librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-23 18:27:52	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 492 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-23 18:25:26	card stringlengths 11 1.01M
transformersbook/pegasus-samsum	transformersbook	2022-02-05T17:05:28Z	75,124	6	transformers	[ "transformers", "pytorch", "tensorboard", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum-test 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. --> # pegasus-samsum-test This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. The model is trained in Chapter 6: Summarization in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/06_summarization.ipynb). It achieves the following results on the evaluation set: - Loss: 1.4875 ## Model description More information needed ## Intended uses & 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: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.7012 \| 0.54 \| 500 \| 1.4875 \| ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
transformersbook/bert-base-uncased-issues-128	transformersbook	2022-02-05T16:57:43Z	9	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-uncased-issues-128 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GitHub issues dataset. The model is used in Chapter 9: Dealing with Few to No Labels in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/09_few-to-no-labels.ipynb). It achieves the following results on the evaluation set: - Loss: 1.2520 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.0949 \| 1.0 \| 291 \| 1.7072 \| \| 1.649 \| 2.0 \| 582 \| 1.4409 \| \| 1.4835 \| 3.0 \| 873 \| 1.4099 \| \| 1.3938 \| 4.0 \| 1164 \| 1.3858 \| \| 1.3326 \| 5.0 \| 1455 \| 1.2004 \| \| 1.2949 \| 6.0 \| 1746 \| 1.2955 \| \| 1.2451 \| 7.0 \| 2037 \| 1.2682 \| \| 1.1992 \| 8.0 \| 2328 \| 1.1938 \| \| 1.1784 \| 9.0 \| 2619 \| 1.1686 \| \| 1.1397 \| 10.0 \| 2910 \| 1.2050 \| \| 1.1293 \| 11.0 \| 3201 \| 1.2058 \| \| 1.1006 \| 12.0 \| 3492 \| 1.1680 \| \| 1.0835 \| 13.0 \| 3783 \| 1.2414 \| \| 1.0757 \| 14.0 \| 4074 \| 1.1522 \| \| 1.062 \| 15.0 \| 4365 \| 1.1176 \| \| 1.0535 \| 16.0 \| 4656 \| 1.2520 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu102 - Datasets 1.13.0 - Tokenizers 0.10.3
transformersbook/distilbert-base-uncased-distilled-clinc	transformersbook	2022-02-05T16:47:39Z	199	3	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- 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: 0.9393548387096774 --- <!-- 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 with knowledge distillation version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. The model is used in Chapter 8: Making Transformers Efficient in Production in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/08_model-compression.ipynb). It achieves the following results on the evaluation set: - Loss: 0.1005 - Accuracy: 0.9394 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.9031 \| 1.0 \| 318 \| 0.5745 \| 0.7365 \| \| 0.4481 \| 2.0 \| 636 \| 0.2856 \| 0.8748 \| \| 0.2528 \| 3.0 \| 954 \| 0.1798 \| 0.9187 \| \| 0.176 \| 4.0 \| 1272 \| 0.1398 \| 0.9294 \| \| 0.1416 \| 5.0 \| 1590 \| 0.1211 \| 0.9348 \| \| 0.1243 \| 6.0 \| 1908 \| 0.1116 \| 0.9348 \| \| 0.1133 \| 7.0 \| 2226 \| 0.1062 \| 0.9377 \| \| 0.1075 \| 8.0 \| 2544 \| 0.1035 \| 0.9387 \| \| 0.1039 \| 9.0 \| 2862 \| 0.1014 \| 0.9381 \| \| 0.1018 \| 10.0 \| 3180 \| 0.1005 \| 0.9394 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1+cu102 - Datasets 1.13.0 - Tokenizers 0.10.3
transformersbook/codeparrot-small	transformersbook	2022-02-05T16:28:36Z	4	1	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	# CodeParrot CodeParrot (small) is a 110M parameter GPT-2 model trained on the [CodeParrot Python code dataset](https://huggingface.co/datasets/transformersbook/codeparrot). The model is trained in Chapter 10: Training Transformers from Scratch in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/10_transformers-from-scratch.ipynb).
transformersbook/codeparrot	transformersbook	2022-02-05T16:27:42Z	18	5	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	# CodeParrot CodeParrot (large) is a 1.5B parameter GPT-2 model trained on the [CodeParrot Python code dataset](https://huggingface.co/datasets/transformersbook/codeparrot). The model is trained in Chapter 10: Training Transformers from Scratch in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/10_transformers-from-scratch.ipynb).
groar/distilgpt2-finetuned-escape	groar	2022-02-05T14:44:47Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-escape results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-escape This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
Ayham/distilbert_distilgpt2_summarization_cnn_dailymail	Ayham	2022-02-05T11:39:58Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "encoder-decoder", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: distilbert_distilgpt2_summarization_cnn_dailymail 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_distilgpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
omoekan/opus-tatoeba-eng-yor	omoekan	2022-02-05T10:15:11Z	3	0	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	## OPUS Tatoeba English-Yoruba This model was obtained by running the script convert_marian_to_pytorch.py with the flag -m eng-yor. The original models were trained by Jörg Tiedemann using the MarianNMT library. See all available MarianMTModel models on the profile of the Helsinki NLP group. --- - tags: translation - source language: English - target language: Yoruba - dataset: opus+bt -model: transformer-align -pre-processing: normalization + SentencePiece (spm12k,spm12k) -download original weights: [opus+bt-2021-04-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-yor/opus+bt-2021-04-10.zip) -test set translations: [opus+bt-2021-04-10.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-yor/opus+bt-2021-04-10.test.txt) -test set scores: [opus+bt-2021-04-10.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-yor/opus+bt-2021-04-10.eval.txt) -Benchmarks \|test set\|BLEU\|chr-F\| \|:---\|:---\|:---\| \|Tatoeba-test.eng-yor\|13.0\|0.333\| ---
ajitrajasekharan/biomedical	ajitrajasekharan	2022-02-05T08:44:05Z	6	1	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: - {en} # Example: fr license: mit widget: - text: "Lou Gehrig who works for XCorp and lives in New York suffers from [MASK]" example_title: "Test for entity type: Disease" - text: "Overexpression of [MASK] occurs across a wide range of cancers" example_title: "Test for entity type: Gene" - text: "Patients treated with [MASK] are vulnerable to infectious diseases" example_title: "Test for entity type: Drug" - text: "A eGFR level below [MASK] indicates chronic kidney disease" example_title: "Test for entity type: Measure " - text: "In the [MASK], increased daily imatinib dose induced MMR" example_title: "Test for entity type: STUDY/TRIAL" - text: "Paul Erdos died at [MASK]" example_title: "Test for entity type: TIME" inference: parameters: top_k: 10 tags: - {fill-mask} # Example: audio - exbert --- This cased model was pretrained from scratch using a custom vocabulary on the following corpora - Pubmed - Clinical trials corpus - and a small subset of Bookcorpus The pretrained model was used to do NER as is, with no fine-tuning. The approach is described [in this post](https://ajitrajasekharan.github.io/2021/01/02/my-first-post.html). [Towards Data Science review](https://twitter.com/TDataScience/status/1486300137366466560?s=20) [App in Spaces](https://huggingface.co/spaces/ajitrajasekharan/self-supervised-ner-biomedical) demonstrates this approach. [Github link](https://github.com/ajitrajasekharan/unsupervised_NER) to perform NER using this model in an ensemble with bert-base cased. The ensemble detects 69 entity subtypes (17 broad entity groups) <img src="https://ajitrajasekharan.github.io/images/1.png" width="600"> ### Ensemble model performance <img src="https://ajitrajasekharan.github.io/images/6.png" width="600"> ### Additional notes - The model predictions on the right do not include [CLS] predictions. Hosted inference API only returns the masked position predictions. In practice, the [CLS] predictions are just as useful as the model predictions for the masked position _(if the next sentence prediction loss was low during pretraining)_ and are used for NER. - Some of the top model predictions like "a", "the", punctuations, etc. while valid predictions, bear no entity information. These are filtered when harvesting descriptors for NER. The examples on the right are unfiltered results. - [Use this link](https://huggingface.co/spaces/ajitrajasekharan/Qualitative-pretrained-model-evaluation) to examine both fill-mask prediction and [CLS] predictions ### License MIT license <a href="https://huggingface.co/exbert/?model=ajitrajasekharan/biomedical&modelKind=bidirectional&sentence=Gefitinib%20is%20an%20EGFR%20tyrosine%20kinase%20inhibitor,%20which%20is%20often%20used%20for%20breast%20cancer%20and%20NSCLC%20treatment.&layer=3&heads=..0,1,2,3,4,5,6,7,8,9,10,11&threshold=0.7&tokenInd=17&tokenSide=right&maskInds=..&hideClsSep=true"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
HenryHXR/t5-base-finetuned-scitldr	HenryHXR	2022-02-05T05:48:10Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-finetuned-scitldr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-finetuned-scitldr This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0232 - Rouge1: 35.2134 - Rouge2: 16.8919 - Rougel: 30.8442 - Rougelsum: 30.9316 - Gen Len: 18.7981 ## Model description More information needed ## Intended uses & 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-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 2.0533 \| 1.0 \| 996 \| 2.0285 \| 34.9774 \| 16.6163 \| 30.6177 \| 30.7038 \| 18.7981 \| \| 2.0994 \| 2.0 \| 1992 \| 2.0232 \| 35.2134 \| 16.8919 \| 30.8442 \| 30.9316 \| 18.7981 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
MaggieXM/distilbert-base-uncased-finetuned-squad	MaggieXM	2022-02-05T04:50:41Z	16	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.01 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 0.01 \| 56 \| 4.8054 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
aogara/slai_transformer	aogara	2022-02-05T00:26:24Z	0	0	null	[ "region:us" ]	null	2022-03-02T23:29:05Z	# Building a HuggingFace Transformer NLP Model ## Running this Repo
BigSalmon/InformalToFormalLincoln20	BigSalmon	2022-02-04T20:56:17Z	10	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:04Z	Informal to Formal: Wordy to Concise: Fill Missing Phrase: ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln20") model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln20") ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space changes over time) ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time) ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ```` ``` infill: increasing the number of sidewalks in suburban areas will [MASK]. Translated into the Style of Abraham Lincoln: increasing the number of sidewalks in suburban areas will ( ( enhance / maximize ) community cohesion / facilitate ( communal ties / the formation of neighborhood camaraderie ) / forge neighborly relations / lend themselves to the advancement of neighborly ties / plant the seeds of community building / flower anew the bonds of friendship / invite the budding of neighborhood rapport / enrich neighborhood life ). infill: corn fields [MASK], [MASK] visibly as one ventures beyond chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), ( manifesting themselves ) visibly as one ventures beyond chicago. infill: the [MASK] the SAT will soon be [MASK]. [MASK] an examination undertaken on one's laptop. [MASK] will allow students to retrieve test results promptly. Translated into the Style of Abraham Lincoln: the ( conventional form of ) the SAT will soon be ( consigned to history ). ( replacing it will be ) an examination undertaken on one's laptop. ( so doing ) will allow students to retrieve test results promptly. infill: ``` ``` * wordy: chancing upon a linux user is a rare occurrence in the present day. Translate into Concise Text: present-day linux users are rare. * wordy: an interest in classical music is becoming more and more less popular. Translate into Concise Text: classical music appreciation is dwindling. Translate into Concise Text: waning interest in classic music persists. Translate into Concise Text: interest in classic music is fading. * wordy: the ice cream was only one dollar, but it was not a good value for the size. Translate into Concise Text: the one dollar ice cream was overpriced for its size. Translate into Concise Text: overpriced, the one dollar ice cream was small. * wordy: ```
MarioPenguin/bert-model-english	MarioPenguin	2022-02-04T20:12:58Z	6	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: bert-model-english results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # bert-model-english 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: - Train Loss: 0.1408 - Train Sparse Categorical Accuracy: 0.9512 - Validation Loss: nan - Validation Sparse Categorical Accuracy: 0.0 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Sparse Categorical Accuracy \| Validation Loss \| Validation Sparse Categorical Accuracy \| Epoch \| \|:----------:\|:---------------------------------:\|:---------------:\|:--------------------------------------:\|:-----:\| \| 0.2775 \| 0.8887 \| nan \| 0.0 \| 0 \| \| 0.1702 \| 0.9390 \| nan \| 0.0 \| 1 \| \| 0.1300 \| 0.9555 \| nan \| 0.0 \| 2 \| \| 0.1346 \| 0.9544 \| nan \| 0.0 \| 3 \| \| 0.1408 \| 0.9512 \| nan \| 0.0 \| 4 \| ### Framework versions - Transformers 4.16.2 - TensorFlow 2.7.0 - Datasets 1.18.3 - Tokenizers 0.11.0
tesemnikov-av/NER-RUBERT-Per-Loc-Org	tesemnikov-av	2022-02-04T19:40:56Z	7	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- widget: - text: "В город Сергиев Посад приехал Курт Кобейн." --- Fine-tuning [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model on sentences from Wiki auto annotated with PER, LOC, ORG tags [corus/WiNER](https://pypi.org/project/corus/#reference) language: RU NER Class: - PER - LOC - ORG license: mit
LenaSchmidt/distilbert-base-uncased-finetuned-squad	LenaSchmidt	2022-02-04T19:20:11Z	11	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7713 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.0325 \| 1.0 \| 585 \| 1.7520 \| \| 1.609 \| 2.0 \| 1170 \| 1.7713 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
mrm8488/roberta-base-bne-finetuned-sqac-retriever	mrm8488	2022-02-04T17:59:07Z	4	1	sentence-transformers	[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 939 with parameters: ``` {'batch_size': 16} ``` Loss*: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 93, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
huggingtweets/loverachelle2	huggingtweets	2022-02-04T17:51:57Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/loverachelle2/1643997109994/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/1371211513323749377/ABF4NRhC_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">LoveRachelle2</div> <div style="text-align: center; font-size: 14px;">@loverachelle2</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 LoveRachelle2. \| Data \| LoveRachelle2 \| \| --- \| --- \| \| Tweets downloaded \| 1440 \| \| Retweets \| 102 \| \| Short tweets \| 92 \| \| Tweets kept \| 1246 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1liqzipo/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 @loverachelle2's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/284b8u8q) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/284b8u8q/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/loverachelle2') 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)
samx18/demo	samx18	2022-02-04T17:23:34Z	0	0	null	[ "region:us" ]	null	2022-03-02T23:29:05Z	# Dummy This is a dummy model for testing - do not use
dkurt/wav2vec2-base-ft-keyword-spotting-int8	dkurt	2022-02-04T16:40:37Z	7	2	transformers	[ "transformers", "wav2vec2", "audio-classification", "endpoints_compatible", "region:us" ]	audio-classification	2022-03-02T23:29:05Z	[anton-l/wav2vec2-base-ft-keyword-spotting](https://huggingface.co/anton-l/wav2vec2-base-ft-keyword-spotting) model quantized with [Optimum OpenVINO](https://github.com/dkurt/optimum-openvino/). \| Accuracy on eval (baseline) \| Accuracy on eval (quantized) \| \|-----------------------------\|----------------------------------------\| \| 0.9828 \| 0.9553 (-0.0274) \|
Rolv-Arild/xls-r-300m-npsc-4	Rolv-Arild	2022-02-04T16:36:33Z	32	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "NbAiLab/NPSC", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - automatic-speech-recognition - NbAiLab/NPSC - generated_from_trainer model-index: - name: '' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set: - Loss: 0.1957 - Wer: 0.1697 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 4.4527 \| 0.28 \| 250 \| 4.0144 \| 1.0 \| \| 3.1828 \| 0.56 \| 500 \| 3.1369 \| 1.0 \| \| 2.9927 \| 0.85 \| 750 \| 3.0183 \| 1.0 \| \| 2.9591 \| 1.13 \| 1000 \| 2.9991 \| 1.0 \| \| 2.8989 \| 1.41 \| 1250 \| 2.9000 \| 1.0000 \| \| 2.4286 \| 1.69 \| 1500 \| 1.7688 \| 0.9550 \| \| 1.6765 \| 1.98 \| 1750 \| 0.6842 \| 0.4855 \| \| 1.4521 \| 2.26 \| 2000 \| 0.5096 \| 0.3736 \| \| 1.3589 \| 2.54 \| 2250 \| 0.4479 \| 0.3335 \| \| 1.3136 \| 2.82 \| 2500 \| 0.4056 \| 0.3123 \| \| 1.2856 \| 3.11 \| 2750 \| 0.3870 \| 0.2987 \| \| 1.2283 \| 3.39 \| 3000 \| 0.3646 \| 0.2828 \| \| 1.2053 \| 3.67 \| 3250 \| 0.3499 \| 0.2748 \| \| 1.2087 \| 3.95 \| 3500 \| 0.3345 \| 0.2603 \| \| 1.2002 \| 4.24 \| 3750 \| 0.3320 \| 0.2523 \| \| 1.1383 \| 4.52 \| 4000 \| 0.3117 \| 0.2439 \| \| 1.1364 \| 4.8 \| 4250 \| 0.3198 \| 0.2383 \| \| 1.158 \| 5.08 \| 4500 \| 0.3071 \| 0.2342 \| \| 1.108 \| 5.37 \| 4750 \| 0.3011 \| 0.2314 \| \| 1.1025 \| 5.65 \| 5000 \| 0.2875 \| 0.2289 \| \| 1.0697 \| 5.93 \| 5250 \| 0.2926 \| 0.2256 \| \| 1.0904 \| 6.21 \| 5500 \| 0.2695 \| 0.2245 \| \| 1.0802 \| 6.5 \| 5750 \| 0.2602 \| 0.2189 \| \| 1.0882 \| 6.78 \| 6000 \| 0.2603 \| 0.2168 \| \| 1.0881 \| 7.06 \| 6250 \| 0.2540 \| 0.2293 \| \| 1.0378 \| 7.34 \| 6500 \| 0.2614 \| 0.2193 \| \| 1.0397 \| 7.63 \| 6750 \| 0.2707 \| 0.2104 \| \| 1.0296 \| 7.91 \| 7000 \| 0.2483 \| 0.2119 \| \| 1.0249 \| 8.19 \| 7250 \| 0.2483 \| 0.2047 \| \| 1.013 \| 8.47 \| 7500 \| 0.2487 \| 0.2042 \| \| 1.0064 \| 8.76 \| 7750 \| 0.2456 \| 0.2016 \| \| 1.0668 \| 9.04 \| 8000 \| 0.2397 \| 0.1995 \| \| 1.0129 \| 9.32 \| 8250 \| 0.2374 \| 0.1994 \| \| 1.0164 \| 9.6 \| 8500 \| 0.2206 \| 0.1992 \| \| 0.975 \| 9.89 \| 8750 \| 0.2247 \| 0.1973 \| \| 0.9849 \| 10.17 \| 9000 \| 0.2325 \| 0.1953 \| \| 0.9826 \| 10.45 \| 9250 \| 0.2301 \| 0.1934 \| \| 0.9835 \| 10.73 \| 9500 \| 0.2192 \| 0.1942 \| \| 0.9676 \| 11.02 \| 9750 \| 0.2266 \| 0.1913 \| \| 0.9627 \| 11.3 \| 10000 \| 0.2193 \| 0.1921 \| \| 0.976 \| 11.58 \| 10250 \| 0.2309 \| 0.1882 \| \| 0.969 \| 11.86 \| 10500 \| 0.2268 \| 0.1886 \| \| 0.9611 \| 12.15 \| 10750 \| 0.2322 \| 0.1863 \| \| 0.9397 \| 12.43 \| 11000 \| 0.2197 \| 0.1844 \| \| 0.9601 \| 12.71 \| 11250 \| 0.2211 \| 0.1871 \| \| 0.9718 \| 12.99 \| 11500 \| 0.2079 \| 0.1898 \| \| 0.9347 \| 13.28 \| 11750 \| 0.2054 \| 0.1843 \| \| 0.9377 \| 13.56 \| 12000 \| 0.2031 \| 0.1842 \| \| 0.934 \| 13.84 \| 12250 \| 0.2059 \| 0.1806 \| \| 0.9295 \| 14.12 \| 12500 \| 0.2122 \| 0.1861 \| \| 0.935 \| 14.41 \| 12750 \| 0.2072 \| 0.1787 \| \| 0.9021 \| 14.69 \| 13000 \| 0.2105 \| 0.1781 \| \| 0.9193 \| 14.97 \| 13250 \| 0.2035 \| 0.1786 \| \| 0.9214 \| 15.25 \| 13500 \| 0.2035 \| 0.1766 \| \| 0.9048 \| 15.54 \| 13750 \| 0.1964 \| 0.1758 \| \| 0.9006 \| 15.82 \| 14000 \| 0.1984 \| 0.1757 \| \| 0.9027 \| 16.1 \| 14250 \| 0.2022 \| 0.1743 \| \| 0.9083 \| 16.38 \| 14500 \| 0.1969 \| 0.1744 \| \| 0.9761 \| 16.67 \| 14750 \| 0.1963 \| 0.1728 \| \| 0.9311 \| 16.95 \| 15000 \| 0.1960 \| 0.1737 \| \| 0.886 \| 17.23 \| 15250 \| 0.1929 \| 0.1726 \| \| 0.8969 \| 17.51 \| 15500 \| 0.1928 \| 0.1734 \| \| 0.9084 \| 17.8 \| 15750 \| 0.1937 \| 0.1713 \| \| 0.8795 \| 18.08 \| 16000 \| 0.1978 \| 0.1709 \| \| 0.8883 \| 18.36 \| 16250 \| 0.1956 \| 0.1703 \| \| 0.8901 \| 18.64 \| 16500 \| 0.1933 \| 0.1705 \| \| 0.8922 \| 18.93 \| 16750 \| 0.1962 \| 0.1711 \| \| 0.8765 \| 19.21 \| 17000 \| 0.1962 \| 0.1711 \| \| 0.8992 \| 19.49 \| 17250 \| 0.1965 \| 0.1703 \| \| 0.8778 \| 19.77 \| 17500 \| 0.1957 \| 0.1699 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.0+cu113 - Datasets 1.18.1 - Tokenizers 0.11.0
groar/distilgpt2-finetuned-wikitext2	groar	2022-02-04T16:27:05Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6895 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.7852 \| 1.0 \| 2334 \| 3.6895 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
cahya/wav2vec2-base-turkish-cv8	cahya	2022-02-04T14:30:19Z	5	1	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "tr", "dataset:common_voice", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - tr tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer datasets: - common_voice model-index: - name: '' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [./checkpoint-1000](https://huggingface.co/./checkpoint-1000) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.3282 - Wer: 0.2836 ## Model description More information needed ## Intended uses & 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: 96 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 192 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 1.0671 \| 2.04 \| 200 \| 0.3079 \| 0.2752 \| \| 0.6433 \| 4.08 \| 400 \| 0.2728 \| 0.2848 \| \| 0.5687 \| 6.12 \| 600 \| 0.2882 \| 0.3036 \| \| 0.5355 \| 8.16 \| 800 \| 0.2778 \| 0.2920 \| \| 0.5116 \| 10.2 \| 1000 \| 0.2906 \| 0.3014 \| \| 0.5313 \| 9.16 \| 1200 \| 0.2984 \| 0.3273 \| \| 0.4996 \| 10.69 \| 1400 \| 0.3170 \| 0.3344 \| \| 0.4845 \| 12.21 \| 1600 \| 0.3202 \| 0.3634 \| \| 0.5092 \| 13.74 \| 1800 \| 0.3167 \| 0.3373 \| \| 0.4777 \| 15.27 \| 2000 \| 0.3292 \| 0.3386 \| \| 0.4651 \| 16.79 \| 2200 \| 0.3070 \| 0.3427 \| \| 0.461 \| 18.32 \| 2400 \| 0.3149 \| 0.3561 \| \| 0.4481 \| 19.85 \| 2600 \| 0.3292 \| 0.3441 \| \| 0.4479 \| 21.37 \| 2800 \| 0.3142 \| 0.3209 \| \| 0.4305 \| 22.9 \| 3000 \| 0.3525 \| 0.3547 \| \| 0.4254 \| 24.43 \| 3200 \| 0.3414 \| 0.3400 \| \| 0.4066 \| 25.95 \| 3400 \| 0.3118 \| 0.3207 \| \| 0.4043 \| 27.48 \| 3600 \| 0.3418 \| 0.3483 \| \| 0.3985 \| 29.01 \| 3800 \| 0.3254 \| 0.3166 \| \| 0.3982 \| 30.53 \| 4000 \| 0.3306 \| 0.3453 \| \| 0.3929 \| 32.06 \| 4200 \| 0.3262 \| 0.3229 \| \| 0.378 \| 33.59 \| 4400 \| 0.3546 \| 0.3336 \| \| 0.4062 \| 35.11 \| 4600 \| 0.3174 \| 0.3457 \| \| 0.3648 \| 36.64 \| 4800 \| 0.3377 \| 0.3357 \| \| 0.3609 \| 38.17 \| 5000 \| 0.3346 \| 0.3520 \| \| 0.3483 \| 39.69 \| 5200 \| 0.3350 \| 0.3526 \| \| 0.3548 \| 41.22 \| 5400 \| 0.3330 \| 0.3406 \| \| 0.3446 \| 42.75 \| 5600 \| 0.3398 \| 0.3372 \| \| 0.3346 \| 44.27 \| 5800 \| 0.3449 \| 0.3288 \| \| 0.3309 \| 45.8 \| 6000 \| 0.3320 \| 0.3144 \| \| 0.326 \| 47.33 \| 6200 \| 0.3400 \| 0.3279 \| \| 0.3189 \| 48.85 \| 6400 \| 0.3400 \| 0.3150 \| \| 0.3165 \| 50.38 \| 6600 \| 0.3359 \| 0.2995 \| \| 0.3132 \| 51.91 \| 6800 \| 0.3343 \| 0.3096 \| \| 0.3092 \| 53.44 \| 7000 \| 0.3224 \| 0.3029 \| \| 0.2995 \| 54.96 \| 7200 \| 0.3205 \| 0.2985 \| \| 0.304 \| 56.49 \| 7400 \| 0.3523 \| 0.3034 \| \| 0.2952 \| 58.02 \| 7600 \| 0.3289 \| 0.2934 \| \| 0.2875 \| 59.54 \| 7800 \| 0.3350 \| 0.3008 \| \| 0.2868 \| 61.07 \| 8000 \| 0.3537 \| 0.3227 \| \| 0.2875 \| 62.6 \| 8200 \| 0.3389 \| 0.2970 \| \| 0.2778 \| 64.12 \| 8400 \| 0.3370 \| 0.2960 \| \| 0.2706 \| 65.65 \| 8600 \| 0.3250 \| 0.2802 \| \| 0.2669 \| 67.18 \| 8800 \| 0.3351 \| 0.2903 \| \| 0.2615 \| 68.7 \| 9000 \| 0.3382 \| 0.2989 \| \| 0.2563 \| 70.23 \| 9200 \| 0.3312 \| 0.2975 \| \| 0.2546 \| 71.76 \| 9400 \| 0.3212 \| 0.3003 \| \| 0.2482 \| 73.28 \| 9600 \| 0.3337 \| 0.3091 \| \| 0.2504 \| 74.81 \| 9800 \| 0.3308 \| 0.3110 \| \| 0.2456 \| 76.34 \| 10000 \| 0.3157 \| 0.3118 \| \| 0.2363 \| 77.86 \| 10200 \| 0.3251 \| 0.3144 \| \| 0.2319 \| 79.39 \| 10400 \| 0.3253 \| 0.3038 \| \| 0.2266 \| 80.92 \| 10600 \| 0.3374 \| 0.3038 \| \| 0.2279 \| 82.44 \| 10800 \| 0.3268 \| 0.2964 \| \| 0.2231 \| 83.97 \| 11000 \| 0.3278 \| 0.2950 \| \| 0.2185 \| 85.5 \| 11200 \| 0.3462 \| 0.2981 \| \| 0.2245 \| 87.02 \| 11400 \| 0.3311 \| 0.2895 \| \| 0.223 \| 88.55 \| 11600 \| 0.3325 \| 0.2877 \| \| 0.2121 \| 90.08 \| 11800 \| 0.3337 \| 0.2828 \| \| 0.2126 \| 91.6 \| 12000 \| 0.3325 \| 0.2808 \| \| 0.2027 \| 93.13 \| 12200 \| 0.3277 \| 0.2820 \| \| 0.2058 \| 94.66 \| 12400 \| 0.3308 \| 0.2827 \| \| 0.1991 \| 96.18 \| 12600 \| 0.3279 \| 0.2820 \| \| 0.1991 \| 97.71 \| 12800 \| 0.3300 \| 0.2822 \| \| 0.1986 \| 99.24 \| 13000 \| 0.3285 \| 0.2835 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0
abhishek/autonlp-imdb-roberta-base-3662644	abhishek	2022-02-04T14:25:35Z	16	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "autonlp", "unk", "dataset:abhishek/autonlp-data-imdb-roberta-base", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - abhishek/autonlp-data-imdb-roberta-base co2_eq_emissions: 25.894117734124272 --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 3662644 - CO2 Emissions (in grams): 25.894117734124272 ## Validation Metrics - Loss: 0.20277436077594757 - Accuracy: 0.92604 - Precision: 0.9560674830864092 - Recall: 0.89312 - AUC: 0.9814625504000001 - F1: 0.9235223559581421 ## 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 AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-imdb-roberta-base-3662644 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-imdb-roberta-base-3662644", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-imdb-roberta-base-3662644", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
Language-Media-Lab/mt5-small-jpn-ain-mt	Language-Media-Lab	2022-02-04T14:23:13Z	5	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "translation", "jpn", "ain", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - jpn - ain tags: - translation --- mt5-small-jpn-ain-mt is a machine translation model pretrained with [Google's mT5-small](https://huggingface.co/google/mt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Japanese to Ainu language.
Language-Media-Lab/mt5-small-ain-jpn-mt	Language-Media-Lab	2022-02-04T13:20:55Z	5	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "translation", "jpn", "ain", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - jpn - ain tags: - translation --- mt5-small-ain-jpn-mt is a machine translation model pretrained with [Google's mT5-small](https://huggingface.co/google/mt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Ainu language to Japanese.
Language-Media-Lab/byt5-small-ain-jpn-mt	Language-Media-Lab	2022-02-04T13:03:14Z	7	2	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "translation", "ain", "ja", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - ain - ja tags: - translation --- Byt5-small-ain-jpn-mt is a machine translation model pretrained with [Google's ByT5-small](https://huggingface.co/google/byt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Ainu language to Japanese.
Language-Media-Lab/byt5-small-jpn-ain-mt	Language-Media-Lab	2022-02-04T13:02:58Z	14	1	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "translation", "jpn", "ain", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - jpn - ain tags: - translation --- Byt5-small-jpn-ain-mt is a machine translation model pretrained with [Google's ByT5-small](https://huggingface.co/google/byt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Japanese to Ainu language.
huggingtweets/ir_rkp	huggingtweets	2022-02-04T12:03:54Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/ir_rkp/1643976228944/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/1432037158072856578/a_Fty68E_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">Riikka Purra</div> <div style="text-align: center; font-size: 14px;">@ir_rkp</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 Riikka Purra. \| Data \| Riikka Purra \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 141 \| \| Short tweets \| 78 \| \| Tweets kept \| 3031 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1w0bzvgu/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 @ir_rkp's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1nj4v31w) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1nj4v31w/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/ir_rkp') 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)
Plim/xls-r-1b-fr	Plim	2022-02-04T11:45:21Z	5	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "fr", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- language: - fr license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer model-index: - name: '' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - FR dataset. It achieves the following results on the evaluation set: - Loss: 0.2464 - Wer: 0.2220 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 1.0326 \| 0.32 \| 1000 \| 0.3092 \| 0.2718 \| \| 1.0828 \| 0.65 \| 2000 \| 0.2843 \| 0.2606 \| \| 1.0771 \| 0.97 \| 3000 \| 0.2774 \| 0.2488 \| \| 1.0306 \| 1.3 \| 4000 \| 0.2588 \| 0.2351 \| \| 1.0052 \| 1.62 \| 5000 \| 0.2483 \| 0.2284 \| \| 0.9865 \| 1.94 \| 6000 \| 0.2464 \| 0.2220 \| \| 0.978 \| 2.27 \| 7000 \| 0.2514 \| 0.2172 \| \| 1.7438 \| 2.59 \| 8000 \| 0.7983 \| 0.5072 \| \| 2.3309 \| 2.92 \| 9000 \| 1.8917 \| 0.9416 \| \| 2.1834 \| 3.24 \| 10000 \| 1.7496 \| 0.9030 \| \| 2.3047 \| 3.56 \| 11000 \| 1.5377 \| 0.8747 \| \| 2.1378 \| 3.89 \| 12000 \| 1.3501 \| 0.7923 \| \| 1.9812 \| 4.21 \| 13000 \| 1.2662 \| 0.7697 \| \| 2.6855 \| 4.54 \| 14000 \| 2.4120 \| 0.9902 \| \| 2.7482 \| 4.86 \| 15000 \| 2.5341 \| 0.9874 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0
Yanzhu/bertweetfr_offensiveness	Yanzhu	2022-02-04T11:42:54Z	0	0	null	[ "region:us" ]	null	2022-03-02T23:29:05Z	French roBERTa-base model fine-tuned for Offensive Language Identification on COVID-19 tweets.
Subhashini17/wav2vec2-large-xls-r-300m-ta-colab-new1	Subhashini17	2022-02-04T11:14:25Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-ta-colab-new1 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-ta-colab-new1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6642 - eval_wer: 0.7611 - eval_runtime: 152.4412 - eval_samples_per_second: 11.683 - eval_steps_per_second: 1.463 - epoch: 10.11 - step: 960 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.13.3 - Tokenizers 0.10.3
yohida/yoshida_gpt	yohida	2022-02-04T10:13:45Z	4	0	transformers	[ "transformers", "gpt2", "text-generation", "ja", "japanese", "gpt", "lm", "nlp", "dataset:cc100", "dataset:wikipedia", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: ja thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png tags: - ja - japanese - gpt - text-generation - lm - nlp license: mit datasets: - cc100 - wikipedia widget: - text: "西田幾多郎は、" --- # japanese-gpt-1b ![rinna-icon](./rinna.png) This repository provides a 1.3B-parameter Japanese GPT model. The model was trained by [rinna Co., Ltd.](https://corp.rinna.co.jp/) # How to use the model NOTE: Use `T5Tokenizer` to initiate the tokenizer. ~~~~ import torch from transformers import T5Tokenizer, AutoModelForCausalLM tokenizer = T5Tokenizer.from_pretrained("rinna/japanese-gpt-1b") model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt-1b") if torch.cuda.is_available(): model = model.to("cuda") text = "西田幾多郎は、" token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt") with torch.no_grad(): output_ids = model.generate( token_ids.to(model.device), max_length=100, min_length=100, do_sample=True, top_k=500, top_p=0.95, pad_token_id=tokenizer.pad_token_id, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, bad_word_ids=[[tokenizer.unk_token_id]] ) output = tokenizer.decode(output_ids.tolist()[0]) print(output) # sample output: 西田幾多郎は、その主著の「善の研究」などで、人間の内面に自然とその根源があると指摘し、その根源的な性格は、この西田哲学を象徴しているとして、カントの「純粋理性批判」と「判断力批判」を対比して捉えます。それは、「人が理性的存在であるかぎりにおいて、人はその当人に固有な道徳的に自覚された善悪の基準を持っている」とするもので、この理性的な善悪の観念を否定するのがカントの ~~~~ # Model architecture A 24-layer, 2048-hidden-size transformer-based language model. # Training The model was trained on [Japanese C4](https://huggingface.co/datasets/allenai/c4), [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz) and [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) to optimize a traditional language modelling objective. It reaches around 14 perplexity on a chosen validation set from the same data. # Tokenization The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer. The vocabulary was first trained on a selected subset from the training data using the official sentencepiece training script, and then augmented with emojis and symbols. # Licenese [The MIT license](https://opensource.org/licenses/MIT)
huggingtweets/dril-drilbot_neo-jril_bot	huggingtweets	2022-02-04T09:52:05Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/dril-drilbot_neo-jril_bot/1643968320729/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/847818629840228354/VXyQHfn0_400x400.jpg')"> </div> <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/1468502340634296326/gbl8-ltv_400x400.png')"> </div> <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/1374924360780242944/-Q8NfgEr_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">wint & Jril & wintbot_neo</div> <div style="text-align: center; font-size: 14px;">@dril-drilbot_neo-jril_bot</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 wint & Jril & wintbot_neo. \| Data \| wint \| Jril \| wintbot_neo \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3228 \| 113 \| 3241 \| \| Retweets \| 475 \| 0 \| 315 \| \| Short tweets \| 305 \| 0 \| 453 \| \| Tweets kept \| 2448 \| 113 \| 2473 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/27nmrlyy/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 @dril-drilbot_neo-jril_bot's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/i64hq9wb) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/i64hq9wb/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/dril-drilbot_neo-jril_bot') 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)
LegolasTheElf/Wav2Vec2_xls_r_openslr_Hi_V2	LegolasTheElf	2022-02-04T07:53:30Z	7	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "Harveenchadha/indic-voice", "generated_from_trainer", "hi", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- license: apache-2.0 language: - hi tags: - automatic-speech-recognition - Harveenchadha/indic-voice - generated_from_trainer model-index: - name: Wav2Vec2_xls_r_openslr_Hi_V2 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_xls_r_openslr_Hi_V2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [Harveenchadha/indic-voice](https://huggingface.co/datasets/Harveenchadha/indic-voice) dataset. It achieves the following results on the evaluation set: - Loss: 0.3184 - Wer: 0.3104 - Cer: 0.0958 ## Model description More information needed ## Intended uses & 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Cer \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:------:\|:---------------:\|:------:\| \| 7.1097 \| 0.48 \| 300 \| 0.9965 \| 3.3989 \| 1.0 \| \| 3.0235 \| 0.96 \| 600 \| 0.3163 \| 1.3183 \| 0.7977 \| \| 1.1419 \| 1.44 \| 900 \| 0.1913 \| 0.6416 \| 0.5543 \| \| 0.8242 \| 1.92 \| 1200 \| 0.1608 \| 0.5063 \| 0.4804 \| \| 0.6876 \| 2.56 \| 1600 \| 0.1387 \| 0.4401 \| 0.4280 \| \| 0.5868 \| 3.21 \| 2000 \| 0.1249 \| 0.3940 \| 0.3907 \| \| 0.5285 \| 3.85 \| 2400 \| 0.1200 \| 0.3661 \| 0.3763 \| \| 0.5 \| 4.49 \| 2800 \| 0.3528 \| 0.3610 \| 0.1136 \| \| 0.4538 \| 5.13 \| 3200 \| 0.3403 \| 0.3485 \| 0.1086 \| \| 0.4165 \| 5.77 \| 3600 \| 0.3335 \| 0.3439 \| 0.1062 \| \| 0.3989 \| 6.41 \| 4000 \| 0.3264 \| 0.3340 \| 0.1036 \| \| 0.3679 \| 7.05 \| 4400 \| 0.3256 \| 0.3287 \| 0.1013 \| \| 0.3517 \| 7.69 \| 4800 \| 0.3212 \| 0.3223 \| 0.1002 \| \| 0.3357 \| 8.33 \| 5200 \| 0.3173 \| 0.3196 \| 0.0986 \| \| 0.3225 \| 8.97 \| 5600 \| 0.3142 \| 0.3177 \| 0.0985 \| \| 0.3057 \| 9.62 \| 6000 \| 0.3199 \| 0.3156 \| 0.0975 \| \| 0.2972 \| 10.26 \| 6400 \| 0.3139 \| 0.3128 \| 0.0967 \| \| 0.2881 \| 10.9 \| 6800 \| 0.3184 \| 0.3107 \| 0.0957 \| \| 0.2791 \| 11.54 \| 7200 \| 0.3184 \| 0.3104 \| 0.0958 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0
Ayham/xlnet_distilgpt2_summarization_cnn_dailymail	Ayham	2022-02-04T06:33:59Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "encoder-decoder", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: xlnet_distilgpt2_summarization_cnn_dailymail 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. --> # xlnet_distilgpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
jonc/distilbert-base-uncased-finetuned-emotion	jonc	2022-02-04T06:15:55Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- 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: 0.9230733583303665 --- <!-- 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: 0.2159 - Accuracy: 0.923 - F1: 0.9231 ## Model description More information needed ## Intended uses & 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8494 \| 1.0 \| 250 \| 0.3134 \| 0.907 \| 0.9051 \| \| 0.2504 \| 2.0 \| 500 \| 0.2159 \| 0.923 \| 0.9231 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
Mapcar/pegasus-samsum	Mapcar	2022-02-04T03:27:33Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum 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. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4844 ## Model description More information needed ## Intended uses & 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: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.6936 \| 0.54 \| 500 \| 1.4844 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
ghofrani/common7	ghofrani	2022-02-04T01:32:24Z	5	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "fa", "dataset:common_voice", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - fa tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer datasets: - common_voice model-index: - name: common7 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. --> # common7 This model is a fine-tuned version of [common7/checkpoint-18500](https://huggingface.co/common7/checkpoint-18500) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - FA dataset. It achieves the following results on the evaluation set: - Loss: 0.3448 - Wer: 0.3478 ## Model description More information needed ## Intended uses & 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: 32 - eval_batch_size: 16 - 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_steps: 100 - num_epochs: 150.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\| \| 2.957 \| 3.29 \| 500 \| 2.9503 \| 1.0 \| \| 1.7225 \| 6.58 \| 1000 \| 0.8860 \| 0.7703 \| \| 1.4907 \| 9.86 \| 1500 \| 0.6555 \| 0.6673 \| \| 1.4177 \| 13.16 \| 2000 \| 0.5784 \| 0.6076 \| \| 1.3425 \| 16.45 \| 2500 \| 0.5379 \| 0.5718 \| \| 1.33 \| 19.73 \| 3000 \| 0.4962 \| 0.5245 \| \| 1.4378 \| 23.03 \| 3500 \| 0.4699 \| 0.5098 \| \| 1.1894 \| 26.31 \| 4000 \| 0.4527 \| 0.4848 \| \| 1.1844 \| 29.6 \| 4500 \| 0.4309 \| 0.4651 \| \| 1.1795 \| 32.89 \| 5000 \| 0.4131 \| 0.4524 \| \| 1.1471 \| 36.18 \| 5500 \| 0.4052 \| 0.4435 \| \| 1.1337 \| 39.47 \| 6000 \| 0.3927 \| 0.4363 \| \| 1.1896 \| 42.76 \| 6500 \| 0.3811 \| 0.4254 \| \| 1.1847 \| 46.05 \| 7000 \| 0.3855 \| 0.4129 \| \| 0.9954 \| 49.34 \| 7500 \| 0.3729 \| 0.3981 \| \| 1.0293 \| 52.63 \| 8000 \| 0.3637 \| 0.4014 \| \| 1.0224 \| 55.92 \| 8500 \| 0.3578 \| 0.3885 \| \| 1.012 \| 59.21 \| 9000 \| 0.3629 \| 0.3930 \| \| 1.0772 \| 62.5 \| 9500 \| 0.3635 \| 0.3906 \| \| 1.0344 \| 65.79 \| 10000 \| 0.3469 \| 0.3771 \| \| 0.9457 \| 69.08 \| 10500 \| 0.3435 \| 0.3735 \| \| 0.9307 \| 72.37 \| 11000 \| 0.3519 \| 0.3762 \| \| 0.9523 \| 75.65 \| 11500 \| 0.3443 \| 0.3666 \| \| 0.9523 \| 78.94 \| 12000 \| 0.3502 \| 0.3757 \| \| 0.9475 \| 82.24 \| 12500 \| 0.3509 \| 0.3643 \| \| 0.9971 \| 85.52 \| 13000 \| 0.3502 \| 0.3626 \| \| 0.9058 \| 88.81 \| 13500 \| 0.3472 \| 0.3605 \| \| 0.8922 \| 92.1 \| 14000 \| 0.3530 \| 0.3618 \| \| 0.9 \| 95.39 \| 14500 \| 0.3500 \| 0.3574 \| \| 0.9051 \| 98.68 \| 15000 \| 0.3456 \| 0.3535 \| \| 0.9304 \| 101.97 \| 15500 \| 0.3438 \| 0.3578 \| \| 0.9433 \| 105.26 \| 16000 \| 0.3396 \| 0.3530 \| \| 0.8988 \| 108.55 \| 16500 \| 0.3436 \| 0.3539 \| \| 0.8789 \| 111.84 \| 17000 \| 0.3426 \| 0.3516 \| \| 0.8667 \| 115.13 \| 17500 \| 0.3438 \| 0.3506 \| \| 0.8895 \| 118.42 \| 18000 \| 0.3434 \| 0.3503 \| \| 0.8888 \| 121.71 \| 18500 \| 0.3425 \| 0.3494 \| \| 0.9453 \| 125.0 \| 19000 \| 0.3415 \| 0.3480 \| \| 0.9267 \| 128.29 \| 19500 \| 0.3477 \| 0.3503 \| \| 0.8315 \| 131.58 \| 20000 \| 0.3476 \| 0.3505 \| \| 0.8542 \| 134.86 \| 20500 \| 0.3475 \| 0.3506 \| \| 0.8478 \| 138.16 \| 21000 \| 0.3430 \| 0.3481 \| \| 0.8643 \| 141.45 \| 21500 \| 0.3451 \| 0.3485 \| \| 0.8705 \| 144.73 \| 22000 \| 0.3444 \| 0.3474 \| \| 0.9869 \| 148.03 \| 22500 \| 0.3441 \| 0.3493 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2 - Datasets 1.18.3.dev0 - Tokenizers 0.10.3
edugp/data2vec-nlp-base	edugp	2022-02-03T23:23:15Z	8	0	transformers	[ "transformers", "pytorch", "data2vec", "fill-mask", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: model-index: - name: data2vec-nlp-base results: [] --- # Data2Vec NLP Base This model was converted from `fairseq`. The original weights can be found in https://dl.fbaipublicfiles.com/fairseq/data2vec/nlp_base.pt Example usage: ```python from transformers import RobertaTokenizer, Data2VecForSequenceClassification, Data2VecConfig import torch tokenizer = RobertaTokenizer.from_pretrained("roberta-large") config = Data2VecConfig.from_pretrained("edugp/data2vec-nlp-base") model = Data2VecForSequenceClassification.from_pretrained("edugp/data2vec-nlp-base", config=config) # Fine-tune this model inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) prediction_logits = outputs.logits ```
ArBert/albert-base-v2-finetuned-ner	ArBert	2022-02-03T14:26:33Z	22	4	transformers	[ "transformers", "pytorch", "tensorboard", "albert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: albert-base-v2-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9301181102362205 - name: Recall type: recall value: 0.9376033513394334 - name: F1 type: f1 value: 0.9338457315399397 - name: Accuracy type: accuracy value: 0.9851613086447802 --- <!-- 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. --> # albert-base-v2-finetuned-ner This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0700 - Precision: 0.9301 - Recall: 0.9376 - F1: 0.9338 - Accuracy: 0.9852 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.096 \| 1.0 \| 1756 \| 0.0752 \| 0.9163 \| 0.9201 \| 0.9182 \| 0.9811 \| \| 0.0481 \| 2.0 \| 3512 \| 0.0761 \| 0.9169 \| 0.9293 \| 0.9231 \| 0.9830 \| \| 0.0251 \| 3.0 \| 5268 \| 0.0700 \| 0.9301 \| 0.9376 \| 0.9338 \| 0.9852 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.1 - Datasets 1.17.0 - Tokenizers 0.10.3
anuragshas/wav2vec2-xls-r-300m-pa-IN-cv8-with-lm	anuragshas	2022-02-03T12:28:34Z	5	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - pa-IN license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer datasets: - common_voice model-index: - name: '' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PA-IN dataset. It achieves the following results on the evaluation set: - Loss: 0.6864 - Wer: 0.6707 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 200.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 4.3322 \| 14.81 \| 400 \| 3.7450 \| 1.0 \| \| 3.2662 \| 29.63 \| 800 \| 3.2571 \| 0.9996 \| \| 1.6408 \| 44.44 \| 1200 \| 0.9098 \| 0.8162 \| \| 1.2289 \| 59.26 \| 1600 \| 0.6757 \| 0.7099 \| \| 1.0551 \| 74.07 \| 2000 \| 0.6417 \| 0.7044 \| \| 0.966 \| 88.89 \| 2400 \| 0.6365 \| 0.6789 \| \| 0.8713 \| 103.7 \| 2800 \| 0.6617 \| 0.6954 \| \| 0.8055 \| 118.52 \| 3200 \| 0.6371 \| 0.6762 \| \| 0.7489 \| 133.33 \| 3600 \| 0.6798 \| 0.6911 \| \| 0.7073 \| 148.15 \| 4000 \| 0.6567 \| 0.6731 \| \| 0.6609 \| 162.96 \| 4400 \| 0.6742 \| 0.6840 \| \| 0.6435 \| 177.78 \| 4800 \| 0.6862 \| 0.6633 \| \| 0.6282 \| 192.59 \| 5200 \| 0.6865 \| 0.6731 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.4.dev0 - Tokenizers 0.11.0
Baybars/wav2vec2-xls-r-1b-turkish	Baybars	2022-02-03T10:09:31Z	17	1	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "common_voice", "generated_from_trainer", "tr", "dataset:common_voice", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- language: - tr tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: '' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [./checkpoint-10500](https://huggingface.co/./checkpoint-10500) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.7540 - Wer: 0.4647 - Cer: 0.1318 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.999,0.9999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 120.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Cer \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:-----:\|:------:\|:---------------:\|:------:\| \| 1.0779 \| 4.59 \| 500 \| 0.2354 \| 0.8260 \| 0.7395 \| \| 0.7573 \| 9.17 \| 1000 \| 0.2100 \| 0.7544 \| 0.6960 \| \| 0.8225 \| 13.76 \| 1500 \| 0.2021 \| 0.6867 \| 0.6672 \| \| 0.621 \| 18.35 \| 2000 \| 0.1874 \| 0.6824 \| 0.6209 \| \| 0.6362 \| 22.94 \| 2500 \| 0.1904 \| 0.6712 \| 0.6286 \| \| 0.624 \| 27.52 \| 3000 \| 0.1820 \| 0.6940 \| 0.6116 \| \| 0.4781 \| 32.11 \| 3500 \| 0.1735 \| 0.6966 \| 0.5989 \| \| 0.5685 \| 36.7 \| 4000 \| 0.1769 \| 0.6742 \| 0.5971 \| \| 0.4384 \| 41.28 \| 4500 \| 0.1767 \| 0.6904 \| 0.5999 \| \| 0.5509 \| 45.87 \| 5000 \| 0.1692 \| 0.6734 \| 0.5641 \| \| 0.3665 \| 50.46 \| 5500 \| 0.1680 \| 0.7018 \| 0.5662 \| \| 0.3914 \| 55.05 \| 6000 \| 0.1631 \| 0.7121 \| 0.5552 \| \| 0.2467 \| 59.63 \| 6500 \| 0.1563 \| 0.6657 \| 0.5374 \| \| 0.2576 \| 64.22 \| 7000 \| 0.1554 \| 0.6920 \| 0.5316 \| \| 0.2711 \| 68.81 \| 7500 \| 0.1495 \| 0.6900 \| 0.5176 \| \| 0.2626 \| 73.39 \| 8000 \| 0.1454 \| 0.6843 \| 0.5043 \| \| 0.1377 \| 77.98 \| 8500 \| 0.1470 \| 0.7383 \| 0.5101 \| \| 0.2005 \| 82.57 \| 9000 \| 0.1430 \| 0.7228 \| 0.5045 \| \| 0.1355 \| 87.16 \| 9500 \| 0.1375 \| 0.7231 \| 0.4869 \| \| 0.0431 \| 91.74 \| 10000 \| 0.1350 \| 0.7397 \| 0.4749 \| \| 0.0586 \| 96.33 \| 10500 \| 0.1339 \| 0.7360 \| 0.4754 \| \| 0.0896 \| 100.92 \| 11000 \| 0.7187 \| 0.4885 \| 0.1398 \| \| 0.183 \| 105.5 \| 11500 \| 0.7310 \| 0.4838 \| 0.1392 \| \| 0.0963 \| 110.09 \| 12000 \| 0.7643 \| 0.4759 \| 0.1362 \| \| 0.0437 \| 114.68 \| 12500 \| 0.7525 \| 0.4641 \| 0.1328 \| \| 0.1122 \| 119.27 \| 13000 \| 0.7535 \| 0.4651 \| 0.1317 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0
Hetarth/marian-finetuned-hi-hinglish	Hetarth	2022-02-03T09:54:31Z	8	0	transformers	[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: marian-finetuned-hi-hinglish results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # marian-finetuned-hi-hinglish This model is a fine-tuned version of [Helsinki-NLP/opus-mt-hi-en](https://huggingface.co/Helsinki-NLP/opus-mt-hi-en) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.1869 - Validation Loss: 4.0607 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 279, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 4.1869 \| 4.0607 \| 0 \| ### Framework versions - Transformers 4.16.2 - TensorFlow 2.7.0 - Datasets 1.18.3 - Tokenizers 0.11.0
Rajan/Nepali_Word2Vec	Rajan	2022-02-03T08:32:41Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-03-02T23:29:04Z	--- license: mit --- https://github.com/R4j4n/Nepali-Word2Vec-from-scratch How to clone : ``` git lfs install git clone https://huggingface.co/Rajan/Nepali_Word2Vec ```
Atiqah/Atiqah	Atiqah	2022-02-03T07:04:44Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2022-03-02T23:29:04Z	--- license: artistic-2.0 ---
pritoms/distilroberta-base-YTTranscript23	pritoms	2022-02-03T05:52:25Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-YTTranscript23 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-YTTranscript23 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9258 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 70 \| 2.9007 \| \| No log \| 2.0 \| 140 \| 2.9651 \| \| No log \| 3.0 \| 210 \| 2.9374 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
sunitha/distilbert-base-uncased-3feb-2022-finetuned-squad	sunitha	2022-02-03T05:06:27Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-3feb-2022-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-3feb-2022-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1470 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.2276 \| 1.0 \| 5533 \| 1.1641 \| \| 0.9614 \| 2.0 \| 11066 \| 1.1225 \| \| 0.7769 \| 3.0 \| 16599 \| 1.1470 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
pritoms/distilgpt2-YTTranscriptTrial2	pritoms	2022-02-03T04:46:19Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-YTTranscriptTrial2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-YTTranscriptTrial2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.8738 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 70 \| 6.0027 \| \| No log \| 2.0 \| 140 \| 5.9072 \| \| No log \| 3.0 \| 210 \| 5.8738 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
huggingtweets/denyah_	huggingtweets	2022-02-03T01:43:56Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/denyah_/1643852632266/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/1484264819049959425/siOsFP3t_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">Den</div> <div style="text-align: center; font-size: 14px;">@denyah_</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 Den. \| Data \| Den \| \| --- \| --- \| \| Tweets downloaded \| 3244 \| \| Retweets \| 464 \| \| Short tweets \| 795 \| \| Tweets kept \| 1985 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3e5c08gr/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 @denyah_'s tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1438ocp8) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1438ocp8/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/denyah_') 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)
Plim/xls-r-300m-lm-fr	Plim	2022-02-02T23:29:54Z	5	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "fr", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- language: - fr tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer model-index: - name: '' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [./checkpoint-6000](https://huggingface.co/./checkpoint-6000) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - FR dataset. It achieves the following results on the evaluation set: - Loss: 0.2619 - Wer: 0.2457 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.495 \| 0.16 \| 500 \| 3.3883 \| 1.0 \| \| 2.9095 \| 0.32 \| 1000 \| 2.9152 \| 1.0000 \| \| 1.8434 \| 0.49 \| 1500 \| 1.0473 \| 0.7446 \| \| 1.4298 \| 0.65 \| 2000 \| 0.5729 \| 0.5130 \| \| 1.1937 \| 0.81 \| 2500 \| 0.3795 \| 0.3450 \| \| 1.1248 \| 0.97 \| 3000 \| 0.3321 \| 0.3052 \| \| 1.0835 \| 1.13 \| 3500 \| 0.3038 \| 0.2805 \| \| 1.0479 \| 1.3 \| 4000 \| 0.2910 \| 0.2689 \| \| 1.0413 \| 1.46 \| 4500 \| 0.2798 \| 0.2593 \| \| 1.014 \| 1.62 \| 5000 \| 0.2727 \| 0.2512 \| \| 1.004 \| 1.78 \| 5500 \| 0.2646 \| 0.2471 \| \| 0.9949 \| 1.94 \| 6000 \| 0.2619 \| 0.2457 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0
cahya/wav2vec2-base-turkish-artificial	cahya	2022-02-02T15:44:36Z	5	1	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning-week", "tr", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: tr datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Wav2Vec2 Base Turkish with Artificial Voices by Cahya results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice tr type: common_voice args: tr metrics: - name: Test WER type: wer value: 57.60 --- # Wav2Vec2-Large-XLSR-Turkish Fine-tuned [ceyda/wav2vec2-base-760](https://huggingface.co/ceyda/wav2vec2-base-760) on the [Turkish Artificial Common Voice dataset](https://cloud.uncool.ai/index.php/f/2165181). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "tr", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial") # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset[:2]["sentence"]) ``` ## Evaluation The model can be evaluated as follows on the Turkish test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "tr", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]' # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` Test Result: 57.60 % ## Training The Artificial Common Voice `train`, `validation` is used to fine tune the model The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
arjuntheprogrammer/distilbert-base-multilingual-cased-sentiment-2	arjuntheprogrammer	2022-02-02T15:16:39Z	35	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy - f1 model-index: - name: distilbert-base-multilingual-cased-sentiment-2 results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi args: en metrics: - name: Accuracy type: accuracy value: 0.7614 - name: F1 type: f1 value: 0.7614 --- <!-- 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-multilingual-cased-sentiment-2 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.5882 - Accuracy: 0.7614 - F1: 0.7614 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00024 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - distributed_type: sagemaker_data_parallel - num_devices: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - 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 ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.15.1 - Tokenizers 0.10.3
kmfoda/staging-pegasus-gmeetsamsum	kmfoda	2022-02-02T14:34:58Z	12	0	transformers	[ "transformers", "pytorch", "pegasus", "feature-extraction", "summarization", "en", "arxiv:1912.08777", "endpoints_compatible", "region:us" ]	summarization	2022-03-02T23:29:05Z	--- language: en tags: - summarization --- ### Pegasus Models See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html) Original TF 1 code [here](https://github.com/google-research/pegasus) Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019 Maintained by: [@sshleifer](https://twitter.com/sam_shleifer) Task: Summarization The following is copied from the authors' README. # Mixed & Stochastic Checkpoints We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table. \| dataset \| C4 \| HugeNews \| Mixed & Stochastic\| \| ---- \| ---- \| ---- \| ----\| \| xsum \| 45.20/22.06/36.99 \| 47.21/24.56/39.25 \| 47.60/24.83/39.64\| \| cnn_dailymail \| 43.90/21.20/40.76 \| 44.17/21.47/41.11 \| 44.16/21.56/41.30\| \| newsroom \| 45.07/33.39/41.28 \| 45.15/33.51/41.33 \| 45.98/34.20/42.18\| \| multi_news \| 46.74/17.95/24.26 \| 47.52/18.72/24.91 \| 47.65/18.75/24.95\| \| gigaword \| 38.75/19.96/36.14 \| 39.12/19.86/36.24 \| 39.65/20.47/36.76\| \| wikihow \| 43.07/19.70/34.79 \| 41.35/18.51/33.42 \| 46.39/22.12/38.41 \| \| reddit_tifu \| 26.54/8.94/21.64 \| 26.63/9.01/21.60 \| 27.99/9.81/22.94\| \| big_patent \| 53.63/33.16/42.25 \| 53.41/32.89/42.07 \| 52.29/33.08/41.66 \| \| arxiv \| 44.70/17.27/25.80 \| 44.67/17.18/25.73 \| 44.21/16.95/25.67\| \| pubmed \| 45.49/19.90/27.69 \| 45.09/19.56/27.42 \| 45.97/20.15/28.25\| \| aeslc \| 37.69/21.85/36.84 \| 37.40/21.22/36.45 \| 37.68/21.25/36.51\| \| billsum \| 57.20/39.56/45.80 \| 57.31/40.19/45.82 \| 59.67/41.58/47.59\| The "Mixed & Stochastic" model has the following changes: - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). - the model uniformly sample a gap sentence ratio between 15% and 45%. - importance sentences are sampled using a 20% uniform noise to importance scores. - the sentencepiece tokenizer is updated to be able to encode newline character. (*) the numbers of wikihow and big_patent datasets are not comparable because of change in tokenization and data: - wikihow dataset contains newline characters which is useful for paragraph segmentation, the C4 and HugeNews model's sentencepiece tokenizer doesn't encode newline and loose this information. - we update the BigPatent dataset to preserve casing, some format cleanings are also changed, please refer to change in TFDS. The "Mixed & Stochastic" model has the following changes (from pegasus-large in the paper): trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). the model uniformly sample a gap sentence ratio between 15% and 45%. importance sentences are sampled using a 20% uniform noise to importance scores. the sentencepiece tokenizer is updated to be able to encode newline character. Citation ``` @misc{zhang2019pegasus, title={PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization}, author={Jingqing Zhang and Yao Zhao and Mohammad Saleh and Peter J. Liu}, year={2019}, eprint={1912.08777}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
shaina/covid_qa_mpnet	shaina	2022-02-02T14:33:18Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "mpnet", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer widget: - text: "What is COVID-19?" context: "Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019.[7] The disease has since spread worldwide, leading to an ongoing pandemic." - text: "Where was COVID-19 first discovered?" context: "The first known infections from SARS-CoV-2 were discovered in Wuhan, China. The original source of viral transmission to humans remains unclear, as does whether the virus became pathogenic before or after the spillover event." - text: "What is Post-COVID syndrome?" context: "Long COVID, also known as post-COVID-19 syndrome, post-acute sequelae of COVID-19 (PASC), or chronic COVID syndrome (CCS) is a condition characterized by long-term sequelae appearing or persisting after the typical convalescence period of COVID-19. Long COVID can affect nearly every organ system, with sequelae including respiratory system disorders, nervous system and neurocognitive disorders, mental health disorders, metabolic disorders, cardiovascular disorders, gastrointestinal disorders, malaise, fatigue, musculoskeletal pain, and anemia. A wide range of symptoms are commonly reported, including fatigue, headaches, shortness of breath, anosmia (loss of smell), parosmia (distorted smell), muscle weakness, low fever and cognitive dysfunction." --- # covid_qa_mpnet This model is a fine-tuned version of [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on our COVID-19 dataset. It achieves the following results on the evaluation set: - Loss: 0.1352 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.2477 \| 1.0 \| 3895 \| 0.1869 \| \| 0.1838 \| 2.0 \| 7790 \| 0.1352 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
NbAiLab/wav2vec2-xlsr-300M-NPSC-OH	NbAiLab	2022-02-02T06:10:42Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "NbAiLab/NPSC", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - automatic-speech-recognition - NbAiLab/NPSC - generated_from_trainer model-index: - name: wav2vec2-xlsr-300M-NPSC-OH 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-xlsr-300M-NPSC-OH This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set: - Loss: 0.1692 - Wer: 0.1663 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 15.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 3.1638 \| 0.66 \| 500 \| 3.0686 \| 1.0 \| \| 2.9311 \| 1.31 \| 1000 \| 2.9208 \| 1.0 \| \| 2.4175 \| 1.97 \| 1500 \| 1.5009 \| 0.9049 \| \| 1.4442 \| 2.63 \| 2000 \| 0.4426 \| 0.3783 \| \| 1.2624 \| 3.28 \| 2500 \| 0.3193 \| 0.2998 \| \| 1.1889 \| 3.94 \| 3000 \| 0.2867 \| 0.2630 \| \| 1.1315 \| 4.6 \| 3500 \| 0.2566 \| 0.2444 \| \| 1.0864 \| 5.26 \| 4000 \| 0.2368 \| 0.2294 \| \| 1.093 \| 5.91 \| 4500 \| 0.2240 \| 0.2151 \| \| 1.0368 \| 6.57 \| 5000 \| 0.2117 \| 0.2056 \| \| 1.0178 \| 7.23 \| 5500 \| 0.2020 \| 0.1954 \| \| 1.0035 \| 7.88 \| 6000 \| 0.2005 \| 0.1924 \| \| 0.9759 \| 8.54 \| 6500 \| 0.1971 \| 0.1863 \| \| 0.9795 \| 9.2 \| 7000 \| 0.1892 \| 0.1812 \| \| 0.9601 \| 9.85 \| 7500 \| 0.1863 \| 0.1795 \| \| 0.9673 \| 10.51 \| 8000 \| 0.1809 \| 0.1761 \| \| 0.9233 \| 11.17 \| 8500 \| 0.1818 \| 0.1755 \| \| 0.9382 \| 11.83 \| 9000 \| 0.1767 \| 0.1741 \| \| 0.9242 \| 12.48 \| 9500 \| 0.1743 \| 0.1703 \| \| 0.9703 \| 13.14 \| 10000 \| 0.1711 \| 0.1711 \| \| 0.9139 \| 13.8 \| 10500 \| 0.1718 \| 0.1672 \| \| 0.9073 \| 14.45 \| 11000 \| 0.1700 \| 0.1665 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0
navsad/navid_test_bert	navsad	2022-02-02T04:52:11Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: navid_test_bert results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5834463254140851 --- <!-- 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. --> # navid_test_bert This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8149 - Matthews Correlation: 0.5834 ## Model description More information needed ## Intended uses & 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 \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.4598 \| 1.0 \| 1069 \| 0.4919 \| 0.5314 \| \| 0.3228 \| 2.0 \| 2138 \| 0.6362 \| 0.5701 \| \| 0.17 \| 3.0 \| 3207 \| 0.8149 \| 0.5834 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0
BigSalmon/InfillFormalLincoln	BigSalmon	2022-02-02T03:45:03Z	10	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:04Z	Informal to Formal: ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InfillFormalLincoln") model = AutoModelWithLMHead.from_pretrained("BigSalmon/InfillFormalLincoln") ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space changes over time) ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time) ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2Space (The model for this space changes over time) ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ```` ``` infill: increasing the number of sidewalks in suburban areas will [MASK]. Translated into the Style of Abraham Lincoln: increasing the number of sidewalks in suburban areas will ( ( enhance / maximize ) community cohesion / facilitate ( communal ties / the formation of neighborhood camaraderie ) / forge neighborly relations / lend themselves to the advancement of neighborly ties / plant the seeds of community building / flower anew the bonds of friendship / invite the budding of neighborhood rapport / enrich neighborhood life ). infill: corn fields [MASK], [MASK] visibly as one ventures beyond chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), ( manifesting themselves ) visibly as one ventures beyond chicago. infill: the [MASK] the SAT will soon be [MASK]. [MASK] an examination undertaken on one's laptop. [MASK] will allow students to retrieve test results promptly. Translated into the Style of Abraham Lincoln: the ( conventional form of ) the SAT will soon be ( consigned to history ). ( replacing it will be ) an examination undertaken on one's laptop. ( so doing ) will allow students to retrieve test results promptly. infill: ```
pdroberts/distilbert-base-uncased-finetuned-emotion	pdroberts	2022-02-01T23:48:03Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion model-index: - name: distilbert-base-uncased-finetuned-emotion 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-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.2+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
mattmcclean/distilbert-base-uncased-finetuned-emotion	mattmcclean	2022-02-01T19:48:01Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- 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: 0.925 - name: F1 type: f1 value: 0.9252235175634111 --- <!-- 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: 0.2173 - Accuracy: 0.925 - F1: 0.9252 ## Model description More information needed ## Intended uses & 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.825 \| 1.0 \| 250 \| 0.2925 \| 0.915 \| 0.9134 \| \| 0.2444 \| 2.0 \| 500 \| 0.2173 \| 0.925 \| 0.9252 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0 - Datasets 1.16.1 - Tokenizers 0.10.3
naleraphael/rasr_sample	naleraphael	2022-02-01T18:18:16Z	4	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - sv-SE license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer datasets: - common_voice model-index: - name: rasr_sample 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. --> # rasr_sample This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 0.3147 - Wer: 0.2676 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 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: 2000 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 3.3332 \| 1.45 \| 500 \| 3.3031 \| 1.0 \| \| 2.9272 \| 2.91 \| 1000 \| 2.9353 \| 0.9970 \| \| 2.0736 \| 4.36 \| 1500 \| 1.1565 \| 0.8714 \| \| 1.7339 \| 5.81 \| 2000 \| 0.7156 \| 0.6688 \| \| 1.5989 \| 7.27 \| 2500 \| 0.5791 \| 0.5519 \| \| 1.4916 \| 8.72 \| 3000 \| 0.5038 \| 0.5169 \| \| 1.4562 \| 10.17 \| 3500 \| 0.4861 \| 0.4805 \| \| 1.3893 \| 11.63 \| 4000 \| 0.4584 \| 0.4761 \| \| 1.3797 \| 13.08 \| 4500 \| 0.4298 \| 0.4686 \| \| 1.3508 \| 14.53 \| 5000 \| 0.4138 \| 0.3744 \| \| 1.3165 \| 15.99 \| 5500 \| 0.4015 \| 0.3578 \| \| 1.281 \| 17.44 \| 6000 \| 0.3883 \| 0.3472 \| \| 1.2682 \| 18.89 \| 6500 \| 0.3904 \| 0.3434 \| \| 1.2477 \| 20.35 \| 7000 \| 0.3726 \| 0.3321 \| \| 1.2364 \| 21.8 \| 7500 \| 0.3685 \| 0.3281 \| \| 1.2041 \| 23.26 \| 8000 \| 0.3597 \| 0.3194 \| \| 1.1901 \| 24.71 \| 8500 \| 0.3542 \| 0.3203 \| \| 1.1903 \| 26.16 \| 9000 \| 0.3500 \| 0.3138 \| \| 1.1677 \| 27.61 \| 9500 \| 0.3458 \| 0.3067 \| \| 1.1718 \| 29.07 \| 10000 \| 0.3595 \| 0.3112 \| \| 1.1562 \| 30.52 \| 10500 \| 0.3433 \| 0.3022 \| \| 1.1392 \| 31.97 \| 11000 \| 0.3440 \| 0.2936 \| \| 1.1258 \| 33.43 \| 11500 \| 0.3396 \| 0.2950 \| \| 1.1067 \| 34.88 \| 12000 \| 0.3379 \| 0.2939 \| \| 1.0953 \| 36.34 \| 12500 \| 0.3370 \| 0.2868 \| \| 1.0835 \| 37.79 \| 13000 \| 0.3317 \| 0.2860 \| \| 1.0772 \| 39.24 \| 13500 \| 0.3302 \| 0.2854 \| \| 1.0853 \| 40.7 \| 14000 \| 0.3265 \| 0.2783 \| \| 1.0689 \| 42.15 \| 14500 \| 0.3306 \| 0.2770 \| \| 1.0394 \| 43.6 \| 15000 \| 0.3233 \| 0.2757 \| \| 1.0581 \| 45.06 \| 15500 \| 0.3199 \| 0.2713 \| \| 1.0362 \| 46.51 \| 16000 \| 0.3154 \| 0.2683 \| \| 1.0406 \| 47.96 \| 16500 \| 0.3176 \| 0.2688 \| \| 1.0082 \| 49.42 \| 17000 \| 0.3149 \| 0.2679 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0
cahya/output	cahya	2022-02-01T15:40:45Z	8	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "common_voice", "generated_from_trainer", "tr", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - tr license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: output 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. --> # output This model is a fine-tuned version of [cahya/wav2vec2-base-turkish-artificial-cv](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial-cv) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.1822 - Wer: 0.1423 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-07 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.18.2 - Tokenizers 0.10.3
moussaKam/frugalscore_tiny_roberta_bert-score	moussaKam	2022-02-01T10:50:57Z	6	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "arxiv:2110.08559", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	# FrugalScore FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance Paper: https://arxiv.org/abs/2110.08559?context=cs Project github: https://github.com/moussaKam/FrugalScore The pretrained checkpoints presented in the paper : \| FrugalScore \| Student \| Teacher \| Method \| \|----------------------------------------------------\|-------------\|----------------\|------------\| \| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) \| BERT-tiny \| BERT-Base \| BERTScore \| \| [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) \| BERT-small \| BERT-Base \| BERTScore \| \| [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) \| BERT-medium \| BERT-Base \| BERTScore \| \| [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) \| BERT-tiny \| RoBERTa-Large \| BERTScore \| \| [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) \| BERT-small \| RoBERTa-Large \| BERTScore \| \| [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) \| BERT-medium \| RoBERTa-Large \| BERTScore \| \| [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) \| BERT-tiny \| DeBERTa-XLarge \| BERTScore \| \| [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) \| BERT-small \| DeBERTa-XLarge \| BERTScore \| \| [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) \| BERT-medium \| DeBERTa-XLarge \| BERTScore \| \| [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) \| BERT-tiny \| BERT-Base \| MoverScore \| \| [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) \| BERT-small \| BERT-Base \| MoverScore \| \| [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) \| BERT-medium \| BERT-Base \| MoverScore \|
moussaKam/frugalscore_medium_bert-base_bert-score	moussaKam	2022-02-01T10:50:43Z	12	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "arxiv:2110.08559", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	# FrugalScore FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance Paper: https://arxiv.org/abs/2110.08559?context=cs Project github: https://github.com/moussaKam/FrugalScore The pretrained checkpoints presented in the paper : \| FrugalScore \| Student \| Teacher \| Method \| \|----------------------------------------------------\|-------------\|----------------\|------------\| \| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) \| BERT-tiny \| BERT-Base \| BERTScore \| \| [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) \| BERT-small \| BERT-Base \| BERTScore \| \| [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) \| BERT-medium \| BERT-Base \| BERTScore \| \| [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) \| BERT-tiny \| RoBERTa-Large \| BERTScore \| \| [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) \| BERT-small \| RoBERTa-Large \| BERTScore \| \| [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) \| BERT-medium \| RoBERTa-Large \| BERTScore \| \| [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) \| BERT-tiny \| DeBERTa-XLarge \| BERTScore \| \| [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) \| BERT-small \| DeBERTa-XLarge \| BERTScore \| \| [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) \| BERT-medium \| DeBERTa-XLarge \| BERTScore \| \| [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) \| BERT-tiny \| BERT-Base \| MoverScore \| \| [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) \| BERT-small \| BERT-Base \| MoverScore \| \| [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) \| BERT-medium \| BERT-Base \| MoverScore \|
moussaKam/frugalscore_small_bert-base_bert-score	moussaKam	2022-02-01T10:50:31Z	5	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "arxiv:2110.08559", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	# FrugalScore FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance Paper: https://arxiv.org/abs/2110.08559?context=cs Project github: https://github.com/moussaKam/FrugalScore The pretrained checkpoints presented in the paper : \| FrugalScore \| Student \| Teacher \| Method \| \|----------------------------------------------------\|-------------\|----------------\|------------\| \| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) \| BERT-tiny \| BERT-Base \| BERTScore \| \| [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) \| BERT-small \| BERT-Base \| BERTScore \| \| [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) \| BERT-medium \| BERT-Base \| BERTScore \| \| [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) \| BERT-tiny \| RoBERTa-Large \| BERTScore \| \| [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) \| BERT-small \| RoBERTa-Large \| BERTScore \| \| [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) \| BERT-medium \| RoBERTa-Large \| BERTScore \| \| [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) \| BERT-tiny \| DeBERTa-XLarge \| BERTScore \| \| [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) \| BERT-small \| DeBERTa-XLarge \| BERTScore \| \| [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) \| BERT-medium \| DeBERTa-XLarge \| BERTScore \| \| [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) \| BERT-tiny \| BERT-Base \| MoverScore \| \| [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) \| BERT-small \| BERT-Base \| MoverScore \| \| [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) \| BERT-medium \| BERT-Base \| MoverScore \|
moussaKam/frugalscore_tiny_bert-base_bert-score	moussaKam	2022-02-01T10:50:21Z	4,310	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "arxiv:2110.08559", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	# FrugalScore FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance Paper: https://arxiv.org/abs/2110.08559?context=cs Project github: https://github.com/moussaKam/FrugalScore The pretrained checkpoints presented in the paper : \| FrugalScore \| Student \| Teacher \| Method \| \|----------------------------------------------------\|-------------\|----------------\|------------\| \| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) \| BERT-tiny \| BERT-Base \| BERTScore \| \| [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) \| BERT-small \| BERT-Base \| BERTScore \| \| [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) \| BERT-medium \| BERT-Base \| BERTScore \| \| [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) \| BERT-tiny \| RoBERTa-Large \| BERTScore \| \| [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) \| BERT-small \| RoBERTa-Large \| BERTScore \| \| [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) \| BERT-medium \| RoBERTa-Large \| BERTScore \| \| [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) \| BERT-tiny \| DeBERTa-XLarge \| BERTScore \| \| [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) \| BERT-small \| DeBERTa-XLarge \| BERTScore \| \| [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) \| BERT-medium \| DeBERTa-XLarge \| BERTScore \| \| [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) \| BERT-tiny \| BERT-Base \| MoverScore \| \| [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) \| BERT-small \| BERT-Base \| MoverScore \| \| [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) \| BERT-medium \| BERT-Base \| MoverScore \|
MaryaAI/opus-mt-en-ar-finetunedSTEM-v4-en-to-ar	MaryaAI	2022-02-01T08:51:38Z	4	0	transformers	[ "transformers", "tf", "tensorboard", "marian", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: MaryaAI/opus-mt-en-ar-finetunedSTEM-v4-en-to-ar results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # MaryaAI/opus-mt-en-ar-finetunedSTEM-v4-en-to-ar This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.0589 - Validation Loss: 5.3227 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 2.0589 \| 5.3227 \| 0 \| ### Framework versions - Transformers 4.17.0.dev0 - TensorFlow 2.7.0 - Datasets 1.18.3.dev0 - Tokenizers 0.10.3
vachonni/wav2vec2-large-xls-r-300m-dansk-CV-80	vachonni	2022-02-01T07:55:36Z	4	2	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-dansk-CV-80 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-dansk-CV-80 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for Danish, using the [mozilla-foundation/common_voice_8_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6394 - eval_wer: 0.3682 - eval_runtime: 104.0466 - eval_samples_per_second: 13.359 - eval_steps_per_second: 1.672 - epoch: 21.28 - step: 2000 ## Model description ASR Danish model ## Intended uses & limitations More information needed ## Training and evaluation data Danish subset of [mozilla-foundation/common_voice_8_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0
huggingtweets/clamtime-madramami	huggingtweets	2022-02-01T07:09:05Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/clamtime-madramami/1643699341002/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/1486460616927858690/H_L_HiW-_400x400.jpg')"> </div> <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/1486839044906618880/x1Q9ED9b_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> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">clementine!!!! & riley, twink eliminator 🐾🏳️‍⚧️</div> <div style="text-align: center; font-size: 14px;">@clamtime-madramami</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 clementine!!!! & riley, twink eliminator 🐾🏳️‍⚧️. \| Data \| clementine!!!! \| riley, twink eliminator 🐾🏳️‍⚧️ \| \| --- \| --- \| --- \| \| Tweets downloaded \| 3239 \| 3247 \| \| Retweets \| 340 \| 114 \| \| Short tweets \| 872 \| 607 \| \| Tweets kept \| 2027 \| 2526 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1lh3p7v6/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 @clamtime-madramami's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1gman3fy) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1gman3fy/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/clamtime-madramami') 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)
hady/wav2vec2-base-timit-demo-colab	hady	2022-02-01T07:01:28Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
Priyajay/xls-r-ab-test	Priyajay	2022-02-01T04:29:17Z	9	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "common_voice", "generated_from_trainer", "hi", "dataset:common_voice", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- language: - hi tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: '' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the COMMON_VOICE - HI dataset. It achieves the following results on the evaluation set: - Loss: 248.1278 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0
jonfd/electra-small-is-no	jonfd	2022-01-31T23:41:45Z	8	0	transformers	[ "transformers", "pytorch", "tf", "electra", "pretraining", "is", "no", "dataset:igc", "dataset:ic3", "dataset:jonfd/ICC", "dataset:mc4", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: - is - no license: cc-by-4.0 datasets: - igc - ic3 - jonfd/ICC - mc4 --- # Icelandic-Norwegian ELECTRA-Small This model was pretrained on the following corpora: * The [Icelandic Gigaword Corpus](http://igc.arnastofnun.is/) (IGC) * The Icelandic Common Crawl Corpus (IC3) * The [Icelandic Crawled Corpus](https://huggingface.co/datasets/jonfd/ICC) (ICC) * The [Multilingual Colossal Clean Crawled Corpus](https://huggingface.co/datasets/mc4) (mC4) - Icelandic and Norwegian text obtained from .is and .no domains, respectively The total size of the corpus after document-level deduplication and filtering was 7.41B tokens, split equally between the two languages. The model was trained using a WordPiece tokenizer with a vocabulary size of 64,105 for 1.1 million steps, and otherwise with default settings. # Acknowledgments This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC). This project was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by [Almannarómur](https://almannaromur.is/), is funded by the Icelandic Ministry of Education, Science and Culture.
philschmid/bert-mini-sst2-distilled	philschmid	2022-01-31T23:34:03Z	256	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert-mini-sst2-distilled results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.856651376146789 --- <!-- 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-mini-sst2-distilled This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.1792 - Accuracy: 0.8567 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00021185586235152412 - train_batch_size: 1024 - eval_batch_size: 1024 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.1552 \| 1.0 \| 66 \| 1.4847 \| 0.8349 \| \| 0.8451 \| 2.0 \| 132 \| 1.3495 \| 0.8624 \| \| 0.5864 \| 3.0 \| 198 \| 1.2257 \| 0.8532 \| \| 0.4553 \| 4.0 \| 264 \| 1.2571 \| 0.8544 \| \| 0.3708 \| 5.0 \| 330 \| 1.2132 \| 0.8658 \| \| 0.3086 \| 6.0 \| 396 \| 1.2370 \| 0.8589 \| \| 0.2701 \| 7.0 \| 462 \| 1.1900 \| 0.8635 \| \| 0.246 \| 8.0 \| 528 \| 1.1792 \| 0.8567 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.15.1 - Tokenizers 0.10.3
paintingpeter/distilbert-base-uncased-finetuned-clinc	paintingpeter	2022-01-31T21:55:25Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- 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: 0.9174193548387096 --- <!-- 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: 0.7713 - Accuracy: 0.9174 ## Model description More information needed ## Intended uses & 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.2892 \| 1.0 \| 318 \| 3.2831 \| 0.7426 \| \| 2.6244 \| 2.0 \| 636 \| 1.8739 \| 0.8335 \| \| 1.5442 \| 3.0 \| 954 \| 1.1525 \| 0.8926 \| \| 1.0096 \| 4.0 \| 1272 \| 0.8569 \| 0.91 \| \| 0.793 \| 5.0 \| 1590 \| 0.7713 \| 0.9174 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
glob-asr/wav2vec2-large-xls-r-300m-spanish-small	glob-asr	2022-01-31T20:58:46Z	4	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-spanish-small 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-spanish-small This model is a fine-tuned version of [jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom](https://huggingface.co/jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3596 - Wer: 0.2105 ## Model description More information needed ## Intended uses & 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.1971 \| 0.79 \| 400 \| 0.2169 \| 0.2077 \| \| 0.2293 \| 1.58 \| 800 \| 0.2507 \| 0.2418 \| \| 0.2065 \| 2.37 \| 1200 \| 0.2703 \| 0.2459 \| \| 0.1842 \| 3.16 \| 1600 \| 0.2716 \| 0.2495 \| \| 0.1634 \| 3.95 \| 2000 \| 0.2695 \| 0.2510 \| \| 0.1443 \| 4.74 \| 2400 \| 0.2754 \| 0.2435 \| \| 0.1345 \| 5.53 \| 2800 \| 0.3119 \| 0.2654 \| \| 0.1267 \| 6.32 \| 3200 \| 0.3154 \| 0.2573 \| \| 0.1237 \| 7.11 \| 3600 \| 0.3251 \| 0.2666 \| \| 0.1118 \| 7.91 \| 4000 \| 0.3139 \| 0.2503 \| \| 0.1051 \| 8.7 \| 4400 \| 0.3286 \| 0.2573 \| \| 0.0964 \| 9.49 \| 4800 \| 0.3348 \| 0.2587 \| \| 0.0946 \| 10.28 \| 5200 \| 0.3357 \| 0.2587 \| \| 0.0897 \| 11.07 \| 5600 \| 0.3408 \| 0.2590 \| \| 0.0812 \| 11.86 \| 6000 \| 0.3380 \| 0.2560 \| \| 0.079 \| 12.65 \| 6400 \| 0.3304 \| 0.2415 \| \| 0.0753 \| 13.44 \| 6800 \| 0.3557 \| 0.2540 \| \| 0.0717 \| 14.23 \| 7200 \| 0.3507 \| 0.2519 \| \| 0.0691 \| 15.02 \| 7600 \| 0.3554 \| 0.2587 \| \| 0.0626 \| 15.81 \| 8000 \| 0.3619 \| 0.2520 \| \| 0.0661 \| 16.6 \| 8400 \| 0.3609 \| 0.2564 \| \| 0.0582 \| 17.39 \| 8800 \| 0.3818 \| 0.2520 \| \| 0.0556 \| 18.18 \| 9200 \| 0.3685 \| 0.2410 \| \| 0.0515 \| 18.97 \| 9600 \| 0.3658 \| 0.2367 \| \| 0.0478 \| 19.76 \| 10000 \| 0.3701 \| 0.2413 \| \| 0.0486 \| 20.55 \| 10400 \| 0.3681 \| 0.2371 \| \| 0.0468 \| 21.34 \| 10800 \| 0.3607 \| 0.2370 \| \| 0.0452 \| 22.13 \| 11200 \| 0.3499 \| 0.2286 \| \| 0.0399 \| 22.92 \| 11600 \| 0.3647 \| 0.2282 \| \| 0.0393 \| 23.72 \| 12000 \| 0.3638 \| 0.2255 \| \| 0.0381 \| 24.51 \| 12400 \| 0.3359 \| 0.2202 \| \| 0.0332 \| 25.3 \| 12800 \| 0.3488 \| 0.2177 \| \| 0.033 \| 26.09 \| 13200 \| 0.3628 \| 0.2175 \| \| 0.0311 \| 26.88 \| 13600 \| 0.3695 \| 0.2195 \| \| 0.0294 \| 27.67 \| 14000 \| 0.3624 \| 0.2164 \| \| 0.0281 \| 28.46 \| 14400 \| 0.3688 \| 0.2113 \| \| 0.0274 \| 29.25 \| 14800 \| 0.3596 \| 0.2105 \| ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0
shaina/CoQUAD_MPNet	shaina	2022-01-31T18:22:46Z	0	0	null	[ "MPNet", "en", "license:apache-2.0", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: en tags: - MPNet license: apache-2.0 dataset: - covid-19 --- # CoQUAD_MPNet : MPNet model for COVID-19 ## Introduction It is a state-of-the-art language model for MPNet for Covid-19 dataset with focus on post-covid. ## How to use for Deepset Haystack ```python # Load data from datasets import load_dataset dataset = load_dataset("shaina/covid19") # Haystack pipeline !sudo apt-get install git-lfs !git lfs install !git clone https://huggingface.co/shaina/CoQUAD_MPNet GIT_LFS_SKIP_SMUDGE=1 from haystack.nodes import ElasticsearchRetriever retriever = ElasticsearchRetriever(document_store=document_store) reader = FARMReader(model_name_or_path="/content/drive/MyDrive/CoQUAD_MPNet", use_gpu=True) from haystack.pipelines import ExtractiveQAPipeline pipe = ExtractiveQAPipeline(reader, retriever) prediction = pipe.run( query="What is post-COVID?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}} ) from pprint import pprint pprint(prediction) ``` --- ## Authors Shaina Raza ---
peter-explosion-ai/en_pipeline	peter-explosion-ai	2022-01-31T17:04:42Z	5	0	spacy	[ "spacy", "text-classification", "en", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- tags: - spacy - text-classification language: - en model-index: - name: en_pipeline results: [] --- \| Feature \| Description \| \| --- \| --- \| \| Name \| `en_pipeline` \| \| Version \| `0.0.0` \| \| spaCy \| `>=3.2.1,<3.3.0` \| \| Default Pipeline \| `textcat` \| \| Components \| `textcat` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| n/a \| \| License \| n/a \| \| Author \| [n/a]() \| ### Label Scheme <details> <summary>View label scheme (2 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `textcat` \| `POSITIVE`, `NEGATIVE` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `CATS_SCORE` \| 55.70 \| \| `CATS_MICRO_P` \| 58.65 \| \| `CATS_MICRO_R` \| 58.65 \| \| `CATS_MICRO_F` \| 58.65 \| \| `CATS_MACRO_P` \| 61.88 \| \| `CATS_MACRO_R` \| 58.69 \| \| `CATS_MACRO_F` \| 55.70 \| \| `CATS_MACRO_AUC` \| 63.53 \| \| `CATS_MACRO_AUC_PER_TYPE` \| 0.00 \| \| `TEXTCAT_LOSS` \| 3.74 \|
osanseviero/test_meta	osanseviero	2022-01-31T15:21:09Z	0	0	spacy	[ "spacy", "token-classification", "license:lgpl-lr", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification languages: - fr license: lgpl-lr other-thing: test ---
huggingtweets/tks	huggingtweets	2022-01-31T10:20:15Z	0	0	null	[ "huggingtweets", "en", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/tks/1643624411056/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/1044664291050344449/vKKJxtBF_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">高須正和@NT深圳コミュニティ/TAKASU@NT Shenzhen</div> <div style="text-align: center; font-size: 14px;">@tks</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 高須正和@NT深圳コミュニティ/TAKASU@NT Shenzhen. \| Data \| 高須正和@NT深圳コミュニティ/TAKASU@NT Shenzhen \| \| --- \| --- \| \| Tweets downloaded \| 3248 \| \| Retweets \| 1831 \| \| Short tweets \| 825 \| \| Tweets kept \| 592 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1lg0mgsp/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 @tks's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/j1ak5d5p) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/j1ak5d5p/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/tks') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/goando-tsuchinao83-za09313103	huggingtweets	2022-01-31T09:56:33Z	0	0	null	[ "huggingtweets", "en", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/goando-tsuchinao83-za09313103/1643622988627/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/715665333218979842/fLLzpFee_400x400.jpg')"> </div> <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/1145832571214815232/KYNcOP04_400x400.jpg')"> </div> <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/1281544202627674112/zglo72WL_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">土屋尚史 / Goodpatch & Go Ando / PREDUCTS / THE GUILD & shun nozaki / Goodpatch</div> <div style="text-align: center; font-size: 14px;">@goando-tsuchinao83-za09313103</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 土屋尚史 / Goodpatch & Go Ando / PREDUCTS / THE GUILD & shun nozaki / Goodpatch. \| Data \| 土屋尚史 / Goodpatch \| Go Ando / PREDUCTS / THE GUILD \| shun nozaki / Goodpatch \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3236 \| 3250 \| 798 \| \| Retweets \| 1577 \| 97 \| 34 \| \| Short tweets \| 914 \| 1729 \| 458 \| \| Tweets kept \| 745 \| 1424 \| 306 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/31bsh75f/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 @goando-tsuchinao83-za09313103's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/26i8c30r) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/26i8c30r/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/goando-tsuchinao83-za09313103') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/ggreenwald	huggingtweets	2022-01-31T09:49:22Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/ggreenwald/1643622558420/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/1092582027994509312/cpYWuYI9_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">Glenn Greenwald</div> <div style="text-align: center; font-size: 14px;">@ggreenwald</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 Glenn Greenwald. \| Data \| Glenn Greenwald \| \| --- \| --- \| \| Tweets downloaded \| 3248 \| \| Retweets \| 324 \| \| Short tweets \| 160 \| \| Tweets kept \| 2764 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/y433olp5/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 @ggreenwald's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/duljho5y) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/duljho5y/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/ggreenwald') 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)
TajMahaladeen/pokemon_gptj	TajMahaladeen	2022-01-31T06:12:31Z	9	0	transformers	[ "transformers", "pytorch", "gptj", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 ---
NbAiLab/xls-r-1b-npsc	NbAiLab	2022-01-31T04:33:39Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- license: apache-2.0 ---
huggingtweets/alphaxchange-coinmarketcap-techcrunch	huggingtweets	2022-01-31T01:31:27Z	4	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/alphaxchange-coinmarketcap-techcrunch/1643592683390/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/1475337078544248835/JRWM0Hsl_400x400.jpg')"> </div> <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/1096066608034918401/m8wnTWsX_400x400.png')"> </div> <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/1469027897209987081/fCdlufKH_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">CoinMarketCap & TechCrunch & AlphaExchange</div> <div style="text-align: center; font-size: 14px;">@alphaxchange-coinmarketcap-techcrunch</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 CoinMarketCap & TechCrunch & AlphaExchange. \| Data \| CoinMarketCap \| TechCrunch \| AlphaExchange \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3249 \| 3250 \| 185 \| \| Retweets \| 247 \| 29 \| 25 \| \| Short tweets \| 209 \| 9 \| 17 \| \| Tweets kept \| 2793 \| 3212 \| 143 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ii2008f/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 @alphaxchange-coinmarketcap-techcrunch's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/28z1wzo5) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/28z1wzo5/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/alphaxchange-coinmarketcap-techcrunch') 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)
eldor-97/MarianMix_en-10	eldor-97	2022-01-30T23:25:27Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: MarianMix_en-10 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. --> # MarianMix_en-10 This model is a fine-tuned version of [Helsinki-NLP/opus-tatoeba-en-ja](https://huggingface.co/Helsinki-NLP/opus-tatoeba-en-ja) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0752 - Bleu: 14.601 - Gen Len: 45.8087 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 99 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:--------:\| \| 2.1136 \| 0.44 \| 500 \| 2.0044 \| 0.2655 \| 109.0201 \| \| 1.1422 \| 0.89 \| 1000 \| 1.7516 \| 1.4123 \| 71.0 \| \| 0.9666 \| 1.33 \| 1500 \| 1.5219 \| 3.6611 \| 64.6888 \| \| 0.8725 \| 1.78 \| 2000 \| 1.3606 \| 4.6539 \| 77.1641 \| \| 0.7655 \| 2.22 \| 2500 \| 1.2586 \| 8.3456 \| 60.3837 \| \| 0.7149 \| 2.67 \| 3000 \| 1.1953 \| 11.2247 \| 50.5921 \| \| 0.6719 \| 3.11 \| 3500 \| 1.1541 \| 10.4303 \| 54.3776 \| \| 0.6265 \| 3.56 \| 4000 \| 1.1186 \| 13.3231 \| 48.283 \| \| 0.6157 \| 4.0 \| 4500 \| 1.0929 \| 13.8467 \| 46.569 \| \| 0.5736 \| 4.44 \| 5000 \| 1.0848 \| 14.2731 \| 45.5035 \| \| 0.5683 \| 4.89 \| 5500 \| 1.0752 \| 14.601 \| 45.8087 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.17.0 - Tokenizers 0.10.3
fgaim/t5-small-squad-v2	fgaim	2022-01-30T21:35:54Z	34	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "en", "dataset:c4", "dataset:squad", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: - en datasets: - c4 - squad tags: - text2text-generation widget: - text: "question: What is the atomic number for oxygen? context: Oxygen is a chemical element with symbol O and atomic number 8." - text: "question: What is the chemical symbol of Oxygen? context: Oxygen is a chemical element with symbol O and atomic number 8." license: apache-2.0 --- T5-small for QA --- [Google's T5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) pre-trained on the [C4](https://huggingface.co/datasets/c4) dataset, fine-tuned for Question-Answering on [SQuAD v2](https://huggingface.co/datasets/squad_v2) with the following hyperparameters: ``` optimizer=adamw_hf learning_rate=3e-5 adam_beta1=0.9 adam_beta2=0.999 adam_epsilon=1e-08 num_train_epochs=2 per_device_train_batch_size=12 ``` Usage --- The input [context and question] has to be prepared in a specific way as follows: ```python from transformers import pipeline def prep_input(_context, _question): return " ".join(["question:", _question.strip(), "context:", _context.strip()]) t5qa = pipeline("text2text-generation", "fgaim/t5-small-squad-v2") context = """ Oxygen is a chemical element with symbol O and atomic number 8. It is a member of the chalcogen group on the periodic table and is a highly reactive nonmetal and oxidizing agent that readily forms compounds (notably oxides) with most elements. By mass, oxygen is the third-most abundant element in the universe, after hydrogen and helium. At standard temperature and pressure, two atoms of the element bind to form dioxygen, a colorless and odorless diatomic gas with the formula O. """ t5qa(prep_input(context, "How many atoms combine to form dioxygen?")) # [{'generated_text': 'two'}] t5qa(prep_input(context, "What element makes up almost half of the earth's crust by mass?")) # [{'generated_text': 'oxygen'}] t5qa(prep_input(context, "What are the most abundent elements of the universe by mass?")) # [{'generated_text': 'hydrogen and helium'}] ```
osama7/t5-summarization-multinews	osama7	2022-01-30T20:42:51Z	5	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	This is a t5-base model trained on the multi_news dataset for abstraction summarization
gagan3012/xls-r-300m-hi	gagan3012	2022-01-30T20:39:40Z	10	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "hi", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - hi license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer datasets: - common_voice model-index: - name: xls-r-300m-hi 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. --> # xls-r-300m-hi This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset. It achieves the following results on the evaluation set: - Loss: 0.7522 - Wer: 1.0091 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 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: 2000 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 5.0417 \| 2.59 \| 500 \| 5.1484 \| 1.0 \| \| 3.3722 \| 5.18 \| 1000 \| 3.3380 \| 1.0001 \| \| 1.9752 \| 7.77 \| 1500 \| 1.3910 \| 1.0074 \| \| 1.5868 \| 10.36 \| 2000 \| 1.0298 \| 1.0084 \| \| 1.4413 \| 12.95 \| 2500 \| 0.9313 \| 1.0175 \| \| 1.3296 \| 15.54 \| 3000 \| 0.8966 \| 1.0194 \| \| 1.2746 \| 18.13 \| 3500 \| 0.8875 \| 1.0097 \| \| 1.2147 \| 20.73 \| 4000 \| 0.8746 \| 1.0089 \| \| 1.1774 \| 23.32 \| 4500 \| 0.8383 \| 1.0198 \| \| 1.129 \| 25.91 \| 5000 \| 0.7848 \| 1.0167 \| \| 1.0995 \| 28.5 \| 5500 \| 0.7992 \| 1.0210 \| \| 1.0665 \| 31.09 \| 6000 \| 0.7878 \| 1.0107 \| \| 1.0321 \| 33.68 \| 6500 \| 0.7653 \| 1.0082 \| \| 1.0068 \| 36.27 \| 7000 \| 0.7635 \| 1.0065 \| \| 0.9916 \| 38.86 \| 7500 \| 0.7728 \| 1.0090 \| \| 0.9735 \| 41.45 \| 8000 \| 0.7688 \| 1.0070 \| \| 0.9745 \| 44.04 \| 8500 \| 0.7455 \| 1.0097 \| \| 0.9677 \| 46.63 \| 9000 \| 0.7605 \| 1.0099 \| \| 0.9313 \| 49.22 \| 9500 \| 0.7527 \| 1.0097 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0
Kayvane/distilbert-complaints-product	Kayvane	2022-01-30T19:15:13Z	33	3	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:consumer_complaints", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer datasets: - consumer_complaints model-index: - name: distilbert-complaints-product 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-complaints-product This model was trained from the [CFBP](https://www.consumerfinance.gov/data-research/consumer-complaints/) dataset, also made available on the HuggingFace Datasets library. This model predicts the type of financial complaint based on the text provided ## Model description A DistilBert Text Classification Model, with 18 possible classes to determine the nature of a financial customer complaint. ## Intended uses & limitations This model is used as part of.a demonstration for E2E Machine Learning Projects focused on Contact Centre Automation: - Infrastructure: Terraform - ML Ops: HuggingFace (Datasets, Hub, Transformers) - Ml Explainability: SHAP - Cloud: AWS - Model Hosting: Lambda - DB Backend: DynamoDB - Orchestration: Step-Functions - UI Hosting: EC2 - Routing: API Gateway - UI: Budibase ## Training and evaluation data consumer_complaints dataset ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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 ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0
Sindhu/rembert-squad2	Sindhu	2022-01-30T18:35:08Z	5	3	transformers	[ "transformers", "pytorch", "rembert", "question-answering", "multilingual", "dataset:squad2", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- language: - multilingual tags: - question-answering datasets: - squad2 metrics: - squad2 --- # Rembert Squad2 This model is finetuned for QA task on Squad2 from [Rembert checkpoint](https://huggingface.co/google/rembert). ## Hyperparameters ``` Batch Size: 4 Grad Accumulation Steps = 8 Total epochs = 3 MLM Checkpoint = "rembert" max_seq_len = 256 learning_rate = 1e-5 lr_schedule = LinearWarmup warmup_ratio = 0.1 doc_stride = 128 ``` ## Squad 2 Evaluation stats: Metrics generated from [the official Squad2 evaluation script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/) ```json { "exact": 84.51107554956624, "f1": 87.46644042781853, "total": 11873, "HasAns_exact": 80.97165991902834, "HasAns_f1": 86.89086491219469, "HasAns_total": 5928, "NoAns_exact": 88.04037005887301, "NoAns_f1": 88.04037005887301, "NoAns_total": 5945 } ``` For any questions, you can reach out to me [on Twitter](https://twitter.com/batw0man)
Erfan/mT5-base_Farsi_Title_Generator	Erfan	2022-01-30T18:00:42Z	11	2	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "Title-Generation", "fa", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- language: - fa tags: - Title-Generation metrics: - ROUGH ---
tomascufaro/wav2vec2-large-xls-r-300m-spanish-small	tomascufaro	2022-01-30T17:23:59Z	14	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-spanish-small 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-spanish-small This model is a fine-tuned version of [jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom](https://huggingface.co/jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3763 - Wer: 0.1791 ## Model description More information needed ## Intended uses & 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.2277 \| 0.26 \| 400 \| 0.2601 \| 0.2291 \| \| 0.2932 \| 0.53 \| 800 \| 0.2950 \| 0.2670 \| \| 0.3019 \| 0.79 \| 1200 \| 0.3247 \| 0.2766 \| \| 0.2987 \| 1.05 \| 1600 \| 0.3031 \| 0.2606 \| \| 0.261 \| 1.32 \| 2000 \| 0.2994 \| 0.2620 \| \| 0.2651 \| 1.58 \| 2400 \| 0.3134 \| 0.2700 \| \| 0.264 \| 1.85 \| 2800 \| 0.3016 \| 0.2641 \| \| 0.2475 \| 2.11 \| 3200 \| 0.3135 \| 0.2661 \| \| 0.2269 \| 2.37 \| 3600 \| 0.3029 \| 0.2562 \| \| 0.2389 \| 2.64 \| 4000 \| 0.3035 \| 0.2549 \| \| 0.2319 \| 2.9 \| 4400 \| 0.3022 \| 0.2551 \| \| 0.2123 \| 3.16 \| 4800 \| 0.3256 \| 0.2638 \| \| 0.2094 \| 3.43 \| 5200 \| 0.3227 \| 0.2712 \| \| 0.2121 \| 3.69 \| 5600 \| 0.3085 \| 0.2596 \| \| 0.207 \| 3.96 \| 6000 \| 0.3041 \| 0.2597 \| \| 0.1809 \| 4.22 \| 6400 \| 0.3122 \| 0.2524 \| \| 0.1846 \| 4.48 \| 6800 \| 0.3254 \| 0.2579 \| \| 0.1885 \| 4.75 \| 7200 \| 0.2958 \| 0.2437 \| \| 0.1923 \| 5.01 \| 7600 \| 0.3136 \| 0.2502 \| \| 0.1626 \| 5.27 \| 8000 \| 0.3059 \| 0.2488 \| \| 0.1704 \| 5.54 \| 8400 \| 0.3082 \| 0.2515 \| \| 0.1674 \| 5.8 \| 8800 \| 0.3196 \| 0.2509 \| \| 0.1691 \| 6.06 \| 9200 \| 0.3193 \| 0.25 \| \| 0.1499 \| 6.33 \| 9600 \| 0.3529 \| 0.2635 \| \| 0.1568 \| 6.59 \| 10000 \| 0.3241 \| 0.2481 \| \| 0.1538 \| 6.86 \| 10400 \| 0.3354 \| 0.2476 \| \| 0.1503 \| 7.12 \| 10800 \| 0.3180 \| 0.2402 \| \| 0.136 \| 7.38 \| 11200 \| 0.3230 \| 0.2397 \| \| 0.1413 \| 7.65 \| 11600 \| 0.3178 \| 0.2451 \| \| 0.147 \| 7.91 \| 12000 \| 0.3170 \| 0.2389 \| \| 0.1341 \| 8.17 \| 12400 \| 0.3380 \| 0.2501 \| \| 0.1329 \| 8.44 \| 12800 \| 0.3265 \| 0.2414 \| \| 0.1314 \| 8.7 \| 13200 \| 0.3281 \| 0.2482 \| \| 0.1312 \| 8.97 \| 13600 \| 0.3259 \| 0.2539 \| \| 0.12 \| 9.23 \| 14000 \| 0.3291 \| 0.2424 \| \| 0.1193 \| 9.49 \| 14400 \| 0.3302 \| 0.2412 \| \| 0.1189 \| 9.76 \| 14800 \| 0.3376 \| 0.2407 \| \| 0.1217 \| 10.02 \| 15200 \| 0.3334 \| 0.2400 \| \| 0.1118 \| 10.28 \| 15600 \| 0.3359 \| 0.2368 \| \| 0.1139 \| 10.55 \| 16000 \| 0.3239 \| 0.2335 \| \| 0.1106 \| 10.81 \| 16400 \| 0.3374 \| 0.2352 \| \| 0.1081 \| 11.07 \| 16800 \| 0.3585 \| 0.2434 \| \| 0.1063 \| 11.34 \| 17200 \| 0.3639 \| 0.2472 \| \| 0.1041 \| 11.6 \| 17600 \| 0.3399 \| 0.2423 \| \| 0.1062 \| 11.87 \| 18000 \| 0.3410 \| 0.2388 \| \| 0.1012 \| 12.13 \| 18400 \| 0.3597 \| 0.2413 \| \| 0.0953 \| 12.39 \| 18800 \| 0.3440 \| 0.2296 \| \| 0.097 \| 12.66 \| 19200 \| 0.3440 \| 0.2269 \| \| 0.0968 \| 12.92 \| 19600 \| 0.3498 \| 0.2333 \| \| 0.0902 \| 13.18 \| 20000 \| 0.3471 \| 0.2290 \| \| 0.0868 \| 13.45 \| 20400 \| 0.3462 \| 0.2266 \| \| 0.0892 \| 13.71 \| 20800 \| 0.3373 \| 0.2227 \| \| 0.0902 \| 13.97 \| 21200 \| 0.3377 \| 0.2240 \| \| 0.0846 \| 14.24 \| 21600 \| 0.3484 \| 0.2237 \| \| 0.0839 \| 14.5 \| 22000 \| 0.3706 \| 0.2260 \| \| 0.0834 \| 14.77 \| 22400 \| 0.3430 \| 0.2268 \| \| 0.0841 \| 15.03 \| 22800 \| 0.3489 \| 0.2259 \| \| 0.076 \| 15.29 \| 23200 \| 0.3626 \| 0.2281 \| \| 0.0771 \| 15.56 \| 23600 \| 0.3624 \| 0.2268 \| \| 0.0773 \| 15.82 \| 24000 \| 0.3440 \| 0.2252 \| \| 0.0759 \| 16.08 \| 24400 \| 0.3532 \| 0.2170 \| \| 0.0745 \| 16.35 \| 24800 \| 0.3686 \| 0.2188 \| \| 0.0713 \| 16.61 \| 25200 \| 0.3691 \| 0.2195 \| \| 0.0718 \| 16.88 \| 25600 \| 0.3470 \| 0.2108 \| \| 0.0685 \| 17.14 \| 26000 \| 0.3756 \| 0.2179 \| \| 0.0689 \| 17.4 \| 26400 \| 0.3542 \| 0.2149 \| \| 0.0671 \| 17.67 \| 26800 \| 0.3461 \| 0.2165 \| \| 0.0737 \| 17.93 \| 27200 \| 0.3473 \| 0.2238 \| \| 0.0669 \| 18.19 \| 27600 \| 0.3441 \| 0.2138 \| \| 0.0629 \| 18.46 \| 28000 \| 0.3721 \| 0.2155 \| \| 0.0632 \| 18.72 \| 28400 \| 0.3667 \| 0.2126 \| \| 0.0647 \| 18.98 \| 28800 \| 0.3579 \| 0.2097 \| \| 0.0603 \| 19.25 \| 29200 \| 0.3670 \| 0.2130 \| \| 0.0604 \| 19.51 \| 29600 \| 0.3750 \| 0.2142 \| \| 0.0619 \| 19.78 \| 30000 \| 0.3804 \| 0.2160 \| \| 0.0603 \| 20.04 \| 30400 \| 0.3764 \| 0.2124 \| \| 0.0577 \| 20.3 \| 30800 \| 0.3858 \| 0.2097 \| \| 0.0583 \| 20.57 \| 31200 \| 0.3520 \| 0.2089 \| \| 0.0561 \| 20.83 \| 31600 \| 0.3615 \| 0.2079 \| \| 0.0545 \| 21.09 \| 32000 \| 0.3824 \| 0.2032 \| \| 0.0525 \| 21.36 \| 32400 \| 0.3858 \| 0.2091 \| \| 0.0524 \| 21.62 \| 32800 \| 0.3956 \| 0.2099 \| \| 0.0527 \| 21.89 \| 33200 \| 0.3667 \| 0.2025 \| \| 0.0514 \| 22.15 \| 33600 \| 0.3708 \| 0.2032 \| \| 0.0506 \| 22.41 \| 34000 \| 0.3815 \| 0.2053 \| \| 0.0478 \| 22.68 \| 34400 \| 0.3671 \| 0.2007 \| \| 0.049 \| 22.94 \| 34800 \| 0.3758 \| 0.2003 \| \| 0.0477 \| 23.2 \| 35200 \| 0.3786 \| 0.2014 \| \| 0.045 \| 23.47 \| 35600 \| 0.3732 \| 0.1998 \| \| 0.0426 \| 23.73 \| 36000 \| 0.3737 \| 0.2010 \| \| 0.0444 \| 23.99 \| 36400 \| 0.3600 \| 0.1990 \| \| 0.0433 \| 24.26 \| 36800 \| 0.3689 \| 0.1976 \| \| 0.0442 \| 24.52 \| 37200 \| 0.3787 \| 0.1968 \| \| 0.0419 \| 24.79 \| 37600 \| 0.3652 \| 0.1961 \| \| 0.042 \| 25.05 \| 38000 \| 0.3820 \| 0.1964 \| \| 0.0419 \| 25.31 \| 38400 \| 0.3786 \| 0.1919 \| \| 0.0376 \| 25.58 \| 38800 \| 0.3842 \| 0.1934 \| \| 0.0385 \| 25.84 \| 39200 \| 0.3767 \| 0.1900 \| \| 0.0396 \| 26.1 \| 39600 \| 0.3688 \| 0.1888 \| \| 0.0371 \| 26.37 \| 40000 \| 0.3815 \| 0.1894 \| \| 0.0363 \| 26.63 \| 40400 \| 0.3748 \| 0.1878 \| \| 0.0377 \| 26.9 \| 40800 \| 0.3713 \| 0.1852 \| \| 0.0352 \| 27.16 \| 41200 \| 0.3734 \| 0.1851 \| \| 0.0355 \| 27.42 \| 41600 \| 0.3776 \| 0.1874 \| \| 0.0333 \| 27.69 \| 42000 \| 0.3867 \| 0.1841 \| \| 0.0348 \| 27.95 \| 42400 \| 0.3823 \| 0.1839 \| \| 0.0329 \| 28.21 \| 42800 \| 0.3795 \| 0.1822 \| \| 0.0325 \| 28.48 \| 43200 \| 0.3711 \| 0.1813 \| \| 0.0328 \| 28.74 \| 43600 \| 0.3721 \| 0.1781 \| \| 0.0312 \| 29.0 \| 44000 \| 0.3803 \| 0.1816 \| \| 0.0318 \| 29.27 \| 44400 \| 0.3758 \| 0.1794 \| \| 0.0302 \| 29.53 \| 44800 \| 0.3792 \| 0.1784 \| \| 0.0339 \| 29.8 \| 45200 \| 0.3763 \| 0.1791 \| ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0
jiobiala24/wav2vec2-base-checkpoint-10	jiobiala24	2022-01-30T16:10:56Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-base-checkpoint-10 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-checkpoint-10 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-9](https://huggingface.co/jiobiala24/wav2vec2-base-checkpoint-9) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9567 - Wer: 0.3292 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.2892 \| 1.62 \| 1000 \| 0.5745 \| 0.3467 \| \| 0.235 \| 3.23 \| 2000 \| 0.6156 \| 0.3423 \| \| 0.1782 \| 4.85 \| 3000 \| 0.6299 \| 0.3484 \| \| 0.1504 \| 6.46 \| 4000 \| 0.6475 \| 0.3446 \| \| 0.133 \| 8.08 \| 5000 \| 0.6753 \| 0.3381 \| \| 0.115 \| 9.69 \| 6000 \| 0.7834 \| 0.3529 \| \| 0.101 \| 11.31 \| 7000 \| 0.7924 \| 0.3426 \| \| 0.0926 \| 12.92 \| 8000 \| 0.7887 \| 0.3465 \| \| 0.0863 \| 14.54 \| 9000 \| 0.7674 \| 0.3439 \| \| 0.0788 \| 16.16 \| 10000 \| 0.8648 \| 0.3435 \| \| 0.0728 \| 17.77 \| 11000 \| 0.8460 \| 0.3395 \| \| 0.0693 \| 19.39 \| 12000 \| 0.8941 \| 0.3451 \| \| 0.0637 \| 21.0 \| 13000 \| 0.9079 \| 0.3356 \| \| 0.0584 \| 22.62 \| 14000 \| 0.8851 \| 0.3336 \| \| 0.055 \| 24.23 \| 15000 \| 0.9400 \| 0.3338 \| \| 0.0536 \| 25.85 \| 16000 \| 0.9387 \| 0.3335 \| \| 0.0481 \| 27.46 \| 17000 \| 0.9664 \| 0.3337 \| \| 0.0485 \| 29.08 \| 18000 \| 0.9567 \| 0.3292 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
imvladikon/charbert-bert-wiki	imvladikon	2022-01-30T11:35:48Z	63	3	transformers	[ "transformers", "pytorch", "language model", "en", "dataset:wikipedia", "arxiv:2011.01513", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: - en tags: - language model datasets: - wikipedia --- pre-trained model from [CharBERT: Character-aware Pre-trained Language Model](https://github.com/wtma/CharBERT) ``` @misc{ma2020charbert, title={CharBERT: Character-aware Pre-trained Language Model}, author={Wentao Ma and Yiming Cui and Chenglei Si and Ting Liu and Shijin Wang and Guoping Hu}, year={2020}, eprint={2011.01513}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
sshasnain/finetune-wav2vec2-large-xlsr-bengali	sshasnain	2022-01-30T07:55:29Z	3	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "bn", "audio", "speech", "dataset:custom", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: Bengali datasets: - custom metrics: - wer tags: - bn - audio - automatic-speech-recognition - speech license: apache-2.0 model-index: - name: finetune-wav2vec2-large-xlsr-bengali results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: custom type: custom args: ben metrics: - name: Test WER type: wer value: 0.011 --- # finetune-wav2vec2-large-xlsr-bengali * ## Usage *
pinecone/mpnet-retriever-discourse	pinecone	2022-01-30T07:23:58Z	4	2	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "question-answering", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - question-answering --- # MPNet Retriever (Discourse) This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used as a retriever model in open-domain question-answering tasks. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Training The model was fine-tuned on question-answer pairs scraper from several ML-focused Discourse forums \[HuggingFace, PyTorch, Streamlit, TensorFlow\]. The model was trained with the parameters: DataLoader: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 105 with parameters: ``` {'batch_size': 12} ``` Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors Fine-tuned by [James Briggs](https://www.youtube.com/c/jamesbriggs) at [Pinecone](https://www.pinecone.io). Learn more about the [fine-tuning process here](https://www.pinecone.io/learn/retriever-models/).
jogonba2/mbarthez-copy_mechanism-hal_articles	jogonba2	2022-01-30T03:52:27Z	3	0	transformers	[ "transformers", "pytorch", "mbart", "generated_from_trainer", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: mbarthez-copy_mechanism-hal_articles results: - task: name: Summarization type: summarization metrics: - name: Rouge1 type: rouge value: 36.548 --- <!-- 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. --> # mbarthez-davide_articles-copy_enhanced This model is a fine-tuned version of [moussaKam/mbarthez](https://huggingface.co/moussaKam/mbarthez) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4905 - Rouge1: 36.548 - Rouge2: 19.6282 - Rougel: 30.2513 - Rougelsum: 30.2765 - Gen Len: 25.7238 ## Model description More information needed ## Intended uses & 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.6706 \| 1.0 \| 33552 \| 1.5690 \| 31.2477 \| 16.5455 \| 26.9855 \| 26.9754 \| 18.6217 \| \| 1.3446 \| 2.0 \| 67104 \| 1.5060 \| 32.1108 \| 17.1408 \| 27.7833 \| 27.7703 \| 18.9115 \| \| 1.3245 \| 3.0 \| 100656 \| 1.4905 \| 32.9084 \| 17.7027 \| 28.2912 \| 28.2975 \| 18.9801 \| ### Framework versions - Transformers 4.10.2 - Pytorch 1.7.1+cu110 - Datasets 1.11.0 - Tokenizers 0.10.3
anton-l/wav2vec2-xls-r-common_voice-tr-ft-100sh	anton-l	2022-01-30T02:42:22Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "common_voice", "generated_from_trainer", "tr", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - tr license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer model-index: - name: wav2vec2-xls-r-common_voice-tr-ft 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-xls-r-common_voice-tr-ft This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.5806 - Wer: 0.3998 - Cer: 0.1053 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\|:------:\| \| 0.5369 \| 17.0 \| 500 \| 0.6021 \| 0.6366 \| 0.1727 \| \| 0.3542 \| 34.0 \| 1000 \| 0.5265 \| 0.4906 \| 0.1278 \| \| 0.1866 \| 51.0 \| 1500 \| 0.5805 \| 0.4768 \| 0.1261 \| \| 0.1674 \| 68.01 \| 2000 \| 0.5336 \| 0.4518 \| 0.1186 \| \| 0.19 \| 86.0 \| 2500 \| 0.5676 \| 0.4427 \| 0.1151 \| \| 0.0815 \| 103.0 \| 3000 \| 0.5510 \| 0.4268 \| 0.1125 \| \| 0.0545 \| 120.0 \| 3500 \| 0.5608 \| 0.4175 \| 0.1099 \| \| 0.0299 \| 137.01 \| 4000 \| 0.5875 \| 0.4222 \| 0.1124 \| \| 0.0267 \| 155.0 \| 4500 \| 0.5882 \| 0.4026 \| 0.1063 \| \| 0.025 \| 172.0 \| 5000 \| 0.5806 \| 0.3998 \| 0.1053 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2 - Datasets 1.18.2 - Tokenizers 0.10.3
huggingtweets/hashimoto_lo	huggingtweets	2022-01-30T01:43:17Z	0	0	null	[ "huggingtweets", "en", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/hashimoto_lo/1643506993033/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/922396157493383169/LLKd_U72_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">橋下徹</div> <div style="text-align: center; font-size: 14px;">@hashimoto_lo</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 橋下徹. \| Data \| 橋下徹 \| \| --- \| --- \| \| Tweets downloaded \| 3247 \| \| Retweets \| 759 \| \| Short tweets \| 137 \| \| Tweets kept \| 2351 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1wi9n714/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 @hashimoto_lo's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/240mb7l6) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/240mb7l6/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/hashimoto_lo') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/tjonthefloor	huggingtweets	2022-01-29T22:53:02Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/tjonthefloor/1643496777814/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/1466388620256948228/kkRWm2mR_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">ash ψ</div> <div style="text-align: center; font-size: 14px;">@tjonthefloor</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 ash ψ. \| Data \| ash ψ \| \| --- \| --- \| \| Tweets downloaded \| 470 \| \| Retweets \| 144 \| \| Short tweets \| 99 \| \| Tweets kept \| 227 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/20bqlhah/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 @tjonthefloor's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ntjhfs1) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ntjhfs1/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/tjonthefloor') 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)

transformersbook/pegasus-samsum

transformersbook

2022-02-05T17:05:28Z

75,124

6

transformers

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

text2text-generation

2022-03-02T23:29:05Z

--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum-test results: [] ---  # pegasus-samsum-test This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. The model is trained in Chapter 6: Summarization in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/06_summarization.ipynb). It achieves the following results on the evaluation set: - Loss: 1.4875 ## Model description More information needed ## Intended uses & 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: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.7012 | 0.54 | 500 | 1.4875 | ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3

transformersbook/bert-base-uncased-issues-128

transformersbook

2022-02-05T16:57:43Z

9

0

transformers

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

fill-mask

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-uncased-issues-128 results: [] ---  # bert-base-uncased-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GitHub issues dataset. The model is used in Chapter 9: Dealing with Few to No Labels in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/09_few-to-no-labels.ipynb). It achieves the following results on the evaluation set: - Loss: 1.2520 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0949 | 1.0 | 291 | 1.7072 | | 1.649 | 2.0 | 582 | 1.4409 | | 1.4835 | 3.0 | 873 | 1.4099 | | 1.3938 | 4.0 | 1164 | 1.3858 | | 1.3326 | 5.0 | 1455 | 1.2004 | | 1.2949 | 6.0 | 1746 | 1.2955 | | 1.2451 | 7.0 | 2037 | 1.2682 | | 1.1992 | 8.0 | 2328 | 1.1938 | | 1.1784 | 9.0 | 2619 | 1.1686 | | 1.1397 | 10.0 | 2910 | 1.2050 | | 1.1293 | 11.0 | 3201 | 1.2058 | | 1.1006 | 12.0 | 3492 | 1.1680 | | 1.0835 | 13.0 | 3783 | 1.2414 | | 1.0757 | 14.0 | 4074 | 1.1522 | | 1.062 | 15.0 | 4365 | 1.1176 | | 1.0535 | 16.0 | 4656 | 1.2520 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu102 - Datasets 1.13.0 - Tokenizers 0.10.3

transformersbook/distilbert-base-uncased-distilled-clinc

transformersbook

2022-02-05T16:47:39Z

199

3

transformers

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

text-classification

2022-03-02T23:29:05Z

--- 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: 0.9393548387096774 ---  # distilbert-base-uncased-distilled-clinc This model is a fine-tuned with knowledge distillation version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. The model is used in Chapter 8: Making Transformers Efficient in Production in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/08_model-compression.ipynb). It achieves the following results on the evaluation set: - Loss: 0.1005 - Accuracy: 0.9394 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9031 | 1.0 | 318 | 0.5745 | 0.7365 | | 0.4481 | 2.0 | 636 | 0.2856 | 0.8748 | | 0.2528 | 3.0 | 954 | 0.1798 | 0.9187 | | 0.176 | 4.0 | 1272 | 0.1398 | 0.9294 | | 0.1416 | 5.0 | 1590 | 0.1211 | 0.9348 | | 0.1243 | 6.0 | 1908 | 0.1116 | 0.9348 | | 0.1133 | 7.0 | 2226 | 0.1062 | 0.9377 | | 0.1075 | 8.0 | 2544 | 0.1035 | 0.9387 | | 0.1039 | 9.0 | 2862 | 0.1014 | 0.9381 | | 0.1018 | 10.0 | 3180 | 0.1005 | 0.9394 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1+cu102 - Datasets 1.13.0 - Tokenizers 0.10.3

transformersbook/codeparrot-small

transformersbook

2022-02-05T16:28:36Z

4

1

transformers

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

text-generation

2022-03-02T23:29:05Z

# CodeParrot CodeParrot (small) is a 110M parameter GPT-2 model trained on the [CodeParrot Python code dataset](https://huggingface.co/datasets/transformersbook/codeparrot). The model is trained in Chapter 10: Training Transformers from Scratch in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/10_transformers-from-scratch.ipynb).

transformersbook/codeparrot

transformersbook

2022-02-05T16:27:42Z

18

5

transformers

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

text-generation

2022-03-02T23:29:05Z

# CodeParrot CodeParrot (large) is a 1.5B parameter GPT-2 model trained on the [CodeParrot Python code dataset](https://huggingface.co/datasets/transformersbook/codeparrot). The model is trained in Chapter 10: Training Transformers from Scratch in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/10_transformers-from-scratch.ipynb).

groar/distilgpt2-finetuned-escape

groar

2022-02-05T14:44:47Z

6

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-escape results: [] ---  # distilgpt2-finetuned-escape This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

Ayham/distilbert_distilgpt2_summarization_cnn_dailymail

Ayham

2022-02-05T11:39:58Z

7

0

transformers

[ "transformers", "pytorch", "tensorboard", "encoder-decoder", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-02T23:29:04Z

--- tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: distilbert_distilgpt2_summarization_cnn_dailymail results: [] ---  # distilbert_distilgpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

omoekan/opus-tatoeba-eng-yor

omoekan

2022-02-05T10:15:11Z

3

0

transformers

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

text2text-generation

2022-03-02T23:29:05Z

## OPUS Tatoeba English-Yoruba This model was obtained by running the script convert_marian_to_pytorch.py with the flag -m eng-yor. The original models were trained by Jörg Tiedemann using the MarianNMT library. See all available MarianMTModel models on the profile of the Helsinki NLP group. --- - tags: translation - source language: English - target language: Yoruba - dataset: opus+bt -model: transformer-align -pre-processing: normalization + SentencePiece (spm12k,spm12k) -download original weights: [opus+bt-2021-04-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-yor/opus+bt-2021-04-10.zip) -test set translations: [opus+bt-2021-04-10.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-yor/opus+bt-2021-04-10.test.txt) -test set scores: [opus+bt-2021-04-10.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-yor/opus+bt-2021-04-10.eval.txt) -Benchmarks |test set|BLEU|chr-F| |:---|:---|:---| |Tatoeba-test.eng-yor|13.0|0.333| ---

ajitrajasekharan/biomedical

ajitrajasekharan

2022-02-05T08:44:05Z

6

1

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:05Z

--- language: - {en} # Example: fr license: mit widget: - text: "Lou Gehrig who works for XCorp and lives in New York suffers from [MASK]" example_title: "Test for entity type: Disease" - text: "Overexpression of [MASK] occurs across a wide range of cancers" example_title: "Test for entity type: Gene" - text: "Patients treated with [MASK] are vulnerable to infectious diseases" example_title: "Test for entity type: Drug" - text: "A eGFR level below [MASK] indicates chronic kidney disease" example_title: "Test for entity type: Measure " - text: "In the [MASK], increased daily imatinib dose induced MMR" example_title: "Test for entity type: STUDY/TRIAL" - text: "Paul Erdos died at [MASK]" example_title: "Test for entity type: TIME" inference: parameters: top_k: 10 tags: - {fill-mask} # Example: audio - exbert --- This **cased model** was pretrained from scratch using a custom vocabulary on the following corpora - Pubmed - Clinical trials corpus - and a small subset of Bookcorpus The pretrained model was used to do NER **as is, with no fine-tuning**. The approach is described [in this post](https://ajitrajasekharan.github.io/2021/01/02/my-first-post.html). [Towards Data Science review](https://twitter.com/TDataScience/status/1486300137366466560?s=20) [App in Spaces](https://huggingface.co/spaces/ajitrajasekharan/self-supervised-ner-biomedical) demonstrates this approach. [Github link](https://github.com/ajitrajasekharan/unsupervised_NER) to perform NER using this model in an ensemble with bert-base cased. The ensemble detects 69 entity subtypes (17 broad entity groups) <img src="https://ajitrajasekharan.github.io/images/1.png" width="600"> ### Ensemble model performance <img src="https://ajitrajasekharan.github.io/images/6.png" width="600"> ### Additional notes - The model predictions on the right do not include [CLS] predictions. Hosted inference API only returns the masked position predictions. In practice, the [CLS] predictions are just as useful as the model predictions for the masked position _(if the next sentence prediction loss was low during pretraining)_ and are used for NER. - Some of the top model predictions like "a", "the", punctuations, etc. while valid predictions, bear no entity information. These are filtered when harvesting descriptors for NER. The examples on the right are unfiltered results. - [Use this link](https://huggingface.co/spaces/ajitrajasekharan/Qualitative-pretrained-model-evaluation) to examine both fill-mask prediction and [CLS] predictions ### License MIT license <a href="https://huggingface.co/exbert/?model=ajitrajasekharan/biomedical&modelKind=bidirectional&sentence=Gefitinib%20is%20an%20EGFR%20tyrosine%20kinase%20inhibitor,%20which%20is%20often%20used%20for%20breast%20cancer%20and%20NSCLC%20treatment.&layer=3&heads=..0,1,2,3,4,5,6,7,8,9,10,11&threshold=0.7&tokenInd=17&tokenSide=right&maskInds=..&hideClsSep=true"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

HenryHXR/t5-base-finetuned-scitldr

HenryHXR

2022-02-05T05:48:10Z

9

0

transformers

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

text2text-generation

2022-03-02T23:29:04Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-finetuned-scitldr results: [] ---  # t5-base-finetuned-scitldr This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0232 - Rouge1: 35.2134 - Rouge2: 16.8919 - Rougel: 30.8442 - Rougelsum: 30.9316 - Gen Len: 18.7981 ## Model description More information needed ## Intended uses & 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-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.0533 | 1.0 | 996 | 2.0285 | 34.9774 | 16.6163 | 30.6177 | 30.7038 | 18.7981 | | 2.0994 | 2.0 | 1992 | 2.0232 | 35.2134 | 16.8919 | 30.8442 | 30.9316 | 18.7981 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

MaggieXM/distilbert-base-uncased-finetuned-squad

MaggieXM

2022-02-05T04:50:41Z

16

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-03-02T23:29:04Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. ## Model description More information needed ## Intended uses & 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: 0.01 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.01 | 56 | 4.8054 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

aogara/slai_transformer

aogara

2022-02-05T00:26:24Z

0

null

[ "region:us" ]

null

2022-03-02T23:29:05Z

# Building a HuggingFace Transformer NLP Model ## Running this Repo

BigSalmon/InformalToFormalLincoln20

BigSalmon

2022-02-04T20:56:17Z

10

0

transformers

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

text-generation

2022-03-02T23:29:04Z

Informal to Formal: Wordy to Concise: Fill Missing Phrase: ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln20") model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln20") ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space changes over time) ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time) ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ```` ``` infill: increasing the number of sidewalks in suburban areas will [MASK]. Translated into the Style of Abraham Lincoln: increasing the number of sidewalks in suburban areas will ( ( enhance / maximize ) community cohesion / facilitate ( communal ties / the formation of neighborhood camaraderie ) / forge neighborly relations / lend themselves to the advancement of neighborly ties / plant the seeds of community building / flower anew the bonds of friendship / invite the budding of neighborhood rapport / enrich neighborhood life ). infill: corn fields [MASK], [MASK] visibly as one ventures beyond chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), ( manifesting themselves ) visibly as one ventures beyond chicago. infill: the [MASK] the SAT will soon be [MASK]. [MASK] an examination undertaken on one's laptop. [MASK] will allow students to retrieve test results promptly. Translated into the Style of Abraham Lincoln: the ( conventional form of ) the SAT will soon be ( consigned to history ). ( replacing it will be ) an examination undertaken on one's laptop. ( so doing ) will allow students to retrieve test results promptly. infill: ``` ``` *** wordy: chancing upon a linux user is a rare occurrence in the present day. Translate into Concise Text: present-day linux users are rare. *** wordy: an interest in classical music is becoming more and more less popular. Translate into Concise Text: classical music appreciation is dwindling. Translate into Concise Text: waning interest in classic music persists. Translate into Concise Text: interest in classic music is fading. *** wordy: the ice cream was only one dollar, but it was not a good value for the size. Translate into Concise Text: the one dollar ice cream was overpriced for its size. Translate into Concise Text: overpriced, the one dollar ice cream was small. *** wordy: ```

MarioPenguin/bert-model-english

MarioPenguin

2022-02-04T20:12:58Z

6

0

transformers

[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:04Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: bert-model-english results: [] ---  # bert-model-english 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: - Train Loss: 0.1408 - Train Sparse Categorical Accuracy: 0.9512 - Validation Loss: nan - Validation Sparse Categorical Accuracy: 0.0 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| | 0.2775 | 0.8887 | nan | 0.0 | 0 | | 0.1702 | 0.9390 | nan | 0.0 | 1 | | 0.1300 | 0.9555 | nan | 0.0 | 2 | | 0.1346 | 0.9544 | nan | 0.0 | 3 | | 0.1408 | 0.9512 | nan | 0.0 | 4 | ### Framework versions - Transformers 4.16.2 - TensorFlow 2.7.0 - Datasets 1.18.3 - Tokenizers 0.11.0

tesemnikov-av/NER-RUBERT-Per-Loc-Org

tesemnikov-av

2022-02-04T19:40:56Z

7

0

transformers

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

token-classification

2022-03-02T23:29:05Z

--- widget: - text: "В город Сергиев Посад приехал Курт Кобейн." --- Fine-tuning [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model on sentences from Wiki auto annotated with PER, LOC, ORG tags [corus/WiNER](https://pypi.org/project/corus/#reference) language: RU NER Class: - PER - LOC - ORG license: mit

LenaSchmidt/distilbert-base-uncased-finetuned-squad

LenaSchmidt

2022-02-04T19:20:11Z

11

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-03-02T23:29:04Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7713 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0325 | 1.0 | 585 | 1.7520 | | 1.609 | 2.0 | 1170 | 1.7713 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

mrm8488/roberta-base-bne-finetuned-sqac-retriever

mrm8488

2022-02-04T17:59:07Z

4

1

sentence-transformers

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

sentence-similarity

2022-03-02T23:29:05Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 939 with parameters: ``` {'batch_size': 16} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 93, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

huggingtweets/loverachelle2

huggingtweets

2022-02-04T17:51:57Z

4

0

transformers

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

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/loverachelle2/1643997109994/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/1371211513323749377/ABF4NRhC_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">LoveRachelle2</div> <div style="text-align: center; font-size: 14px;">@loverachelle2</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 LoveRachelle2. | Data | LoveRachelle2 | | --- | --- | | Tweets downloaded | 1440 | | Retweets | 102 | | Short tweets | 92 | | Tweets kept | 1246 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1liqzipo/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 @loverachelle2's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/284b8u8q) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/284b8u8q/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/loverachelle2') 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)

samx18/demo

samx18

2022-02-04T17:23:34Z

0

null

[ "region:us" ]

null

2022-03-02T23:29:05Z

# Dummy This is a dummy model for testing - do not use

dkurt/wav2vec2-base-ft-keyword-spotting-int8

dkurt

2022-02-04T16:40:37Z

7

2

transformers

[ "transformers", "wav2vec2", "audio-classification", "endpoints_compatible", "region:us" ]

audio-classification

2022-03-02T23:29:05Z

[anton-l/wav2vec2-base-ft-keyword-spotting](https://huggingface.co/anton-l/wav2vec2-base-ft-keyword-spotting) model quantized with [Optimum OpenVINO](https://github.com/dkurt/optimum-openvino/). | Accuracy on eval (baseline) | Accuracy on eval (quantized) | |-----------------------------|----------------------------------------| | 0.9828 | 0.9553 (-0.0274) |

Rolv-Arild/xls-r-300m-npsc-4

Rolv-Arild

2022-02-04T16:36:33Z

32

0

transformers

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

automatic-speech-recognition

2022-03-02T23:29:04Z

--- license: apache-2.0 tags: - automatic-speech-recognition - NbAiLab/NPSC - generated_from_trainer model-index: - name: '' results: [] ---  # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set: - Loss: 0.1957 - Wer: 0.1697 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.4527 | 0.28 | 250 | 4.0144 | 1.0 | | 3.1828 | 0.56 | 500 | 3.1369 | 1.0 | | 2.9927 | 0.85 | 750 | 3.0183 | 1.0 | | 2.9591 | 1.13 | 1000 | 2.9991 | 1.0 | | 2.8989 | 1.41 | 1250 | 2.9000 | 1.0000 | | 2.4286 | 1.69 | 1500 | 1.7688 | 0.9550 | | 1.6765 | 1.98 | 1750 | 0.6842 | 0.4855 | | 1.4521 | 2.26 | 2000 | 0.5096 | 0.3736 | | 1.3589 | 2.54 | 2250 | 0.4479 | 0.3335 | | 1.3136 | 2.82 | 2500 | 0.4056 | 0.3123 | | 1.2856 | 3.11 | 2750 | 0.3870 | 0.2987 | | 1.2283 | 3.39 | 3000 | 0.3646 | 0.2828 | | 1.2053 | 3.67 | 3250 | 0.3499 | 0.2748 | | 1.2087 | 3.95 | 3500 | 0.3345 | 0.2603 | | 1.2002 | 4.24 | 3750 | 0.3320 | 0.2523 | | 1.1383 | 4.52 | 4000 | 0.3117 | 0.2439 | | 1.1364 | 4.8 | 4250 | 0.3198 | 0.2383 | | 1.158 | 5.08 | 4500 | 0.3071 | 0.2342 | | 1.108 | 5.37 | 4750 | 0.3011 | 0.2314 | | 1.1025 | 5.65 | 5000 | 0.2875 | 0.2289 | | 1.0697 | 5.93 | 5250 | 0.2926 | 0.2256 | | 1.0904 | 6.21 | 5500 | 0.2695 | 0.2245 | | 1.0802 | 6.5 | 5750 | 0.2602 | 0.2189 | | 1.0882 | 6.78 | 6000 | 0.2603 | 0.2168 | | 1.0881 | 7.06 | 6250 | 0.2540 | 0.2293 | | 1.0378 | 7.34 | 6500 | 0.2614 | 0.2193 | | 1.0397 | 7.63 | 6750 | 0.2707 | 0.2104 | | 1.0296 | 7.91 | 7000 | 0.2483 | 0.2119 | | 1.0249 | 8.19 | 7250 | 0.2483 | 0.2047 | | 1.013 | 8.47 | 7500 | 0.2487 | 0.2042 | | 1.0064 | 8.76 | 7750 | 0.2456 | 0.2016 | | 1.0668 | 9.04 | 8000 | 0.2397 | 0.1995 | | 1.0129 | 9.32 | 8250 | 0.2374 | 0.1994 | | 1.0164 | 9.6 | 8500 | 0.2206 | 0.1992 | | 0.975 | 9.89 | 8750 | 0.2247 | 0.1973 | | 0.9849 | 10.17 | 9000 | 0.2325 | 0.1953 | | 0.9826 | 10.45 | 9250 | 0.2301 | 0.1934 | | 0.9835 | 10.73 | 9500 | 0.2192 | 0.1942 | | 0.9676 | 11.02 | 9750 | 0.2266 | 0.1913 | | 0.9627 | 11.3 | 10000 | 0.2193 | 0.1921 | | 0.976 | 11.58 | 10250 | 0.2309 | 0.1882 | | 0.969 | 11.86 | 10500 | 0.2268 | 0.1886 | | 0.9611 | 12.15 | 10750 | 0.2322 | 0.1863 | | 0.9397 | 12.43 | 11000 | 0.2197 | 0.1844 | | 0.9601 | 12.71 | 11250 | 0.2211 | 0.1871 | | 0.9718 | 12.99 | 11500 | 0.2079 | 0.1898 | | 0.9347 | 13.28 | 11750 | 0.2054 | 0.1843 | | 0.9377 | 13.56 | 12000 | 0.2031 | 0.1842 | | 0.934 | 13.84 | 12250 | 0.2059 | 0.1806 | | 0.9295 | 14.12 | 12500 | 0.2122 | 0.1861 | | 0.935 | 14.41 | 12750 | 0.2072 | 0.1787 | | 0.9021 | 14.69 | 13000 | 0.2105 | 0.1781 | | 0.9193 | 14.97 | 13250 | 0.2035 | 0.1786 | | 0.9214 | 15.25 | 13500 | 0.2035 | 0.1766 | | 0.9048 | 15.54 | 13750 | 0.1964 | 0.1758 | | 0.9006 | 15.82 | 14000 | 0.1984 | 0.1757 | | 0.9027 | 16.1 | 14250 | 0.2022 | 0.1743 | | 0.9083 | 16.38 | 14500 | 0.1969 | 0.1744 | | 0.9761 | 16.67 | 14750 | 0.1963 | 0.1728 | | 0.9311 | 16.95 | 15000 | 0.1960 | 0.1737 | | 0.886 | 17.23 | 15250 | 0.1929 | 0.1726 | | 0.8969 | 17.51 | 15500 | 0.1928 | 0.1734 | | 0.9084 | 17.8 | 15750 | 0.1937 | 0.1713 | | 0.8795 | 18.08 | 16000 | 0.1978 | 0.1709 | | 0.8883 | 18.36 | 16250 | 0.1956 | 0.1703 | | 0.8901 | 18.64 | 16500 | 0.1933 | 0.1705 | | 0.8922 | 18.93 | 16750 | 0.1962 | 0.1711 | | 0.8765 | 19.21 | 17000 | 0.1962 | 0.1711 | | 0.8992 | 19.49 | 17250 | 0.1965 | 0.1703 | | 0.8778 | 19.77 | 17500 | 0.1957 | 0.1699 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.0+cu113 - Datasets 1.18.1 - Tokenizers 0.11.0

groar/distilgpt2-finetuned-wikitext2

groar

2022-02-04T16:27:05Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] ---  # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6895 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7852 | 1.0 | 2334 | 3.6895 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

cahya/wav2vec2-base-turkish-cv8

cahya

2022-02-04T14:30:19Z

5

1

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "tr", "dataset:common_voice", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:05Z

--- language: - tr tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer datasets: - common_voice model-index: - name: '' results: [] ---  # This model is a fine-tuned version of [./checkpoint-1000](https://huggingface.co/./checkpoint-1000) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.3282 - Wer: 0.2836 ## Model description More information needed ## Intended uses & 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: 96 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 192 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.0671 | 2.04 | 200 | 0.3079 | 0.2752 | | 0.6433 | 4.08 | 400 | 0.2728 | 0.2848 | | 0.5687 | 6.12 | 600 | 0.2882 | 0.3036 | | 0.5355 | 8.16 | 800 | 0.2778 | 0.2920 | | 0.5116 | 10.2 | 1000 | 0.2906 | 0.3014 | | 0.5313 | 9.16 | 1200 | 0.2984 | 0.3273 | | 0.4996 | 10.69 | 1400 | 0.3170 | 0.3344 | | 0.4845 | 12.21 | 1600 | 0.3202 | 0.3634 | | 0.5092 | 13.74 | 1800 | 0.3167 | 0.3373 | | 0.4777 | 15.27 | 2000 | 0.3292 | 0.3386 | | 0.4651 | 16.79 | 2200 | 0.3070 | 0.3427 | | 0.461 | 18.32 | 2400 | 0.3149 | 0.3561 | | 0.4481 | 19.85 | 2600 | 0.3292 | 0.3441 | | 0.4479 | 21.37 | 2800 | 0.3142 | 0.3209 | | 0.4305 | 22.9 | 3000 | 0.3525 | 0.3547 | | 0.4254 | 24.43 | 3200 | 0.3414 | 0.3400 | | 0.4066 | 25.95 | 3400 | 0.3118 | 0.3207 | | 0.4043 | 27.48 | 3600 | 0.3418 | 0.3483 | | 0.3985 | 29.01 | 3800 | 0.3254 | 0.3166 | | 0.3982 | 30.53 | 4000 | 0.3306 | 0.3453 | | 0.3929 | 32.06 | 4200 | 0.3262 | 0.3229 | | 0.378 | 33.59 | 4400 | 0.3546 | 0.3336 | | 0.4062 | 35.11 | 4600 | 0.3174 | 0.3457 | | 0.3648 | 36.64 | 4800 | 0.3377 | 0.3357 | | 0.3609 | 38.17 | 5000 | 0.3346 | 0.3520 | | 0.3483 | 39.69 | 5200 | 0.3350 | 0.3526 | | 0.3548 | 41.22 | 5400 | 0.3330 | 0.3406 | | 0.3446 | 42.75 | 5600 | 0.3398 | 0.3372 | | 0.3346 | 44.27 | 5800 | 0.3449 | 0.3288 | | 0.3309 | 45.8 | 6000 | 0.3320 | 0.3144 | | 0.326 | 47.33 | 6200 | 0.3400 | 0.3279 | | 0.3189 | 48.85 | 6400 | 0.3400 | 0.3150 | | 0.3165 | 50.38 | 6600 | 0.3359 | 0.2995 | | 0.3132 | 51.91 | 6800 | 0.3343 | 0.3096 | | 0.3092 | 53.44 | 7000 | 0.3224 | 0.3029 | | 0.2995 | 54.96 | 7200 | 0.3205 | 0.2985 | | 0.304 | 56.49 | 7400 | 0.3523 | 0.3034 | | 0.2952 | 58.02 | 7600 | 0.3289 | 0.2934 | | 0.2875 | 59.54 | 7800 | 0.3350 | 0.3008 | | 0.2868 | 61.07 | 8000 | 0.3537 | 0.3227 | | 0.2875 | 62.6 | 8200 | 0.3389 | 0.2970 | | 0.2778 | 64.12 | 8400 | 0.3370 | 0.2960 | | 0.2706 | 65.65 | 8600 | 0.3250 | 0.2802 | | 0.2669 | 67.18 | 8800 | 0.3351 | 0.2903 | | 0.2615 | 68.7 | 9000 | 0.3382 | 0.2989 | | 0.2563 | 70.23 | 9200 | 0.3312 | 0.2975 | | 0.2546 | 71.76 | 9400 | 0.3212 | 0.3003 | | 0.2482 | 73.28 | 9600 | 0.3337 | 0.3091 | | 0.2504 | 74.81 | 9800 | 0.3308 | 0.3110 | | 0.2456 | 76.34 | 10000 | 0.3157 | 0.3118 | | 0.2363 | 77.86 | 10200 | 0.3251 | 0.3144 | | 0.2319 | 79.39 | 10400 | 0.3253 | 0.3038 | | 0.2266 | 80.92 | 10600 | 0.3374 | 0.3038 | | 0.2279 | 82.44 | 10800 | 0.3268 | 0.2964 | | 0.2231 | 83.97 | 11000 | 0.3278 | 0.2950 | | 0.2185 | 85.5 | 11200 | 0.3462 | 0.2981 | | 0.2245 | 87.02 | 11400 | 0.3311 | 0.2895 | | 0.223 | 88.55 | 11600 | 0.3325 | 0.2877 | | 0.2121 | 90.08 | 11800 | 0.3337 | 0.2828 | | 0.2126 | 91.6 | 12000 | 0.3325 | 0.2808 | | 0.2027 | 93.13 | 12200 | 0.3277 | 0.2820 | | 0.2058 | 94.66 | 12400 | 0.3308 | 0.2827 | | 0.1991 | 96.18 | 12600 | 0.3279 | 0.2820 | | 0.1991 | 97.71 | 12800 | 0.3300 | 0.2822 | | 0.1986 | 99.24 | 13000 | 0.3285 | 0.2835 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0

abhishek/autonlp-imdb-roberta-base-3662644

abhishek

2022-02-04T14:25:35Z

16

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "autonlp", "unk", "dataset:abhishek/autonlp-data-imdb-roberta-base", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:05Z

--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - abhishek/autonlp-data-imdb-roberta-base co2_eq_emissions: 25.894117734124272 --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 3662644 - CO2 Emissions (in grams): 25.894117734124272 ## Validation Metrics - Loss: 0.20277436077594757 - Accuracy: 0.92604 - Precision: 0.9560674830864092 - Recall: 0.89312 - AUC: 0.9814625504000001 - F1: 0.9235223559581421 ## 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 AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-imdb-roberta-base-3662644 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-imdb-roberta-base-3662644", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-imdb-roberta-base-3662644", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```

Language-Media-Lab/mt5-small-jpn-ain-mt

Language-Media-Lab

2022-02-04T14:23:13Z

5

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "translation", "jpn", "ain", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2022-03-02T23:29:05Z

--- language: - jpn - ain tags: - translation --- mt5-small-jpn-ain-mt is a machine translation model pretrained with [Google's mT5-small](https://huggingface.co/google/mt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Japanese to Ainu language.

Language-Media-Lab/mt5-small-ain-jpn-mt

Language-Media-Lab

2022-02-04T13:20:55Z

5

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "translation", "jpn", "ain", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2022-03-02T23:29:05Z

--- language: - jpn - ain tags: - translation --- mt5-small-ain-jpn-mt is a machine translation model pretrained with [Google's mT5-small](https://huggingface.co/google/mt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Ainu language to Japanese.

Language-Media-Lab/byt5-small-ain-jpn-mt

Language-Media-Lab

2022-02-04T13:03:14Z

7

2

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "translation", "ain", "ja", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

translation

2022-03-02T23:29:05Z

--- language: - ain - ja tags: - translation --- Byt5-small-ain-jpn-mt is a machine translation model pretrained with [Google's ByT5-small](https://huggingface.co/google/byt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Ainu language to Japanese.

Language-Media-Lab/byt5-small-jpn-ain-mt

Language-Media-Lab

2022-02-04T13:02:58Z

14

1

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "translation", "jpn", "ain", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

translation

2022-03-02T23:29:05Z

--- language: - jpn - ain tags: - translation --- Byt5-small-jpn-ain-mt is a machine translation model pretrained with [Google's ByT5-small](https://huggingface.co/google/byt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Japanese to Ainu language.

huggingtweets/ir_rkp

huggingtweets

2022-02-04T12:03:54Z

3

0

transformers

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

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/ir_rkp/1643976228944/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/1432037158072856578/a_Fty68E_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">Riikka Purra</div> <div style="text-align: center; font-size: 14px;">@ir_rkp</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 Riikka Purra. | Data | Riikka Purra | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 141 | | Short tweets | 78 | | Tweets kept | 3031 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1w0bzvgu/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 @ir_rkp's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1nj4v31w) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1nj4v31w/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/ir_rkp') 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)

Plim/xls-r-1b-fr

Plim

2022-02-04T11:45:21Z

5

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "fr", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:04Z

--- language: - fr license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer model-index: - name: '' results: [] ---  # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - FR dataset. It achieves the following results on the evaluation set: - Loss: 0.2464 - Wer: 0.2220 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.0326 | 0.32 | 1000 | 0.3092 | 0.2718 | | 1.0828 | 0.65 | 2000 | 0.2843 | 0.2606 | | 1.0771 | 0.97 | 3000 | 0.2774 | 0.2488 | | 1.0306 | 1.3 | 4000 | 0.2588 | 0.2351 | | 1.0052 | 1.62 | 5000 | 0.2483 | 0.2284 | | 0.9865 | 1.94 | 6000 | 0.2464 | 0.2220 | | 0.978 | 2.27 | 7000 | 0.2514 | 0.2172 | | 1.7438 | 2.59 | 8000 | 0.7983 | 0.5072 | | 2.3309 | 2.92 | 9000 | 1.8917 | 0.9416 | | 2.1834 | 3.24 | 10000 | 1.7496 | 0.9030 | | 2.3047 | 3.56 | 11000 | 1.5377 | 0.8747 | | 2.1378 | 3.89 | 12000 | 1.3501 | 0.7923 | | 1.9812 | 4.21 | 13000 | 1.2662 | 0.7697 | | 2.6855 | 4.54 | 14000 | 2.4120 | 0.9902 | | 2.7482 | 4.86 | 15000 | 2.5341 | 0.9874 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

Yanzhu/bertweetfr_offensiveness

Yanzhu

2022-02-04T11:42:54Z

0

null

[ "region:us" ]

null

2022-03-02T23:29:05Z

French roBERTa-base model fine-tuned for Offensive Language Identification on COVID-19 tweets.

Subhashini17/wav2vec2-large-xls-r-300m-ta-colab-new1

Subhashini17

2022-02-04T11:14:25Z

5

0

transformers

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

automatic-speech-recognition

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-ta-colab-new1 results: [] ---  # wav2vec2-large-xls-r-300m-ta-colab-new1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6642 - eval_wer: 0.7611 - eval_runtime: 152.4412 - eval_samples_per_second: 11.683 - eval_steps_per_second: 1.463 - epoch: 10.11 - step: 960 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.13.3 - Tokenizers 0.10.3

yohida/yoshida_gpt

yohida

2022-02-04T10:13:45Z

4

0

transformers

[ "transformers", "gpt2", "text-generation", "ja", "japanese", "gpt", "lm", "nlp", "dataset:cc100", "dataset:wikipedia", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-03-02T23:29:05Z

--- language: ja thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png tags: - ja - japanese - gpt - text-generation - lm - nlp license: mit datasets: - cc100 - wikipedia widget: - text: "西田幾多郎は、" --- # japanese-gpt-1b ![rinna-icon](./rinna.png) This repository provides a 1.3B-parameter Japanese GPT model. The model was trained by [rinna Co., Ltd.](https://corp.rinna.co.jp/) # How to use the model *NOTE:* Use `T5Tokenizer` to initiate the tokenizer. ~~~~ import torch from transformers import T5Tokenizer, AutoModelForCausalLM tokenizer = T5Tokenizer.from_pretrained("rinna/japanese-gpt-1b") model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt-1b") if torch.cuda.is_available(): model = model.to("cuda") text = "西田幾多郎は、" token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt") with torch.no_grad(): output_ids = model.generate( token_ids.to(model.device), max_length=100, min_length=100, do_sample=True, top_k=500, top_p=0.95, pad_token_id=tokenizer.pad_token_id, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, bad_word_ids=[[tokenizer.unk_token_id]] ) output = tokenizer.decode(output_ids.tolist()[0]) print(output) # sample output: 西田幾多郎は、その主著の「善の研究」などで、人間の内面に自然とその根源があると指摘し、その根源的な性格は、この西田哲学を象徴しているとして、カントの「純粋理性批判」と「判断力批判」を対比して捉えます。それは、「人が理性的存在であるかぎりにおいて、人はその当人に固有な道徳的に自覚された善悪の基準を持っている」とするもので、この理性的な善悪の観念を否定するのがカントの ~~~~ # Model architecture A 24-layer, 2048-hidden-size transformer-based language model. # Training The model was trained on [Japanese C4](https://huggingface.co/datasets/allenai/c4), [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz) and [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) to optimize a traditional language modelling objective. It reaches around 14 perplexity on a chosen validation set from the same data. # Tokenization The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer. The vocabulary was first trained on a selected subset from the training data using the official sentencepiece training script, and then augmented with emojis and symbols. # Licenese [The MIT license](https://opensource.org/licenses/MIT)

huggingtweets/dril-drilbot_neo-jril_bot

huggingtweets

2022-02-04T09:52:05Z

3

0

transformers

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

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/dril-drilbot_neo-jril_bot/1643968320729/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/847818629840228354/VXyQHfn0_400x400.jpg')"> </div> <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/1468502340634296326/gbl8-ltv_400x400.png')"> </div> <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/1374924360780242944/-Q8NfgEr_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">wint & Jril & wintbot_neo</div> <div style="text-align: center; font-size: 14px;">@dril-drilbot_neo-jril_bot</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 wint & Jril & wintbot_neo. | Data | wint | Jril | wintbot_neo | | --- | --- | --- | --- | | Tweets downloaded | 3228 | 113 | 3241 | | Retweets | 475 | 0 | 315 | | Short tweets | 305 | 0 | 453 | | Tweets kept | 2448 | 113 | 2473 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/27nmrlyy/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 @dril-drilbot_neo-jril_bot's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/i64hq9wb) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/i64hq9wb/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/dril-drilbot_neo-jril_bot') 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)

LegolasTheElf/Wav2Vec2_xls_r_openslr_Hi_V2

LegolasTheElf

2022-02-04T07:53:30Z

7

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "Harveenchadha/indic-voice", "generated_from_trainer", "hi", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:04Z

--- license: apache-2.0 language: - hi tags: - automatic-speech-recognition - Harveenchadha/indic-voice - generated_from_trainer model-index: - name: Wav2Vec2_xls_r_openslr_Hi_V2 results: [] ---  # Wav2Vec2_xls_r_openslr_Hi_V2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [Harveenchadha/indic-voice](https://huggingface.co/datasets/Harveenchadha/indic-voice) dataset. It achieves the following results on the evaluation set: - Loss: 0.3184 - Wer: 0.3104 - Cer: 0.0958 ## Model description More information needed ## Intended uses & 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |:-------------:|:-----:|:----:|:------:|:---------------:|:------:| | 7.1097 | 0.48 | 300 | 0.9965 | 3.3989 | 1.0 | | 3.0235 | 0.96 | 600 | 0.3163 | 1.3183 | 0.7977 | | 1.1419 | 1.44 | 900 | 0.1913 | 0.6416 | 0.5543 | | 0.8242 | 1.92 | 1200 | 0.1608 | 0.5063 | 0.4804 | | 0.6876 | 2.56 | 1600 | 0.1387 | 0.4401 | 0.4280 | | 0.5868 | 3.21 | 2000 | 0.1249 | 0.3940 | 0.3907 | | 0.5285 | 3.85 | 2400 | 0.1200 | 0.3661 | 0.3763 | | 0.5 | 4.49 | 2800 | 0.3528 | 0.3610 | 0.1136 | | 0.4538 | 5.13 | 3200 | 0.3403 | 0.3485 | 0.1086 | | 0.4165 | 5.77 | 3600 | 0.3335 | 0.3439 | 0.1062 | | 0.3989 | 6.41 | 4000 | 0.3264 | 0.3340 | 0.1036 | | 0.3679 | 7.05 | 4400 | 0.3256 | 0.3287 | 0.1013 | | 0.3517 | 7.69 | 4800 | 0.3212 | 0.3223 | 0.1002 | | 0.3357 | 8.33 | 5200 | 0.3173 | 0.3196 | 0.0986 | | 0.3225 | 8.97 | 5600 | 0.3142 | 0.3177 | 0.0985 | | 0.3057 | 9.62 | 6000 | 0.3199 | 0.3156 | 0.0975 | | 0.2972 | 10.26 | 6400 | 0.3139 | 0.3128 | 0.0967 | | 0.2881 | 10.9 | 6800 | 0.3184 | 0.3107 | 0.0957 | | 0.2791 | 11.54 | 7200 | 0.3184 | 0.3104 | 0.0958 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

Ayham/xlnet_distilgpt2_summarization_cnn_dailymail

Ayham

2022-02-04T06:33:59Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "encoder-decoder", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-02T23:29:04Z

--- tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: xlnet_distilgpt2_summarization_cnn_dailymail results: [] ---  # xlnet_distilgpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

jonc/distilbert-base-uncased-finetuned-emotion

jonc

2022-02-04T06:15:55Z

4

0

transformers

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

text-classification

2022-03-02T23:29:05Z

--- 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: 0.9230733583303665 ---  # 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: 0.2159 - Accuracy: 0.923 - F1: 0.9231 ## Model description More information needed ## Intended uses & 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8494 | 1.0 | 250 | 0.3134 | 0.907 | 0.9051 | | 0.2504 | 2.0 | 500 | 0.2159 | 0.923 | 0.9231 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

Mapcar/pegasus-samsum

Mapcar

2022-02-04T03:27:33Z

6

0

transformers

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

text2text-generation

2022-03-02T23:29:04Z

--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum results: [] ---  # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4844 ## Model description More information needed ## Intended uses & 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: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.6936 | 0.54 | 500 | 1.4844 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

ghofrani/common7

ghofrani

2022-02-04T01:32:24Z

5

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "fa", "dataset:common_voice", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:05Z

--- language: - fa tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer datasets: - common_voice model-index: - name: common7 results: [] ---  # common7 This model is a fine-tuned version of [common7/checkpoint-18500](https://huggingface.co/common7/checkpoint-18500) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - FA dataset. It achieves the following results on the evaluation set: - Loss: 0.3448 - Wer: 0.3478 ## Model description More information needed ## Intended uses & 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: 32 - eval_batch_size: 16 - 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_steps: 100 - num_epochs: 150.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 2.957 | 3.29 | 500 | 2.9503 | 1.0 | | 1.7225 | 6.58 | 1000 | 0.8860 | 0.7703 | | 1.4907 | 9.86 | 1500 | 0.6555 | 0.6673 | | 1.4177 | 13.16 | 2000 | 0.5784 | 0.6076 | | 1.3425 | 16.45 | 2500 | 0.5379 | 0.5718 | | 1.33 | 19.73 | 3000 | 0.4962 | 0.5245 | | 1.4378 | 23.03 | 3500 | 0.4699 | 0.5098 | | 1.1894 | 26.31 | 4000 | 0.4527 | 0.4848 | | 1.1844 | 29.6 | 4500 | 0.4309 | 0.4651 | | 1.1795 | 32.89 | 5000 | 0.4131 | 0.4524 | | 1.1471 | 36.18 | 5500 | 0.4052 | 0.4435 | | 1.1337 | 39.47 | 6000 | 0.3927 | 0.4363 | | 1.1896 | 42.76 | 6500 | 0.3811 | 0.4254 | | 1.1847 | 46.05 | 7000 | 0.3855 | 0.4129 | | 0.9954 | 49.34 | 7500 | 0.3729 | 0.3981 | | 1.0293 | 52.63 | 8000 | 0.3637 | 0.4014 | | 1.0224 | 55.92 | 8500 | 0.3578 | 0.3885 | | 1.012 | 59.21 | 9000 | 0.3629 | 0.3930 | | 1.0772 | 62.5 | 9500 | 0.3635 | 0.3906 | | 1.0344 | 65.79 | 10000 | 0.3469 | 0.3771 | | 0.9457 | 69.08 | 10500 | 0.3435 | 0.3735 | | 0.9307 | 72.37 | 11000 | 0.3519 | 0.3762 | | 0.9523 | 75.65 | 11500 | 0.3443 | 0.3666 | | 0.9523 | 78.94 | 12000 | 0.3502 | 0.3757 | | 0.9475 | 82.24 | 12500 | 0.3509 | 0.3643 | | 0.9971 | 85.52 | 13000 | 0.3502 | 0.3626 | | 0.9058 | 88.81 | 13500 | 0.3472 | 0.3605 | | 0.8922 | 92.1 | 14000 | 0.3530 | 0.3618 | | 0.9 | 95.39 | 14500 | 0.3500 | 0.3574 | | 0.9051 | 98.68 | 15000 | 0.3456 | 0.3535 | | 0.9304 | 101.97 | 15500 | 0.3438 | 0.3578 | | 0.9433 | 105.26 | 16000 | 0.3396 | 0.3530 | | 0.8988 | 108.55 | 16500 | 0.3436 | 0.3539 | | 0.8789 | 111.84 | 17000 | 0.3426 | 0.3516 | | 0.8667 | 115.13 | 17500 | 0.3438 | 0.3506 | | 0.8895 | 118.42 | 18000 | 0.3434 | 0.3503 | | 0.8888 | 121.71 | 18500 | 0.3425 | 0.3494 | | 0.9453 | 125.0 | 19000 | 0.3415 | 0.3480 | | 0.9267 | 128.29 | 19500 | 0.3477 | 0.3503 | | 0.8315 | 131.58 | 20000 | 0.3476 | 0.3505 | | 0.8542 | 134.86 | 20500 | 0.3475 | 0.3506 | | 0.8478 | 138.16 | 21000 | 0.3430 | 0.3481 | | 0.8643 | 141.45 | 21500 | 0.3451 | 0.3485 | | 0.8705 | 144.73 | 22000 | 0.3444 | 0.3474 | | 0.9869 | 148.03 | 22500 | 0.3441 | 0.3493 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2 - Datasets 1.18.3.dev0 - Tokenizers 0.10.3

edugp/data2vec-nlp-base

edugp

2022-02-03T23:23:15Z

8

0

transformers

[ "transformers", "pytorch", "data2vec", "fill-mask", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: model-index: - name: data2vec-nlp-base results: [] --- # Data2Vec NLP Base This model was converted from `fairseq`. The original weights can be found in https://dl.fbaipublicfiles.com/fairseq/data2vec/nlp_base.pt Example usage: ```python from transformers import RobertaTokenizer, Data2VecForSequenceClassification, Data2VecConfig import torch tokenizer = RobertaTokenizer.from_pretrained("roberta-large") config = Data2VecConfig.from_pretrained("edugp/data2vec-nlp-base") model = Data2VecForSequenceClassification.from_pretrained("edugp/data2vec-nlp-base", config=config) # Fine-tune this model inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) prediction_logits = outputs.logits ```

ArBert/albert-base-v2-finetuned-ner

ArBert

2022-02-03T14:26:33Z

22

4

transformers

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

token-classification

2022-03-02T23:29:04Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: albert-base-v2-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9301181102362205 - name: Recall type: recall value: 0.9376033513394334 - name: F1 type: f1 value: 0.9338457315399397 - name: Accuracy type: accuracy value: 0.9851613086447802 ---  # albert-base-v2-finetuned-ner This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0700 - Precision: 0.9301 - Recall: 0.9376 - F1: 0.9338 - Accuracy: 0.9852 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.096 | 1.0 | 1756 | 0.0752 | 0.9163 | 0.9201 | 0.9182 | 0.9811 | | 0.0481 | 2.0 | 3512 | 0.0761 | 0.9169 | 0.9293 | 0.9231 | 0.9830 | | 0.0251 | 3.0 | 5268 | 0.0700 | 0.9301 | 0.9376 | 0.9338 | 0.9852 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.1 - Datasets 1.17.0 - Tokenizers 0.10.3

anuragshas/wav2vec2-xls-r-300m-pa-IN-cv8-with-lm

anuragshas

2022-02-03T12:28:34Z

5

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:05Z

--- language: - pa-IN license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer datasets: - common_voice model-index: - name: '' results: [] ---  # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PA-IN dataset. It achieves the following results on the evaluation set: - Loss: 0.6864 - Wer: 0.6707 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 200.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 4.3322 | 14.81 | 400 | 3.7450 | 1.0 | | 3.2662 | 29.63 | 800 | 3.2571 | 0.9996 | | 1.6408 | 44.44 | 1200 | 0.9098 | 0.8162 | | 1.2289 | 59.26 | 1600 | 0.6757 | 0.7099 | | 1.0551 | 74.07 | 2000 | 0.6417 | 0.7044 | | 0.966 | 88.89 | 2400 | 0.6365 | 0.6789 | | 0.8713 | 103.7 | 2800 | 0.6617 | 0.6954 | | 0.8055 | 118.52 | 3200 | 0.6371 | 0.6762 | | 0.7489 | 133.33 | 3600 | 0.6798 | 0.6911 | | 0.7073 | 148.15 | 4000 | 0.6567 | 0.6731 | | 0.6609 | 162.96 | 4400 | 0.6742 | 0.6840 | | 0.6435 | 177.78 | 4800 | 0.6862 | 0.6633 | | 0.6282 | 192.59 | 5200 | 0.6865 | 0.6731 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.4.dev0 - Tokenizers 0.11.0

Baybars/wav2vec2-xls-r-1b-turkish

Baybars

2022-02-03T10:09:31Z

17

1

transformers

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

automatic-speech-recognition

2022-03-02T23:29:04Z

--- language: - tr tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: '' results: [] ---  # This model is a fine-tuned version of [./checkpoint-10500](https://huggingface.co/./checkpoint-10500) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.7540 - Wer: 0.4647 - Cer: 0.1318 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.999,0.9999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 120.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |:-------------:|:------:|:-----:|:------:|:---------------:|:------:| | 1.0779 | 4.59 | 500 | 0.2354 | 0.8260 | 0.7395 | | 0.7573 | 9.17 | 1000 | 0.2100 | 0.7544 | 0.6960 | | 0.8225 | 13.76 | 1500 | 0.2021 | 0.6867 | 0.6672 | | 0.621 | 18.35 | 2000 | 0.1874 | 0.6824 | 0.6209 | | 0.6362 | 22.94 | 2500 | 0.1904 | 0.6712 | 0.6286 | | 0.624 | 27.52 | 3000 | 0.1820 | 0.6940 | 0.6116 | | 0.4781 | 32.11 | 3500 | 0.1735 | 0.6966 | 0.5989 | | 0.5685 | 36.7 | 4000 | 0.1769 | 0.6742 | 0.5971 | | 0.4384 | 41.28 | 4500 | 0.1767 | 0.6904 | 0.5999 | | 0.5509 | 45.87 | 5000 | 0.1692 | 0.6734 | 0.5641 | | 0.3665 | 50.46 | 5500 | 0.1680 | 0.7018 | 0.5662 | | 0.3914 | 55.05 | 6000 | 0.1631 | 0.7121 | 0.5552 | | 0.2467 | 59.63 | 6500 | 0.1563 | 0.6657 | 0.5374 | | 0.2576 | 64.22 | 7000 | 0.1554 | 0.6920 | 0.5316 | | 0.2711 | 68.81 | 7500 | 0.1495 | 0.6900 | 0.5176 | | 0.2626 | 73.39 | 8000 | 0.1454 | 0.6843 | 0.5043 | | 0.1377 | 77.98 | 8500 | 0.1470 | 0.7383 | 0.5101 | | 0.2005 | 82.57 | 9000 | 0.1430 | 0.7228 | 0.5045 | | 0.1355 | 87.16 | 9500 | 0.1375 | 0.7231 | 0.4869 | | 0.0431 | 91.74 | 10000 | 0.1350 | 0.7397 | 0.4749 | | 0.0586 | 96.33 | 10500 | 0.1339 | 0.7360 | 0.4754 | | 0.0896 | 100.92 | 11000 | 0.7187 | 0.4885 | 0.1398 | | 0.183 | 105.5 | 11500 | 0.7310 | 0.4838 | 0.1392 | | 0.0963 | 110.09 | 12000 | 0.7643 | 0.4759 | 0.1362 | | 0.0437 | 114.68 | 12500 | 0.7525 | 0.4641 | 0.1328 | | 0.1122 | 119.27 | 13000 | 0.7535 | 0.4651 | 0.1317 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

Hetarth/marian-finetuned-hi-hinglish

Hetarth

2022-02-03T09:54:31Z

8

0

transformers

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

text2text-generation

2022-03-02T23:29:04Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: marian-finetuned-hi-hinglish results: [] ---  # marian-finetuned-hi-hinglish This model is a fine-tuned version of [Helsinki-NLP/opus-mt-hi-en](https://huggingface.co/Helsinki-NLP/opus-mt-hi-en) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.1869 - Validation Loss: 4.0607 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 279, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.1869 | 4.0607 | 0 | ### Framework versions - Transformers 4.16.2 - TensorFlow 2.7.0 - Datasets 1.18.3 - Tokenizers 0.11.0

Rajan/Nepali_Word2Vec

Rajan

2022-02-03T08:32:41Z

0

null

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

null

2022-03-02T23:29:04Z

--- license: mit --- https://github.com/R4j4n/Nepali-Word2Vec-from-scratch How to clone : ``` git lfs install git clone https://huggingface.co/Rajan/Nepali_Word2Vec ```

Atiqah/Atiqah

Atiqah

2022-02-03T07:04:44Z

0

null

[ "license:artistic-2.0", "region:us" ]

null

2022-03-02T23:29:04Z

--- license: artistic-2.0 ---

pritoms/distilroberta-base-YTTranscript23

pritoms

2022-02-03T05:52:25Z

5

0

transformers

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

fill-mask

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-YTTranscript23 results: [] ---  # distilroberta-base-YTTranscript23 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9258 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 70 | 2.9007 | | No log | 2.0 | 140 | 2.9651 | | No log | 3.0 | 210 | 2.9374 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

sunitha/distilbert-base-uncased-3feb-2022-finetuned-squad

sunitha

2022-02-03T05:06:27Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-3feb-2022-finetuned-squad results: [] ---  # distilbert-base-uncased-3feb-2022-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1470 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2276 | 1.0 | 5533 | 1.1641 | | 0.9614 | 2.0 | 11066 | 1.1225 | | 0.7769 | 3.0 | 16599 | 1.1470 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

pritoms/distilgpt2-YTTranscriptTrial2

pritoms

2022-02-03T04:46:19Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-YTTranscriptTrial2 results: [] ---  # distilgpt2-YTTranscriptTrial2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.8738 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 70 | 6.0027 | | No log | 2.0 | 140 | 5.9072 | | No log | 3.0 | 210 | 5.8738 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

huggingtweets/denyah_

huggingtweets

2022-02-03T01:43:56Z

4

0

transformers

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

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/denyah_/1643852632266/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/1484264819049959425/siOsFP3t_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">Den</div> <div style="text-align: center; font-size: 14px;">@denyah_</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 Den. | Data | Den | | --- | --- | | Tweets downloaded | 3244 | | Retweets | 464 | | Short tweets | 795 | | Tweets kept | 1985 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3e5c08gr/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 @denyah_'s tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1438ocp8) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1438ocp8/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/denyah_') 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)

Plim/xls-r-300m-lm-fr

Plim

2022-02-02T23:29:54Z

5

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "fr", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:04Z

--- language: - fr tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer model-index: - name: '' results: [] ---  # This model is a fine-tuned version of [./checkpoint-6000](https://huggingface.co/./checkpoint-6000) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - FR dataset. It achieves the following results on the evaluation set: - Loss: 0.2619 - Wer: 0.2457 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.495 | 0.16 | 500 | 3.3883 | 1.0 | | 2.9095 | 0.32 | 1000 | 2.9152 | 1.0000 | | 1.8434 | 0.49 | 1500 | 1.0473 | 0.7446 | | 1.4298 | 0.65 | 2000 | 0.5729 | 0.5130 | | 1.1937 | 0.81 | 2500 | 0.3795 | 0.3450 | | 1.1248 | 0.97 | 3000 | 0.3321 | 0.3052 | | 1.0835 | 1.13 | 3500 | 0.3038 | 0.2805 | | 1.0479 | 1.3 | 4000 | 0.2910 | 0.2689 | | 1.0413 | 1.46 | 4500 | 0.2798 | 0.2593 | | 1.014 | 1.62 | 5000 | 0.2727 | 0.2512 | | 1.004 | 1.78 | 5500 | 0.2646 | 0.2471 | | 0.9949 | 1.94 | 6000 | 0.2619 | 0.2457 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

cahya/wav2vec2-base-turkish-artificial

cahya

2022-02-02T15:44:36Z

5

1

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning-week", "tr", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:05Z

--- language: tr datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Wav2Vec2 Base Turkish with Artificial Voices by Cahya results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice tr type: common_voice args: tr metrics: - name: Test WER type: wer value: 57.60 --- # Wav2Vec2-Large-XLSR-Turkish Fine-tuned [ceyda/wav2vec2-base-760](https://huggingface.co/ceyda/wav2vec2-base-760) on the [Turkish Artificial Common Voice dataset](https://cloud.uncool.ai/index.php/f/2165181). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "tr", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial") # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset[:2]["sentence"]) ``` ## Evaluation The model can be evaluated as follows on the Turkish test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "tr", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]' # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 57.60 % ## Training The Artificial Common Voice `train`, `validation` is used to fine tune the model The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)

arjuntheprogrammer/distilbert-base-multilingual-cased-sentiment-2

arjuntheprogrammer

2022-02-02T15:16:39Z

35

0

transformers

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

text-classification

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy - f1 model-index: - name: distilbert-base-multilingual-cased-sentiment-2 results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi args: en metrics: - name: Accuracy type: accuracy value: 0.7614 - name: F1 type: f1 value: 0.7614 ---  # distilbert-base-multilingual-cased-sentiment-2 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.5882 - Accuracy: 0.7614 - F1: 0.7614 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00024 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - distributed_type: sagemaker_data_parallel - num_devices: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - 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 ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.15.1 - Tokenizers 0.10.3

kmfoda/staging-pegasus-gmeetsamsum

kmfoda

2022-02-02T14:34:58Z

12

0

transformers

[ "transformers", "pytorch", "pegasus", "feature-extraction", "summarization", "en", "arxiv:1912.08777", "endpoints_compatible", "region:us" ]

summarization

2022-03-02T23:29:05Z

--- language: en tags: - summarization --- ### Pegasus Models See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html) Original TF 1 code [here](https://github.com/google-research/pegasus) Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019 Maintained by: [@sshleifer](https://twitter.com/sam_shleifer) Task: Summarization The following is copied from the authors' README. # Mixed & Stochastic Checkpoints We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table. | dataset | C4 | HugeNews | Mixed & Stochastic| | ---- | ---- | ---- | ----| | xsum | 45.20/22.06/36.99 | 47.21/24.56/39.25 | 47.60/24.83/39.64| | cnn_dailymail | 43.90/21.20/40.76 | 44.17/21.47/41.11 | 44.16/21.56/41.30| | newsroom | 45.07/33.39/41.28 | 45.15/33.51/41.33 | 45.98/34.20/42.18| | multi_news | 46.74/17.95/24.26 | 47.52/18.72/24.91 | 47.65/18.75/24.95| | gigaword | 38.75/19.96/36.14 | 39.12/19.86/36.24 | 39.65/20.47/36.76| | wikihow | 43.07/19.70/34.79 | 41.35/18.51/33.42 | 46.39/22.12/38.41 *| | reddit_tifu | 26.54/8.94/21.64 | 26.63/9.01/21.60 | 27.99/9.81/22.94| | big_patent | 53.63/33.16/42.25 | 53.41/32.89/42.07 | 52.29/33.08/41.66 *| | arxiv | 44.70/17.27/25.80 | 44.67/17.18/25.73 | 44.21/16.95/25.67| | pubmed | 45.49/19.90/27.69 | 45.09/19.56/27.42 | 45.97/20.15/28.25| | aeslc | 37.69/21.85/36.84 | 37.40/21.22/36.45 | 37.68/21.25/36.51| | billsum | 57.20/39.56/45.80 | 57.31/40.19/45.82 | 59.67/41.58/47.59| The "Mixed & Stochastic" model has the following changes: - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). - the model uniformly sample a gap sentence ratio between 15% and 45%. - importance sentences are sampled using a 20% uniform noise to importance scores. - the sentencepiece tokenizer is updated to be able to encode newline character. (*) the numbers of wikihow and big_patent datasets are not comparable because of change in tokenization and data: - wikihow dataset contains newline characters which is useful for paragraph segmentation, the C4 and HugeNews model's sentencepiece tokenizer doesn't encode newline and loose this information. - we update the BigPatent dataset to preserve casing, some format cleanings are also changed, please refer to change in TFDS. The "Mixed & Stochastic" model has the following changes (from pegasus-large in the paper): trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). the model uniformly sample a gap sentence ratio between 15% and 45%. importance sentences are sampled using a 20% uniform noise to importance scores. the sentencepiece tokenizer is updated to be able to encode newline character. Citation ``` @misc{zhang2019pegasus, title={PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization}, author={Jingqing Zhang and Yao Zhao and Mohammad Saleh and Peter J. Liu}, year={2019}, eprint={1912.08777}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

shaina/covid_qa_mpnet

shaina

2022-02-02T14:33:18Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "mpnet", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]

question-answering

2022-03-02T23:29:05Z

--- tags: - generated_from_trainer widget: - text: "What is COVID-19?" context: "Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019.[7] The disease has since spread worldwide, leading to an ongoing pandemic." - text: "Where was COVID-19 first discovered?" context: "The first known infections from SARS-CoV-2 were discovered in Wuhan, China. The original source of viral transmission to humans remains unclear, as does whether the virus became pathogenic before or after the spillover event." - text: "What is Post-COVID syndrome?" context: "Long COVID, also known as post-COVID-19 syndrome, post-acute sequelae of COVID-19 (PASC), or chronic COVID syndrome (CCS) is a condition characterized by long-term sequelae appearing or persisting after the typical convalescence period of COVID-19. Long COVID can affect nearly every organ system, with sequelae including respiratory system disorders, nervous system and neurocognitive disorders, mental health disorders, metabolic disorders, cardiovascular disorders, gastrointestinal disorders, malaise, fatigue, musculoskeletal pain, and anemia. A wide range of symptoms are commonly reported, including fatigue, headaches, shortness of breath, anosmia (loss of smell), parosmia (distorted smell), muscle weakness, low fever and cognitive dysfunction." --- # covid_qa_mpnet This model is a fine-tuned version of [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on our COVID-19 dataset. It achieves the following results on the evaluation set: - Loss: 0.1352 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.2477 | 1.0 | 3895 | 0.1869 | | 0.1838 | 2.0 | 7790 | 0.1352 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

NbAiLab/wav2vec2-xlsr-300M-NPSC-OH

NbAiLab

2022-02-02T06:10:42Z

8

0

transformers

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

automatic-speech-recognition

2022-03-02T23:29:04Z

--- license: apache-2.0 tags: - automatic-speech-recognition - NbAiLab/NPSC - generated_from_trainer model-index: - name: wav2vec2-xlsr-300M-NPSC-OH results: [] ---  # wav2vec2-xlsr-300M-NPSC-OH This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set: - Loss: 0.1692 - Wer: 0.1663 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 15.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.1638 | 0.66 | 500 | 3.0686 | 1.0 | | 2.9311 | 1.31 | 1000 | 2.9208 | 1.0 | | 2.4175 | 1.97 | 1500 | 1.5009 | 0.9049 | | 1.4442 | 2.63 | 2000 | 0.4426 | 0.3783 | | 1.2624 | 3.28 | 2500 | 0.3193 | 0.2998 | | 1.1889 | 3.94 | 3000 | 0.2867 | 0.2630 | | 1.1315 | 4.6 | 3500 | 0.2566 | 0.2444 | | 1.0864 | 5.26 | 4000 | 0.2368 | 0.2294 | | 1.093 | 5.91 | 4500 | 0.2240 | 0.2151 | | 1.0368 | 6.57 | 5000 | 0.2117 | 0.2056 | | 1.0178 | 7.23 | 5500 | 0.2020 | 0.1954 | | 1.0035 | 7.88 | 6000 | 0.2005 | 0.1924 | | 0.9759 | 8.54 | 6500 | 0.1971 | 0.1863 | | 0.9795 | 9.2 | 7000 | 0.1892 | 0.1812 | | 0.9601 | 9.85 | 7500 | 0.1863 | 0.1795 | | 0.9673 | 10.51 | 8000 | 0.1809 | 0.1761 | | 0.9233 | 11.17 | 8500 | 0.1818 | 0.1755 | | 0.9382 | 11.83 | 9000 | 0.1767 | 0.1741 | | 0.9242 | 12.48 | 9500 | 0.1743 | 0.1703 | | 0.9703 | 13.14 | 10000 | 0.1711 | 0.1711 | | 0.9139 | 13.8 | 10500 | 0.1718 | 0.1672 | | 0.9073 | 14.45 | 11000 | 0.1700 | 0.1665 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

navsad/navid_test_bert

navsad

2022-02-02T04:52:11Z

4

0

transformers

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

text-classification

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: navid_test_bert results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5834463254140851 ---  # navid_test_bert This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8149 - Matthews Correlation: 0.5834 ## Model description More information needed ## Intended uses & 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 | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4598 | 1.0 | 1069 | 0.4919 | 0.5314 | | 0.3228 | 2.0 | 2138 | 0.6362 | 0.5701 | | 0.17 | 3.0 | 3207 | 0.8149 | 0.5834 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

BigSalmon/InfillFormalLincoln

BigSalmon

2022-02-02T03:45:03Z

10

0

transformers

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

text-generation

2022-03-02T23:29:04Z

Informal to Formal: ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InfillFormalLincoln") model = AutoModelWithLMHead.from_pretrained("BigSalmon/InfillFormalLincoln") ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space changes over time) ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time) ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2Space (The model for this space changes over time) ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ```` ``` infill: increasing the number of sidewalks in suburban areas will [MASK]. Translated into the Style of Abraham Lincoln: increasing the number of sidewalks in suburban areas will ( ( enhance / maximize ) community cohesion / facilitate ( communal ties / the formation of neighborhood camaraderie ) / forge neighborly relations / lend themselves to the advancement of neighborly ties / plant the seeds of community building / flower anew the bonds of friendship / invite the budding of neighborhood rapport / enrich neighborhood life ). infill: corn fields [MASK], [MASK] visibly as one ventures beyond chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), ( manifesting themselves ) visibly as one ventures beyond chicago. infill: the [MASK] the SAT will soon be [MASK]. [MASK] an examination undertaken on one's laptop. [MASK] will allow students to retrieve test results promptly. Translated into the Style of Abraham Lincoln: the ( conventional form of ) the SAT will soon be ( consigned to history ). ( replacing it will be ) an examination undertaken on one's laptop. ( so doing ) will allow students to retrieve test results promptly. infill: ```

pdroberts/distilbert-base-uncased-finetuned-emotion

pdroberts

2022-02-01T23:48:03Z

3

0

transformers

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

text-classification

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] ---  # 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. ## Model description More information needed ## Intended uses & 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 ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.2+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

mattmcclean/distilbert-base-uncased-finetuned-emotion

mattmcclean

2022-02-01T19:48:01Z

6

0

transformers

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

text-classification

2022-03-02T23:29:05Z

--- 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: 0.925 - name: F1 type: f1 value: 0.9252235175634111 ---  # 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: 0.2173 - Accuracy: 0.925 - F1: 0.9252 ## Model description More information needed ## Intended uses & 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.825 | 1.0 | 250 | 0.2925 | 0.915 | 0.9134 | | 0.2444 | 2.0 | 500 | 0.2173 | 0.925 | 0.9252 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0 - Datasets 1.16.1 - Tokenizers 0.10.3

naleraphael/rasr_sample

naleraphael

2022-02-01T18:18:16Z

4

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:05Z

--- language: - sv-SE license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer datasets: - common_voice model-index: - name: rasr_sample results: [] ---  # rasr_sample This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 0.3147 - Wer: 0.2676 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 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: 2000 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.3332 | 1.45 | 500 | 3.3031 | 1.0 | | 2.9272 | 2.91 | 1000 | 2.9353 | 0.9970 | | 2.0736 | 4.36 | 1500 | 1.1565 | 0.8714 | | 1.7339 | 5.81 | 2000 | 0.7156 | 0.6688 | | 1.5989 | 7.27 | 2500 | 0.5791 | 0.5519 | | 1.4916 | 8.72 | 3000 | 0.5038 | 0.5169 | | 1.4562 | 10.17 | 3500 | 0.4861 | 0.4805 | | 1.3893 | 11.63 | 4000 | 0.4584 | 0.4761 | | 1.3797 | 13.08 | 4500 | 0.4298 | 0.4686 | | 1.3508 | 14.53 | 5000 | 0.4138 | 0.3744 | | 1.3165 | 15.99 | 5500 | 0.4015 | 0.3578 | | 1.281 | 17.44 | 6000 | 0.3883 | 0.3472 | | 1.2682 | 18.89 | 6500 | 0.3904 | 0.3434 | | 1.2477 | 20.35 | 7000 | 0.3726 | 0.3321 | | 1.2364 | 21.8 | 7500 | 0.3685 | 0.3281 | | 1.2041 | 23.26 | 8000 | 0.3597 | 0.3194 | | 1.1901 | 24.71 | 8500 | 0.3542 | 0.3203 | | 1.1903 | 26.16 | 9000 | 0.3500 | 0.3138 | | 1.1677 | 27.61 | 9500 | 0.3458 | 0.3067 | | 1.1718 | 29.07 | 10000 | 0.3595 | 0.3112 | | 1.1562 | 30.52 | 10500 | 0.3433 | 0.3022 | | 1.1392 | 31.97 | 11000 | 0.3440 | 0.2936 | | 1.1258 | 33.43 | 11500 | 0.3396 | 0.2950 | | 1.1067 | 34.88 | 12000 | 0.3379 | 0.2939 | | 1.0953 | 36.34 | 12500 | 0.3370 | 0.2868 | | 1.0835 | 37.79 | 13000 | 0.3317 | 0.2860 | | 1.0772 | 39.24 | 13500 | 0.3302 | 0.2854 | | 1.0853 | 40.7 | 14000 | 0.3265 | 0.2783 | | 1.0689 | 42.15 | 14500 | 0.3306 | 0.2770 | | 1.0394 | 43.6 | 15000 | 0.3233 | 0.2757 | | 1.0581 | 45.06 | 15500 | 0.3199 | 0.2713 | | 1.0362 | 46.51 | 16000 | 0.3154 | 0.2683 | | 1.0406 | 47.96 | 16500 | 0.3176 | 0.2688 | | 1.0082 | 49.42 | 17000 | 0.3149 | 0.2679 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

cahya/output

cahya

2022-02-01T15:40:45Z

8

0

transformers

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

automatic-speech-recognition

2022-03-02T23:29:05Z

--- language: - tr license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: output results: [] ---  # output This model is a fine-tuned version of [cahya/wav2vec2-base-turkish-artificial-cv](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial-cv) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.1822 - Wer: 0.1423 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-07 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.18.2 - Tokenizers 0.10.3

moussaKam/frugalscore_tiny_roberta_bert-score

moussaKam

2022-02-01T10:50:57Z

6

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "arxiv:2110.08559", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:05Z

# FrugalScore FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance Paper: https://arxiv.org/abs/2110.08559?context=cs Project github: https://github.com/moussaKam/FrugalScore The pretrained checkpoints presented in the paper : | FrugalScore | Student | Teacher | Method | |----------------------------------------------------|-------------|----------------|------------| | [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore | | [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore | | [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore | | [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore | | [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore | | [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore | | [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore | | [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore | | [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore | | [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore | | [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore | | [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore |

moussaKam/frugalscore_medium_bert-base_bert-score

moussaKam

2022-02-01T10:50:43Z

12

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "arxiv:2110.08559", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:05Z

# FrugalScore FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance Paper: https://arxiv.org/abs/2110.08559?context=cs Project github: https://github.com/moussaKam/FrugalScore The pretrained checkpoints presented in the paper : | FrugalScore | Student | Teacher | Method | |----------------------------------------------------|-------------|----------------|------------| | [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore | | [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore | | [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore | | [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore | | [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore | | [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore | | [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore | | [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore | | [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore | | [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore | | [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore | | [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore |

moussaKam/frugalscore_small_bert-base_bert-score

moussaKam

2022-02-01T10:50:31Z

5

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "arxiv:2110.08559", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:05Z

# FrugalScore FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance Paper: https://arxiv.org/abs/2110.08559?context=cs Project github: https://github.com/moussaKam/FrugalScore The pretrained checkpoints presented in the paper : | FrugalScore | Student | Teacher | Method | |----------------------------------------------------|-------------|----------------|------------| | [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore | | [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore | | [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore | | [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore | | [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore | | [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore | | [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore | | [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore | | [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore | | [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore | | [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore | | [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore |

moussaKam/frugalscore_tiny_bert-base_bert-score

moussaKam

2022-02-01T10:50:21Z

4,310

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "arxiv:2110.08559", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:05Z

# FrugalScore FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance Paper: https://arxiv.org/abs/2110.08559?context=cs Project github: https://github.com/moussaKam/FrugalScore The pretrained checkpoints presented in the paper : | FrugalScore | Student | Teacher | Method | |----------------------------------------------------|-------------|----------------|------------| | [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore | | [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore | | [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore | | [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore | | [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore | | [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore | | [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore | | [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore | | [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore | | [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore | | [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore | | [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore |

MaryaAI/opus-mt-en-ar-finetunedSTEM-v4-en-to-ar

MaryaAI

2022-02-01T08:51:38Z

4

0

transformers

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

text2text-generation

2022-03-02T23:29:04Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: MaryaAI/opus-mt-en-ar-finetunedSTEM-v4-en-to-ar results: [] ---  # MaryaAI/opus-mt-en-ar-finetunedSTEM-v4-en-to-ar This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.0589 - Validation Loss: 5.3227 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.0589 | 5.3227 | 0 | ### Framework versions - Transformers 4.17.0.dev0 - TensorFlow 2.7.0 - Datasets 1.18.3.dev0 - Tokenizers 0.10.3

vachonni/wav2vec2-large-xls-r-300m-dansk-CV-80

vachonni

2022-02-01T07:55:36Z

4

2

transformers

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

automatic-speech-recognition

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-dansk-CV-80 results: [] ---  # wav2vec2-large-xls-r-300m-dansk-CV-80 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for Danish, using the [mozilla-foundation/common_voice_8_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6394 - eval_wer: 0.3682 - eval_runtime: 104.0466 - eval_samples_per_second: 13.359 - eval_steps_per_second: 1.672 - epoch: 21.28 - step: 2000 ## Model description ASR Danish model ## Intended uses & limitations More information needed ## Training and evaluation data Danish subset of [mozilla-foundation/common_voice_8_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

huggingtweets/clamtime-madramami

huggingtweets

2022-02-01T07:09:05Z

3

0

transformers

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

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/clamtime-madramami/1643699341002/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/1486460616927858690/H_L_HiW-_400x400.jpg')"> </div> <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/1486839044906618880/x1Q9ED9b_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> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">clementine!!!! & riley, twink eliminator 🐾🏳️‍⚧️</div> <div style="text-align: center; font-size: 14px;">@clamtime-madramami</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 clementine!!!! & riley, twink eliminator 🐾🏳️‍⚧️. | Data | clementine!!!! | riley, twink eliminator 🐾🏳️‍⚧️ | | --- | --- | --- | | Tweets downloaded | 3239 | 3247 | | Retweets | 340 | 114 | | Short tweets | 872 | 607 | | Tweets kept | 2027 | 2526 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1lh3p7v6/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 @clamtime-madramami's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1gman3fy) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1gman3fy/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/clamtime-madramami') 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)

hady/wav2vec2-base-timit-demo-colab

hady

2022-02-01T07:01:28Z

4

0

transformers

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

automatic-speech-recognition

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] ---  # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

Priyajay/xls-r-ab-test

Priyajay

2022-02-01T04:29:17Z

9

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "common_voice", "generated_from_trainer", "hi", "dataset:common_voice", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:04Z

--- language: - hi tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: '' results: [] ---  # This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the COMMON_VOICE - HI dataset. It achieves the following results on the evaluation set: - Loss: 248.1278 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

jonfd/electra-small-is-no

jonfd

2022-01-31T23:41:45Z

8

0

transformers

[ "transformers", "pytorch", "tf", "electra", "pretraining", "is", "no", "dataset:igc", "dataset:ic3", "dataset:jonfd/ICC", "dataset:mc4", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

null

2022-03-02T23:29:05Z

--- language: - is - no license: cc-by-4.0 datasets: - igc - ic3 - jonfd/ICC - mc4 --- # Icelandic-Norwegian ELECTRA-Small This model was pretrained on the following corpora: * The [Icelandic Gigaword Corpus](http://igc.arnastofnun.is/) (IGC) * The Icelandic Common Crawl Corpus (IC3) * The [Icelandic Crawled Corpus](https://huggingface.co/datasets/jonfd/ICC) (ICC) * The [Multilingual Colossal Clean Crawled Corpus](https://huggingface.co/datasets/mc4) (mC4) - Icelandic and Norwegian text obtained from .is and .no domains, respectively The total size of the corpus after document-level deduplication and filtering was 7.41B tokens, split equally between the two languages. The model was trained using a WordPiece tokenizer with a vocabulary size of 64,105 for 1.1 million steps, and otherwise with default settings. # Acknowledgments This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC). This project was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by [Almannarómur](https://almannaromur.is/), is funded by the Icelandic Ministry of Education, Science and Culture.

philschmid/bert-mini-sst2-distilled

philschmid

2022-01-31T23:34:03Z

256

0

transformers

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

text-classification

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert-mini-sst2-distilled results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.856651376146789 ---  # bert-mini-sst2-distilled This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.1792 - Accuracy: 0.8567 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00021185586235152412 - train_batch_size: 1024 - eval_batch_size: 1024 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.1552 | 1.0 | 66 | 1.4847 | 0.8349 | | 0.8451 | 2.0 | 132 | 1.3495 | 0.8624 | | 0.5864 | 3.0 | 198 | 1.2257 | 0.8532 | | 0.4553 | 4.0 | 264 | 1.2571 | 0.8544 | | 0.3708 | 5.0 | 330 | 1.2132 | 0.8658 | | 0.3086 | 6.0 | 396 | 1.2370 | 0.8589 | | 0.2701 | 7.0 | 462 | 1.1900 | 0.8635 | | 0.246 | 8.0 | 528 | 1.1792 | 0.8567 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.15.1 - Tokenizers 0.10.3

paintingpeter/distilbert-base-uncased-finetuned-clinc

paintingpeter

2022-01-31T21:55:25Z

3

0

transformers

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

text-classification

2022-03-02T23:29:05Z

--- 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: 0.9174193548387096 ---  # 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: 0.7713 - Accuracy: 0.9174 ## Model description More information needed ## Intended uses & 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2892 | 1.0 | 318 | 3.2831 | 0.7426 | | 2.6244 | 2.0 | 636 | 1.8739 | 0.8335 | | 1.5442 | 3.0 | 954 | 1.1525 | 0.8926 | | 1.0096 | 4.0 | 1272 | 0.8569 | 0.91 | | 0.793 | 5.0 | 1590 | 0.7713 | 0.9174 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

glob-asr/wav2vec2-large-xls-r-300m-spanish-small

glob-asr

2022-01-31T20:58:46Z

4

0

transformers

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

automatic-speech-recognition

2022-03-02T23:29:05Z

--- tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-spanish-small results: [] ---  # wav2vec2-large-xls-r-300m-spanish-small This model is a fine-tuned version of [jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom](https://huggingface.co/jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3596 - Wer: 0.2105 ## Model description More information needed ## Intended uses & 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.1971 | 0.79 | 400 | 0.2169 | 0.2077 | | 0.2293 | 1.58 | 800 | 0.2507 | 0.2418 | | 0.2065 | 2.37 | 1200 | 0.2703 | 0.2459 | | 0.1842 | 3.16 | 1600 | 0.2716 | 0.2495 | | 0.1634 | 3.95 | 2000 | 0.2695 | 0.2510 | | 0.1443 | 4.74 | 2400 | 0.2754 | 0.2435 | | 0.1345 | 5.53 | 2800 | 0.3119 | 0.2654 | | 0.1267 | 6.32 | 3200 | 0.3154 | 0.2573 | | 0.1237 | 7.11 | 3600 | 0.3251 | 0.2666 | | 0.1118 | 7.91 | 4000 | 0.3139 | 0.2503 | | 0.1051 | 8.7 | 4400 | 0.3286 | 0.2573 | | 0.0964 | 9.49 | 4800 | 0.3348 | 0.2587 | | 0.0946 | 10.28 | 5200 | 0.3357 | 0.2587 | | 0.0897 | 11.07 | 5600 | 0.3408 | 0.2590 | | 0.0812 | 11.86 | 6000 | 0.3380 | 0.2560 | | 0.079 | 12.65 | 6400 | 0.3304 | 0.2415 | | 0.0753 | 13.44 | 6800 | 0.3557 | 0.2540 | | 0.0717 | 14.23 | 7200 | 0.3507 | 0.2519 | | 0.0691 | 15.02 | 7600 | 0.3554 | 0.2587 | | 0.0626 | 15.81 | 8000 | 0.3619 | 0.2520 | | 0.0661 | 16.6 | 8400 | 0.3609 | 0.2564 | | 0.0582 | 17.39 | 8800 | 0.3818 | 0.2520 | | 0.0556 | 18.18 | 9200 | 0.3685 | 0.2410 | | 0.0515 | 18.97 | 9600 | 0.3658 | 0.2367 | | 0.0478 | 19.76 | 10000 | 0.3701 | 0.2413 | | 0.0486 | 20.55 | 10400 | 0.3681 | 0.2371 | | 0.0468 | 21.34 | 10800 | 0.3607 | 0.2370 | | 0.0452 | 22.13 | 11200 | 0.3499 | 0.2286 | | 0.0399 | 22.92 | 11600 | 0.3647 | 0.2282 | | 0.0393 | 23.72 | 12000 | 0.3638 | 0.2255 | | 0.0381 | 24.51 | 12400 | 0.3359 | 0.2202 | | 0.0332 | 25.3 | 12800 | 0.3488 | 0.2177 | | 0.033 | 26.09 | 13200 | 0.3628 | 0.2175 | | 0.0311 | 26.88 | 13600 | 0.3695 | 0.2195 | | 0.0294 | 27.67 | 14000 | 0.3624 | 0.2164 | | 0.0281 | 28.46 | 14400 | 0.3688 | 0.2113 | | 0.0274 | 29.25 | 14800 | 0.3596 | 0.2105 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0

shaina/CoQUAD_MPNet

shaina

2022-01-31T18:22:46Z

0

null

[ "MPNet", "en", "license:apache-2.0", "region:us" ]

null

2022-03-02T23:29:05Z

--- language: en tags: - MPNet license: apache-2.0 dataset: - covid-19 --- # CoQUAD_MPNet : MPNet model for COVID-19 ## Introduction It is a state-of-the-art language model for MPNet for Covid-19 dataset with focus on post-covid. ## How to use for Deepset Haystack ```python # Load data from datasets import load_dataset dataset = load_dataset("shaina/covid19") # Haystack pipeline !sudo apt-get install git-lfs !git lfs install !git clone https://huggingface.co/shaina/CoQUAD_MPNet GIT_LFS_SKIP_SMUDGE=1 from haystack.nodes import ElasticsearchRetriever retriever = ElasticsearchRetriever(document_store=document_store) reader = FARMReader(model_name_or_path="/content/drive/MyDrive/CoQUAD_MPNet", use_gpu=True) from haystack.pipelines import ExtractiveQAPipeline pipe = ExtractiveQAPipeline(reader, retriever) prediction = pipe.run( query="What is post-COVID?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}} ) from pprint import pprint pprint(prediction) ``` --- ## Authors Shaina Raza ---

peter-explosion-ai/en_pipeline

peter-explosion-ai

2022-01-31T17:04:42Z

5

0

spacy

[ "spacy", "text-classification", "en", "region:us" ]

text-classification

2022-03-02T23:29:05Z

--- tags: - spacy - text-classification language: - en model-index: - name: en_pipeline results: [] --- | Feature | Description | | --- | --- | | **Name** | `en_pipeline` | | **Version** | `0.0.0` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `textcat` | | **Components** | `textcat` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | n/a | | **License** | n/a | | **Author** | [n/a]() | ### Label Scheme <details> <summary>View label scheme (2 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`textcat`** | `POSITIVE`, `NEGATIVE` | </details> ### Accuracy | Type | Score | | --- | --- | | `CATS_SCORE` | 55.70 | | `CATS_MICRO_P` | 58.65 | | `CATS_MICRO_R` | 58.65 | | `CATS_MICRO_F` | 58.65 | | `CATS_MACRO_P` | 61.88 | | `CATS_MACRO_R` | 58.69 | | `CATS_MACRO_F` | 55.70 | | `CATS_MACRO_AUC` | 63.53 | | `CATS_MACRO_AUC_PER_TYPE` | 0.00 | | `TEXTCAT_LOSS` | 3.74 |

osanseviero/test_meta

osanseviero

2022-01-31T15:21:09Z

0

spacy

[ "spacy", "token-classification", "license:lgpl-lr", "region:us" ]

token-classification

2022-03-02T23:29:05Z

--- tags: - spacy - token-classification languages: - fr license: lgpl-lr other-thing: test ---

huggingtweets/tks

huggingtweets

2022-01-31T10:20:15Z

0

null

[ "huggingtweets", "en", "region:us" ]

null

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/tks/1643624411056/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/1044664291050344449/vKKJxtBF_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">高須正和@NT深圳コミュニティ/TAKASU@NT Shenzhen</div> <div style="text-align: center; font-size: 14px;">@tks</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 高須正和@NT深圳コミュニティ/TAKASU@NT Shenzhen. | Data | 高須正和@NT深圳コミュニティ/TAKASU@NT Shenzhen | | --- | --- | | Tweets downloaded | 3248 | | Retweets | 1831 | | Short tweets | 825 | | Tweets kept | 592 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1lg0mgsp/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 @tks's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/j1ak5d5p) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/j1ak5d5p/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/tks') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/goando-tsuchinao83-za09313103

huggingtweets

2022-01-31T09:56:33Z

0

null

[ "huggingtweets", "en", "region:us" ]

null

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/goando-tsuchinao83-za09313103/1643622988627/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/715665333218979842/fLLzpFee_400x400.jpg')"> </div> <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/1145832571214815232/KYNcOP04_400x400.jpg')"> </div> <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/1281544202627674112/zglo72WL_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">土屋尚史 / Goodpatch & Go Ando / PREDUCTS / THE GUILD & shun nozaki / Goodpatch</div> <div style="text-align: center; font-size: 14px;">@goando-tsuchinao83-za09313103</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 土屋尚史 / Goodpatch & Go Ando / PREDUCTS / THE GUILD & shun nozaki / Goodpatch. | Data | 土屋尚史 / Goodpatch | Go Ando / PREDUCTS / THE GUILD | shun nozaki / Goodpatch | | --- | --- | --- | --- | | Tweets downloaded | 3236 | 3250 | 798 | | Retweets | 1577 | 97 | 34 | | Short tweets | 914 | 1729 | 458 | | Tweets kept | 745 | 1424 | 306 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/31bsh75f/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 @goando-tsuchinao83-za09313103's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/26i8c30r) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/26i8c30r/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/goando-tsuchinao83-za09313103') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/ggreenwald

huggingtweets

2022-01-31T09:49:22Z

5

0

transformers

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

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/ggreenwald/1643622558420/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/1092582027994509312/cpYWuYI9_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">Glenn Greenwald</div> <div style="text-align: center; font-size: 14px;">@ggreenwald</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 Glenn Greenwald. | Data | Glenn Greenwald | | --- | --- | | Tweets downloaded | 3248 | | Retweets | 324 | | Short tweets | 160 | | Tweets kept | 2764 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/y433olp5/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 @ggreenwald's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/duljho5y) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/duljho5y/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/ggreenwald') 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)

TajMahaladeen/pokemon_gptj

TajMahaladeen

2022-01-31T06:12:31Z

9

0

transformers

[ "transformers", "pytorch", "gptj", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-03-02T23:29:05Z

--- license: apache-2.0 ---

NbAiLab/xls-r-1b-npsc

NbAiLab

2022-01-31T04:33:39Z

4

0

transformers

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

automatic-speech-recognition

2022-03-02T23:29:04Z

--- license: apache-2.0 ---

huggingtweets/alphaxchange-coinmarketcap-techcrunch

huggingtweets

2022-01-31T01:31:27Z

4

1

transformers

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

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/alphaxchange-coinmarketcap-techcrunch/1643592683390/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/1475337078544248835/JRWM0Hsl_400x400.jpg')"> </div> <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/1096066608034918401/m8wnTWsX_400x400.png')"> </div> <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/1469027897209987081/fCdlufKH_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">CoinMarketCap & TechCrunch & AlphaExchange</div> <div style="text-align: center; font-size: 14px;">@alphaxchange-coinmarketcap-techcrunch</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 CoinMarketCap & TechCrunch & AlphaExchange. | Data | CoinMarketCap | TechCrunch | AlphaExchange | | --- | --- | --- | --- | | Tweets downloaded | 3249 | 3250 | 185 | | Retweets | 247 | 29 | 25 | | Short tweets | 209 | 9 | 17 | | Tweets kept | 2793 | 3212 | 143 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ii2008f/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 @alphaxchange-coinmarketcap-techcrunch's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/28z1wzo5) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/28z1wzo5/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/alphaxchange-coinmarketcap-techcrunch') 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)

eldor-97/MarianMix_en-10

eldor-97

2022-01-30T23:25:27Z

8

0

transformers

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

text2text-generation

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: MarianMix_en-10 results: [] ---  # MarianMix_en-10 This model is a fine-tuned version of [Helsinki-NLP/opus-tatoeba-en-ja](https://huggingface.co/Helsinki-NLP/opus-tatoeba-en-ja) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0752 - Bleu: 14.601 - Gen Len: 45.8087 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 99 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:--------:| | 2.1136 | 0.44 | 500 | 2.0044 | 0.2655 | 109.0201 | | 1.1422 | 0.89 | 1000 | 1.7516 | 1.4123 | 71.0 | | 0.9666 | 1.33 | 1500 | 1.5219 | 3.6611 | 64.6888 | | 0.8725 | 1.78 | 2000 | 1.3606 | 4.6539 | 77.1641 | | 0.7655 | 2.22 | 2500 | 1.2586 | 8.3456 | 60.3837 | | 0.7149 | 2.67 | 3000 | 1.1953 | 11.2247 | 50.5921 | | 0.6719 | 3.11 | 3500 | 1.1541 | 10.4303 | 54.3776 | | 0.6265 | 3.56 | 4000 | 1.1186 | 13.3231 | 48.283 | | 0.6157 | 4.0 | 4500 | 1.0929 | 13.8467 | 46.569 | | 0.5736 | 4.44 | 5000 | 1.0848 | 14.2731 | 45.5035 | | 0.5683 | 4.89 | 5500 | 1.0752 | 14.601 | 45.8087 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.17.0 - Tokenizers 0.10.3

fgaim/t5-small-squad-v2

fgaim

2022-01-30T21:35:54Z

34

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "en", "dataset:c4", "dataset:squad", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-02T23:29:05Z

--- language: - en datasets: - c4 - squad tags: - text2text-generation widget: - text: "question: What is the atomic number for oxygen? context: Oxygen is a chemical element with symbol O and atomic number 8." - text: "question: What is the chemical symbol of Oxygen? context: Oxygen is a chemical element with symbol O and atomic number 8." license: apache-2.0 --- T5-small for QA --- [Google's T5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) pre-trained on the [C4](https://huggingface.co/datasets/c4) dataset, fine-tuned for Question-Answering on [SQuAD v2](https://huggingface.co/datasets/squad_v2) with the following hyperparameters: ``` optimizer=adamw_hf learning_rate=3e-5 adam_beta1=0.9 adam_beta2=0.999 adam_epsilon=1e-08 num_train_epochs=2 per_device_train_batch_size=12 ``` Usage --- The input [context and question] has to be prepared in a specific way as follows: ```python from transformers import pipeline def prep_input(_context, _question): return " ".join(["question:", _question.strip(), "context:", _context.strip()]) t5qa = pipeline("text2text-generation", "fgaim/t5-small-squad-v2") context = """ Oxygen is a chemical element with symbol O and atomic number 8. It is a member of the chalcogen group on the periodic table and is a highly reactive nonmetal and oxidizing agent that readily forms compounds (notably oxides) with most elements. By mass, oxygen is the third-most abundant element in the universe, after hydrogen and helium. At standard temperature and pressure, two atoms of the element bind to form dioxygen, a colorless and odorless diatomic gas with the formula O. """ t5qa(prep_input(context, "How many atoms combine to form dioxygen?")) # [{'generated_text': 'two'}] t5qa(prep_input(context, "What element makes up almost half of the earth's crust by mass?")) # [{'generated_text': 'oxygen'}] t5qa(prep_input(context, "What are the most abundent elements of the universe by mass?")) # [{'generated_text': 'hydrogen and helium'}] ```

osama7/t5-summarization-multinews

osama7

2022-01-30T20:42:51Z

5

0

transformers

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

text2text-generation

2022-03-02T23:29:05Z

This is a t5-base model trained on the multi_news dataset for abstraction summarization

gagan3012/xls-r-300m-hi

gagan3012

2022-01-30T20:39:40Z

10

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "hi", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:05Z

--- language: - hi license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer datasets: - common_voice model-index: - name: xls-r-300m-hi results: [] ---  # xls-r-300m-hi This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset. It achieves the following results on the evaluation set: - Loss: 0.7522 - Wer: 1.0091 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 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: 2000 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.0417 | 2.59 | 500 | 5.1484 | 1.0 | | 3.3722 | 5.18 | 1000 | 3.3380 | 1.0001 | | 1.9752 | 7.77 | 1500 | 1.3910 | 1.0074 | | 1.5868 | 10.36 | 2000 | 1.0298 | 1.0084 | | 1.4413 | 12.95 | 2500 | 0.9313 | 1.0175 | | 1.3296 | 15.54 | 3000 | 0.8966 | 1.0194 | | 1.2746 | 18.13 | 3500 | 0.8875 | 1.0097 | | 1.2147 | 20.73 | 4000 | 0.8746 | 1.0089 | | 1.1774 | 23.32 | 4500 | 0.8383 | 1.0198 | | 1.129 | 25.91 | 5000 | 0.7848 | 1.0167 | | 1.0995 | 28.5 | 5500 | 0.7992 | 1.0210 | | 1.0665 | 31.09 | 6000 | 0.7878 | 1.0107 | | 1.0321 | 33.68 | 6500 | 0.7653 | 1.0082 | | 1.0068 | 36.27 | 7000 | 0.7635 | 1.0065 | | 0.9916 | 38.86 | 7500 | 0.7728 | 1.0090 | | 0.9735 | 41.45 | 8000 | 0.7688 | 1.0070 | | 0.9745 | 44.04 | 8500 | 0.7455 | 1.0097 | | 0.9677 | 46.63 | 9000 | 0.7605 | 1.0099 | | 0.9313 | 49.22 | 9500 | 0.7527 | 1.0097 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

Kayvane/distilbert-complaints-product

Kayvane

2022-01-30T19:15:13Z

33

3

transformers

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

text-classification

2022-03-02T23:29:04Z

--- tags: - generated_from_trainer datasets: - consumer_complaints model-index: - name: distilbert-complaints-product results: [] ---  # distilbert-complaints-product This model was trained from the [CFBP](https://www.consumerfinance.gov/data-research/consumer-complaints/) dataset, also made available on the HuggingFace Datasets library. This model predicts the type of financial complaint based on the text provided ## Model description A DistilBert Text Classification Model, with 18 possible classes to determine the nature of a financial customer complaint. ## Intended uses & limitations This model is used as part of.a demonstration for E2E Machine Learning Projects focused on Contact Centre Automation: - **Infrastructure:** Terraform - **ML Ops:** HuggingFace (Datasets, Hub, Transformers) - **Ml Explainability:** SHAP - **Cloud:** AWS - Model Hosting: Lambda - DB Backend: DynamoDB - Orchestration: Step-Functions - UI Hosting: EC2 - Routing: API Gateway - **UI:** Budibase ## Training and evaluation data consumer_complaints dataset ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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 ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

Sindhu/rembert-squad2

Sindhu

2022-01-30T18:35:08Z

5

3

transformers

[ "transformers", "pytorch", "rembert", "question-answering", "multilingual", "dataset:squad2", "endpoints_compatible", "region:us" ]

question-answering

2022-03-02T23:29:05Z

--- language: - multilingual tags: - question-answering datasets: - squad2 metrics: - squad2 --- # Rembert Squad2 This model is finetuned for QA task on Squad2 from [Rembert checkpoint](https://huggingface.co/google/rembert). ## Hyperparameters ``` Batch Size: 4 Grad Accumulation Steps = 8 Total epochs = 3 MLM Checkpoint = "rembert" max_seq_len = 256 learning_rate = 1e-5 lr_schedule = LinearWarmup warmup_ratio = 0.1 doc_stride = 128 ``` ## Squad 2 Evaluation stats: Metrics generated from [the official Squad2 evaluation script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/) ```json { "exact": 84.51107554956624, "f1": 87.46644042781853, "total": 11873, "HasAns_exact": 80.97165991902834, "HasAns_f1": 86.89086491219469, "HasAns_total": 5928, "NoAns_exact": 88.04037005887301, "NoAns_f1": 88.04037005887301, "NoAns_total": 5945 } ``` For any questions, you can reach out to me [on Twitter](https://twitter.com/batw0man)

Erfan/mT5-base_Farsi_Title_Generator

Erfan

2022-01-30T18:00:42Z

11

2

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "Title-Generation", "fa", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-02T23:29:04Z

--- language: - fa tags: - Title-Generation metrics: - ROUGH ---

tomascufaro/wav2vec2-large-xls-r-300m-spanish-small

tomascufaro

2022-01-30T17:23:59Z

14

0

transformers

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

automatic-speech-recognition

2022-03-02T23:29:05Z

--- tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-spanish-small results: [] ---  # wav2vec2-large-xls-r-300m-spanish-small This model is a fine-tuned version of [jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom](https://huggingface.co/jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3763 - Wer: 0.1791 ## Model description More information needed ## Intended uses & 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.2277 | 0.26 | 400 | 0.2601 | 0.2291 | | 0.2932 | 0.53 | 800 | 0.2950 | 0.2670 | | 0.3019 | 0.79 | 1200 | 0.3247 | 0.2766 | | 0.2987 | 1.05 | 1600 | 0.3031 | 0.2606 | | 0.261 | 1.32 | 2000 | 0.2994 | 0.2620 | | 0.2651 | 1.58 | 2400 | 0.3134 | 0.2700 | | 0.264 | 1.85 | 2800 | 0.3016 | 0.2641 | | 0.2475 | 2.11 | 3200 | 0.3135 | 0.2661 | | 0.2269 | 2.37 | 3600 | 0.3029 | 0.2562 | | 0.2389 | 2.64 | 4000 | 0.3035 | 0.2549 | | 0.2319 | 2.9 | 4400 | 0.3022 | 0.2551 | | 0.2123 | 3.16 | 4800 | 0.3256 | 0.2638 | | 0.2094 | 3.43 | 5200 | 0.3227 | 0.2712 | | 0.2121 | 3.69 | 5600 | 0.3085 | 0.2596 | | 0.207 | 3.96 | 6000 | 0.3041 | 0.2597 | | 0.1809 | 4.22 | 6400 | 0.3122 | 0.2524 | | 0.1846 | 4.48 | 6800 | 0.3254 | 0.2579 | | 0.1885 | 4.75 | 7200 | 0.2958 | 0.2437 | | 0.1923 | 5.01 | 7600 | 0.3136 | 0.2502 | | 0.1626 | 5.27 | 8000 | 0.3059 | 0.2488 | | 0.1704 | 5.54 | 8400 | 0.3082 | 0.2515 | | 0.1674 | 5.8 | 8800 | 0.3196 | 0.2509 | | 0.1691 | 6.06 | 9200 | 0.3193 | 0.25 | | 0.1499 | 6.33 | 9600 | 0.3529 | 0.2635 | | 0.1568 | 6.59 | 10000 | 0.3241 | 0.2481 | | 0.1538 | 6.86 | 10400 | 0.3354 | 0.2476 | | 0.1503 | 7.12 | 10800 | 0.3180 | 0.2402 | | 0.136 | 7.38 | 11200 | 0.3230 | 0.2397 | | 0.1413 | 7.65 | 11600 | 0.3178 | 0.2451 | | 0.147 | 7.91 | 12000 | 0.3170 | 0.2389 | | 0.1341 | 8.17 | 12400 | 0.3380 | 0.2501 | | 0.1329 | 8.44 | 12800 | 0.3265 | 0.2414 | | 0.1314 | 8.7 | 13200 | 0.3281 | 0.2482 | | 0.1312 | 8.97 | 13600 | 0.3259 | 0.2539 | | 0.12 | 9.23 | 14000 | 0.3291 | 0.2424 | | 0.1193 | 9.49 | 14400 | 0.3302 | 0.2412 | | 0.1189 | 9.76 | 14800 | 0.3376 | 0.2407 | | 0.1217 | 10.02 | 15200 | 0.3334 | 0.2400 | | 0.1118 | 10.28 | 15600 | 0.3359 | 0.2368 | | 0.1139 | 10.55 | 16000 | 0.3239 | 0.2335 | | 0.1106 | 10.81 | 16400 | 0.3374 | 0.2352 | | 0.1081 | 11.07 | 16800 | 0.3585 | 0.2434 | | 0.1063 | 11.34 | 17200 | 0.3639 | 0.2472 | | 0.1041 | 11.6 | 17600 | 0.3399 | 0.2423 | | 0.1062 | 11.87 | 18000 | 0.3410 | 0.2388 | | 0.1012 | 12.13 | 18400 | 0.3597 | 0.2413 | | 0.0953 | 12.39 | 18800 | 0.3440 | 0.2296 | | 0.097 | 12.66 | 19200 | 0.3440 | 0.2269 | | 0.0968 | 12.92 | 19600 | 0.3498 | 0.2333 | | 0.0902 | 13.18 | 20000 | 0.3471 | 0.2290 | | 0.0868 | 13.45 | 20400 | 0.3462 | 0.2266 | | 0.0892 | 13.71 | 20800 | 0.3373 | 0.2227 | | 0.0902 | 13.97 | 21200 | 0.3377 | 0.2240 | | 0.0846 | 14.24 | 21600 | 0.3484 | 0.2237 | | 0.0839 | 14.5 | 22000 | 0.3706 | 0.2260 | | 0.0834 | 14.77 | 22400 | 0.3430 | 0.2268 | | 0.0841 | 15.03 | 22800 | 0.3489 | 0.2259 | | 0.076 | 15.29 | 23200 | 0.3626 | 0.2281 | | 0.0771 | 15.56 | 23600 | 0.3624 | 0.2268 | | 0.0773 | 15.82 | 24000 | 0.3440 | 0.2252 | | 0.0759 | 16.08 | 24400 | 0.3532 | 0.2170 | | 0.0745 | 16.35 | 24800 | 0.3686 | 0.2188 | | 0.0713 | 16.61 | 25200 | 0.3691 | 0.2195 | | 0.0718 | 16.88 | 25600 | 0.3470 | 0.2108 | | 0.0685 | 17.14 | 26000 | 0.3756 | 0.2179 | | 0.0689 | 17.4 | 26400 | 0.3542 | 0.2149 | | 0.0671 | 17.67 | 26800 | 0.3461 | 0.2165 | | 0.0737 | 17.93 | 27200 | 0.3473 | 0.2238 | | 0.0669 | 18.19 | 27600 | 0.3441 | 0.2138 | | 0.0629 | 18.46 | 28000 | 0.3721 | 0.2155 | | 0.0632 | 18.72 | 28400 | 0.3667 | 0.2126 | | 0.0647 | 18.98 | 28800 | 0.3579 | 0.2097 | | 0.0603 | 19.25 | 29200 | 0.3670 | 0.2130 | | 0.0604 | 19.51 | 29600 | 0.3750 | 0.2142 | | 0.0619 | 19.78 | 30000 | 0.3804 | 0.2160 | | 0.0603 | 20.04 | 30400 | 0.3764 | 0.2124 | | 0.0577 | 20.3 | 30800 | 0.3858 | 0.2097 | | 0.0583 | 20.57 | 31200 | 0.3520 | 0.2089 | | 0.0561 | 20.83 | 31600 | 0.3615 | 0.2079 | | 0.0545 | 21.09 | 32000 | 0.3824 | 0.2032 | | 0.0525 | 21.36 | 32400 | 0.3858 | 0.2091 | | 0.0524 | 21.62 | 32800 | 0.3956 | 0.2099 | | 0.0527 | 21.89 | 33200 | 0.3667 | 0.2025 | | 0.0514 | 22.15 | 33600 | 0.3708 | 0.2032 | | 0.0506 | 22.41 | 34000 | 0.3815 | 0.2053 | | 0.0478 | 22.68 | 34400 | 0.3671 | 0.2007 | | 0.049 | 22.94 | 34800 | 0.3758 | 0.2003 | | 0.0477 | 23.2 | 35200 | 0.3786 | 0.2014 | | 0.045 | 23.47 | 35600 | 0.3732 | 0.1998 | | 0.0426 | 23.73 | 36000 | 0.3737 | 0.2010 | | 0.0444 | 23.99 | 36400 | 0.3600 | 0.1990 | | 0.0433 | 24.26 | 36800 | 0.3689 | 0.1976 | | 0.0442 | 24.52 | 37200 | 0.3787 | 0.1968 | | 0.0419 | 24.79 | 37600 | 0.3652 | 0.1961 | | 0.042 | 25.05 | 38000 | 0.3820 | 0.1964 | | 0.0419 | 25.31 | 38400 | 0.3786 | 0.1919 | | 0.0376 | 25.58 | 38800 | 0.3842 | 0.1934 | | 0.0385 | 25.84 | 39200 | 0.3767 | 0.1900 | | 0.0396 | 26.1 | 39600 | 0.3688 | 0.1888 | | 0.0371 | 26.37 | 40000 | 0.3815 | 0.1894 | | 0.0363 | 26.63 | 40400 | 0.3748 | 0.1878 | | 0.0377 | 26.9 | 40800 | 0.3713 | 0.1852 | | 0.0352 | 27.16 | 41200 | 0.3734 | 0.1851 | | 0.0355 | 27.42 | 41600 | 0.3776 | 0.1874 | | 0.0333 | 27.69 | 42000 | 0.3867 | 0.1841 | | 0.0348 | 27.95 | 42400 | 0.3823 | 0.1839 | | 0.0329 | 28.21 | 42800 | 0.3795 | 0.1822 | | 0.0325 | 28.48 | 43200 | 0.3711 | 0.1813 | | 0.0328 | 28.74 | 43600 | 0.3721 | 0.1781 | | 0.0312 | 29.0 | 44000 | 0.3803 | 0.1816 | | 0.0318 | 29.27 | 44400 | 0.3758 | 0.1794 | | 0.0302 | 29.53 | 44800 | 0.3792 | 0.1784 | | 0.0339 | 29.8 | 45200 | 0.3763 | 0.1791 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0

jiobiala24/wav2vec2-base-checkpoint-10

jiobiala24

2022-01-30T16:10:56Z

4

0

transformers

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

automatic-speech-recognition

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-base-checkpoint-10 results: [] ---  # wav2vec2-base-checkpoint-10 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-9](https://huggingface.co/jiobiala24/wav2vec2-base-checkpoint-9) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9567 - Wer: 0.3292 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.2892 | 1.62 | 1000 | 0.5745 | 0.3467 | | 0.235 | 3.23 | 2000 | 0.6156 | 0.3423 | | 0.1782 | 4.85 | 3000 | 0.6299 | 0.3484 | | 0.1504 | 6.46 | 4000 | 0.6475 | 0.3446 | | 0.133 | 8.08 | 5000 | 0.6753 | 0.3381 | | 0.115 | 9.69 | 6000 | 0.7834 | 0.3529 | | 0.101 | 11.31 | 7000 | 0.7924 | 0.3426 | | 0.0926 | 12.92 | 8000 | 0.7887 | 0.3465 | | 0.0863 | 14.54 | 9000 | 0.7674 | 0.3439 | | 0.0788 | 16.16 | 10000 | 0.8648 | 0.3435 | | 0.0728 | 17.77 | 11000 | 0.8460 | 0.3395 | | 0.0693 | 19.39 | 12000 | 0.8941 | 0.3451 | | 0.0637 | 21.0 | 13000 | 0.9079 | 0.3356 | | 0.0584 | 22.62 | 14000 | 0.8851 | 0.3336 | | 0.055 | 24.23 | 15000 | 0.9400 | 0.3338 | | 0.0536 | 25.85 | 16000 | 0.9387 | 0.3335 | | 0.0481 | 27.46 | 17000 | 0.9664 | 0.3337 | | 0.0485 | 29.08 | 18000 | 0.9567 | 0.3292 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

imvladikon/charbert-bert-wiki

imvladikon

2022-01-30T11:35:48Z

63

3

transformers

[ "transformers", "pytorch", "language model", "en", "dataset:wikipedia", "arxiv:2011.01513", "endpoints_compatible", "region:us" ]

null

2022-03-02T23:29:05Z

--- language: - en tags: - language model datasets: - wikipedia --- pre-trained model from [CharBERT: Character-aware Pre-trained Language Model](https://github.com/wtma/CharBERT) ``` @misc{ma2020charbert, title={CharBERT: Character-aware Pre-trained Language Model}, author={Wentao Ma and Yiming Cui and Chenglei Si and Ting Liu and Shijin Wang and Guoping Hu}, year={2020}, eprint={2011.01513}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

sshasnain/finetune-wav2vec2-large-xlsr-bengali

sshasnain

2022-01-30T07:55:29Z

3

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "bn", "audio", "speech", "dataset:custom", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-02T23:29:05Z

--- language: Bengali datasets: - custom metrics: - wer tags: - bn - audio - automatic-speech-recognition - speech license: apache-2.0 model-index: - name: finetune-wav2vec2-large-xlsr-bengali results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: custom type: custom args: ben metrics: - name: Test WER type: wer value: 0.011 --- # finetune-wav2vec2-large-xlsr-bengali *** ## Usage ***

pinecone/mpnet-retriever-discourse

pinecone

2022-01-30T07:23:58Z

4

2

sentence-transformers

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

sentence-similarity

2022-03-02T23:29:05Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - question-answering --- # MPNet Retriever (Discourse) This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used as a retriever model in open-domain question-answering tasks.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Training The model was fine-tuned on question-answer pairs scraper from several ML-focused Discourse forums \[HuggingFace, PyTorch, Streamlit, TensorFlow\]. The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 105 with parameters: ``` {'batch_size': 12} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors Fine-tuned by [James Briggs](https://www.youtube.com/c/jamesbriggs) at [Pinecone](https://www.pinecone.io). Learn more about the [fine-tuning process here](https://www.pinecone.io/learn/retriever-models/).

jogonba2/mbarthez-copy_mechanism-hal_articles

jogonba2

2022-01-30T03:52:27Z

3

0

transformers

[ "transformers", "pytorch", "mbart", "generated_from_trainer", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

null

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: mbarthez-copy_mechanism-hal_articles results: - task: name: Summarization type: summarization metrics: - name: Rouge1 type: rouge value: 36.548 ---  # mbarthez-davide_articles-copy_enhanced This model is a fine-tuned version of [moussaKam/mbarthez](https://huggingface.co/moussaKam/mbarthez) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4905 - Rouge1: 36.548 - Rouge2: 19.6282 - Rougel: 30.2513 - Rougelsum: 30.2765 - Gen Len: 25.7238 ## Model description More information needed ## Intended uses & 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.6706 | 1.0 | 33552 | 1.5690 | 31.2477 | 16.5455 | 26.9855 | 26.9754 | 18.6217 | | 1.3446 | 2.0 | 67104 | 1.5060 | 32.1108 | 17.1408 | 27.7833 | 27.7703 | 18.9115 | | 1.3245 | 3.0 | 100656 | 1.4905 | 32.9084 | 17.7027 | 28.2912 | 28.2975 | 18.9801 | ### Framework versions - Transformers 4.10.2 - Pytorch 1.7.1+cu110 - Datasets 1.11.0 - Tokenizers 0.10.3

anton-l/wav2vec2-xls-r-common_voice-tr-ft-100sh

anton-l

2022-01-30T02:42:22Z

5

0

transformers

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

automatic-speech-recognition

2022-03-02T23:29:05Z

--- language: - tr license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer model-index: - name: wav2vec2-xls-r-common_voice-tr-ft results: [] ---  # wav2vec2-xls-r-common_voice-tr-ft This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.5806 - Wer: 0.3998 - Cer: 0.1053 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:----:|:---------------:|:------:|:------:| | 0.5369 | 17.0 | 500 | 0.6021 | 0.6366 | 0.1727 | | 0.3542 | 34.0 | 1000 | 0.5265 | 0.4906 | 0.1278 | | 0.1866 | 51.0 | 1500 | 0.5805 | 0.4768 | 0.1261 | | 0.1674 | 68.01 | 2000 | 0.5336 | 0.4518 | 0.1186 | | 0.19 | 86.0 | 2500 | 0.5676 | 0.4427 | 0.1151 | | 0.0815 | 103.0 | 3000 | 0.5510 | 0.4268 | 0.1125 | | 0.0545 | 120.0 | 3500 | 0.5608 | 0.4175 | 0.1099 | | 0.0299 | 137.01 | 4000 | 0.5875 | 0.4222 | 0.1124 | | 0.0267 | 155.0 | 4500 | 0.5882 | 0.4026 | 0.1063 | | 0.025 | 172.0 | 5000 | 0.5806 | 0.3998 | 0.1053 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2 - Datasets 1.18.2 - Tokenizers 0.10.3

huggingtweets/hashimoto_lo

huggingtweets

2022-01-30T01:43:17Z

0

null

[ "huggingtweets", "en", "region:us" ]

null

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/hashimoto_lo/1643506993033/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/922396157493383169/LLKd_U72_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">橋下徹</div> <div style="text-align: center; font-size: 14px;">@hashimoto_lo</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 橋下徹. | Data | 橋下徹 | | --- | --- | | Tweets downloaded | 3247 | | Retweets | 759 | | Short tweets | 137 | | Tweets kept | 2351 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1wi9n714/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 @hashimoto_lo's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/240mb7l6) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/240mb7l6/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/hashimoto_lo') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/tjonthefloor

huggingtweets

2022-01-29T22:53:02Z

4

0

transformers

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

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/tjonthefloor/1643496777814/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/1466388620256948228/kkRWm2mR_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">ash ψ</div> <div style="text-align: center; font-size: 14px;">@tjonthefloor</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 ash ψ. | Data | ash ψ | | --- | --- | | Tweets downloaded | 470 | | Retweets | 144 | | Short tweets | 99 | | Tweets kept | 227 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/20bqlhah/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 @tjonthefloor's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ntjhfs1) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ntjhfs1/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/tjonthefloor') 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)