Results The result comparing to the original model published for this dataset is available in the table bellow. |Model|dev_accuracy| dev_f1|test_accuracy|test_f1| |--|--|--|--|--| |[m3hrdadfi/bert-fa-base-uncased-wikinli](https://huggingface.co/m3hrdadfi/bert-fa-base-uncased-wikinli)|77.88|77.57|76.64|75.99| |[demoversion/bert-fa-base-uncased-haddad-wikinli](https://huggingface.co/demoversion/bert-fa-base-uncased-haddad-wikinli)|**78.62**|**79.74**|**77.04**|**78.56**| # Notebooks Notebooks used for training and evaluation are available below. [Training ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DemoVersion/persian-nli-trainer/blob/main/notebooks/training.ipynb) [Evaluation ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DemoVersion/persian-nli-trainer/blob/main/notebooks/evaluation.ipynb)

tuhailong/cross_encoder_roberta-wwm-ext_v1

3ac66951c2ca373cc7081624c721515e8b39f6b4

2022-04-20T02:41:23.000Z

[ "pytorch", "bert", "text-classification", "zh", "dataset:dialogue", "transformers", "cross-encoder" ]

text-classification

false

tuhailong

null

tuhailong/cross_encoder_roberta-wwm-ext_v1

7

null

transformers

14,347

--- language: zh tags: - cross-encoder datasets: - dialogue --- # Data train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs. ## Model model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder, pretrained model is hfl/chinese-roberta-wwm-ext. This model structure is as same as [tuhailong/cross_encoder_roberta-wwm-ext_v0](https://huggingface.co/tuhailong/cross_encoder_roberta-wwm-ext_v0)，the difference is changing the order of input sentences and put them in train dataset, the performance is better in my dataset. ### Usage ```python >>> from sentence_transformers.cross_encoder import CrossEncoder >>> model = CrossEncoder(model_save_path, device="cuda", max_length=64) >>> sentences = ["今天天气不错", "今天心情不错"] >>> score = model.predict([sentences]) >>> print(score[0]) ``` #### Code train code from https://github.com/TTurn/cross-encoder

tuhailong/cross_encoder_roberta-wwm-ext_v2

cb0ca1424c3cd02fd1be9a147e959e9b64f0fd98

2022-04-20T02:41:07.000Z

[ "pytorch", "bert", "text-classification", "zh", "dataset:dialogue", "transformers", "cross-encoder" ]

text-classification

false

tuhailong

null

tuhailong/cross_encoder_roberta-wwm-ext_v2

7

null

transformers

14,348

--- language: zh tags: - cross-encoder datasets: - dialogue --- # Data train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs. ## Model model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder, pretrained model is hfl/chinese-roberta-wwm-ext. This model structure is as same as [tuhailong/cross_encoder_roberta-wwm-ext_v1](https://huggingface.co/tuhailong/cross_encoder_roberta-wwm-ext_v1)，the difference is changing the epoch from 5 to 1, the performance is better in my dataset. ### Usage ```python >>> from sentence_transformers.cross_encoder import CrossEncoder >>> model = CrossEncoder(model_save_path, device="cuda", max_length=64) >>> sentences = ["今天天气不错", "今天心情不错"] >>> score = model.predict([sentences]) >>> print(score[0]) ``` #### Code train code from https://github.com/TTurn/cross-encoder

James-kc-min/AGT_Roberta

3abd7ddab489817b17c184e714cf6765af1d01eb

2022-04-20T09:39:04.000Z

[ "pytorch", "roberta", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

James-kc-min

null

James-kc-min/AGT_Roberta

7

null

transformers

14,349

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: AGT_Roberta results: [] ---  # AGT_Roberta This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.0 - Tokenizers 0.12.1

brad1141/Longformer_v5

4b6147fac5cb8316dd03ae9895b5e4fa9b1eff58

2022-04-20T19:13:09.000Z

[ "pytorch", "tensorboard", "longformer", "token-classification", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]

token-classification

false

brad1141

null

brad1141/Longformer_v5

7

null

transformers

14,350

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: Longformer_v5 results: [] ---  # Longformer_v5 This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7919 - Precision: 0.8516 - Recall: 0.8678 - F1: 0.6520 - Accuracy: 0.8259 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.7744 | 1.0 | 1012 | 0.5785 | 0.8375 | 0.8501 | 0.5798 | 0.8098 | | 0.5211 | 2.0 | 2024 | 0.5415 | 0.8434 | 0.8801 | 0.6251 | 0.8282 | | 0.3996 | 3.0 | 3036 | 0.5565 | 0.8500 | 0.8766 | 0.6303 | 0.8274 | | 0.2964 | 4.0 | 4048 | 0.6017 | 0.8617 | 0.8546 | 0.6415 | 0.8240 | | 0.2187 | 5.0 | 5060 | 0.6660 | 0.8485 | 0.8718 | 0.6431 | 0.8271 | | 0.1603 | 6.0 | 6072 | 0.7235 | 0.8493 | 0.8759 | 0.6544 | 0.8290 | | 0.1208 | 7.0 | 7084 | 0.7919 | 0.8516 | 0.8678 | 0.6520 | 0.8259 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1

AntoineB/roberta-tiny-imdb

395a16062c8898922e544bcc4c8f8d9bc369ad4a

2022-04-21T11:45:11.000Z

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

false

AntoineB

null

AntoineB/roberta-tiny-imdb

7

null

transformers

14,351

Entry not found

QuickRead/reward_model_wandb_dynamic_bs_1_idx

a74e679c73030b491b52e331088dca9068bf8139

2022-04-22T10:24:39.000Z

[ "pytorch", "pegasus", "feature-extraction", "transformers" ]

feature-extraction

false

QuickRead

null

QuickRead/reward_model_wandb_dynamic_bs_1_idx

7

null

transformers

14,352

Entry not found

Saisam/gpt-neo-math-small

b748555bc45c89784c29e36cbd952118f035c375

2022-04-22T01:13:57.000Z

[ "pytorch", "gpt_neo", "text-generation", "transformers", "license:apache-2.0" ]

text-generation

false

Saisam

null

Saisam/gpt-neo-math-small

7

null

transformers

14,353

--- license: apache-2.0 --- # GPT-NEO-Model for Lean Tactics In the project, we used an HuggingFace GPT-NEO small model and fine-tuned the tactic dataset. The Input should be of the form ``` <GOAL> Goal <PROOFSTEP> ``` The model can easily be accessed using the following code. ``` from transformers import GPT2Tokenizer, GPTNeoForCausalLM import torch tokenizer = GPT2Tokenizer.from_pretrained("Saisam/gpt-neo-math-small") model = GPTNeoForCausalLM.from_pretrained("Saisam/gpt-neo-math-small") ``` Worked along with Xihao Xhang and Moya Zhu

niuca/DeepDebug

e6bc8bb8a64393e4b6c2a363aaf71c684c65106f

2022-04-22T07:10:27.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

niuca

null

niuca/DeepDebug

7

null

transformers

14,354

Entry not found

abdouaziiz/wav2vec2-WOLOF-2.6K-base

dfe30081849ef2b46421488c532fdb577c12586e

2022-04-22T07:17:17.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

abdouaziiz

null

abdouaziiz/wav2vec2-WOLOF-2.6K-base

7

null

transformers

14,355

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wolof results: [] ---  # wolof This model is a fine-tuned version of [LeBenchmark/wav2vec2-FR-2.6K-base](https://huggingface.co/LeBenchmark/wav2vec2-FR-2.6K-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2816 - Wer: 0.3897 ## 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: 20 - 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: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.9468 | 1.67 | 1500 | 0.7036 | 0.6418 | | 0.5506 | 3.33 | 3000 | 0.4129 | 0.5018 | | 0.3817 | 5.0 | 4500 | 0.3414 | 0.4519 | | 0.2885 | 6.67 | 6000 | 0.3181 | 0.4305 | | 0.2275 | 8.33 | 7500 | 0.2920 | 0.4011 | | 0.1852 | 10.0 | 9000 | 0.2816 | 0.3897 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu102 - Datasets 2.1.0 - Tokenizers 0.12.1

buidung2004/wav2vec-vietnamese-number-digits-finetune

6546a582457cc73c9ecbfeca4554b33ea284fae7

2022-05-03T14:16:38.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]

automatic-speech-recognition

false

buidung2004

null

buidung2004/wav2vec-vietnamese-number-digits-finetune

7

null

transformers

14,356

Entry not found

dapang/distilroberta-base-mic-sym

1b5e930b847f04de70a13a5dfc5603c77e476d37

2022-04-23T03:53:15.000Z

[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

dapang

null

dapang/distilroberta-base-mic-sym

7

null

transformers

14,357

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilroberta-base-mic-sym results: [] ---  # distilroberta-base-mic-sym This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0023 - Accuracy: 0.9997 - F1: 0.9997 ## 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: 2.740146306575944e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 188 | 0.0049 | 0.9990 | 0.9990 | | No log | 2.0 | 376 | 0.0023 | 0.9997 | 0.9997 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.12.0.dev20220422+cu116 - Datasets 2.1.0 - Tokenizers 0.12.1

allenai/aspire-biencoder-biomed-scib

76e5d1c6f0af4d30d3b4340d6cd1affebaec44c0

2022-04-24T19:38:56.000Z

[ "pytorch", "bert", "feature-extraction", "arxiv:2111.08366", "transformers", "license:apache-2.0" ]

feature-extraction

false

allenai

null

allenai/aspire-biencoder-biomed-scib

7

null

transformers

14,358

--- license: apache-2.0 --- ## Overview Model included in a paper for modeling fine grained similarity between documents: **Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity" **Authors**: Sheshera Mysore, Arman Cohan, Tom Hope **Paper**: https://arxiv.org/abs/2111.08366 **Github**: https://github.com/allenai/aspire **Note**: In the context of the paper, this model is referred to as `Specter-CoCite_Scib` and represents a baseline bi-encoder for scientific document similarity. This model is similar in architecture to the [`allenai/specter`](https://github.com/allenai/specter) model but is trained on co-citation data instead of citation data. ## Model Card ### Model description This model is a BERT bi-encoder model trained for similarity of title-abstract pairs in biomedical scientific papers. The model is **initialized with the SciBert model**. This model inputs the title and abstract of a paper and represents it with a single vector obtained by a scalar mix of the CLS token at every layer of the SciBert encoder. These scalar mix parameters can be important for performance in some datasets. Importantly, these scalar mix weights are not included as part of this HF model, if you wish to use these parameters please download the full model at: [`aspire-biencoder-biomed-scib-full.zip`](https://drive.google.com/file/d/1X6S5qwaKUlI3N3RDQSG-tJCzMBWAnqxP/view?usp=sharing). ### Training data The model is trained on pairs of co-cited papers in a contrastive learning setup. The model is trained on 1.2 million biomedical paper pairs. In training the model negative examples for the contrastive loss are obtained as random in-batch negatives. Co-citations are obtained from the full text of papers, for example - the papers in brackets below are all co-cited and each pairs title and abstracts would be used as a training pair: > The idea of distant supervision has been proposed and used widely in Relation Extraction (Mintz et al., 2009; Riedel et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012) , where the source of labels is an external knowledge base. ### Training procedure The model was trained with the Adam Optimizer and a learning rate of 2e-5 with 1000 warm-up steps followed by linear decay of the learning rate. The model training convergence is checked with the loss on a held out dev set consisting of co-cited paper pairs. ### Intended uses & limitations This model is trained for document similarity tasks in **biomedical** scientific text using a single vector per document. Here, the documents are the title and abstract of a paper. With appropriate fine-tuning the model can also be used for other tasks such as classification. Since the training data comes primarily from biomedicine, performance on other domains may be poorer. ### How to use Follow instructions for use detailed on the model github repo: https://github.com/allenai/aspire#specter-cocite ### Variable and metrics This model is evaluated on information retrieval datasets with document level queries. Here we report performance on RELISH (biomedical/English), and TRECCOVID (biomedical/English). These are detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). These datasets represent a abstract level retrieval task, where given a query scientific abstract the task requires the retrieval of relevant candidate abstracts. We rank documents by the L2 distance between the query and candidate documents. ### Evaluation results The released model `aspire-biencoder-biomed-scib` (and `aspire-biencoder-biomed-scib-full`) is compared against `allenai/specter`. `aspire-biencoder-biomed-scib-full`<sup>*</sup> is the performance reported in our paper by averaging over 3 re-runs of the model. The released models `aspire-biencoder-biomed-scib` and `aspire-biencoder-biomed-scib-full` are the single best run among the 3 re-runs. | | TRECCOVID | TRECCOVID | RELISH | RELISH | |-------------------------------------------:|:---------:|:-------:|:------:|:-------:| | | MAP | NDCG%20 | MAP | NDCG%20 | | `specter` | 28.24 | 59.28 | 60.62| 77.20 | | `aspire-biencoder-biomed-scib-full`<sup>*</sup> | 30.60 | 62.07 | 61.43| 78.01 | | `aspire-biencoder-biomed-scib` | 30.74 | 60.16 | 61.52| 78.07 | | `aspire-biencoder-biomed-scib-full` | 31.45 | 63.15 | 61.34| 77.89 | **Alternative models:** Besides the above models consider these alternative models also released in the Aspire paper: [`aspire-biencoder-compsci-spec`](https://huggingface.co/allenai/aspire-biencoder-compsci-spec): If you wanted to run on computer science papers. [`aspire-biencoder-biomed-spec`](https://huggingface.co/allenai/aspire-biencoder-biomed-spec): This is an alternative bi-encoder model identical to the above model, except that it is initialized with `allenai/specter` instead of SciBert. This usually under-performs the model released here.

rdchambers/bert-finetuned-ner

4b81e5bb92b94b5b9d9c73f5db67fbcf175b6695

2022-05-05T20:34:58.000Z

[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

token-classification

false

rdchambers

null

rdchambers/bert-finetuned-ner

7

null

transformers

14,359

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: [] ---  # bert-finetuned-ner 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: 0.0176 - Precision: 0.8418 - Recall: 0.8095 - F1: 0.8253 - Accuracy: 0.9937 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 48 | 0.0268 | 0.7280 | 0.7829 | 0.7544 | 0.9908 | | No log | 2.0 | 96 | 0.0194 | 0.8295 | 0.8050 | 0.8171 | 0.9934 | | No log | 3.0 | 144 | 0.0176 | 0.8418 | 0.8095 | 0.8253 | 0.9937 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

Reproducibility/naacl22_causalDistilBERT_instance_1

6b6e342cc4145642d683ccb0c92e4cbd9fe7c5be

2022-04-23T19:50:56.000Z

[ "pytorch", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

Reproducibility

null

Reproducibility/naacl22_causalDistilBERT_instance_1

7

null

transformers

14,360

Entry not found

avacaondata/maria-exist22-task1

fd5a13a4f89a2f55cbd6fd1ced095886183fb6f0

2022-04-23T23:32:32.000Z

[ "pytorch", "roberta", "text-classification", "transformers" ]

text-classification

false

avacaondata

null

avacaondata/maria-exist22-task1

7

null

transformers

14,361

Entry not found

Ghost1/bert-finetuned-ner-accelerate

3e082f9026d76d2bb1185a8433bd1a44a1396a0d

2022-04-25T10:34:12.000Z

[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

false

Ghost1

null

Ghost1/bert-finetuned-ner-accelerate

7

null

transformers

14,362

Entry not found

xfbai/AMRBART-base

9c3b31e5c1bfedec71f595bb4f7f1a9ccfca07ed

2022-04-26T06:12:47.000Z

[ "pytorch", "bart", "text2text-generation", "en", "arxiv:2203.07836", "transformers", "AMRBART", "license:mit", "autotrain_compatible" ]

text2text-generation

false

xfbai

null

xfbai/AMRBART-base

7

null

transformers

14,363

--- language: en tags: - AMRBART license: mit --- ## AMRBART (base-sized model) AMRBART model is continually pre-trained on the English text and AMR Graphs based on the BART model. It was introduced in the paper: [Graph Pre-training for AMR Parsing and Generation](https://arxiv.org/pdf/2203.07836.pdf) by bai et al. in ACL 2022 and first released in [this repository](https://github.com/muyeby/AMRBART). ## Model description AMRBART follows the BART model which uses a transformer encoder-encoder architecture. AMRBART is pre-trained with 6 tasks: + learning to reconstruct the text based on the corrupted text. + learning to reconstruct AMR graphs based on the corrupted AMR graph. + learning to reconstruct the text based on the corrupted text and its corresponding AMR graph. + learning to reconstruct an AMR graph based on the corrupted AMR graph and its corresponding text. + learning to reconstruct the text based on the corrupted text and its corresponding corrupted AMR graph. + learning to reconstruct an AMR graph based on the corrupted AMR graph and its corresponding corrupted text. AMRBART is particularly effective when fine-tuned for AMR parsing and AMR-to-text generation tasks. ## Training data The AMRBART model is pre-trained on [AMR3.0](https://catalog.ldc.upenn.edu/LDC2020T02), a dataset consisting of 55,635 training instances and [English Gigaword](https://catalog.ldc.upenn.edu/LDC2003T05) (we randomly sampled 200,000 sentences). ## Intended uses & limitations You can use the raw model for either AMR encoding or AMR parsing, but it's mostly intended to be fine-tuned on a downstream task. ## How to use Here is how to initialize this model in PyTorch: ```python from transformers import BartForConditionalGeneration model = BartForConditionalGeneration.from_pretrained("xfbai/AMRBART-base") ``` Please refer to [this repository](https://github.com/muyeby/AMRBART) for tokenizer initialization and data preprocessing. ## BibTeX entry and citation info Please cite this paper if you find this model helpful ```bibtex @inproceedings{bai-etal-2022-graph, title = "Graph Pre-training for {AMR} Parsing and Generation", author = "Bai, Xuefeng and Chen, Yulong and Zhang, Yue", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Online", publisher = "Association for Computational Linguistics", url = "todo", doi = "todo", pages = "todo" } ```

Nithiwat/fake-news-debunker

8f22a53ce662277bb13bf361cacbafc14a0055cb

2022-04-26T13:53:36.000Z

[ "pytorch", "roberta", "text-classification", "en", "dataset:Fake and real news datasets by CLÉMENT BISAILLON", "transformers", "autotrain", "co2_eq_emissions" ]

text-classification

false

Nithiwat

null

Nithiwat/fake-news-debunker

7

1

transformers

14,364

--- tags: autotrain language: en widget: - text: "Bill Gates wants to use mass Covid-19 vaccination campaign to implant microchips to track people" datasets: - Fake and real news datasets by CLÉMENT BISAILLON co2_eq_emissions: 4.415122243239347 --- # Model Trained Using AutoTrain - Problem: Fake News Classification - Problem type: Binary Classification - Model ID: 785124234 - CO2 Emissions (in grams): 4.415122243239347 ## Validation Metrics - Loss: 0.00012586714001372457 - Accuracy: 0.9998886538247411 - Precision: 1.0 - Recall: 0.9997665732959851 - AUC: 0.9999999999999999 - F1: 0.999883273024396 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Nithiwat/autotrain-fake-news-classifier-785124234 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("Nithiwat/autotrain-fake-news-classifier-785124234", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("Nithiwat/autotrain-fake-news-classifier-785124234", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

drsis/pegasus-samsum-tb

6fb60d780e037f6618fcc0b6ff48cff123c306b4

2022-04-26T02:18:42.000Z

[ "pytorch", "pegasus", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

drsis

null

drsis/pegasus-samsum-tb

7

null

transformers

14,365

Entry not found

anablasi/financial_model

d916472c95596b95d033ec69e11b93b122dcaf45

2022-05-10T16:32:38.000Z

[ "pytorch", "roberta", "fill-mask", "transformers", "generated_from_trainer", "license:cc-by-4.0", "model-index", "autotrain_compatible" ]

fill-mask

false

anablasi

null

anablasi/financial_model

7

null

transformers

14,366

--- license: cc-by-4.0 tags: - generated_from_trainer model-index: - name: model2 results: [] ---  # model2 This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Training results ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

sangjeedondrub/tibetan-roberta-base

1c2458923edf160701295b9b8bc6195fa7e4c9aa

2022-05-05T02:18:22.000Z

[ "pytorch", "roberta", "fill-mask", "bo", "transformers", "tibetan", "pretrained language model", "license:mit", "autotrain_compatible" ]

fill-mask

false

sangjeedondrub

null

sangjeedondrub/tibetan-roberta-base

7

0

transformers

14,367

--- language: - bo tags: - tibetan - pretrained language model - roberta widget: - text: "རྫོགས་པའི་ <mask>" - text: "ཆོས་ཀྱི་<mask>་བ" - text: "གངས་རིའི་ <mask>" - text: "བོད་ཀྱི་སྨན་<mask>" license: "mit" --- # Demo in a `fill-mask` task ``` from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline model_name = 'sangjeedondrub/tibetan-roberta-base' model = AutoModelForMaskedLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) fill_mask_pipe = pipeline( "fill-mask", model=model, tokenizer=tokenizer ) samples = """རིན་ <mask> ཆོས་ཀྱི་ <mask> རྫོགས་པའི་ <mask> གངས་རིའི་ <mask> མེ་ལོང་ <mask> བདེན་པའི་ <mask> 'འབྱུང་ <mask>""".splitlines() for idx, sample in enumerate(samples, start=1): outputs = fill_mask_pipe(sample) print(idx, sample) for output in outputs: print(output) ``` # Output ``` 1 རིན་ <mask> {'score': 0.943362832069397, 'token': 459, 'token_str': 'ཐང', 'sequence': 'རིན་ཐང'} {'score': 0.025716140866279602, 'token': 282, 'token_str': 'པ', 'sequence': 'རིན་པ'} {'score': 0.004410382825881243, 'token': 596, 'token_str': 'འཕར', 'sequence': 'རིན་འཕར'} {'score': 0.003161463886499405, 'token': 561, 'token_str': 'ཅང', 'sequence': 'རིན་ཅང'} {'score': 0.0025683969724923372, 'token': 360, 'token_str': 'གནས', 'sequence': 'རིན་གནས'} 2 ཆོས་ཀྱི་ <mask> {'score': 0.08558642119169235, 'token': 476, 'token_str': 'དཔལ', 'sequence': 'ཆོས་ཀྱི་དཔལ'} {'score': 0.0616581067442894, 'token': 323, 'token_str': 'ལས', 'sequence': 'ཆོས་ཀྱི་ལས'} {'score': 0.04617622494697571, 'token': 568, 'token_str': 'ཉམས', 'sequence': 'ཆོས་ཀྱི་ཉམས'} {'score': 0.042447883635759354, 'token': 467, 'token_str': 'དབང', 'sequence': 'ཆོས་ཀྱི་དབང'} {'score': 0.0358237698674202, 'token': 768, 'token_str': 'དད', 'sequence': 'ཆོས་ཀྱི་དད'} 3 རྫོགས་པའི་ <mask> {'score': 0.06635843217372894, 'token': 323, 'token_str': 'ལས', 'sequence': 'རྫོགས་པའི་ལས'} {'score': 0.06410858780145645, 'token': 360, 'token_str': 'གནས', 'sequence': 'རྫོགས་པའི་གནས'} {'score': 0.0570441335439682, 'token': 573, 'token_str': 'གཏམ', 'sequence': 'རྫོགས་པའི་གཏམ'} {'score': 0.05679900944232941, 'token': 397, 'token_str': 'ལམ', 'sequence': 'རྫོགས་པའི་ལམ'} {'score': 0.05157950520515442, 'token': 543, 'token_str': 'མཚན', 'sequence': 'རྫོགས་པའི་མཚན'} 4 གངས་རིའི་ <mask> {'score': 0.21429458260536194, 'token': 971, 'token_str': 'འདབས', 'sequence': 'གངས་རིའི་འདབས'} {'score': 0.05296638607978821, 'token': 360, 'token_str': 'གནས', 'sequence': 'གངས་རིའི་གནས'} {'score': 0.04839177057147026, 'token': 712, 'token_str': 'གངས', 'sequence': 'གངས་རིའི་གངས'} {'score': 0.04389436915516853, 'token': 984, 'token_str': 'འདབ', 'sequence': 'གངས་རིའི་འདབ'} {'score': 0.04158150777220726, 'token': 274, 'token_str': 'ན', 'sequence': 'གངས་རིའི་ན'} 5 མེ་ལོང་ <mask> {'score': 0.19395706057548523, 'token': 323, 'token_str': 'ལས', 'sequence': 'མེ་ལོང་ལས'} {'score': 0.12707622349262238, 'token': 293, 'token_str': 'དང', 'sequence': 'མེ་ལོང་དང'} {'score': 0.08089829981327057, 'token': 280, 'token_str': 'མ', 'sequence': 'མེ་ལོང་མ'} {'score': 0.06481984257698059, 'token': 279, 'token_str': 'ལ', 'sequence': 'མེ་ལོང་ལ'} {'score': 0.0577043853700161, 'token': 362, 'token_str': 'ནང', 'sequence': 'མེ་ལོང་ནང'} 6 བདེན་པའི་ <mask> {'score': 0.12633271515369415, 'token': 573, 'token_str': 'གཏམ', 'sequence': 'བདེན་པའི་གཏམ'} {'score': 0.0909079909324646, 'token': 360, 'token_str': 'གནས', 'sequence': 'བདེན་པའི་གནས'} {'score': 0.08624855428934097, 'token': 397, 'token_str': 'ལམ', 'sequence': 'བདེན་པའི་ལམ'} {'score': 0.07476165890693665, 'token': 362, 'token_str': 'ནང', 'sequence': 'བདེན་པའི་ནང'} {'score': 0.06319335103034973, 'token': 323, 'token_str': 'ལས', 'sequence': 'བདེན་པའི་ལས'} 7 'འབྱུང་ <mask> {'score': 0.8271735906600952, 'token': 360, 'token_str': 'གནས', 'sequence': "'འབྱུང་གནས"} {'score': 0.10802919417619705, 'token': 270, 'token_str': 'བ', 'sequence': "'འབྱུང་བ"} {'score': 0.021947095170617104, 'token': 503, 'token_str': 'ཁམས', 'sequence': "'འབྱུང་ཁམས"} {'score': 0.006081813480705023, 'token': 484, 'token_str': 'རབས', 'sequence': "'འབྱུང་རབས"} {'score': 0.002384472405537963, 'token': 293, 'token_str': 'དང', 'sequence': "'འབྱུང་དང"} ``` # About This model is trained and released by Sangjee Dondrub [sangjeedondrub at live dot com], the mere purpose of conducting these experiments is to improve my familiarity with Transformers APIs.

jenspt/bert_classification_27_04

3f855f9f59c22d02d91b8f737efe1a8e521b6b29

2022-04-27T14:10:02.000Z

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

false

jenspt

null

jenspt/bert_classification_27_04

7

null

transformers

14,368

Entry not found

NeuML/t5-small-bashsql

4e9bed94c0454354aa4fb2db142ba62d413d0fde

2022-04-28T13:12:43.000Z

[ "pytorch", "t5", "text2text-generation", "en", "transformers", "license:apache-2.0", "autotrain_compatible" ]

text2text-generation

false

NeuML

null

NeuML/t5-small-bashsql

7

null

transformers

14,369

--- language: en widget: - text: "translate Bash to SQL: find -name \"feel good story\" -mtime -1" example_title: Last day - text: "translate Bash to SQL: find -name \"show me sports stories\" -mtime -1 -team \"Red Sox\"" example_title: Last day with filter - text: "translate Bash to SQL: find -name \"breaking news\" -summary" example_title: Summary - text: "translate Bash to SQL: find -name \"breaking news\" -translate fr" example_title: Translate to French inference: parameters: max_length: 512 license: apache-2.0 --- # T5-small finedtuned to generate txtai SQL [T5 small](https://huggingface.co/t5-small) fine-tuned to generate [txtai](https://github.com/neuml/txtai) SQL. This model takes [Bash](https://en.wikipedia.org/wiki/Bash_(Unix_shell)) like commands and builds txtai-compatible SQL statements. ``` find -name "feel good story" -mtime -1 find -name "show me sports stories" -mtime -1 -team \"Red Sox\" find -name "breaking news" -summary find -name "breaking news" -translate fr ``` ## Custom query syntax This model is an example of creating a custom query syntax that can be translated into SQL txtai can understand. Any query syntax can be created. This one supports Bash-like commands but a similar strategy can be deployed to support other languages. Natural language can be translated to functions, query clauses, column selection and more. See [t5-small-txtsql](https://huggingface.co/NeuML/t5-small-txtsql) for a model that translates natural language statements into txtai SQL. ## Model training This model was trained using scripts that can be [found here](https://github.com/neuml/txtai/tree/master/models/bashsql). Steps to train: ```bash python generate.py bashsql.csv python train.py bashsql.csv t5-small-bashsql ```

classla/wav2vec2-large-slavic-parlaspeech-hr

6bd500f77d2a9f3b49a79102d4db388041be59c7

2022-05-18T13:58:26.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "hr", "dataset:parlaspeech-hr", "transformers", "audio", "parlaspeech" ]

automatic-speech-recognition

false

classla

null

classla/wav2vec2-large-slavic-parlaspeech-hr

7

1

transformers

14,370

--- language: hr datasets: - parlaspeech-hr tags: - audio - automatic-speech-recognition - parlaspeech widget: - example_title: example 1 src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/1800.m4a - example_title: example 2 src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020578b.flac.wav - example_title: example 3 src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020570a.flac.wav --- # wav2vec2-large-slavic-parlaspeech-hr This model for Croatian ASR is based on the [facebook/wav2vec2-large-slavic-voxpopuli-v2 model](https://huggingface.co/facebook/wav2vec2-large-slavic-voxpopuli-v2) and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset [ParlaSpeech-HR v1.0](http://hdl.handle.net/11356/1494). If you use this model, please cite the following paper: Nikola Ljubešić, Danijel Koržinek, Peter Rupnik, Ivo-Pavao Jazbec. ParlaSpeech-HR -- a freely available ASR dataset for Croatian bootstrapped from the ParlaMint corpus. Accepted at ParlaCLARIN@LREC. ## Metrics Evaluation is performed on the dev and test portions of the [ParlaSpeech-HR v1.0](http://hdl.handle.net/11356/1494) dataset. |split|CER|WER| |---|---|---| |dev|0.0311|0.0921| |test|0.0222|0.0679| ## Usage in `transformers` ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC import soundfile as sf import torch import os device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # load model and tokenizer processor = Wav2Vec2Processor.from_pretrained( "classla/wav2vec2-large-slavic-parlaspeech-hr") model = Wav2Vec2ForCTC.from_pretrained("classla/wav2vec2-large-slavic-parlaspeech-hr") # download the example wav files: os.system("wget https://huggingface.co/classla/wav2vec2-large-slavic-parlaspeech-hr/raw/main/00020570a.flac.wav") # read the wav file speech, sample_rate = sf.read("00020570a.flac.wav") input_values = processor(speech, sampling_rate=sample_rate, return_tensors="pt").input_values.to(device) # remove the raw wav file os.system("rm 00020570a.flac.wav") # retrieve logits logits = model.to(device)(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.decode(predicted_ids[0]).lower() # transcription: 'veliki broj poslovnih subjekata posluje sa minusom velik dio' ``` ## Training hyperparameters In fine-tuning, the following arguments were used: | arg | value | |-------------------------------|-------| | `per_device_train_batch_size` | 16 | | `gradient_accumulation_steps` | 4 | | `num_train_epochs` | 8 | | `learning_rate` | 3e-4 | | `warmup_steps` | 500 |

efederici/cross-encoder-distilbert-it

30142b713ba540668032bd736435536022468203

2022-05-03T13:14:47.000Z

[ "pytorch", "distilbert", "text-classification", "it", "transformers", "cross-encoder", "sentence-similarity", "license:apache-2.0" ]

text-classification

false

efederici

null

efederici/cross-encoder-distilbert-it

7

null

transformers

14,371

--- pipeline_tag: text-classification license: apache-2.0 language: - it tags: - cross-encoder - sentence-similarity - transformers --- # Cross-Encoder The model can be used for Information Retrieval: given a query, encode the query will all possible passages. Then sort the passages in a decreasing order. <p align="center"> <img src="https://www.exibart.com/repository/media/2020/07/bridget-riley-cool-edge.jpg" width="400"> </br> Bridget Riley, COOL EDGE </p> ## Training Data This model was trained on a custom biomedical ranking dataset. ## Usage and Performance ```python from sentence_transformers import CrossEncoder model = CrossEncoder('efederici/cross-encoder-distilbert-it') scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')]) ``` The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.

lilykaw/distilbert-base-uncased-finetuned-stsb

0f0f322c369e9a4edcf95bc841b60b4da5c1d0ca

2022-04-28T22:46:26.000Z

[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

lilykaw

null

lilykaw/distilbert-base-uncased-finetuned-stsb

7

null

transformers

14,372

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: distilbert-base-uncased-finetuned-stsb results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.8651841336703003 ---  # distilbert-base-uncased-finetuned-stsb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5634 - Pearson: 0.8680 - Spearmanr: 0.8652 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | No log | 1.0 | 360 | 0.6646 | 0.8516 | 0.8494 | | 1.0238 | 2.0 | 720 | 0.5617 | 0.8666 | 0.8637 | | 0.3952 | 3.0 | 1080 | 0.6533 | 0.8649 | 0.8646 | | 0.3952 | 4.0 | 1440 | 0.5889 | 0.8651 | 0.8625 | | 0.2488 | 5.0 | 1800 | 0.5634 | 0.8680 | 0.8652 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

thusken/nb-bert-base-target-group

bec6a71230e50ff6df31a195e1fd78da0af14dde

2022-05-06T12:24:27.000Z

[ "pytorch", "bert", "text-classification", "transformers", "generated_from_trainer", "license:cc-by-4.0", "model-index" ]

text-classification

false

thusken

null

thusken/nb-bert-base-target-group

7

null

transformers

14,373

--- license: cc-by-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: nb-bert-base-target-group results: [] ---  # nb-bert-base-target-group This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2820 - Accuracy: 0.8822 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 64 - 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2779 | 1.0 | 2032 | 0.2820 | 0.8822 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.12.1

chiragasarpota/scotus-bert

d93025e5c0fe0810f4f30bd5a1a9d5725916eee6

2022-04-29T16:36:17.000Z

[ "pytorch", "bert", "text-classification", "transformers", "license:apache-2.0" ]

text-classification

false

chiragasarpota

null

chiragasarpota/scotus-bert

7

null

transformers

14,374

--- license: apache-2.0 ---

omar47/wav2vec2-large-xls-r-300m-urdu

1b18aac1552bac1987a72c719267f2e59c38cbb4

2022-05-16T15:20:18.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

omar47

null

omar47/wav2vec2-large-xls-r-300m-urdu

7

null

transformers

14,375

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xls-r-300m-urdu results: [] ---  # wav2vec2-large-xls-r-300m-urdu This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m). It achieves the following results on the evaluation set: - Loss: 0.5285 - Wer: 0.1702 ## 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: 35 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 16.9618 | 0.74 | 32 | 15.0745 | 1.0 | | 9.1928 | 1.49 | 64 | 5.9361 | 1.0 | | 4.9307 | 2.23 | 96 | 4.2924 | 1.0 | | 3.8917 | 2.98 | 128 | 3.5873 | 1.0 | | 3.3867 | 3.72 | 160 | 3.2594 | 1.0 | | 3.2107 | 4.47 | 192 | 3.1718 | 1.0 | | 3.1395 | 5.21 | 224 | 3.1281 | 1.0 | | 3.115 | 5.95 | 256 | 3.1238 | 1.0 | | 3.0801 | 6.7 | 288 | 3.0674 | 1.0 | | 2.9725 | 7.44 | 320 | 2.8277 | 1.0 | | 2.4159 | 8.19 | 352 | 1.7186 | 0.9036 | | 1.3377 | 8.93 | 384 | 1.0271 | 0.6433 | | 0.8591 | 9.67 | 416 | 0.8087 | 0.5441 | | 0.726 | 10.42 | 448 | 0.7263 | 0.4634 | | 0.6242 | 11.16 | 480 | 0.6783 | 0.4156 | | 0.5417 | 11.91 | 512 | 0.6611 | 0.4305 | | 0.4784 | 12.65 | 544 | 0.6300 | 0.3926 | | 0.4198 | 13.4 | 576 | 0.5646 | 0.3499 | | 0.3798 | 14.14 | 608 | 0.5919 | 0.3229 | | 0.3356 | 14.88 | 640 | 0.5715 | 0.3369 | | 0.2954 | 15.63 | 672 | 0.5325 | 0.2728 | | 0.264 | 16.37 | 704 | 0.5535 | 0.2689 | | 0.2535 | 17.12 | 736 | 0.5467 | 0.2366 | | 0.2277 | 17.86 | 768 | 0.5219 | 0.2345 | | 0.2141 | 18.6 | 800 | 0.5314 | 0.2487 | | 0.2036 | 19.35 | 832 | 0.5382 | 0.2236 | | 0.2021 | 20.09 | 864 | 0.5038 | 0.1922 | | 0.1676 | 20.84 | 896 | 0.5238 | 0.2033 | | 0.1544 | 21.58 | 928 | 0.5069 | 0.1866 | | 0.1512 | 22.33 | 960 | 0.5045 | 0.1965 | | 0.1512 | 23.07 | 992 | 0.5167 | 0.1862 | | 0.1399 | 23.81 | 1024 | 0.5236 | 0.1840 | | 0.1291 | 24.56 | 1056 | 0.5234 | 0.1957 | | 0.1274 | 25.3 | 1088 | 0.5348 | 0.1943 | | 0.127 | 26.05 | 1120 | 0.4978 | 0.1719 | | 0.1105 | 26.79 | 1152 | 0.5067 | 0.1767 | | 0.1069 | 27.53 | 1184 | 0.5150 | 0.1758 | | 0.1058 | 28.28 | 1216 | 0.5218 | 0.1844 | | 0.0999 | 29.02 | 1248 | 0.5375 | 0.1852 | | 0.0964 | 29.77 | 1280 | 0.5373 | 0.1843 | | 0.0971 | 30.51 | 1312 | 0.5190 | 0.1776 | | 0.0906 | 31.26 | 1344 | 0.5217 | 0.1747 | | 0.0909 | 32.0 | 1376 | 0.5204 | 0.1778 | | 0.0784 | 32.74 | 1408 | 0.5336 | 0.1756 | | 0.0823 | 33.49 | 1440 | 0.5281 | 0.1699 | | 0.0834 | 34.23 | 1472 | 0.5292 | 0.1700 | | 0.0827 | 34.98 | 1504 | 0.5285 | 0.1702 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

TehranNLP-org/bert-large-hateXplain

e9344baa4889877e63918285b4f55f1e24f5b3d9

2022-05-03T17:01:45.000Z

[ "pytorch", "bert", "text-classification", "en", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

TehranNLP-org

null

TehranNLP-org/bert-large-hateXplain

7

null

transformers

14,376

--- language: - en license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: SEED0042 results: - task: name: Text Classification type: text-classification dataset: name: HATEXPLAIN type: '' args: hatexplain metrics: - name: Accuracy type: accuracy value: 0.40790842872008326 ---  # SEED0042 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the HATEXPLAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.7731 - Accuracy: 0.4079 - Accuracy 0: 0.8027 - Accuracy 1: 0.1869 - Accuracy 2: 0.2956 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: not_parallel - gradient_accumulation_steps: 32 - 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: 150 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy 0 | Accuracy 1 | Accuracy 2 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:----------:|:----------:| | No log | 1.0 | 480 | 0.8029 | 0.4235 | 0.7589 | 0.0461 | 0.5985 | | No log | 2.0 | 960 | 0.7574 | 0.4011 | 0.7470 | 0.1831 | 0.3376 | | No log | 3.0 | 1440 | 0.7731 | 0.4079 | 0.8027 | 0.1869 | 0.2956 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu113 - Datasets 2.1.0 - Tokenizers 0.11.6

cuzeverynameistaken/wav2vec2-base-timit-demo-colab0

fe329a0253f47c7f0e7868d381459f6d3814ea67

2022-05-01T08:59:37.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

cuzeverynameistaken

null

cuzeverynameistaken/wav2vec2-base-timit-demo-colab0

7

null

transformers

14,377

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab0 results: [] ---  # wav2vec2-base-timit-demo-colab0 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: 0.6960 - Wer: 0.5694 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.3196 | 13.89 | 500 | 3.1225 | 1.0 | | 1.2756 | 27.78 | 1000 | 0.6960 | 0.5694 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

pszemraj/mGPT-Peter-2E

7663e8d936855df0c4b9b26a06a46d7b6d54672b

2022-05-18T17:49:12.000Z

[ "pytorch", "gpt2", "text-generation", "dataset:mc4", "dataset:Wikipedia", "transformers", "multilingual", "PyTorch", "Transformers", "gpt3", "Deepspeed", "Megatron", "mGPT", "license:apache-2.0" ]

text-generation

false

pszemraj

null

pszemraj/mGPT-Peter-2E

7

null

transformers

14,378

--- license: apache-2.0 pipeline_tag: text-generation tags: - multilingual - PyTorch - Transformers - gpt3 - gpt2 - Deepspeed - Megatron - mGPT datasets: - mc4 - Wikipedia widget: - text: "Ich weiß, dass du müde bist, aber können wir heute Abend noch einen Spaziergang machen? peter szemraj: ich" example_title: "walk - Deutsch" - text: "peter szemraj: 我喜欢穿很酷的衣服" example_title: "fashion - Chinese" - text: "Wat zei je over mijn moeder? peter szemraj: ik" example_title: "🚎 - Dutch" - text: "Zagadka: Człowiekowi, który przebywał na dworze w deszczu bez parasola czy kapelusza, nie zmoczył się ani jeden włos na głowie. Dlaczego? peter szemraj: czy to" example_title: "brain teaser - Polish" - text: "Minha amiga diz que conhece todas as línguas, mas não fala nenhuma delas... o que há de errado com ela? peter szemraj: eu" example_title: "language - Portuguese" - text: "se potesse vivere ovunque, dove sarebbe? peter szemraj: io" example_title: "dream living place - Italian" - text: "Can you take me for dinner somewhere nice this time? peter szemraj:" example_title: "dinner" - text: "What really makes you angry? peter szemraj:" example_title: "pet peeve" - text: "Jak nazwać aligatora, który właśnie przeszedł operację usunięcia lewego ramienia?peter szemraj: ja" example_title: "alligator - Polish" - text: "Warum sind Transformers für die Sprachmodellierung wichtig? peter szemraj: es ist" example_title: "Transformers - German" - text: "как написать хорошие подсказки для языковых моделей? peter szemraj: сначала вам нужно" example_title: "prompt tutorial - Russian" - text: "Pewien mężczyzna wpycha swój samochód do hotelu i mówi właścicielowi, że jest bankrutem. Dlaczego? peter szemraj: może" example_title: "brain teaser - Polish 2" - text: "Zagadka: Mówię bez ust i słyszę bez uszu. Nie mam ciała, ale ożywiam się wraz z wiatrem. Czym jestem? peter szemraj: czy to" example_title: "brain teaser - Polish 3" - text: "Què t'agrada fer per divertir-te? peter szemraj: m'agrada" example_title: "hobbies - Catalan" - text: "为什么你总是那么累？peter szemraj: 呃，我想" example_title: "tired - Chinese" inference: parameters: min_length: 2 max_length: 64 do_sample: True top_k: 10 top_p: 0.9 temperature: 0.65 repetition_penalty: 3.5 no_repeat_ngram_size: 3 length_penalty: 0.4 pad_token: 1 --- # mGPT: fine-tune on message data - 2E - This model is a fine-tuned version of [sberbank-ai/mGPT](https://huggingface.co/sberbank-ai/mGPT) on 80k messages. This builds on the minimum-working-example checkpoint [here](https://huggingface.co/pszemraj/mGPT-Peter-mwe). - 2E = 2 epochs ## Model description - testing if fine-tuned personality data bleeds over to other languages without being trained in them explicitly **Interesting findings thus far:** - Passing a generic word after the `<name-identifier>` that is in a non-English language helps ensure the model responds in the question language (see: any example). - Model generations (in general) remain semantically consistent, even if the generations switch from `<language>`to English in the middle of the generated text. This demonstrates some sort of "universal concept understanding" ### Usage in python Install the transformers library if you don't have it: ``` pip install -U transformers ``` load the model into a pipeline object: ``` from transformers import pipeline import torch device = 'cuda' if torch.cuda.is_available() else 'cpu' my_chatbot = pipeline('text-generation', 'pszemraj/mGPT-Peter-2E', device=0 if device == 'cuda' else -1, ) ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 1 (in addition to all training on prior checkpoints) ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

dineshmane/bert-finetuned-mrpc

2a3780ca3d2b5a6d0c83fca7066214ab3147c0aa

2022-05-01T17:55:58.000Z

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

false

dineshmane

null

dineshmane/bert-finetuned-mrpc

7

null

transformers

14,379

Entry not found

Ghani-25/SummFinFR

18cf0d7f07f8fffdb0cd5df889da4efd87318a34

2022-05-02T13:15:08.000Z

[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

Ghani-25

null

Ghani-25/SummFinFR

7

null

transformers

14,380

Entry not found

ali2066/DistilBERTFINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False

17e7f04b5fad299a01915670cfafba1682d0f3f0

2022-05-02T13:33:27.000Z

[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers", "generated_from_trainer", "model-index" ]

text-classification

false

ali2066

null

ali2066/DistilBERTFINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False

7

null

transformers

14,381

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: DistilBERTFINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False results: [] ---  # DistilBERTFINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7680 - Precision: 0.9838 - Recall: 0.6632 - F1: 0.7923 - Accuracy: 0.6624 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 130 | 0.2980 | 0.9315 | 0.9533 | 0.9423 | 0.9081 | | No log | 2.0 | 260 | 0.2053 | 0.9537 | 0.9626 | 0.9581 | 0.9338 | | No log | 3.0 | 390 | 0.1873 | 0.9464 | 0.9907 | 0.9680 | 0.9485 | | 0.3064 | 4.0 | 520 | 0.1811 | 0.9585 | 0.9720 | 0.9652 | 0.9449 | | 0.3064 | 5.0 | 650 | 0.1887 | 0.9587 | 0.9766 | 0.9676 | 0.9485 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False

c9dfd66b1bea7eaf4edc170d8deae57807a18d21

2022-05-02T18:27:20.000Z

[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

ali2066

null

ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False

7

null

transformers

14,382

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: DistilBERT_FINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False results: [] ---  # DistilBERT_FINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0703 - Precision: 0.9667 - Recall: 0.0505 - F1: 0.0961 - Accuracy: 0.0766 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 95 | 0.5442 | 0.6667 | 0.1132 | 0.1935 | 0.75 | | No log | 2.0 | 190 | 0.5316 | 0.5385 | 0.1321 | 0.2121 | 0.74 | | No log | 3.0 | 285 | 0.5384 | 0.4615 | 0.2264 | 0.3038 | 0.725 | | No log | 4.0 | 380 | 0.5503 | 0.4286 | 0.2264 | 0.2963 | 0.715 | | No log | 5.0 | 475 | 0.5529 | 0.4286 | 0.2264 | 0.2963 | 0.715 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False

c3b8e2538407cc275c431bbaead1ef9a5039c455

2022-05-02T18:36:09.000Z

[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

ali2066

null

ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False

7

null

transformers

14,383

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False results: [] ---  # DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0699 - Precision: 0.9942 - Recall: 0.9773 - F1: 0.9857 - Accuracy: 0.9725 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 479 | 0.4036 | 0.8333 | 0.9326 | 0.8802 | 0.8054 | | 0.5047 | 2.0 | 958 | 0.3749 | 0.8635 | 0.9339 | 0.8973 | 0.8361 | | 0.3336 | 3.0 | 1437 | 0.3789 | 0.8862 | 0.9184 | 0.9020 | 0.8471 | | 0.2644 | 4.0 | 1916 | 0.4024 | 0.8762 | 0.9171 | 0.8962 | 0.8371 | | 0.2233 | 5.0 | 2395 | 0.4195 | 0.8784 | 0.9171 | 0.8973 | 0.8391 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

laituan245/molt5-base-caption2smiles

c7689836acd99876a7255256505e378110140714

2022-05-03T18:08:45.000Z

[ "pytorch", "t5", "text2text-generation", "arxiv:2204.11817", "transformers", "license:apache-2.0", "autotrain_compatible" ]

text2text-generation

false

laituan245

null

laituan245/molt5-base-caption2smiles

7

null

transformers

14,384

--- license: apache-2.0 --- This model can be used to generate a SMILES string from an input caption. ## Example Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-base-caption2smiles", model_max_length=512) model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-base-caption2smiles') input_text = 'The molecule is a monomethoxybenzene that is 2-methoxyphenol substituted by a hydroxymethyl group at position 4. It has a role as a plant metabolite. It is a member of guaiacols and a member of benzyl alcohols.' input_ids = tokenizer(input_text, return_tensors="pt").input_ids outputs = model.generate(input_ids, num_beams=5, max_length=512) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # The model will generate "COC1=C(C=CC(=C1)CCCO)O". The ground-truth is "COC1=C(C=CC(=C1)CO)O". ``` ## Paper For more information, please take a look at our paper. Paper: [Translation between Molecules and Natural Language](https://arxiv.org/abs/2204.11817) Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*

tartuNLP/EstBERT_NER_v2

fc9726c7b6ef3b876b3a826fa471e9120c19c4c3

2022-05-06T06:27:43.000Z

[ "pytorch", "bert", "token-classification", "et", "transformers", "license:cc-by-4.0", "autotrain_compatible" ]

token-classification

false

tartuNLP

null

tartuNLP/EstBERT_NER_v2

7

null

transformers

14,385

--- language: et license: cc-by-4.0 widget: - text: "Eesti President on Alar Karis." --- # Estonian NER model based on EstBERT This model is a fine-tuned version of [tartuNLP/EstBERT](https://huggingface.co/tartuNLP/EstBERT) on the Estonian NER dataset. The model was trained by tartuNLP, the NLP research group at the institute of Computer Science at the University of Tartu. It achieves the following results on the test set: - Loss: 0.3565 - Precision: 0.7612 - Recall: 0.7744 - F1: 0.7678 - Accuracy: 0.9672 The entity-level results are as follows: | | Precision | Recall | F1 | Number | |---------| --------- | ------- | ------- | ------- | | DATE | 0.7278 | 0.7258 | 0.7268 | 372 | | EVENT | 0.3721 | 0.5714 | 0.4507 | 28 | | GPE | 0.8679 | 0.8369 | 0.8521 | 840 | | LOC | 0.6545 | 0.4832 | 0.5560 | 149 | | MONEY | 0.6625 | 0.6023 | 0.6310 | 88 | | ORG | 0.6761 | 0.7267 | 0.7005 | 589 | | PER | 0.8255 | 0.9068 | 0.8642 | 751 | | PERCENT | 1.0 | 0.9589 | 0.9790 | 73 | | PROD | 0.6030 | 0.5430 | 0.5714 | 221 | | TIME | 0.5682 | 0.5556 | 0.5618 | 45 | | TITLE | 0.7 | 0.8063 | 0.7494 | 191 | ## How to use You can use this model with Transformers pipeline for NER. Post-processing of results may be necessary as the model occasionally tags subword tokens as entities. ``` from transformers import BertTokenizer, BertForTokenClassification from transformers import pipeline tokenizer = BertTokenizer.from_pretrained('tartuNLP/EstBERT_NER') bertner = BertForTokenClassification.from_pretrained('tartuNLP/EstBERT_NER') nlp = pipeline("ner", model=bertner, tokenizer=tokenizer) text = "Kaia Kanepi (WTA 57.) langes USA-s Charlestonis toimuval WTA 500 kategooria tenniseturniiril konkurentsist kaheksandikfinaalis, kaotades poolatarile Magda Linette'ile (WTA 64.) 3 : 6, 6 : 4, 2 : 6." ner_results = new_nlp(text) tokens=tokenizer(text) tokens=tokenizer.convert_ids_to_tokens(tokens['input_ids']) print(f'tokens: {tokens}') print(f'NER model:{ner_results}') ``` ``` tokens: ['[CLS]', 'kai', '##a', 'kanepi', '(', 'w', '##ta', '57', '.', ')', 'langes', 'usa', '-', 's', 'cha', '##rl', '##est', '##onis', 'toimuval', 'w', '##ta', '500', 'kategooria', 'tennise', '##turniiril', 'konkurentsist', 'kaheksandik', '##finaalis', ',', 'kaotades', 'poola', '##tari', '##le', 'ma', '##gda', 'line', '##tte', "'", 'ile', '(', 'w', '##ta', '64', '.', ')', '3', ':', '6', ',', '6', ':', '4', ',', '2', ':', '6', '.', '[SEP]'] ``` ``` NER model: [{'entity': 'B-PER', 'score': 0.99999887, 'index': 1, 'word': 'kai', 'start': None, 'end': None}, {'entity': 'B-PER', 'score': 0.97371966, 'index': 2, 'word': '##a', 'start': None, 'end': None}, {'entity': 'I-PER', 'score': 0.99999815, 'index': 3, 'word': 'kanepi', 'start': None, 'end': None}, {'entity': 'B-ORG', 'score': 0.63085276, 'index': 5, 'word': 'w', 'start': None, 'end': None}, {'entity': 'B-GPE', 'score': 0.99999934, 'index': 11, 'word': 'usa', 'start': None, 'end': None}, {'entity': 'B-GPE', 'score': 0.9999685, 'index': 14, 'word': 'cha', 'start': None, 'end': None}, {'entity': 'I-GPE', 'score': 0.8875574, 'index': 15, 'word': '##rl', 'start': None, 'end': None}, {'entity': 'I-GPE', 'score': 0.9996168, 'index': 16, 'word': '##est', 'start': None, 'end': None}, {'entity': 'I-GPE', 'score': 0.9992657, 'index': 17, 'word': '##onis', 'start': None, 'end': None}, {'entity': 'B-EVENT', 'score': 0.99999064, 'index': 19, 'word': 'w', 'start': None, 'end': None}, {'entity': 'I-EVENT', 'score': 0.9772493, 'index': 20, 'word': '##ta', 'start': None, 'end': None}, {'entity': 'I-EVENT', 'score': 0.99999076, 'index': 21, 'word': '500', 'start': None, 'end': None}, {'entity': 'I-EVENT', 'score': 0.99955636, 'index': 22, 'word': 'kategooria', 'start': None, 'end': None}, {'entity': 'B-TITLE', 'score': 0.8771319, 'index': 30, 'word': 'poola', 'start': None, 'end': None}, {'entity': 'B-PER', 'score': 0.99999785, 'index': 33, 'word': 'ma', 'start': None, 'end': None}, {'entity': 'B-PER', 'score': 0.9998398, 'index': 34, 'word': '##gda', 'start': None, 'end': None}, {'entity': 'I-PER', 'score': 0.9999987, 'index': 35, 'word': 'line', 'start': None, 'end': None}, {'entity': 'I-PER', 'score': 0.9999976, 'index': 36, 'word': '##tte', 'start': None, 'end': None}, {'entity': 'I-PER', 'score': 0.99999285, 'index': 37, 'word': "'", 'start': None, 'end': None}, {'entity': 'I-PER', 'score': 0.9999794, 'index': 38, 'word': 'ile', 'start': None, 'end': None}, {'entity': 'B-ORG', 'score': 0.7664479, 'index': 40, 'word': 'w', 'start': None, 'end': None}] ``` ## Intended uses & limitations This model can be used to find named entities from Estonian texts. The model is free to use for anyone. TartuNLP does not guarantee that the model is useful for anyone or anything. TartuNLP is not responsible for any results it generates. ## Training and evaluation data The model was trained on two Estonian NER datasets: - [The Reannotated Estonian NER corpus](https://metashare.ut.ee/repository/browse/reannotated-estonian-ner-corpus/bd43f1f614a511eca6e4fa163e9d45477d086613d2894fd5af79bf13e3f13594/) - [The New Estonian NER corpus](https://metashare.ut.ee/repository/browse/new-estonian-ner-corpus/98b6706c963c11eba6e4fa163e9d45470bcd0533b6994c93ab8b8c628516ffed/) Both datasets have been annotated with the same annotation scheme. For training this model, the datasets were joined. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 1024 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 - lr_scheduler_type: polynomial - max num_epochs: 150 - early stopping limit: 20 - early stopping tol: 0.0001 - mixed_precision_training: Native AMP ### Training results The final model was saved after epoch 53 (shown in bold) where the overall F1 was the highest on the development set. | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Date Precision | Date Recall | Date F1 | Date Number | Event Precision | Event Recall | Event F1 | Event Number | Gpe Precision | Gpe Recall | Gpe F1 | Gpe Number | Loc Precision | Loc Recall | Loc F1 | Loc Number | Money Precision | Money Recall | Money F1 | Money Number | Org Precision | Org Recall | Org F1 | Org Number | Per Precision | Per Recall | Per F1 | Per Number | Percent Precision | Percent Recall | Percent F1 | Percent Number | Prod Precision | Prod Recall | Prod F1 | Prod Number | Time Precision | Time Recall | Time F1 | Time Number | Title Precision | Title Recall | Title F1 | Title Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|:--------------:|:-----------:|:-------:|:-----------:|:---------------:|:------------:|:--------:|:------------:|:-------------:|:----------:|:------:|:----------:|:-------------:|:----------:|:------:|:----------:|:---------------:|:------------:|:--------:|:------------:|:-------------:|:----------:|:------:|:----------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:--------------:|:--------------:|:-----------:|:-------:|:-----------:|:--------------:|:-----------:|:-------:|:-----------:|:---------------:|:------------:|:--------:|:------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.3252 | 1 | 1061 | 0.1628 | 0.6835 | 0.6083 | 0.6437 | 0.9526 | 0.5910 | 0.6022 | 0.5965 | 372 | 0.0 | 0.0 | 0.0 | 28 | 0.8073 | 0.7631 | 0.7846 | 840 | 0.1389 | 0.0336 | 0.0541 | 149 | 0.4217 | 0.3977 | 0.4094 | 88 | 0.5381 | 0.5280 | 0.5330 | 589 | 0.7917 | 0.8655 | 0.8270 | 751 | 0.6471 | 0.3014 | 0.4112 | 73 | 0.2581 | 0.0724 | 0.1131 | 221 | 0.1429 | 0.0889 | 0.1096 | 45 | 0.7805 | 0.6702 | 0.7211 | 191 | 0.6835 | 0.6083 | 0.6437 | 0.9526 | | 0.1513 | 2 | 2122 | 0.1332 | 0.6906 | 0.7329 | 0.7111 | 0.9615 | 0.6185 | 0.7366 | 0.6724 | 372 | 0.0857 | 0.1071 | 0.0952 | 28 | 0.7874 | 0.8595 | 0.8219 | 840 | 0.4767 | 0.2752 | 0.3489 | 149 | 0.6848 | 0.7159 | 0.7000 | 88 | 0.6158 | 0.6231 | 0.6194 | 589 | 0.7770 | 0.9001 | 0.8341 | 751 | 0.9565 | 0.9041 | 0.9296 | 73 | 0.5 | 0.3620 | 0.4199 | 221 | 0.3571 | 0.3333 | 0.3448 | 45 | 0.6033 | 0.7644 | 0.6744 | 191 | 0.6906 | 0.7329 | 0.7111 | 0.9615 | | 0.1131 | 3 | 3183 | 0.1281 | 0.7224 | 0.7338 | 0.7280 | 0.9638 | 0.7054 | 0.7339 | 0.7194 | 372 | 0.1053 | 0.1429 | 0.1212 | 28 | 0.8013 | 0.85 | 0.8250 | 840 | 0.5476 | 0.3087 | 0.3948 | 149 | 0.6386 | 0.6023 | 0.6199 | 88 | 0.6371 | 0.6469 | 0.6420 | 589 | 0.8235 | 0.8762 | 0.8490 | 751 | 0.9859 | 0.9589 | 0.9722 | 73 | 0.5148 | 0.3937 | 0.4462 | 221 | 0.5116 | 0.4889 | 0.5 | 45 | 0.6245 | 0.7749 | 0.6916 | 191 | 0.7224 | 0.7338 | 0.7280 | 0.9638 | | 0.0884 | 4 | 4244 | 0.1354 | 0.7283 | 0.7386 | 0.7334 | 0.9639 | 0.6785 | 0.6694 | 0.6739 | 372 | 0.1795 | 0.25 | 0.2090 | 28 | 0.8231 | 0.8310 | 0.8270 | 840 | 0.6020 | 0.3960 | 0.4777 | 149 | 0.6092 | 0.6023 | 0.6057 | 88 | 0.6473 | 0.7012 | 0.6732 | 589 | 0.8351 | 0.8628 | 0.8487 | 751 | 1.0 | 0.9726 | 0.9861 | 73 | 0.5899 | 0.4751 | 0.5263 | 221 | 0.4524 | 0.4222 | 0.4368 | 45 | 0.6 | 0.7853 | 0.6803 | 191 | 0.7283 | 0.7386 | 0.7334 | 0.9639 | | 0.0685 | 5 | 5305 | 0.1383 | 0.7224 | 0.7696 | 0.7453 | 0.9644 | 0.6635 | 0.7473 | 0.7029 | 372 | 0.26 | 0.4643 | 0.3333 | 28 | 0.8259 | 0.8357 | 0.8308 | 840 | 0.5913 | 0.4564 | 0.5152 | 149 | 0.6437 | 0.6364 | 0.64 | 88 | 0.6540 | 0.7284 | 0.6892 | 589 | 0.8070 | 0.8961 | 0.8492 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.5693 | 0.5204 | 0.5437 | 221 | 0.5192 | 0.6 | 0.5567 | 45 | 0.6320 | 0.7644 | 0.6919 | 191 | 0.7224 | 0.7696 | 0.7453 | 0.9644 | | 0.0532 | 6 | 6366 | 0.1493 | 0.7099 | 0.7613 | 0.7347 | 0.9631 | 0.6727 | 0.6962 | 0.6843 | 372 | 0.2308 | 0.5357 | 0.3226 | 28 | 0.8242 | 0.8262 | 0.8252 | 840 | 0.5877 | 0.4497 | 0.5095 | 149 | 0.6410 | 0.5682 | 0.6024 | 88 | 0.6232 | 0.7470 | 0.6795 | 589 | 0.8087 | 0.8895 | 0.8472 | 751 | 0.9672 | 0.8082 | 0.8806 | 73 | 0.5107 | 0.5385 | 0.5242 | 221 | 0.6190 | 0.5778 | 0.5977 | 45 | 0.6371 | 0.7906 | 0.7056 | 191 | 0.7099 | 0.7613 | 0.7347 | 0.9631 | | 0.0403 | 7 | 7427 | 0.1592 | 0.7239 | 0.7592 | 0.7411 | 0.9642 | 0.6923 | 0.7016 | 0.6969 | 372 | 0.2857 | 0.5714 | 0.3810 | 28 | 0.8272 | 0.8262 | 0.8267 | 840 | 0.5752 | 0.4362 | 0.4962 | 149 | 0.6265 | 0.5909 | 0.6082 | 88 | 0.6402 | 0.6978 | 0.6677 | 589 | 0.8404 | 0.8762 | 0.8579 | 751 | 0.9859 | 0.9589 | 0.9722 | 73 | 0.5257 | 0.6018 | 0.5612 | 221 | 0.5870 | 0.6 | 0.5934 | 45 | 0.6235 | 0.8063 | 0.7032 | 191 | 0.7239 | 0.7592 | 0.7411 | 0.9642 | | 0.0304 | 8 | 8488 | 0.1738 | 0.7301 | 0.7484 | 0.7392 | 0.9644 | 0.6866 | 0.6774 | 0.6820 | 372 | 0.3409 | 0.5357 | 0.4167 | 28 | 0.8393 | 0.8083 | 0.8235 | 840 | 0.5882 | 0.4698 | 0.5224 | 149 | 0.6429 | 0.6136 | 0.6279 | 88 | 0.6608 | 0.6978 | 0.6788 | 589 | 0.8268 | 0.8708 | 0.8482 | 751 | 0.9595 | 0.9726 | 0.9660 | 73 | 0.5351 | 0.5520 | 0.5434 | 221 | 0.5208 | 0.5556 | 0.5376 | 45 | 0.6204 | 0.7958 | 0.6972 | 191 | 0.7301 | 0.7484 | 0.7392 | 0.9644 | | 0.0234 | 9 | 9549 | 0.1860 | 0.7248 | 0.7625 | 0.7432 | 0.9641 | 0.6947 | 0.7097 | 0.7021 | 372 | 0.2963 | 0.5714 | 0.3902 | 28 | 0.8317 | 0.8298 | 0.8308 | 840 | 0.5913 | 0.4564 | 0.5152 | 149 | 0.6118 | 0.5909 | 0.6012 | 88 | 0.6361 | 0.7063 | 0.6693 | 589 | 0.8410 | 0.8735 | 0.8570 | 751 | 0.9859 | 0.9589 | 0.9722 | 73 | 0.5212 | 0.6109 | 0.5625 | 221 | 0.5417 | 0.5778 | 0.5591 | 45 | 0.6414 | 0.7958 | 0.7103 | 191 | 0.7248 | 0.7625 | 0.7432 | 0.9641 | | 0.0178 | 10 | 10610 | 0.2037 | 0.7434 | 0.7383 | 0.7408 | 0.9640 | 0.7159 | 0.6774 | 0.6961 | 372 | 0.2857 | 0.4286 | 0.3429 | 28 | 0.8333 | 0.8333 | 0.8333 | 840 | 0.6262 | 0.4497 | 0.5234 | 149 | 0.6324 | 0.4886 | 0.5513 | 88 | 0.6568 | 0.6757 | 0.6661 | 589 | 0.8291 | 0.8722 | 0.8501 | 751 | 1.0 | 0.8219 | 0.9023 | 73 | 0.5672 | 0.5158 | 0.5403 | 221 | 0.5 | 0.5333 | 0.5161 | 45 | 0.6952 | 0.7644 | 0.7282 | 191 | 0.7434 | 0.7383 | 0.7408 | 0.9640 | | 0.0147 | 11 | 11671 | 0.2114 | 0.7440 | 0.7233 | 0.7335 | 0.9643 | 0.7009 | 0.6613 | 0.6805 | 372 | 0.3030 | 0.3571 | 0.3279 | 28 | 0.8352 | 0.8024 | 0.8185 | 840 | 0.6238 | 0.4228 | 0.504 | 149 | 0.65 | 0.5909 | 0.6190 | 88 | 0.6436 | 0.6469 | 0.6452 | 589 | 0.8407 | 0.8575 | 0.8490 | 751 | 0.9315 | 0.9315 | 0.9315 | 73 | 0.5812 | 0.5023 | 0.5388 | 221 | 0.5476 | 0.5111 | 0.5287 | 45 | 0.6835 | 0.7801 | 0.7286 | 191 | 0.7440 | 0.7233 | 0.7335 | 0.9643 | | 0.0118 | 12 | 12732 | 0.2218 | 0.7331 | 0.7532 | 0.7430 | 0.9649 | 0.7119 | 0.6909 | 0.7012 | 372 | 0.3488 | 0.5357 | 0.4225 | 28 | 0.8325 | 0.8405 | 0.8365 | 840 | 0.5303 | 0.4698 | 0.4982 | 149 | 0.65 | 0.5909 | 0.6190 | 88 | 0.6690 | 0.6587 | 0.6638 | 589 | 0.8178 | 0.8908 | 0.8528 | 751 | 0.9677 | 0.8219 | 0.8889 | 73 | 0.5408 | 0.5701 | 0.5551 | 221 | 0.5102 | 0.5556 | 0.5319 | 45 | 0.6567 | 0.8010 | 0.7217 | 191 | 0.7331 | 0.7532 | 0.7430 | 0.9649 | | 0.0093 | 13 | 13793 | 0.2283 | 0.7495 | 0.7359 | 0.7427 | 0.9644 | 0.7163 | 0.6989 | 0.7075 | 372 | 0.3810 | 0.5714 | 0.4571 | 28 | 0.8612 | 0.7905 | 0.8243 | 840 | 0.6111 | 0.4430 | 0.5136 | 149 | 0.6145 | 0.5795 | 0.5965 | 88 | 0.6775 | 0.6740 | 0.6757 | 589 | 0.8346 | 0.8802 | 0.8568 | 751 | 0.9710 | 0.9178 | 0.9437 | 73 | 0.5619 | 0.5339 | 0.5476 | 221 | 0.4 | 0.4889 | 0.4400 | 45 | 0.6812 | 0.7382 | 0.7085 | 191 | 0.7495 | 0.7359 | 0.7427 | 0.9644 | | 0.0079 | 14 | 14854 | 0.2383 | 0.7371 | 0.7490 | 0.7430 | 0.9647 | 0.6727 | 0.7016 | 0.6868 | 372 | 0.3261 | 0.5357 | 0.4054 | 28 | 0.8453 | 0.8 | 0.8220 | 840 | 0.5963 | 0.4362 | 0.5039 | 149 | 0.625 | 0.5682 | 0.5952 | 88 | 0.6634 | 0.6927 | 0.6777 | 589 | 0.8433 | 0.8815 | 0.8620 | 751 | 0.9853 | 0.9178 | 0.9504 | 73 | 0.5427 | 0.5747 | 0.5582 | 221 | 0.5814 | 0.5556 | 0.5682 | 45 | 0.6513 | 0.8115 | 0.7226 | 191 | 0.7371 | 0.7490 | 0.7430 | 0.9647 | | 0.0068 | 15 | 15915 | 0.2511 | 0.7255 | 0.7359 | 0.7306 | 0.9639 | 0.6826 | 0.6532 | 0.6676 | 372 | 0.3590 | 0.5 | 0.4179 | 28 | 0.8295 | 0.8167 | 0.8230 | 840 | 0.5263 | 0.4698 | 0.4965 | 149 | 0.6575 | 0.5455 | 0.5963 | 88 | 0.6549 | 0.6604 | 0.6577 | 589 | 0.8242 | 0.8802 | 0.8513 | 751 | 0.9833 | 0.8082 | 0.8872 | 73 | 0.5398 | 0.5520 | 0.5459 | 221 | 0.36 | 0.4 | 0.3789 | 45 | 0.6511 | 0.8010 | 0.7183 | 191 | 0.7255 | 0.7359 | 0.7306 | 0.9639 | | 0.0061 | 16 | 16976 | 0.2497 | 0.7253 | 0.7690 | 0.7465 | 0.9648 | 0.6824 | 0.6989 | 0.6906 | 372 | 0.3333 | 0.5357 | 0.4110 | 28 | 0.8473 | 0.8321 | 0.8396 | 840 | 0.4583 | 0.5168 | 0.4858 | 149 | 0.6494 | 0.5682 | 0.6061 | 88 | 0.6556 | 0.7368 | 0.6938 | 589 | 0.8382 | 0.8828 | 0.8599 | 751 | 0.9841 | 0.8493 | 0.9118 | 73 | 0.5341 | 0.6380 | 0.5814 | 221 | 0.5 | 0.5333 | 0.5161 | 45 | 0.6622 | 0.7801 | 0.7163 | 191 | 0.7253 | 0.7690 | 0.7465 | 0.9648 | | 0.0054 | 17 | 18037 | 0.2554 | 0.7323 | 0.7625 | 0.7471 | 0.9650 | 0.6870 | 0.6962 | 0.6916 | 372 | 0.3421 | 0.4643 | 0.3939 | 28 | 0.8463 | 0.8262 | 0.8361 | 840 | 0.5902 | 0.4832 | 0.5314 | 149 | 0.6753 | 0.5909 | 0.6303 | 88 | 0.6640 | 0.7148 | 0.6885 | 589 | 0.8317 | 0.8948 | 0.8621 | 751 | 0.9437 | 0.9178 | 0.9306 | 73 | 0.5210 | 0.5611 | 0.5403 | 221 | 0.5 | 0.5111 | 0.5055 | 45 | 0.6102 | 0.8115 | 0.6966 | 191 | 0.7323 | 0.7625 | 0.7471 | 0.9650 | | 0.005 | 18 | 19098 | 0.2601 | 0.7273 | 0.7747 | 0.7503 | 0.9654 | 0.6970 | 0.7608 | 0.7275 | 372 | 0.2830 | 0.5357 | 0.3704 | 28 | 0.8320 | 0.8488 | 0.8403 | 840 | 0.5841 | 0.4430 | 0.5038 | 149 | 0.6477 | 0.6477 | 0.6477 | 88 | 0.6378 | 0.6995 | 0.6672 | 589 | 0.8501 | 0.8908 | 0.8700 | 751 | 0.9722 | 0.9589 | 0.9655 | 73 | 0.5323 | 0.5973 | 0.5629 | 221 | 0.4444 | 0.4444 | 0.4444 | 45 | 0.624 | 0.8168 | 0.7075 | 191 | 0.7273 | 0.7747 | 0.7503 | 0.9654 | | 0.0044 | 19 | 20159 | 0.2602 | 0.7369 | 0.7616 | 0.7490 | 0.9656 | 0.7124 | 0.7124 | 0.7124 | 372 | 0.3415 | 0.5 | 0.4058 | 28 | 0.8239 | 0.8631 | 0.8430 | 840 | 0.6355 | 0.4564 | 0.5313 | 149 | 0.6667 | 0.6136 | 0.6391 | 88 | 0.6517 | 0.6638 | 0.6577 | 589 | 0.8405 | 0.8842 | 0.8618 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.5144 | 0.5656 | 0.5388 | 221 | 0.5217 | 0.5333 | 0.5275 | 45 | 0.6550 | 0.7853 | 0.7143 | 191 | 0.7369 | 0.7616 | 0.7490 | 0.9656 | | 0.004 | 20 | 21220 | 0.2677 | 0.7347 | 0.7702 | 0.7520 | 0.9658 | 0.7374 | 0.7097 | 0.7233 | 372 | 0.2857 | 0.4286 | 0.3429 | 28 | 0.8466 | 0.8345 | 0.8405 | 840 | 0.6050 | 0.4832 | 0.5373 | 149 | 0.6667 | 0.6136 | 0.6391 | 88 | 0.6593 | 0.7131 | 0.6852 | 589 | 0.8240 | 0.8975 | 0.8591 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.4981 | 0.5837 | 0.5375 | 221 | 0.5102 | 0.5556 | 0.5319 | 45 | 0.6371 | 0.8272 | 0.7198 | 191 | 0.7347 | 0.7702 | 0.7520 | 0.9658 | | 0.0034 | 21 | 22281 | 0.2743 | 0.7386 | 0.7717 | 0.7548 | 0.9657 | 0.6984 | 0.7097 | 0.704 | 372 | 0.3784 | 0.5 | 0.4308 | 28 | 0.8475 | 0.8333 | 0.8403 | 840 | 0.6333 | 0.5101 | 0.5651 | 149 | 0.6190 | 0.5909 | 0.6047 | 88 | 0.6512 | 0.7385 | 0.6921 | 589 | 0.8428 | 0.8921 | 0.8668 | 751 | 0.9846 | 0.8767 | 0.9275 | 73 | 0.5513 | 0.5837 | 0.5670 | 221 | 0.5106 | 0.5333 | 0.5217 | 45 | 0.6379 | 0.8115 | 0.7143 | 191 | 0.7386 | 0.7717 | 0.7548 | 0.9657 | | 0.0033 | 22 | 23342 | 0.2788 | 0.7418 | 0.7520 | 0.7469 | 0.9652 | 0.7143 | 0.6989 | 0.7065 | 372 | 0.3182 | 0.5 | 0.3889 | 28 | 0.8367 | 0.8298 | 0.8332 | 840 | 0.6168 | 0.4430 | 0.5156 | 149 | 0.6235 | 0.6023 | 0.6127 | 88 | 0.6758 | 0.6689 | 0.6724 | 589 | 0.8327 | 0.8815 | 0.8564 | 751 | 0.9714 | 0.9315 | 0.9510 | 73 | 0.5458 | 0.5928 | 0.5683 | 221 | 0.4783 | 0.4889 | 0.4835 | 45 | 0.6637 | 0.7853 | 0.7194 | 191 | 0.7418 | 0.7520 | 0.7469 | 0.9652 | | 0.0033 | 23 | 24403 | 0.2831 | 0.7342 | 0.7535 | 0.7437 | 0.9650 | 0.6981 | 0.6962 | 0.6972 | 372 | 0.3784 | 0.5 | 0.4308 | 28 | 0.8499 | 0.8024 | 0.8255 | 840 | 0.5034 | 0.4966 | 0.5 | 149 | 0.6067 | 0.6136 | 0.6102 | 88 | 0.6581 | 0.6961 | 0.6766 | 589 | 0.8350 | 0.8961 | 0.8645 | 751 | 0.9714 | 0.9315 | 0.9510 | 73 | 0.5424 | 0.5792 | 0.5602 | 221 | 0.3774 | 0.4444 | 0.4082 | 45 | 0.7048 | 0.7749 | 0.7382 | 191 | 0.7342 | 0.7535 | 0.7437 | 0.9650 | | 0.0029 | 24 | 25464 | 0.2931 | 0.7544 | 0.7380 | 0.7461 | 0.9648 | 0.7365 | 0.6989 | 0.7172 | 372 | 0.3590 | 0.5 | 0.4179 | 28 | 0.8535 | 0.7976 | 0.8246 | 840 | 0.5849 | 0.4161 | 0.4863 | 149 | 0.6622 | 0.5568 | 0.6049 | 88 | 0.6672 | 0.6706 | 0.6689 | 589 | 0.8474 | 0.8802 | 0.8635 | 751 | 0.9701 | 0.8904 | 0.9286 | 73 | 0.5550 | 0.5475 | 0.5513 | 221 | 0.4889 | 0.4889 | 0.4889 | 45 | 0.7023 | 0.7906 | 0.7438 | 191 | 0.7544 | 0.7380 | 0.7461 | 0.9648 | | 0.0028 | 25 | 26525 | 0.2899 | 0.7489 | 0.7574 | 0.7531 | 0.9654 | 0.7021 | 0.7097 | 0.7059 | 372 | 0.3902 | 0.5714 | 0.4638 | 28 | 0.8635 | 0.8131 | 0.8375 | 840 | 0.6182 | 0.4564 | 0.5251 | 149 | 0.6471 | 0.625 | 0.6358 | 88 | 0.6613 | 0.6995 | 0.6799 | 589 | 0.8454 | 0.9028 | 0.8731 | 751 | 0.9583 | 0.9452 | 0.9517 | 73 | 0.5681 | 0.5475 | 0.5576 | 221 | 0.4222 | 0.4222 | 0.4222 | 45 | 0.6608 | 0.7853 | 0.7177 | 191 | 0.7489 | 0.7574 | 0.7531 | 0.9654 | | 0.0023 | 26 | 27586 | 0.2922 | 0.7413 | 0.7532 | 0.7472 | 0.9649 | 0.6897 | 0.6989 | 0.6943 | 372 | 0.35 | 0.5 | 0.4118 | 28 | 0.85 | 0.8298 | 0.8398 | 840 | 0.6161 | 0.4631 | 0.5287 | 149 | 0.6486 | 0.5455 | 0.5926 | 88 | 0.6486 | 0.6927 | 0.6700 | 589 | 0.8457 | 0.8828 | 0.8638 | 751 | 0.9853 | 0.9178 | 0.9504 | 73 | 0.5636 | 0.5611 | 0.5624 | 221 | 0.3958 | 0.4222 | 0.4086 | 45 | 0.6638 | 0.7958 | 0.7238 | 191 | 0.7413 | 0.7532 | 0.7472 | 0.9649 | | 0.0021 | 27 | 28647 | 0.2967 | 0.7514 | 0.7568 | 0.7541 | 0.9656 | 0.7081 | 0.7043 | 0.7062 | 372 | 0.3659 | 0.5357 | 0.4348 | 28 | 0.8547 | 0.8190 | 0.8365 | 840 | 0.5641 | 0.4430 | 0.4962 | 149 | 0.6582 | 0.5909 | 0.6228 | 88 | 0.6677 | 0.7097 | 0.6881 | 589 | 0.8459 | 0.8842 | 0.8646 | 751 | 0.9710 | 0.9178 | 0.9437 | 73 | 0.5806 | 0.5701 | 0.5753 | 221 | 0.4898 | 0.5333 | 0.5106 | 45 | 0.7089 | 0.7906 | 0.7475 | 191 | 0.7514 | 0.7568 | 0.7541 | 0.9656 | | 0.0025 | 28 | 29708 | 0.2957 | 0.7335 | 0.7622 | 0.7475 | 0.9651 | 0.7060 | 0.7231 | 0.7145 | 372 | 0.3077 | 0.4286 | 0.3582 | 28 | 0.8459 | 0.8429 | 0.8444 | 840 | 0.5069 | 0.4899 | 0.4983 | 149 | 0.6438 | 0.5341 | 0.5839 | 88 | 0.6838 | 0.7012 | 0.6924 | 589 | 0.8413 | 0.8895 | 0.8647 | 751 | 0.9552 | 0.8767 | 0.9143 | 73 | 0.4901 | 0.5611 | 0.5232 | 221 | 0.3818 | 0.4667 | 0.42 | 45 | 0.6580 | 0.7958 | 0.7204 | 191 | 0.7335 | 0.7622 | 0.7475 | 0.9651 | | 0.0023 | 29 | 30769 | 0.3049 | 0.7455 | 0.7544 | 0.7499 | 0.9654 | 0.6997 | 0.7392 | 0.7190 | 372 | 0.3182 | 0.5 | 0.3889 | 28 | 0.8483 | 0.8119 | 0.8297 | 840 | 0.5630 | 0.5101 | 0.5352 | 149 | 0.6579 | 0.5682 | 0.6098 | 88 | 0.6791 | 0.7114 | 0.6949 | 589 | 0.8583 | 0.8628 | 0.8606 | 751 | 0.9853 | 0.9178 | 0.9504 | 73 | 0.5234 | 0.5566 | 0.5395 | 221 | 0.4565 | 0.4667 | 0.4615 | 45 | 0.7009 | 0.7853 | 0.7407 | 191 | 0.7455 | 0.7544 | 0.7499 | 0.9654 | | 0.0018 | 30 | 31830 | 0.3042 | 0.7415 | 0.7679 | 0.7544 | 0.9654 | 0.6935 | 0.7419 | 0.7169 | 372 | 0.3333 | 0.5 | 0.4 | 28 | 0.8563 | 0.8226 | 0.8391 | 840 | 0.5878 | 0.5168 | 0.55 | 149 | 0.6582 | 0.5909 | 0.6228 | 88 | 0.6677 | 0.7470 | 0.7051 | 589 | 0.8544 | 0.8828 | 0.8684 | 751 | 0.9710 | 0.9178 | 0.9437 | 73 | 0.5300 | 0.5204 | 0.5251 | 221 | 0.4375 | 0.4667 | 0.4516 | 45 | 0.6417 | 0.8063 | 0.7146 | 191 | 0.7415 | 0.7679 | 0.7544 | 0.9654 | | 0.0017 | 31 | 32891 | 0.3071 | 0.7540 | 0.7481 | 0.7510 | 0.9660 | 0.7083 | 0.7312 | 0.7196 | 372 | 0.4054 | 0.5357 | 0.4615 | 28 | 0.8552 | 0.8226 | 0.8386 | 840 | 0.6311 | 0.4362 | 0.5159 | 149 | 0.6220 | 0.5795 | 0.6 | 88 | 0.6734 | 0.6757 | 0.6746 | 589 | 0.8626 | 0.8775 | 0.8700 | 751 | 0.9855 | 0.9315 | 0.9577 | 73 | 0.5307 | 0.5475 | 0.5390 | 221 | 0.3830 | 0.4 | 0.3913 | 45 | 0.7019 | 0.7644 | 0.7318 | 191 | 0.7540 | 0.7481 | 0.7510 | 0.9660 | | 0.0018 | 32 | 33952 | 0.3190 | 0.7499 | 0.7553 | 0.7526 | 0.9656 | 0.7182 | 0.7124 | 0.7152 | 372 | 0.3333 | 0.5357 | 0.4110 | 28 | 0.8586 | 0.7952 | 0.8257 | 840 | 0.6116 | 0.4966 | 0.5481 | 149 | 0.6463 | 0.6023 | 0.6235 | 88 | 0.6805 | 0.6978 | 0.6890 | 589 | 0.8360 | 0.8895 | 0.8619 | 751 | 0.9855 | 0.9315 | 0.9577 | 73 | 0.5633 | 0.5837 | 0.5733 | 221 | 0.5106 | 0.5333 | 0.5217 | 45 | 0.6711 | 0.8010 | 0.7303 | 191 | 0.7499 | 0.7553 | 0.7526 | 0.9656 | | 0.0018 | 33 | 35013 | 0.3094 | 0.7460 | 0.7774 | 0.7614 | 0.9665 | 0.7147 | 0.7473 | 0.7306 | 372 | 0.3659 | 0.5357 | 0.4348 | 28 | 0.8556 | 0.8393 | 0.8474 | 840 | 0.6273 | 0.4631 | 0.5328 | 149 | 0.6506 | 0.6136 | 0.6316 | 88 | 0.6787 | 0.7351 | 0.7058 | 589 | 0.8344 | 0.8988 | 0.8654 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.5702 | 0.6063 | 0.5877 | 221 | 0.3036 | 0.3778 | 0.3366 | 45 | 0.6567 | 0.8010 | 0.7217 | 191 | 0.7460 | 0.7774 | 0.7614 | 0.9665 | | 0.0015 | 34 | 36074 | 0.3091 | 0.7441 | 0.7759 | 0.7597 | 0.9665 | 0.7113 | 0.7285 | 0.7198 | 372 | 0.3404 | 0.5714 | 0.4267 | 28 | 0.8266 | 0.8512 | 0.8387 | 840 | 0.5405 | 0.5369 | 0.5387 | 149 | 0.6707 | 0.625 | 0.6471 | 88 | 0.6856 | 0.7182 | 0.7015 | 589 | 0.8517 | 0.8868 | 0.8689 | 751 | 1.0 | 0.9452 | 0.9718 | 73 | 0.5752 | 0.5882 | 0.5817 | 221 | 0.3878 | 0.4222 | 0.4043 | 45 | 0.6830 | 0.8010 | 0.7373 | 191 | 0.7441 | 0.7759 | 0.7597 | 0.9665 | | 0.0015 | 35 | 37135 | 0.3185 | 0.7487 | 0.7619 | 0.7552 | 0.9660 | 0.6982 | 0.7339 | 0.7156 | 372 | 0.3415 | 0.5 | 0.4058 | 28 | 0.8685 | 0.8179 | 0.8424 | 840 | 0.5504 | 0.4765 | 0.5108 | 149 | 0.6353 | 0.6136 | 0.6243 | 88 | 0.6636 | 0.7267 | 0.6937 | 589 | 0.8654 | 0.8815 | 0.8734 | 751 | 1.0 | 0.9315 | 0.9645 | 73 | 0.55 | 0.5475 | 0.5488 | 221 | 0.3673 | 0.4 | 0.3830 | 45 | 0.6937 | 0.8063 | 0.7458 | 191 | 0.7487 | 0.7619 | 0.7552 | 0.9660 | | 0.0015 | 36 | 38196 | 0.3203 | 0.7438 | 0.7649 | 0.7542 | 0.9660 | 0.6961 | 0.7204 | 0.7081 | 372 | 0.3659 | 0.5357 | 0.4348 | 28 | 0.8617 | 0.8381 | 0.8497 | 840 | 0.5203 | 0.5168 | 0.5185 | 149 | 0.6667 | 0.5909 | 0.6265 | 88 | 0.6710 | 0.7063 | 0.6882 | 589 | 0.8495 | 0.8868 | 0.8678 | 751 | 0.9710 | 0.9178 | 0.9437 | 73 | 0.5561 | 0.5385 | 0.5471 | 221 | 0.42 | 0.4667 | 0.4421 | 45 | 0.6568 | 0.8115 | 0.7260 | 191 | 0.7438 | 0.7649 | 0.7542 | 0.9660 | | 0.0013 | 37 | 39257 | 0.3298 | 0.7315 | 0.7732 | 0.7518 | 0.9656 | 0.6915 | 0.7231 | 0.7070 | 372 | 0.3333 | 0.5714 | 0.4211 | 28 | 0.8654 | 0.8190 | 0.8416 | 840 | 0.4793 | 0.5436 | 0.5094 | 149 | 0.6582 | 0.5909 | 0.6228 | 88 | 0.6656 | 0.7267 | 0.6948 | 589 | 0.8289 | 0.9028 | 0.8642 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.5574 | 0.5928 | 0.5746 | 221 | 0.4043 | 0.4222 | 0.4130 | 45 | 0.6408 | 0.8220 | 0.7202 | 191 | 0.7315 | 0.7732 | 0.7518 | 0.9656 | | 0.0012 | 38 | 40318 | 0.3311 | 0.7533 | 0.7610 | 0.7571 | 0.9664 | 0.7060 | 0.7231 | 0.7145 | 372 | 0.3571 | 0.5357 | 0.4286 | 28 | 0.8613 | 0.8357 | 0.8483 | 840 | 0.6339 | 0.4765 | 0.5441 | 149 | 0.6543 | 0.6023 | 0.6272 | 88 | 0.6528 | 0.7182 | 0.6839 | 589 | 0.8424 | 0.8828 | 0.8622 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.6031 | 0.5294 | 0.5639 | 221 | 0.4130 | 0.4222 | 0.4176 | 45 | 0.7122 | 0.7644 | 0.7374 | 191 | 0.7533 | 0.7610 | 0.7571 | 0.9664 | | 0.0012 | 39 | 41379 | 0.3328 | 0.7444 | 0.7553 | 0.7498 | 0.9657 | 0.6818 | 0.7258 | 0.7031 | 372 | 0.3478 | 0.5714 | 0.4324 | 28 | 0.8561 | 0.8143 | 0.8347 | 840 | 0.6055 | 0.4430 | 0.5116 | 149 | 0.6582 | 0.5909 | 0.6228 | 88 | 0.6715 | 0.7046 | 0.6877 | 589 | 0.8461 | 0.8708 | 0.8583 | 751 | 0.9706 | 0.9041 | 0.9362 | 73 | 0.5665 | 0.5973 | 0.5815 | 221 | 0.4082 | 0.4444 | 0.4255 | 45 | 0.6770 | 0.8010 | 0.7338 | 191 | 0.7444 | 0.7553 | 0.7498 | 0.9657 | | 0.0014 | 40 | 42440 | 0.3415 | 0.7421 | 0.7437 | 0.7429 | 0.9641 | 0.6931 | 0.7043 | 0.6987 | 372 | 0.3488 | 0.5357 | 0.4225 | 28 | 0.8422 | 0.8262 | 0.8341 | 840 | 0.6190 | 0.4362 | 0.5118 | 149 | 0.6622 | 0.5568 | 0.6049 | 88 | 0.6888 | 0.6350 | 0.6608 | 589 | 0.8175 | 0.8828 | 0.8489 | 751 | 1.0 | 0.9178 | 0.9571 | 73 | 0.5584 | 0.5837 | 0.5708 | 221 | 0.4043 | 0.4222 | 0.4130 | 45 | 0.6580 | 0.7958 | 0.7204 | 191 | 0.7421 | 0.7437 | 0.7429 | 0.9641 | | 0.0013 | 41 | 43501 | 0.3401 | 0.7501 | 0.7487 | 0.7494 | 0.9651 | 0.6915 | 0.7231 | 0.7070 | 372 | 0.3421 | 0.4643 | 0.3939 | 28 | 0.8545 | 0.8179 | 0.8358 | 840 | 0.6346 | 0.4430 | 0.5217 | 149 | 0.6812 | 0.5341 | 0.5987 | 88 | 0.6728 | 0.6808 | 0.6768 | 589 | 0.8380 | 0.8748 | 0.8560 | 751 | 0.9710 | 0.9178 | 0.9437 | 73 | 0.5860 | 0.5701 | 0.5780 | 221 | 0.4423 | 0.5111 | 0.4742 | 45 | 0.6787 | 0.7853 | 0.7282 | 191 | 0.7501 | 0.7487 | 0.7494 | 0.9651 | | 0.0011 | 42 | 44562 | 0.3468 | 0.7426 | 0.7687 | 0.7554 | 0.9650 | 0.6965 | 0.7527 | 0.7235 | 372 | 0.3488 | 0.5357 | 0.4225 | 28 | 0.8667 | 0.8202 | 0.8428 | 840 | 0.6408 | 0.4430 | 0.5238 | 149 | 0.6709 | 0.6023 | 0.6347 | 88 | 0.6902 | 0.7148 | 0.7023 | 589 | 0.8404 | 0.8975 | 0.8680 | 751 | 0.9444 | 0.9315 | 0.9379 | 73 | 0.5191 | 0.6154 | 0.5631 | 221 | 0.3469 | 0.3778 | 0.3617 | 45 | 0.6210 | 0.8063 | 0.7016 | 191 | 0.7426 | 0.7687 | 0.7554 | 0.9650 | | 0.0015 | 43 | 45623 | 0.3440 | 0.7566 | 0.7422 | 0.7493 | 0.9648 | 0.6937 | 0.7366 | 0.7145 | 372 | 0.3846 | 0.5357 | 0.4478 | 28 | 0.8608 | 0.8095 | 0.8344 | 840 | 0.6082 | 0.3960 | 0.4797 | 149 | 0.7 | 0.5568 | 0.6203 | 88 | 0.6766 | 0.6570 | 0.6667 | 589 | 0.8317 | 0.8881 | 0.8590 | 751 | 0.9701 | 0.8904 | 0.9286 | 73 | 0.6224 | 0.5520 | 0.5851 | 221 | 0.3913 | 0.4 | 0.3956 | 45 | 0.7081 | 0.7749 | 0.74 | 191 | 0.7566 | 0.7422 | 0.7493 | 0.9648 | | 0.0011 | 44 | 46684 | 0.3354 | 0.7565 | 0.7640 | 0.7602 | 0.9664 | 0.7062 | 0.7366 | 0.7211 | 372 | 0.3659 | 0.5357 | 0.4348 | 28 | 0.8483 | 0.8452 | 0.8468 | 840 | 0.6095 | 0.4295 | 0.5039 | 149 | 0.6883 | 0.6023 | 0.6424 | 88 | 0.6880 | 0.6740 | 0.6810 | 589 | 0.8517 | 0.8948 | 0.8727 | 751 | 0.9710 | 0.9178 | 0.9437 | 73 | 0.6238 | 0.5928 | 0.6079 | 221 | 0.3830 | 0.4 | 0.3913 | 45 | 0.65 | 0.8168 | 0.7239 | 191 | 0.7565 | 0.7640 | 0.7602 | 0.9664 | | 0.0011 | 45 | 47745 | 0.3347 | 0.7485 | 0.7622 | 0.7553 | 0.9655 | 0.7088 | 0.7392 | 0.7237 | 372 | 0.3636 | 0.5714 | 0.4444 | 28 | 0.8603 | 0.8286 | 0.8441 | 840 | 0.5882 | 0.4698 | 0.5224 | 149 | 0.6023 | 0.6023 | 0.6023 | 88 | 0.6770 | 0.6689 | 0.6729 | 589 | 0.8417 | 0.8921 | 0.8662 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.6037 | 0.5928 | 0.5982 | 221 | 0.4583 | 0.4889 | 0.4731 | 45 | 0.6275 | 0.8115 | 0.7078 | 191 | 0.7485 | 0.7622 | 0.7553 | 0.9655 | | 0.0011 | 46 | 48806 | 0.3421 | 0.7481 | 0.7640 | 0.7559 | 0.9657 | 0.7261 | 0.7339 | 0.7299 | 372 | 0.3171 | 0.4643 | 0.3768 | 28 | 0.8570 | 0.8202 | 0.8382 | 840 | 0.5691 | 0.4698 | 0.5147 | 149 | 0.6429 | 0.6136 | 0.6279 | 88 | 0.6769 | 0.7114 | 0.6937 | 589 | 0.8311 | 0.8908 | 0.8599 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.5714 | 0.5611 | 0.5662 | 221 | 0.5 | 0.5556 | 0.5263 | 45 | 0.6638 | 0.7958 | 0.7238 | 191 | 0.7481 | 0.7640 | 0.7559 | 0.9657 | | 0.0009 | 47 | 49867 | 0.3487 | 0.7496 | 0.7604 | 0.7550 | 0.9656 | 0.7158 | 0.7043 | 0.7100 | 372 | 0.3409 | 0.5357 | 0.4167 | 28 | 0.86 | 0.8190 | 0.8390 | 840 | 0.5496 | 0.4832 | 0.5143 | 149 | 0.7162 | 0.6023 | 0.6543 | 88 | 0.6745 | 0.7284 | 0.7004 | 589 | 0.8346 | 0.8802 | 0.8568 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.5566 | 0.5339 | 0.5450 | 221 | 0.5349 | 0.5111 | 0.5227 | 45 | 0.6828 | 0.8115 | 0.7416 | 191 | 0.7496 | 0.7604 | 0.7550 | 0.9656 | | 0.0009 | 48 | 50928 | 0.3470 | 0.7414 | 0.7649 | 0.7529 | 0.9651 | 0.7092 | 0.7473 | 0.7277 | 372 | 0.3333 | 0.5357 | 0.4110 | 28 | 0.8541 | 0.8226 | 0.8381 | 840 | 0.5847 | 0.4631 | 0.5169 | 149 | 0.6835 | 0.6136 | 0.6467 | 88 | 0.6801 | 0.7148 | 0.6970 | 589 | 0.8319 | 0.8895 | 0.8597 | 751 | 0.9571 | 0.9178 | 0.9371 | 73 | 0.5307 | 0.5475 | 0.5390 | 221 | 0.4583 | 0.4889 | 0.4731 | 45 | 0.6364 | 0.8063 | 0.7113 | 191 | 0.7414 | 0.7649 | 0.7529 | 0.9651 | | 0.0011 | 49 | 51989 | 0.3389 | 0.7435 | 0.7664 | 0.7547 | 0.9659 | 0.6957 | 0.7312 | 0.7130 | 372 | 0.3590 | 0.5 | 0.4179 | 28 | 0.8561 | 0.8286 | 0.8421 | 840 | 0.6636 | 0.4899 | 0.5637 | 149 | 0.6136 | 0.6136 | 0.6136 | 88 | 0.6732 | 0.6995 | 0.6861 | 589 | 0.8251 | 0.8921 | 0.8573 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.5746 | 0.5928 | 0.5835 | 221 | 0.4348 | 0.4444 | 0.4396 | 45 | 0.6390 | 0.8063 | 0.7130 | 191 | 0.7435 | 0.7664 | 0.7547 | 0.9659 | | 0.0009 | 50 | 53050 | 0.3557 | 0.7490 | 0.7640 | 0.7564 | 0.9659 | 0.6948 | 0.6855 | 0.6901 | 372 | 0.3947 | 0.5357 | 0.4545 | 28 | 0.8584 | 0.8298 | 0.8438 | 840 | 0.6455 | 0.4765 | 0.5483 | 149 | 0.6933 | 0.5909 | 0.6380 | 88 | 0.6745 | 0.7317 | 0.7020 | 589 | 0.8296 | 0.8948 | 0.8610 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.6082 | 0.5339 | 0.5687 | 221 | 0.4043 | 0.4222 | 0.4130 | 45 | 0.6270 | 0.8272 | 0.7133 | 191 | 0.7490 | 0.7640 | 0.7564 | 0.9659 | | 0.0008 | 51 | 54111 | 0.3492 | 0.7516 | 0.7601 | 0.7558 | 0.9662 | 0.7104 | 0.6989 | 0.7046 | 372 | 0.3714 | 0.4643 | 0.4127 | 28 | 0.8545 | 0.8321 | 0.8432 | 840 | 0.6496 | 0.5101 | 0.5714 | 149 | 0.625 | 0.5682 | 0.5952 | 88 | 0.6722 | 0.6893 | 0.6806 | 589 | 0.8413 | 0.8895 | 0.8647 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.5611 | 0.5611 | 0.5611 | 221 | 0.4792 | 0.5111 | 0.4946 | 45 | 0.6724 | 0.8168 | 0.7376 | 191 | 0.7516 | 0.7601 | 0.7558 | 0.9662 | | 0.0008 | 52 | 55172 | 0.3432 | 0.7526 | 0.7625 | 0.7575 | 0.9661 | 0.7044 | 0.7366 | 0.7201 | 372 | 0.3571 | 0.5357 | 0.4286 | 28 | 0.8610 | 0.8262 | 0.8433 | 840 | 0.6140 | 0.4698 | 0.5323 | 149 | 0.6667 | 0.5909 | 0.6265 | 88 | 0.6766 | 0.6927 | 0.6846 | 589 | 0.8403 | 0.8895 | 0.8642 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.5849 | 0.5611 | 0.5727 | 221 | 0.46 | 0.5111 | 0.4842 | 45 | 0.6681 | 0.8115 | 0.7329 | 191 | 0.7526 | 0.7625 | 0.7575 | 0.9661 | | **0.0006** | **53** | **56233** | **0.3565** | **0.7615** | **0.7747** | **0.7681** | **0.9672** | **0.7305** | **0.7285** | **0.7295** | **372** | **0.3721** | **0.5714** | **0.4507** | **28** | **0.8679** | **0.8369** | **0.8521** | **840** | **0.6545** | **0.4832** | **0.5560** | **149** | **0.6625** | **0.6023** | **0.6310** | **88** | **0.6761** | **0.7267** | **0.7005** | **589** | **0.8255** | **0.9068** | **0.8642** | **751** | **1.0** | **0.9589** | **0.9790** | **73** | **0.6030** | **0.5430** | **0.5714** | **221** | **0.5682** | **0.5556** | **0.5618** | **45** | **0.7** | **0.8063** | **0.7494** | **191** | **0.7615** | **0.7747** | **0.7681** | **0.9672** | | 0.0008 | 54 | 57294 | 0.3480 | 0.7590 | 0.7631 | 0.7610 | 0.9668 | 0.7452 | 0.7312 | 0.7381 | 372 | 0.3409 | 0.5357 | 0.4167 | 28 | 0.8589 | 0.8190 | 0.8385 | 840 | 0.5935 | 0.4899 | 0.5368 | 149 | 0.7027 | 0.5909 | 0.6420 | 88 | 0.6924 | 0.6842 | 0.6883 | 589 | 0.8432 | 0.8948 | 0.8682 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.5856 | 0.5882 | 0.5869 | 221 | 0.5102 | 0.5556 | 0.5319 | 45 | 0.6513 | 0.8115 | 0.7226 | 191 | 0.7590 | 0.7631 | 0.7610 | 0.9668 | | 0.0008 | 55 | 58355 | 0.3568 | 0.7601 | 0.7622 | 0.7612 | 0.9663 | 0.7228 | 0.7151 | 0.7189 | 372 | 0.3571 | 0.5357 | 0.4286 | 28 | 0.8429 | 0.8429 | 0.8429 | 840 | 0.6634 | 0.4497 | 0.536 | 149 | 0.7 | 0.5568 | 0.6203 | 88 | 0.6828 | 0.7165 | 0.6993 | 589 | 0.8655 | 0.8828 | 0.8741 | 751 | 0.9853 | 0.9178 | 0.9504 | 73 | 0.5909 | 0.5294 | 0.5585 | 221 | 0.5106 | 0.5333 | 0.5217 | 45 | 0.6429 | 0.8010 | 0.7133 | 191 | 0.7601 | 0.7622 | 0.7612 | 0.9663 | | 0.0009 | 56 | 59416 | 0.3498 | 0.7542 | 0.7580 | 0.7561 | 0.9661 | 0.7178 | 0.7043 | 0.7110 | 372 | 0.3409 | 0.5357 | 0.4167 | 28 | 0.8379 | 0.8429 | 0.8404 | 840 | 0.6634 | 0.4497 | 0.536 | 149 | 0.6322 | 0.625 | 0.6286 | 88 | 0.6895 | 0.6825 | 0.6860 | 589 | 0.8513 | 0.8842 | 0.8674 | 751 | 0.9577 | 0.9315 | 0.9444 | 73 | 0.5613 | 0.5385 | 0.5497 | 221 | 0.5111 | 0.5111 | 0.5111 | 45 | 0.6667 | 0.8063 | 0.7299 | 191 | 0.7542 | 0.7580 | 0.7561 | 0.9661 | | 0.0007 | 57 | 60477 | 0.3486 | 0.7479 | 0.7711 | 0.7593 | 0.9663 | 0.7143 | 0.7392 | 0.7266 | 372 | 0.3571 | 0.5357 | 0.4286 | 28 | 0.8417 | 0.8417 | 0.8417 | 840 | 0.5923 | 0.5168 | 0.5520 | 149 | 0.6667 | 0.6136 | 0.6391 | 88 | 0.6720 | 0.7165 | 0.6935 | 589 | 0.8562 | 0.8802 | 0.8680 | 751 | 0.9714 | 0.9315 | 0.9510 | 73 | 0.5670 | 0.5747 | 0.5708 | 221 | 0.4583 | 0.4889 | 0.4731 | 45 | 0.6623 | 0.8010 | 0.7251 | 191 | 0.7479 | 0.7711 | 0.7593 | 0.9663 | | 0.0007 | 58 | 61538 | 0.3497 | 0.7539 | 0.7744 | 0.7640 | 0.9667 | 0.7143 | 0.7392 | 0.7266 | 372 | 0.3659 | 0.5357 | 0.4348 | 28 | 0.8449 | 0.8429 | 0.8439 | 840 | 0.6429 | 0.4832 | 0.5517 | 149 | 0.6667 | 0.5909 | 0.6265 | 88 | 0.6708 | 0.7267 | 0.6976 | 589 | 0.8499 | 0.8975 | 0.8731 | 751 | 0.9714 | 0.9315 | 0.9510 | 73 | 0.6108 | 0.5611 | 0.5849 | 221 | 0.5 | 0.4889 | 0.4944 | 45 | 0.6525 | 0.8063 | 0.7213 | 191 | 0.7539 | 0.7744 | 0.7640 | 0.9667 | | 0.0008 | 59 | 62599 | 0.3581 | 0.7474 | 0.7762 | 0.7615 | 0.9662 | 0.7183 | 0.7473 | 0.7325 | 372 | 0.3409 | 0.5357 | 0.4167 | 28 | 0.8439 | 0.8429 | 0.8434 | 840 | 0.5467 | 0.5503 | 0.5485 | 149 | 0.6709 | 0.6023 | 0.6347 | 88 | 0.6693 | 0.7250 | 0.6960 | 589 | 0.8454 | 0.8881 | 0.8662 | 751 | 0.9714 | 0.9315 | 0.9510 | 73 | 0.5961 | 0.5475 | 0.5708 | 221 | 0.5 | 0.5333 | 0.5161 | 45 | 0.6769 | 0.8115 | 0.7381 | 191 | 0.7474 | 0.7762 | 0.7615 | 0.9662 | | 0.0007 | 60 | 63660 | 0.3636 | 0.7494 | 0.7676 | 0.7584 | 0.9662 | 0.7016 | 0.7204 | 0.7109 | 372 | 0.3488 | 0.5357 | 0.4225 | 28 | 0.8489 | 0.8357 | 0.8422 | 840 | 0.6 | 0.4832 | 0.5353 | 149 | 0.6538 | 0.5795 | 0.6145 | 88 | 0.6828 | 0.7199 | 0.7008 | 589 | 0.8476 | 0.8815 | 0.8642 | 751 | 0.9714 | 0.9315 | 0.9510 | 73 | 0.5579 | 0.5882 | 0.5727 | 221 | 0.4762 | 0.4444 | 0.4598 | 45 | 0.6797 | 0.8220 | 0.7441 | 191 | 0.7494 | 0.7676 | 0.7584 | 0.9662 | | 0.0008 | 61 | 64721 | 0.3646 | 0.7538 | 0.7574 | 0.7556 | 0.9660 | 0.6854 | 0.7204 | 0.7025 | 372 | 0.3659 | 0.5357 | 0.4348 | 28 | 0.8573 | 0.8369 | 0.8470 | 840 | 0.6306 | 0.4698 | 0.5385 | 149 | 0.6667 | 0.5909 | 0.6265 | 88 | 0.6896 | 0.6978 | 0.6937 | 589 | 0.8495 | 0.8722 | 0.8607 | 751 | 0.9714 | 0.9315 | 0.9510 | 73 | 0.5728 | 0.5520 | 0.5622 | 221 | 0.375 | 0.4 | 0.3871 | 45 | 0.6830 | 0.8010 | 0.7373 | 191 | 0.7538 | 0.7574 | 0.7556 | 0.9660 | | 0.0006 | 62 | 65782 | 0.3697 | 0.7510 | 0.7460 | 0.7485 | 0.9651 | 0.6885 | 0.7070 | 0.6976 | 372 | 0.4286 | 0.5357 | 0.4762 | 28 | 0.8663 | 0.7869 | 0.8247 | 840 | 0.5902 | 0.4832 | 0.5314 | 149 | 0.6757 | 0.5682 | 0.6173 | 88 | 0.6667 | 0.6927 | 0.6794 | 589 | 0.8432 | 0.8948 | 0.8682 | 751 | 0.9851 | 0.9041 | 0.9429 | 73 | 0.5829 | 0.5566 | 0.5694 | 221 | 0.3673 | 0.4 | 0.3830 | 45 | 0.6995 | 0.7801 | 0.7376 | 191 | 0.7510 | 0.7460 | 0.7485 | 0.9651 | | 0.0006 | 63 | 66843 | 0.3661 | 0.7504 | 0.7502 | 0.7503 | 0.9655 | 0.6909 | 0.6909 | 0.6909 | 372 | 0.4286 | 0.5357 | 0.4762 | 28 | 0.8571 | 0.8143 | 0.8352 | 840 | 0.5814 | 0.5034 | 0.5396 | 149 | 0.6582 | 0.5909 | 0.6228 | 88 | 0.7013 | 0.6655 | 0.6829 | 589 | 0.8348 | 0.8948 | 0.8638 | 751 | 0.9571 | 0.9178 | 0.9371 | 73 | 0.5570 | 0.5747 | 0.5657 | 221 | 0.3830 | 0.4 | 0.3913 | 45 | 0.6786 | 0.7958 | 0.7325 | 191 | 0.7504 | 0.7502 | 0.7503 | 0.9655 | | 0.0006 | 64 | 67904 | 0.3711 | 0.7404 | 0.7628 | 0.7514 | 0.9656 | 0.6911 | 0.7097 | 0.7003 | 372 | 0.3784 | 0.5 | 0.4308 | 28 | 0.8455 | 0.8405 | 0.8430 | 840 | 0.6 | 0.5034 | 0.5474 | 149 | 0.65 | 0.5909 | 0.6190 | 88 | 0.6667 | 0.7029 | 0.6843 | 589 | 0.8350 | 0.8961 | 0.8645 | 751 | 0.9714 | 0.9315 | 0.9510 | 73 | 0.5673 | 0.5339 | 0.5501 | 221 | 0.2917 | 0.3111 | 0.3011 | 45 | 0.6568 | 0.8115 | 0.7260 | 191 | 0.7404 | 0.7628 | 0.7514 | 0.9656 | | 0.0007 | 65 | 68965 | 0.3672 | 0.7377 | 0.7696 | 0.7533 | 0.9661 | 0.7005 | 0.7419 | 0.7206 | 372 | 0.3333 | 0.5357 | 0.4110 | 28 | 0.8433 | 0.8393 | 0.8413 | 840 | 0.5839 | 0.5369 | 0.5594 | 149 | 0.6506 | 0.6136 | 0.6316 | 88 | 0.6840 | 0.7131 | 0.6983 | 589 | 0.8412 | 0.8815 | 0.8609 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.5427 | 0.5747 | 0.5582 | 221 | 0.3019 | 0.3556 | 0.3265 | 45 | 0.6360 | 0.7958 | 0.7070 | 191 | 0.7377 | 0.7696 | 0.7533 | 0.9661 | | 0.0005 | 66 | 70026 | 0.3768 | 0.7496 | 0.7520 | 0.7508 | 0.9657 | 0.6903 | 0.7070 | 0.6985 | 372 | 0.3415 | 0.5 | 0.4058 | 28 | 0.8454 | 0.8333 | 0.8393 | 840 | 0.6372 | 0.4832 | 0.5496 | 149 | 0.6795 | 0.6023 | 0.6386 | 88 | 0.6914 | 0.6655 | 0.6782 | 589 | 0.8483 | 0.8788 | 0.8633 | 751 | 0.9577 | 0.9315 | 0.9444 | 73 | 0.5714 | 0.5792 | 0.5753 | 221 | 0.3 | 0.3333 | 0.3158 | 45 | 0.6696 | 0.7958 | 0.7273 | 191 | 0.7496 | 0.7520 | 0.7508 | 0.9657 | | 0.0007 | 67 | 71087 | 0.3682 | 0.7461 | 0.7664 | 0.7561 | 0.9656 | 0.7094 | 0.7285 | 0.7188 | 372 | 0.3409 | 0.5357 | 0.4167 | 28 | 0.8563 | 0.8369 | 0.8465 | 840 | 0.6290 | 0.5235 | 0.5714 | 149 | 0.6974 | 0.6023 | 0.6463 | 88 | 0.6935 | 0.6876 | 0.6905 | 589 | 0.8363 | 0.8842 | 0.8595 | 751 | 0.9437 | 0.9178 | 0.9306 | 73 | 0.5175 | 0.6018 | 0.5565 | 221 | 0.4694 | 0.5111 | 0.4894 | 45 | 0.6483 | 0.8010 | 0.7166 | 191 | 0.7461 | 0.7664 | 0.7561 | 0.9656 | | 0.0005 | 68 | 72148 | 0.3815 | 0.7590 | 0.7416 | 0.7502 | 0.9654 | 0.7092 | 0.7016 | 0.7054 | 372 | 0.4054 | 0.5357 | 0.4615 | 28 | 0.8489 | 0.8095 | 0.8288 | 840 | 0.6796 | 0.4698 | 0.5556 | 149 | 0.6456 | 0.5795 | 0.6108 | 88 | 0.6801 | 0.6570 | 0.6684 | 589 | 0.8476 | 0.8815 | 0.8642 | 751 | 0.9571 | 0.9178 | 0.9371 | 73 | 0.615 | 0.5566 | 0.5843 | 221 | 0.4348 | 0.4444 | 0.4396 | 45 | 0.6759 | 0.7644 | 0.7174 | 191 | 0.7590 | 0.7416 | 0.7502 | 0.9654 | | 0.0006 | 69 | 73209 | 0.3919 | 0.7494 | 0.7487 | 0.7491 | 0.9650 | 0.6888 | 0.6962 | 0.6925 | 372 | 0.3590 | 0.5 | 0.4179 | 28 | 0.8416 | 0.8095 | 0.8252 | 840 | 0.5865 | 0.5235 | 0.5532 | 149 | 0.6901 | 0.5568 | 0.6164 | 88 | 0.6950 | 0.6808 | 0.6878 | 589 | 0.8490 | 0.8908 | 0.8694 | 751 | 1.0 | 0.9041 | 0.9496 | 73 | 0.5662 | 0.5611 | 0.5636 | 221 | 0.3265 | 0.3556 | 0.3404 | 45 | 0.6881 | 0.7853 | 0.7335 | 191 | 0.7494 | 0.7487 | 0.7491 | 0.9650 | | 0.0006 | 70 | 74270 | 0.3704 | 0.7587 | 0.7619 | 0.7603 | 0.9666 | 0.6891 | 0.7151 | 0.7018 | 372 | 0.3947 | 0.5357 | 0.4545 | 28 | 0.8376 | 0.8536 | 0.8455 | 840 | 0.6697 | 0.4899 | 0.5659 | 149 | 0.6420 | 0.5909 | 0.6154 | 88 | 0.7018 | 0.6791 | 0.6903 | 589 | 0.8491 | 0.8842 | 0.8663 | 751 | 0.9857 | 0.9452 | 0.9650 | 73 | 0.6219 | 0.5656 | 0.5924 | 221 | 0.3913 | 0.4 | 0.3956 | 45 | 0.6802 | 0.7906 | 0.7312 | 191 | 0.7587 | 0.7619 | 0.7603 | 0.9666 | | 0.0005 | 71 | 75331 | 0.3841 | 0.7501 | 0.7634 | 0.7567 | 0.9659 | 0.7005 | 0.6855 | 0.6929 | 372 | 0.4054 | 0.5357 | 0.4615 | 28 | 0.8531 | 0.8298 | 0.8413 | 840 | 0.6293 | 0.4899 | 0.5509 | 149 | 0.6410 | 0.5682 | 0.6024 | 88 | 0.6774 | 0.7165 | 0.6964 | 589 | 0.8264 | 0.9001 | 0.8617 | 751 | 0.9706 | 0.9041 | 0.9362 | 73 | 0.5882 | 0.5882 | 0.5882 | 221 | 0.4545 | 0.4444 | 0.4494 | 45 | 0.6864 | 0.7906 | 0.7348 | 191 | 0.7501 | 0.7634 | 0.7567 | 0.9659 | | 0.0005 | 72 | 76392 | 0.3830 | 0.7605 | 0.7496 | 0.7550 | 0.9655 | 0.7036 | 0.6828 | 0.6930 | 372 | 0.3824 | 0.4643 | 0.4194 | 28 | 0.8618 | 0.8238 | 0.8424 | 840 | 0.6542 | 0.4698 | 0.5469 | 149 | 0.6582 | 0.5909 | 0.6228 | 88 | 0.6935 | 0.6723 | 0.6828 | 589 | 0.8476 | 0.8815 | 0.8642 | 751 | 0.9577 | 0.9315 | 0.9444 | 73 | 0.5830 | 0.5882 | 0.5856 | 221 | 0.4043 | 0.4222 | 0.4130 | 45 | 0.6892 | 0.8010 | 0.7409 | 191 | 0.7605 | 0.7496 | 0.7550 | 0.9655 | | 0.0006 | 73 | 77453 | 0.3839 | 0.7611 | 0.7547 | 0.7579 | 0.9661 | 0.712 | 0.7177 | 0.7149 | 372 | 0.3429 | 0.4286 | 0.3810 | 28 | 0.8494 | 0.8393 | 0.8443 | 840 | 0.6542 | 0.4698 | 0.5469 | 149 | 0.6538 | 0.5795 | 0.6145 | 88 | 0.6877 | 0.6655 | 0.6764 | 589 | 0.8428 | 0.8921 | 0.8668 | 751 | 0.9710 | 0.9178 | 0.9437 | 73 | 0.6257 | 0.5294 | 0.5735 | 221 | 0.4468 | 0.4667 | 0.4565 | 45 | 0.6814 | 0.8063 | 0.7386 | 191 | 0.7611 | 0.7547 | 0.7579 | 0.9661 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0

pietrolesci/t5v1_1-base-mnli

1cc56642ced2f861390ae57d00dcd0cd703a204b

2022-05-03T14:53:23.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

pietrolesci

null

pietrolesci/t5v1_1-base-mnli

7

null

transformers

14,386

## Overview T5-Base v1.1 model trained to generate hypotheses given a premise and a label. Below the settings used to train it ```yaml Experiment configurations ├── datasets │ └── mnli_train: │ dataset_name: multi_nli │ dataset_config_name: null │ cache_dir: null │ input_fields: │ - premise │ - hypothesis │ target_field: label │ train_subset_names: null │ val_subset_names: validation_matched │ test_subset_names: none │ train_val_split: null │ limit_train_samples: null │ limit_val_samples: null │ limit_test_samples: null │ sampling_kwargs: │ sampling_strategy: random │ seed: 42 │ replace: false │ align_labels_with_mapping: null │ avoid_consistency_check: false │ predict_label_mapping: null │ mnli: │ dataset_name: multi_nli │ dataset_config_name: null │ cache_dir: null │ input_fields: │ - premise │ - hypothesis │ target_field: label │ train_subset_names: none │ val_subset_names: none │ test_subset_names: validation_mismatched │ train_val_split: null │ limit_train_samples: null │ limit_val_samples: null │ limit_test_samples: null │ sampling_kwargs: │ sampling_strategy: random │ seed: 42 │ replace: false │ align_labels_with_mapping: null │ avoid_consistency_check: false │ predict_label_mapping: null │ ├── data │ └── _target_: src.task.nli.data.NLIGenerationData.from_config │ main_dataset_name: null │ use_additional_as_test: null │ dataloader: │ batch_size: 64 │ eval_batch_size: 100 │ num_workers: 16 │ pin_memory: true │ drop_last: false │ persistent_workers: false │ shuffle: true │ seed_dataloader: 42 │ replacement: false │ processing: │ preprocessing_num_workers: 16 │ preprocessing_batch_size: 1000 │ load_from_cache_file: true │ padding: longest │ truncation: longest_first │ max_source_length: 128 │ max_target_length: 128 │ template: 'premise: $premise $label hypothesis: ' │ tokenizer: │ _target_: transformers.AutoTokenizer.from_pretrained │ pretrained_model_name_or_path: google/t5-v1_1-base │ use_fast: true │ ├── task │ └── optimizer: │ name: Adafactor │ lr: 0.001 │ weight_decay: 0.0 │ no_decay: │ - bias │ - LayerNorm.weight │ decay_rate: -0.8 │ clip_threshold: 1.0 │ relative_step: false │ scale_parameter: false │ warmup_init: false │ scheduler: │ name: constant_schedule │ model: │ model_name_or_path: google/t5-v1_1-base │ checkpoint_path: null │ freeze: false │ seed_init_weight: 42 │ _target_: src.task.nli.NLIGenerationTask.from_config │ generation: │ max_length: 128 │ min_length: 3 │ do_sample: true │ early_stopping: false │ num_beams: 1 │ temperature: 1.0 │ top_k: 50 │ top_p: 0.95 │ repetition_penalty: null │ length_penalty: null │ no_repeat_ngram_size: null │ encoder_no_repeat_ngram_size: null │ num_return_sequences: 1 │ max_time: null │ max_new_tokens: null │ decoder_start_token_id: null │ use_cache: null │ num_beam_groups: null │ diversity_penalty: null │ ├── trainer │ └── _target_: pytorch_lightning.Trainer │ callbacks: │ lr_monitor: │ _target_: pytorch_lightning.callbacks.LearningRateMonitor │ logging_interval: step │ log_momentum: false │ model_checkpoint: │ _target_: pytorch_lightning.callbacks.ModelCheckpoint │ dirpath: ./checkpoints/ │ filename: nli_generator_mnli-epoch={epoch:02d}-val_loss={val/aggregated_loss:.2f} │ monitor: val/aggregated_loss │ mode: min │ verbose: false │ save_last: true │ save_top_k: 1 │ auto_insert_metric_name: false │ save_on_train_epoch_end: false │ rich_model_summary: │ _target_: pytorch_lightning.callbacks.RichModelSummary │ max_depth: 1 │ log_grad_norm: │ _target_: src.core.callbacks.LogGradNorm │ norm_type: 2 │ group_separator: / │ only_total: true │ on_step: true │ on_epoch: false │ prog_bar: true │ log_generated_text: │ _target_: src.core.callbacks.GenerateAndLogText │ dirpath: ./generated_text │ type: generated_text │ pop_keys_after_logging: true │ on_train: false │ on_validation: false │ on_test: true │ log_to_wandb: true │ wandb_log_dataset_sizes: │ _target_: src.core.callbacks.WandbLogDatasetSizes │ logger: │ wandb: │ _target_: pytorch_lightning.loggers.WandbLogger │ project: nli_debiasing │ entity: team_brushino │ name: nli_generator_mnli │ save_dir: ./ │ offline: false │ log_model: false │ group: mnli │ job_type: generator │ tags: │ - nli_generator_mnli │ - seed=42 │ - seed_dataloader=42 │ notes: nli_generator_mnli_time=02-24-53 │ enable_checkpointing: true │ enable_progress_bar: true │ enable_model_summary: true │ gradient_clip_val: 0.0 │ gradient_clip_algorithm: null │ accelerator: gpu │ devices: auto │ gpus: null │ auto_select_gpus: true │ accumulate_grad_batches: 1 │ max_epochs: 3 │ min_epochs: 1 │ max_steps: -1 │ min_steps: null │ max_time: null │ num_sanity_val_steps: 2 │ overfit_batches: 0.0 │ fast_dev_run: false │ limit_train_batches: 1.0 │ limit_val_batches: 1.0 │ limit_test_batches: 1.0 │ profiler: null │ detect_anomaly: false │ deterministic: false │ check_val_every_n_epoch: 1 │ val_check_interval: 0.1 │ log_every_n_steps: 10 │ move_metrics_to_cpu: false │ └── training └── run_val_before_fit: false run_val_after_fit: false run_test_before_fit: false run_test_after_fit: true lr: 0.001 seed: 42 show_batch: false batch_size: 64 eval_batch_size: 100 num_workers: 16 pin_memory: true drop_last: false persistent_workers: false shuffle: true seed_dataloader: 42 ignore_warnings: true experiment_name: nli_generator_mnli ```

laituan245/molt5-small-caption2smiles

5c4d1d5b1d819185a8e43d77cec8e9ebf2ae5853

2022-05-03T18:08:09.000Z

[ "pytorch", "t5", "text2text-generation", "arxiv:2204.11817", "transformers", "license:apache-2.0", "autotrain_compatible" ]

text2text-generation

false

laituan245

null

laituan245/molt5-small-caption2smiles

7

null

transformers

14,387

--- license: apache-2.0 --- This model can be used to generate a SMILES string from an input caption. ## Example Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-small-caption2smiles", model_max_length=512) model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-small-caption2smiles') input_text = 'The molecule is a monomethoxybenzene that is 2-methoxyphenol substituted by a hydroxymethyl group at position 4. It has a role as a plant metabolite. It is a member of guaiacols and a member of benzyl alcohols.' input_ids = tokenizer(input_text, return_tensors="pt").input_ids outputs = model.generate(input_ids, num_beams=5, max_length=512) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # The model will generate "COC1=C(C=CC(=C1)CCCO)O". The ground-truth is "COC1=C(C=CC(=C1)CO)O". ``` ## Paper For more information, please take a look at our paper. Paper: [Translation between Molecules and Natural Language](https://arxiv.org/abs/2204.11817) Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*

praf-choub/bart-mofe-rl-xsum

9d07a8534f28cd87b72cc7d303786f658a986dde

2022-06-14T04:52:41.000Z

[ "pytorch", "bart", "text2text-generation", "en", "dataset:xsum", "arxiv:2110.07166", "transformers", "summarization", "license:bsd-3-clause", "autotrain_compatible" ]

summarization

false

praf-choub

null

praf-choub/bart-mofe-rl-xsum

7

null

transformers

14,388

--- language: en tags: - summarization license: bsd-3-clause datasets: - xsum --- Citation ``` @article{DBLP:journals/corr/abs-2110-07166, author = {Prafulla Kumar Choubey and Jesse Vig and Wenhao Liu and Nazneen Fatema Rajani}, title = {MoFE: Mixture of Factual Experts for Controlling Hallucinations in Abstractive Summarization}, journal = {CoRR}, volume = {abs/2110.07166}, year = {2021}, url = {https://arxiv.org/abs/2110.07166}, eprinttype = {arXiv}, eprint = {2110.07166}, timestamp = {Fri, 22 Oct 2021 13:33:09 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2110-07166.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

mrm8488/data2vec-text-base-finetuned-sst2

56b5cba6cf53835f194601f285e874875cc76419

2022-05-03T21:52:23.000Z

[ "pytorch", "tensorboard", "data2vec-text", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:mit", "model-index" ]

text-classification

false

mrm8488

null

mrm8488/data2vec-text-base-finetuned-sst2

7

null

transformers

14,389

--- license: mit tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: data2vec-text-base-finetuned-sst2 results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.9231651376146789 ---  # data2vec-text-base-finetuned-sst2 This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/facebook/data2vec-text-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3600 - Accuracy: 0.9232 ## 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: 1.1519343408010398e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2865 | 1.0 | 4210 | 0.2662 | 0.9128 | | 0.2256 | 2.0 | 8420 | 0.3698 | 0.9002 | | 0.1676 | 3.0 | 12630 | 0.3107 | 0.9186 | | 0.1481 | 4.0 | 16840 | 0.3425 | 0.9186 | | 0.1429 | 5.0 | 21050 | 0.3600 | 0.9232 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

chrisvinsen/xlsr-wav2vec2-base-commonvoice-demo-colab-4

faf75890ac2d670314715047e7dc7a73a837814d

2022-05-04T00:35:19.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]

automatic-speech-recognition

false

chrisvinsen

null

chrisvinsen/xlsr-wav2vec2-base-commonvoice-demo-colab-4

7

null

transformers

14,390

Entry not found

eastmountaincode/generate

3f37fe0bd39e6e5a46528ea9ed5e786468b20519

2022-05-03T21:03:45.000Z

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

false

eastmountaincode

null

eastmountaincode/generate

7

null

transformers

14,391

Entry not found

Lauler/sentiment-classifier

8430d75ec3fe42eeaed6b7918b7c1b87a2a1a693

2022-05-03T23:28:00.000Z

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

false

Lauler

null

Lauler/sentiment-classifier

7

null

transformers

14,392

## Sentiment classifier Sentiment classifier for Swedish trained on ScandiSent dataset.

ml4pubmed/BioM-BERT-PubMed-PMC-Large_pub_section

5b4ca9ab5528d34cbaffbc5b693f9f99782d4068

2022-05-04T00:50:46.000Z

[ "pytorch", "electra", "text-classification", "en", "dataset:pubmed", "transformers" ]

text-classification

false

ml4pubmed

null

ml4pubmed/BioM-BERT-PubMed-PMC-Large_pub_section

7

null

transformers

14,393

--- language: - en datasets: - pubmed metrics: - f1 pipeline_tag: text-classification widget: - text: "Many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti-inflammatory actions." example_title: "BACKGROUND example" - text: "A total of 192 MI patients and 140 control persons were included." example_title: "METHODS example" - text: "MI patients had 18 % higher plasma levels of MAp44 (IQR 11-25 %) as compared to the healthy control group (p < 0. 001.)" example_title: "RESULTS example" - text: "The finding that a brief CB group intervention delivered by real-world providers significantly reduced MDD onset relative to both brochure control and bibliotherapy is very encouraging, although effects on continuous outcome measures were small or nonsignificant and approximately half the magnitude of those found in efficacy research, potentially because the present sample reported lower initial depression." example_title: "CONCLUSIONS example" - text: "In order to understand and update the prevalence of myopia in Taiwan, a nationwide survey was performed in 1995." example_title: "OBJECTIVE example" --- # BioM-BERT-PubMed-PMC-Large_pub_section - original model file name: textclassifer_BioM-BERT-PubMed-PMC-Large_pubmed_20k - This is a fine-tuned checkpoint of `sultan/BioM-BERT-PubMed-PMC-Large` for document section text classification - possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS, ## metadata ### training_metrics - date_run: Apr-23-2022_t-04 - huggingface_tag: sultan/BioM-BERT-PubMed-PMC-Large ### training_parameters - date_run: Apr-23-2022_t-04 - huggingface_tag: sultan/BioM-BERT-PubMed-PMC-Large

IsekaiMeta/dapprf4

59dc342176101e0194c043f8dfec8ced902a6413

2022-05-04T02:53:09.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

IsekaiMeta

null

IsekaiMeta/dapprf4

7

null

transformers

14,394

--- tags: - conversational --- #dapprf4

learningdude/wav2vec2-base-sound2

f86577c2acc89b4a19caa010f2d2a0ab0fcd88a7

2022-05-05T04:34:26.000Z

[ "pytorch", "tensorboard", "wav2vec2", "audio-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

audio-classification

false

learningdude

null

learningdude/wav2vec2-base-sound2

7

null

transformers

14,395

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-base-sound2 results: [] ---  # wav2vec2-base-sound2 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5012 - Accuracy: 0.5357 ## 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: 9e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 2.0762 | 0.0714 | | No log | 2.0 | 2 | 2.0638 | 0.1429 | | No log | 3.0 | 3 | 2.0387 | 0.2143 | | No log | 4.0 | 4 | 2.0124 | 0.2143 | | No log | 5.0 | 5 | 1.9864 | 0.2143 | | No log | 6.0 | 6 | 1.9609 | 0.2143 | | No log | 7.0 | 7 | 1.9235 | 0.2143 | | No log | 8.0 | 8 | 1.9379 | 0.2143 | | No log | 9.0 | 9 | 1.8627 | 0.2857 | | 1.9713 | 10.0 | 10 | 1.8277 | 0.3214 | | 1.9713 | 11.0 | 11 | 1.7765 | 0.3571 | | 1.9713 | 12.0 | 12 | 1.7204 | 0.5 | | 1.9713 | 13.0 | 13 | 1.6956 | 0.5 | | 1.9713 | 14.0 | 14 | 1.6602 | 0.5357 | | 1.9713 | 15.0 | 15 | 1.6277 | 0.5714 | | 1.9713 | 16.0 | 16 | 1.6053 | 0.5 | | 1.9713 | 17.0 | 17 | 1.5825 | 0.5 | | 1.9713 | 18.0 | 18 | 1.5656 | 0.4286 | | 1.9713 | 19.0 | 19 | 1.5616 | 0.4643 | | 1.6334 | 20.0 | 20 | 1.5613 | 0.4286 | | 1.6334 | 21.0 | 21 | 1.5419 | 0.5 | | 1.6334 | 22.0 | 22 | 1.5166 | 0.5357 | | 1.6334 | 23.0 | 23 | 1.5088 | 0.5 | | 1.6334 | 24.0 | 24 | 1.5052 | 0.5 | | 1.6334 | 25.0 | 25 | 1.5012 | 0.5357 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 1.14.0 - Tokenizers 0.12.1

eastmountaincode/duneGenerationNoUser

4dec2e4acd76e80845b0f656ab09e61625f7a923

2022-05-05T20:42:32.000Z

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

false

eastmountaincode

null

eastmountaincode/duneGenerationNoUser

7

null

transformers

14,396

Entry not found

xingqiang/macbert-zh-address-match-finetuned

6e19b5ee0da301ef3b37ecb5b32f5873430c2087

2022-05-06T08:48:00.000Z

[ "pytorch", "bert", "feature-extraction", "transformers" ]

feature-extraction

false

xingqiang

null

xingqiang/macbert-zh-address-match-finetuned

7

null

transformers

14,397

Entry not found

DioLiu/distilbert-base-uncased-finetuned-sst2-with-unfamiliar-words

33a9dbc7c1595f282267e4b401cd16253331d7ec

2022-05-06T07:34:54.000Z

[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

false

DioLiu

null

DioLiu/distilbert-base-uncased-finetuned-sst2-with-unfamiliar-words

7

null

transformers

14,398

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-sst2-with-unfamiliar-words results: [] ---  # distilbert-base-uncased-finetuned-sst2-with-unfamiliar-words This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0870 - Accuracy: 0.9866 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2917 | 1.0 | 975 | 0.0703 | 0.9778 | | 0.063 | 2.0 | 1950 | 0.0815 | 0.9821 | | 0.0233 | 3.0 | 2925 | 0.0680 | 0.9866 | | 0.0134 | 4.0 | 3900 | 0.0817 | 0.9866 | | 0.0054 | 5.0 | 4875 | 0.0870 | 0.9866 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

EAST/autotrain-maysix-828926405

78db205d25e32617202947fd8827a0111b39f1a1

2022-05-06T07:13:15.000Z

[ "pytorch", "bert", "text-classification", "zh", "dataset:EAST/autotrain-data-maysix", "transformers", "autotrain", "co2_eq_emissions" ]

text-classification

false

EAST

null

EAST/autotrain-maysix-828926405

7

null

transformers

14,399

--- tags: autotrain language: zh widget: - text: "I love AutoTrain 🤗" datasets: - EAST/autotrain-data-maysix co2_eq_emissions: 0.00258669198292644 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 828926405 - CO2 Emissions (in grams): 0.00258669198292644 ## Validation Metrics - Loss: 0.1797131597995758 - Accuracy: 0.9318181818181818 - Precision: 0.9047619047619048 - Recall: 0.95 - AUC: 0.9875 - F1: 0.9268292682926829 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/EAST/autotrain-maysix-828926405 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("EAST/autotrain-maysix-828926405", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("EAST/autotrain-maysix-828926405", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```