license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-small-subjqa-vanilla-books-qg") output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | 113e7e96e1d38a2de381a2e7cf99bc9c |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-subjqa-vanilla-books-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) | | Score | Type | Dataset | |:-----------|--------:|:-------|:-----------------------------------------------------------------| | BERTScore | 72.1 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_1 | 3.25 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_2 | 0.68 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_3 | 0 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_4 | 0 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | METEOR | 3.87 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | MoverScore | 49.58 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | ROUGE_L | 4.4 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | 3b79691284e239f7d2484fa660a66e4c |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: books - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-small - max_length: 512 - max_length_output: 32 - epoch: 1 - batch: 32 - lr: 5e-05 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 2 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/t5-small-subjqa-vanilla-books-qg/raw/main/trainer_config.json). | 11b1ac4f8fef842de8e588b586ed678e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large-v2 Bulgarian This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 bg dataset. It achieves the following results on the evaluation set: - Loss: 0.3208 - Wer: 13.4040 | 0966bae1834f977fe3bd59e1b8269d19 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0023 | 7.04 | 1000 | 0.3208 | 13.4040 | | 89f86a217b26e1ef92eec6366a95cb78 |
apache-2.0 | ['India', 'politics', 'tweets', 'BJP', 'Congress', 'AAP', 'pytorch', 'gpt2', 'lm-head', 'text-generation'] | false | Model description This is a GPT2 Language model with LM head fine-tuned on tweets crawled from handles which belong predominantly to Indian Politics. For more information about the crawled data, you can go through this [blog](https://bagdeabhishek.github.io/twitterAnalysis) post. This model is finetuned using GPT2-medium instead of the vanilla GPT2 implementation. This model has more parameters but it is able to model language slightly better. | 30dfadd8c83eee537b8cea070aebe523 |
apache-2.0 | ['India', 'politics', 'tweets', 'BJP', 'Congress', 'AAP', 'pytorch', 'gpt2', 'lm-head', 'text-generation'] | false | Training data I used the pre-trained gpt2-medium model from Huggingface transformers repository and fine-tuned it on custom data set crawled from twitter. The method used to identify the political handles is mentioned in detail in a [blog](https://bagdeabhishek.github.io/twitterAnalysis) post. I used tweets from both the Pro-BJP and Anti-BJP clusters mentioned in the blog. | 8c59a70db6257315b1e382f78b15f8f0 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Bulgarian (bg) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza). This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo Last updated 2022-10-12 02:49:02.763 | d19c4e7ab925a2bbe4b246fffd3205cd |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', 'generated_from_trainer'] | false | 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 | d4d871ab3002c803ade20ac44faa3a3c |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', 'generated_from_trainer'] | false | 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 | 04926ae7a231a9cf69c1515c517e95b4 |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', 'generated_from_trainer'] | false | 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 | | ffdeed722137f74d2273e4b85b849757 |
apache-2.0 | ['Vocoder', 'HiFIGAN', 'text-to-speech', 'TTS', 'speech-synthesis', 'speechbrain'] | false | Vocoder with HiFIGAN trained on custom German dataset This repository provides all the necessary tools for using a [HiFIGAN](https://arxiv.org/abs/2010.05646) vocoder trained on a generated German dataset using [mp3_to_training_data](https://github.com/padmalcom/mp3_to_training_data). The pre-trained model (8 epochs so far) takes in input a spectrogram and produces a waveform in output. Typically, a vocoder is used after a TTS model that converts an input text into a spectrogram. | 418550b24c5eeb3baa94725e8aaa63c2 |
apache-2.0 | ['Vocoder', 'HiFIGAN', 'text-to-speech', 'TTS', 'speech-synthesis', 'speechbrain'] | false | How to use Install speechbrain. ```bash pip install speechbrain ``` Use a TTS model (e.g. [tts-tacotron-german](https://huggingface.co/padmalcom/tts-tacotron2-german)), generate a spectrogram and convert it to audio. ```python import torchaudio from speechbrain.pretrained import Tacotron2 from speechbrain.pretrained import HIFIGAN tacotron2 = Tacotron2.from_hparams(source="padmalcom/tts-tacotron2-german", savedir="tmpdir_tts") hifi_gan = HIFIGAN.from_hparams(source="padmalcom/tts-hifigan-german", savedir="tmpdir_vocoder") mel_output, mel_length, alignment = tacotron2.encode_text("Mary had a little lamb") waveforms = hifi_gan.decode_batch(mel_output) torchaudio.save('example_TTS.wav',waveforms.squeeze(1), 22050) ``` | 0f453675baee558e54e2c32be3955a83 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7696 - Matthews Correlation: 0.5136 | ae6b3f2425813092a449570435d1d9ac |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5284 | 1.0 | 535 | 0.4948 | 0.4093 | | 0.3529 | 2.0 | 1070 | 0.5135 | 0.4942 | | 0.2417 | 3.0 | 1605 | 0.6303 | 0.5083 | | 0.1818 | 4.0 | 2140 | 0.7696 | 0.5136 | | 0.1302 | 5.0 | 2675 | 0.8774 | 0.5123 | | 4a7e2a587a12fe569c13f10cfef480cd |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_age_teens-5_sixties-5_s279 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 06185cb9fa2aa56040f1f7fba902e708 |
apache-2.0 | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | segformer-b0-finetuned-segments-sidewalk-2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 2.9327 - Mean Iou: 0.0763 - Mean Accuracy: 0.1260 - Overall Accuracy: 0.5923 - Per Category Iou: [nan, 0.15598158400203022, 0.6233750625153907, 0.0037560777123078824, 0.026995519273962765, 0.027599075064035524, 0.0, 0.0010671752114502803, 0.0, 0.0, 0.503652156236298, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.42226922942999406, 0.0, 0.0005751844669974061, 0.0, 0.0, 0.0, 0.015053303500921295, 0.0, 0.0, 0.0, 0.5380260834627074, 0.2004924888392474, 0.07113330974397604, 7.792680075848753e-05, 0.000328515111695138, 0.0025085129486024, 0.0] - Per Category Accuracy: [nan, 0.17282441039529764, 0.9228726118961177, 0.00408103876916878, 0.028255152590055656, 0.029544523907019265, nan, 0.0010791707371488259, 0.0, 0.0, 0.8681646650418041, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7122996003019028, 0.0, 0.0005801259615003622, 0.0, 0.0, nan, 0.02304960072549563, 0.0, 0.0, 0.0, 0.9348363685365858, 0.2596289024956107, 0.07122958643730157, 8.48216389425569e-05, 0.0005356047133214773, 0.0026059641588056346, 0.0] | 592e9cf7e5aad922d0ce56f7df918f4a |
apache-2.0 | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 0.05 | eeec123a79b5e7a41c8a0d10b2baad6b |
apache-2.0 | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 3.0624 | 0.03 | 10 | 3.1628 | 0.0726 | 0.1219 | 0.5758 | [nan, 0.0878087898079964, 0.611982872765419, 0.0001999765816897758, 0.006930751650791711, 0.0208104329339671, 0.0, 0.0010631316774049914, 0.0, 0.0, 0.4839157481183621, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.39292052415275885, 0.0, 0.0003268797082673576, 0.0011424188270622699, 0.0, 0.0, 0.004317032040472175, 3.142508260307427e-05, 0.0, 0.0, 0.5537894233680722, 0.28184052017073197, 0.015966383939961543, 0.0002995587926924772, 0.0005713078253519804, 0.0035316933149879015, 0.0] | [nan, 0.09656561651317118, 0.9239613003877697, 0.00021265611687132485, 0.007163978434475801, 0.0222089828684614, nan, 0.0010774805715464, 0.0, 0.0, 0.8583517795809614, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.705533848895072, 0.0, 0.00033222625115695, 0.0011495555325644448, 0.0, nan, 0.008061062548807214, 3.244014792707455e-05, 0.0, 0.0, 0.8715627360179777, 0.3828074002074446, 0.01597238073499201, 0.0003298619292210546, 0.0011388100215281895, 0.003805890022240969, 0.0] | | 2.6259 | 0.05 | 20 | 2.9327 | 0.0763 | 0.1260 | 0.5923 | [nan, 0.15598158400203022, 0.6233750625153907, 0.0037560777123078824, 0.026995519273962765, 0.027599075064035524, 0.0, 0.0010671752114502803, 0.0, 0.0, 0.503652156236298, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.42226922942999406, 0.0, 0.0005751844669974061, 0.0, 0.0, 0.0, 0.015053303500921295, 0.0, 0.0, 0.0, 0.5380260834627074, 0.2004924888392474, 0.07113330974397604, 7.792680075848753e-05, 0.000328515111695138, 0.0025085129486024, 0.0] | [nan, 0.17282441039529764, 0.9228726118961177, 0.00408103876916878, 0.028255152590055656, 0.029544523907019265, nan, 0.0010791707371488259, 0.0, 0.0, 0.8681646650418041, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7122996003019028, 0.0, 0.0005801259615003622, 0.0, 0.0, nan, 0.02304960072549563, 0.0, 0.0, 0.0, 0.9348363685365858, 0.2596289024956107, 0.07122958643730157, 8.48216389425569e-05, 0.0005356047133214773, 0.0026059641588056346, 0.0] | | 9e996d74ab5c4d3e60f84afe473f466f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2173 - Accuracy: 0.925 - F1: 0.9252 | 0f4e208afe18f0b696eaa0c2c313defa |
apache-2.0 | ['generated_from_trainer'] | false | 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 | | f3676fc6498d3ceb8fb97a49e232164a |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-finetuned-cnndm_fs0.1-c This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9852 - Recall: 34.0438 - Precision: 33.1906 - F1: 31.9429 - Gen Len: 18.9962 | 85978488a02534d6878529accc4f7376 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP | 86a9ecbcf46f1010476f343e36bb499a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Recall | Precision | F1 | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:-------:|:-------:| | 2.19 | 0.11 | 200 | 1.6689 | 33.0433 | 41.8436 | 35.485 | 18.9695 | | 1.74 | 0.23 | 400 | 1.5476 | 36.2979 | 44.1939 | 38.3491 | 18.9741 | | 1.6352 | 0.34 | 600 | 1.5075 | 33.8389 | 42.4269 | 36.2306 | 18.9848 | | 1.5937 | 0.46 | 800 | 1.4779 | 33.8957 | 42.3366 | 36.2976 | 18.9939 | | 1.5457 | 0.57 | 1000 | 1.4497 | 34.2432 | 42.4519 | 36.5314 | 18.9916 | | 1.522 | 0.69 | 1200 | 1.4360 | 34.8509 | 42.6855 | 36.9827 | 18.9886 | | 1.5091 | 0.8 | 1400 | 1.4210 | 34.5935 | 42.3167 | 36.7092 | 18.9848 | | 1.5015 | 0.92 | 1600 | 1.4013 | 35.3025 | 43.1577 | 37.4461 | 18.9954 | | 1.4897 | 1.03 | 1800 | 1.3980 | 34.498 | 42.2453 | 36.5759 | 18.9886 | | 1.468 | 1.15 | 2000 | 1.3998 | 34.6134 | 42.053 | 36.5715 | 18.9863 | | 1.4812 | 1.26 | 2200 | 1.4014 | 34.5802 | 41.9303 | 36.5025 | 18.9871 | | 1.5264 | 1.38 | 2400 | 1.4729 | 34.0632 | 40.792 | 35.5837 | 18.9863 | | 1.7346 | 1.49 | 2600 | 1.6945 | 33.8488 | 36.3411 | 33.3566 | 18.997 | | 1.9477 | 1.61 | 2800 | 1.8588 | 34.0827 | 34.8631 | 32.749 | 18.9931 | | 2.1295 | 1.72 | 3000 | 1.9741 | 34.6842 | 33.8048 | 32.5274 | 18.9939 | | 2.1759 | 1.84 | 3200 | 1.9805 | 34.4333 | 33.5371 | 32.2921 | 18.9962 | | 2.194 | 1.95 | 3400 | 1.9852 | 34.0438 | 33.1906 | 31.9429 | 18.9962 | | f087a0f6a3e5110b727904a9596c785f |
apache-2.0 | ['generated_from_trainer'] | false | bart-model2-1209 This model is a fine-tuned version of [theojolliffe/bart-paraphrase-v4-e1-feedback](https://huggingface.co/theojolliffe/bart-paraphrase-v4-e1-feedback) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1658 - Rouge1: 55.8035 - Rouge2: 46.8603 - Rougel: 54.6759 - Rougelsum: 55.2072 - Gen Len: 19.6748 | 779e86a9c68b8d4d492b19d4b3cd2174 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.9329 | 1.0 | 650 | 0.1658 | 55.8035 | 46.8603 | 54.6759 | 55.2072 | 19.6748 | | c5d6c948d9aa3df75797a4914799c886 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | abrazaq Dreambooth model trained by raza2 with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars). Sample pictures of this concept: | 5ef2ad332d1fc782dabccd41b9946f13 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-squad This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.0014 | d420d8bf5663dbe6212f0d1f3b9b166f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0927 | 1.0 | 5536 | 1.0290 | | 0.87 | 2.0 | 11072 | 0.9683 | | 0.7335 | 3.0 | 16608 | 1.0014 | | 6d12c2443643595d93f4bdff962f1135 |
apache-2.0 | ['generated_from_trainer'] | false | convnext-tiny-224-finetuned-eurosat-albumentations This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.0727 - Accuracy: 0.9748 | 7715edee87f18312627b1fd1c0f8e607 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.141 | 1.0 | 190 | 0.1496 | 0.9544 | | 0.0736 | 2.0 | 380 | 0.0958 | 0.9719 | | 0.0568 | 3.0 | 570 | 0.0727 | 0.9748 | | 2cc97971d0138fd2ebccac70849ade5e |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_accent_us-2_england-8_s930 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | b61c1ff362aca58a02bf40794bdbd6dd |
mit | ['msmarco', 't5', 'pytorch', 'tensorflow', 'en'] | false | Introduction mT5-base-en-msmarco-v1 is a mT5-based model finetuned on English MS MARCO passage dataset. Further information about the dataset or the translation method can be found on our paper [**mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset**](https://arxiv.org/abs/2108.13897) and [mMARCO](https://github.com/unicamp-dl/mMARCO) repository. | 2c3c6ef27b1522334ec0c7b7b7a80b0b |
mit | ['msmarco', 't5', 'pytorch', 'tensorflow', 'en'] | false | Usage ```python from transformers import T5Tokenizer, MT5ForConditionalGeneration model_name = 'unicamp-dl/mt5-base-en-msmarco' tokenizer = T5Tokenizer.from_pretrained(model_name) model = MT5ForConditionalGeneration.from_pretrained(model_name) ``` | 0416e7785b64d16067f5badabba4d1bd |
mit | ['msmarco', 't5', 'pytorch', 'tensorflow', 'en'] | false | Citation If you use mT5-base-en-msmarco, please cite: @misc{bonifacio2021mmarco, title={mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset}, author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and and Roberto Lotufo and Rodrigo Nogueira}, year={2021}, eprint={2108.13897}, archivePrefix={arXiv}, primaryClass={cs.CL} } | 61f6c333c3b7bea5db6875fbbf06ae16 |
apache-2.0 | ['generated_from_trainer'] | false | twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-14_37_35 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3190 - Precision: 0.1194 - Recall: 0.2563 - F1: 0.1629 - Accuracy: 0.8546 | 53175adbd1947d05d37679c638a838f9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.4963 | 0.0223 | 0.0562 | 0.0319 | 0.7461 | | No log | 2.0 | 60 | 0.4089 | 0.0617 | 0.1359 | 0.0849 | 0.8093 | | No log | 3.0 | 90 | 0.3919 | 0.1053 | 0.2101 | 0.1403 | 0.8219 | | No log | 4.0 | 120 | 0.3787 | 0.1202 | 0.2482 | 0.1619 | 0.8270 | | No log | 5.0 | 150 | 0.3745 | 0.1171 | 0.2391 | 0.1572 | 0.8311 | | c8a6ae42dca172549a009aced9fdc87c |
apache-2.0 | ['generated_from_keras_callback'] | false | risethi/distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9709 - Validation Loss: 1.1167 - Epoch: 1 | 8b486f1e60a16eab94afc158b6dd7753 |
mit | ['generated_from_trainer'] | false | bert_base_tcm_0.9_10_epochs This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0190 - Criterio Julgamento Precision: 0.8170 - Criterio Julgamento Recall: 0.8803 - Criterio Julgamento F1: 0.8475 - Criterio Julgamento Number: 142 - Data Sessao Precision: 0.7798 - Data Sessao Recall: 0.9444 - Data Sessao F1: 0.8543 - Data Sessao Number: 90 - Modalidade Licitacao Precision: 0.9549 - Modalidade Licitacao Recall: 0.9799 - Modalidade Licitacao F1: 0.9673 - Modalidade Licitacao Number: 648 - Numero Exercicio Precision: 0.9559 - Numero Exercicio Recall: 0.9848 - Numero Exercicio F1: 0.9701 - Numero Exercicio Number: 330 - Objeto Licitacao Precision: 0.5496 - Objeto Licitacao Recall: 0.6792 - Objeto Licitacao F1: 0.6076 - Objeto Licitacao Number: 106 - Valor Objeto Precision: 0.8182 - Valor Objeto Recall: 0.8438 - Valor Objeto F1: 0.8308 - Valor Objeto Number: 32 - Overall Precision: 0.8868 - Overall Recall: 0.9414 - Overall F1: 0.9133 - Overall Accuracy: 0.9957 | 27ab0ac16eabe59ad50b4f1761ceea56 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 | 610d409b81958450716883cbb928bcc9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Criterio Julgamento Precision | Criterio Julgamento Recall | Criterio Julgamento F1 | Criterio Julgamento Number | Data Sessao Precision | Data Sessao Recall | Data Sessao F1 | Data Sessao Number | Modalidade Licitacao Precision | Modalidade Licitacao Recall | Modalidade Licitacao F1 | Modalidade Licitacao Number | Numero Exercicio Precision | Numero Exercicio Recall | Numero Exercicio F1 | Numero Exercicio Number | Objeto Licitacao Precision | Objeto Licitacao Recall | Objeto Licitacao F1 | Objeto Licitacao Number | Valor Objeto Precision | Valor Objeto Recall | Valor Objeto F1 | Valor Objeto Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.0187 | 1.0 | 3497 | 0.0201 | 0.776 | 0.6831 | 0.7266 | 142 | 0.7565 | 0.9667 | 0.8488 | 90 | 0.9548 | 0.9784 | 0.9665 | 648 | 0.9329 | 0.9697 | 0.9510 | 330 | 0.375 | 0.5377 | 0.4419 | 106 | 0.7045 | 0.9688 | 0.8158 | 32 | 0.8496 | 0.9095 | 0.8785 | 0.9946 | | 0.0173 | 2.0 | 6994 | 0.0190 | 0.8170 | 0.8803 | 0.8475 | 142 | 0.7798 | 0.9444 | 0.8543 | 90 | 0.9549 | 0.9799 | 0.9673 | 648 | 0.9559 | 0.9848 | 0.9701 | 330 | 0.5496 | 0.6792 | 0.6076 | 106 | 0.8182 | 0.8438 | 0.8308 | 32 | 0.8868 | 0.9414 | 0.9133 | 0.9957 | | 0.0106 | 3.0 | 10491 | 0.0306 | 0.8187 | 0.9225 | 0.8675 | 142 | 0.7890 | 0.9556 | 0.8643 | 90 | 0.9550 | 0.9830 | 0.9688 | 648 | 0.9475 | 0.9848 | 0.9658 | 330 | 0.5373 | 0.6792 | 0.6000 | 106 | 0.7561 | 0.9688 | 0.8493 | 32 | 0.8817 | 0.9510 | 0.9151 | 0.9946 | | 0.0071 | 4.0 | 13988 | 0.0226 | 0.8258 | 0.9014 | 0.8620 | 142 | 0.7830 | 0.9222 | 0.8469 | 90 | 0.9608 | 0.9846 | 0.9726 | 648 | 0.9440 | 0.9697 | 0.9567 | 330 | 0.5522 | 0.6981 | 0.6167 | 106 | 0.9394 | 0.9688 | 0.9538 | 32 | 0.8903 | 0.9451 | 0.9169 | 0.9959 | | 0.0043 | 5.0 | 17485 | 0.0236 | 0.8408 | 0.9296 | 0.8829 | 142 | 0.7766 | 0.8111 | 0.7935 | 90 | 0.9637 | 0.9846 | 0.9740 | 648 | 0.9461 | 0.9576 | 0.9518 | 330 | 0.5682 | 0.7075 | 0.6303 | 106 | 0.7949 | 0.9688 | 0.8732 | 32 | 0.8921 | 0.9384 | 0.9147 | 0.9952 | | 0.0041 | 6.0 | 20982 | 0.0273 | 0.8269 | 0.9085 | 0.8658 | 142 | 0.7838 | 0.9667 | 0.8657 | 90 | 0.9652 | 0.9830 | 0.9740 | 648 | 0.9408 | 0.9636 | 0.9521 | 330 | 0.5827 | 0.7642 | 0.6612 | 106 | 0.7895 | 0.9375 | 0.8571 | 32 | 0.8890 | 0.9510 | 0.9190 | 0.9953 | | 0.0021 | 7.0 | 24479 | 0.0322 | 0.8228 | 0.9155 | 0.8667 | 142 | 0.7810 | 0.9111 | 0.8410 | 90 | 0.9608 | 0.9830 | 0.9718 | 648 | 0.9412 | 0.9697 | 0.9552 | 330 | 0.5507 | 0.7170 | 0.6230 | 106 | 0.8333 | 0.9375 | 0.8824 | 32 | 0.8854 | 0.9458 | 0.9146 | 0.9951 | | 0.0026 | 8.0 | 27976 | 0.0336 | 0.8435 | 0.8732 | 0.8581 | 142 | 0.8039 | 0.9111 | 0.8542 | 90 | 0.9637 | 0.9846 | 0.9740 | 648 | 0.9528 | 0.9788 | 0.9656 | 330 | 0.5620 | 0.7264 | 0.6337 | 106 | 0.8378 | 0.9688 | 0.8986 | 32 | 0.8954 | 0.9458 | 0.9199 | 0.9952 | | 0.001 | 9.0 | 31473 | 0.0326 | 0.8477 | 0.9014 | 0.8737 | 142 | 0.7905 | 0.9222 | 0.8513 | 90 | 0.9665 | 0.9784 | 0.9724 | 648 | 0.9551 | 0.9667 | 0.9608 | 330 | 0.5940 | 0.7453 | 0.6611 | 106 | 0.8611 | 0.9688 | 0.9118 | 32 | 0.9004 | 0.9451 | 0.9222 | 0.9952 | | 0.0011 | 10.0 | 34970 | 0.0338 | 0.8387 | 0.9155 | 0.8754 | 142 | 0.7810 | 0.9111 | 0.8410 | 90 | 0.9650 | 0.9799 | 0.9724 | 648 | 0.9607 | 0.9636 | 0.9622 | 330 | 0.6015 | 0.7547 | 0.6695 | 106 | 0.8857 | 0.9688 | 0.9254 | 32 | 0.9005 | 0.9466 | 0.9230 | 0.9952 | | e9d2b3aab05a620ef03abe47bd724aff |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-mnli-target-glue-cola This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-mnli](https://huggingface.co/muhtasham/tiny-mlm-glue-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7272 - Matthews Correlation: 0.0899 | 689191520f363c56e389660b574ac3f6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6097 | 1.87 | 500 | 0.6214 | 0.0 | | 0.601 | 3.73 | 1000 | 0.6166 | 0.0 | | 0.5829 | 5.6 | 1500 | 0.6181 | 0.0630 | | 0.5537 | 7.46 | 2000 | 0.6384 | 0.0793 | | 0.5231 | 9.33 | 2500 | 0.6629 | 0.1079 | | 0.508 | 11.19 | 3000 | 0.6679 | 0.0949 | | 0.4817 | 13.06 | 3500 | 0.6915 | 0.1062 | | 0.4661 | 14.93 | 4000 | 0.7272 | 0.0899 | | 5cdd734b071cc094e7836450f220b9fe |
creativeml-openrail-m | ['text-to-image', 'v2.0', 'Embedding'] | false | Textual Inversion Embedding by ConflictX For SD 2.0 trained on 768x768 images from midjourney. Install by downloading the step embedding you want, and put it in the \embeddings folder It is slightly overfit on 150 steps so some concepts/keywords will be harder to prompt for (use negatives or weight Kipaki down) but it works amazing for cityscapes, people, gods, and other scifi genres. Very stylized on ancient Egypt, scifi, and orange/blue color scheme but other concepts are definitely possible: More images here: https://imgur.com/a/W2bmBaV Use keyword: Kipaki-xxx xxx is embedding number There are multiple versions, the images below were created with the 150 step version.        Highres Images:     | 0413dadddbb7d7e0593d8108e4fa2f5a |
apache-2.0 | ['generated_from_trainer'] | false | 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_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.3206 | 3a44e5b1a3f74181441d15cbc47809a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2156 | 1.0 | 8235 | 1.1791 | | 0.9413 | 2.0 | 16470 | 1.2182 | | 0.7514 | 3.0 | 24705 | 1.3206 | | a7917c407ca5e10a0f215619ff3b3d20 |
agpl-3.0 | ['generated_from_trainer'] | false | XLMR-ENIS-finetuned-stsb This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5232 - Pearson: 0.8915 - Spearmanr: 0.8888 | dceaef44d8eedef3d9140409e3480fc5 |
agpl-3.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | No log | 1.0 | 360 | 0.6330 | 0.8562 | 0.8570 | | 1.2835 | 2.0 | 720 | 0.6368 | 0.8790 | 0.8781 | | 0.4518 | 3.0 | 1080 | 0.5352 | 0.8883 | 0.8852 | | 0.4518 | 4.0 | 1440 | 0.4881 | 0.8910 | 0.8885 | | 0.288 | 5.0 | 1800 | 0.5232 | 0.8915 | 0.8888 | | 0684143a457ac423321d2bb500d70c06 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-300m-nyanja-test_v2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 0.3734 - Cer: 0.0827 | 2ad001cb657a9db54dd4723f84f236bd |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - 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: 400 - num_epochs: 15 - mixed_precision_training: Native AMP | 542188dc9cc5004dd3eb71a9ec0a91fc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 1.5816 | 0.62 | 400 | inf | 0.5702 | 0.1373 | | 0.6341 | 1.24 | 800 | inf | 0.4383 | 0.1022 | | 0.5103 | 1.86 | 1200 | inf | 0.3782 | 0.0895 | | 0.4553 | 2.48 | 1600 | inf | 0.3734 | 0.0827 | | d3615367e52b6058cbf7111f8e22b21b |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-etc-nlp This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0039 - Accuracy: 0.9993 - F1: 0.9993 | c96f023b3eda0d99b3a12915110c1870 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 262 | 0.0025 | 0.9997 | 0.9997 | | No log | 2.0 | 524 | 0.0039 | 0.9993 | 0.9993 | | 6b8542b504211b5b5fbb4949aea6e08a |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_age_teens-5_sixties-5_s408 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | abaf00c4b6d2d6bef5b2b76e4f9c34cd |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_unispeech-sat_s692 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 933883a31719f00b950e6c0675428f0d |
apache-2.0 | ['generated_from_keras_callback'] | false | kookoobear/distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8602 - Validation Loss: 2.6150 - Epoch: 0 | 65dbf732f61e7ef4d9575345512ccefa |
mit | ['generated_from_trainer'] | false | run-2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1449 - Accuracy: 0.75 - Precision: 0.7115 - Recall: 0.7093 - F1: 0.7103 | 89bf4cdd108cd6b1a4cf08879f5001c3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.9838 | 1.0 | 50 | 0.8621 | 0.645 | 0.6536 | 0.6130 | 0.6124 | | 0.7134 | 2.0 | 100 | 0.8124 | 0.7 | 0.6628 | 0.6421 | 0.6483 | | 0.4911 | 3.0 | 150 | 0.8571 | 0.7 | 0.6726 | 0.6314 | 0.6361 | | 0.3104 | 4.0 | 200 | 0.8228 | 0.76 | 0.7298 | 0.7367 | 0.7294 | | 0.1942 | 5.0 | 250 | 1.1132 | 0.76 | 0.7282 | 0.7031 | 0.7119 | | 0.1409 | 6.0 | 300 | 1.2218 | 0.685 | 0.6516 | 0.6560 | 0.6524 | | 0.0976 | 7.0 | 350 | 1.3648 | 0.715 | 0.6984 | 0.7044 | 0.6946 | | 0.0791 | 8.0 | 400 | 1.5985 | 0.745 | 0.7183 | 0.7113 | 0.7124 | | 0.0647 | 9.0 | 450 | 1.8884 | 0.725 | 0.6818 | 0.6761 | 0.6785 | | 0.0275 | 10.0 | 500 | 1.8639 | 0.725 | 0.6979 | 0.7008 | 0.6958 | | 0.0329 | 11.0 | 550 | 1.8831 | 0.72 | 0.6816 | 0.6869 | 0.6838 | | 0.0169 | 12.0 | 600 | 2.1426 | 0.73 | 0.6864 | 0.6776 | 0.6794 | | 0.0072 | 13.0 | 650 | 2.2483 | 0.725 | 0.7187 | 0.7054 | 0.6968 | | 0.0203 | 14.0 | 700 | 2.2901 | 0.735 | 0.6986 | 0.6885 | 0.6921 | | 0.0093 | 15.0 | 750 | 2.3134 | 0.725 | 0.6830 | 0.6666 | 0.6723 | | 0.0089 | 16.0 | 800 | 2.1598 | 0.73 | 0.6919 | 0.6860 | 0.6885 | | 0.0061 | 17.0 | 850 | 2.0879 | 0.75 | 0.7129 | 0.7132 | 0.7125 | | 0.0024 | 18.0 | 900 | 2.1285 | 0.745 | 0.7062 | 0.7071 | 0.7049 | | 0.0043 | 19.0 | 950 | 2.1386 | 0.74 | 0.7001 | 0.7003 | 0.6985 | | 0.0028 | 20.0 | 1000 | 2.1449 | 0.75 | 0.7115 | 0.7093 | 0.7103 | | f160a1ccc6ae96ea8a45ba15e89b46d4 |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | luigisaetta/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1531 - Wer: 5.5543 | 68bdc98b928dc42c3e95319f51105d29 |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 6000 | 56055e7ec8bc02753c5044f84dbdc1be |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2023 | 0.17 | 1000 | 0.1852 | 7.6354 | | 0.1215 | 0.33 | 2000 | 0.1577 | 6.4088 | | 0.0711 | 1.1 | 3000 | 0.1576 | 6.1324 | | 0.0656 | 1.27 | 4000 | 0.1499 | 5.8786 | | 0.0294 | 2.04 | 5000 | 0.1552 | 5.6234 | | 0.0351 | 2.21 | 6000 | 0.1531 | 5.5543 | | 660df7c2af12a027ea626152a6ce3e7d |
apache-2.0 | [] | false | PaddlePaddle/uie-senta-micro Sentiment analysis is a research hotspot in recent years, aiming at analyzing, processing, summarizing and reasoning emotionally subjective texts. Sentiment analysis has a wide range of application scenarios and can be applied to consumer decision making, public opinion mining, personalized recommendation and so on. According to the analysis granularity, it can be roughly divided into three categories: document-level sentiment analysis, sentence-level sentiment analysis and aspect-level sentiment analysis. Among them, aspect-level sentiment analysis includes multiple subtasks, such as aspect term extraction, opinion term extraction, aspect-opinion-sentiment triplet extraction, etc. UIE-Senta is a type of Chinese sentiment analysis model, which uses UIE as backbone and further trained based on large amount of samples related to sentiment analysis. So it has a stronger ability to understand sentiment knowledge and handle the related samples. Currently, UIE-Senta supports most of basic sentiment analysis capabilities, including sentiment-level sentiment classification, aspect-term extraction, opinion-term extraction, aspect-sentiment pair extraction, aspect-opinion pair extraction, aspect-opinion-sentiment triple extraction. You could perform sentiment analysis with UIE-Senta to improve your business analysis capabilities. <div align="center"> <img src="https://user-images.githubusercontent.com/35913314/199965793-f0933baa-5b82-47da-9271-ba36642119f8.png" /> </div> | 94d3200679d2a7c66db059fddbb6d914 |
apache-2.0 | ['pythae', 'reproducibility'] | false | This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from pythae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_svae") ``` | 44d4aa822d75ac298fd877477e8fc2bc |
apache-2.0 | ['pythae', 'reproducibility'] | false | Reproducibility This trained model reproduces the results of Table 1 in [1]. | Model | Dataset | Metric | Obtained value | Reference value | |:---:|:---:|:---:|:---:|:---:| | SVAE | Dyn. Binarized MNIST | NLL (500 IS) | 93.13 (0.01) | 93.16 (0.31) | [1] Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak. Hyperspherical variational auto-encoders. In 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018, pages 856–865. Association For Uncertainty in Artificial Intelligence (AUAI), 2018. | f8c2e940cb4dee8cc8d3227726ea4e94 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-finetuned-emotion This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1342 - F1: 0.9365 | decf57e3a561872bb6465e25c890cd37 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7357 | 1.0 | 250 | 0.2318 | 0.9224 | | 0.1758 | 2.0 | 500 | 0.1679 | 0.9349 | | 0.1228 | 3.0 | 750 | 0.1385 | 0.9382 | | 0.0961 | 4.0 | 1000 | 0.1452 | 0.9340 | | 0.0805 | 5.0 | 1250 | 0.1342 | 0.9365 | | 5e28be623c085e126e31f1e04b476364 |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | Transcribing your own audio files (in Darija) ```python from speechbrain.pretrained import EncoderASR asr_model = EncoderASR.from_hparams(source="aioxlabs/dvoice-darija", savedir="pretrained_models/asr-wav2vec2-dvoice-dar") asr_model.transcribe_file('./the_path_to_your_audio_file') ``` | 531d5b7bacd0f9326a04824097c69162 |
mit | ['generated_from_trainer', 'deberta-v3'] | false | DeBERTa v3 (small) fine-tuned on SST2 This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2134 - Accuracy: 0.9404 | 01ac19bc7a753b128f861fdaae5a732e |
mit | ['generated_from_trainer', 'deberta-v3'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.176 | 1.0 | 4210 | 0.2134 | 0.9404 | | 0.1254 | 2.0 | 8420 | 0.2362 | 0.9415 | | 0.0957 | 3.0 | 12630 | 0.3187 | 0.9335 | | 0.0673 | 4.0 | 16840 | 0.3039 | 0.9266 | | 0.0457 | 5.0 | 21050 | 0.3521 | 0.9312 | | 0fc1a35aba9681f84bc8f59a1ba64857 |
mit | [] | false | Ancient Greek BERT finetuned for tagging and parsing PROIEL (UD) This is a finetuned checkpoint of [Ancient Greek BERT](https://huggingface.co/pranaydeeps/Ancient-Greek-BERT) by Singh, Rutten and Lefever (2021), which has been trained on the [UD version of PROIEL](https://github.com/UniversalDependencies/UD_Ancient_Greek-PROIEL). The code for training and using the model can be found on [GitHub](https://github.com/clemeth/tagparse). The config file used is here: [`config.py`](config.py). If you use this model for something academic, feel free to cite the master's thesis that it sprung out of: > Clemeth, D. 2022. Tagging and Parsing Old Texts with New Techniques. University of Oslo. URL: http://urn.nb.no/URN:NBN:no-98954. | e49b940f6e4a0f29212b1803449af09a |
mit | [] | false | Performance This is the performance on the [test set of the UD version of PROIEL](https://github.com/UniversalDependencies/UD_Ancient_Greek-PROIEL/blob/master/grc_proiel-ud-test.conllu). | Metric | Accuracy | |:--|:--| | UPOS | 0.9814480997446298 | | XPOS | 0.9821991888237945 | | feats | 0.9254168544389365 | | all tags | 0.9139251915277152 | | UAS | 0.8741925792398979 | | LAS | 0.8402433528616494 | | LA | 0.9063391918281508 | | c036267111e93af9c5bab16c92795db3 |
mit | [] | false | Bibliography - Singh, P., Rutten, G. Lefever, E. 2021. A Pilot Study for BERT Language Modelling and Morphological Analysis for Ancient and Medieval Greek. Proceedings of LaTeCH-CLfL 2021, pp. 128–137. [https://doi.org/10.18653/v1/2021.latechclfl-1.15](https://doi.org/10.18653/v1/2021.latechclfl-1.15). | 7243f681c74a4b11bcd588adac903f82 |
mit | ['generated_from_trainer'] | false | clinical-finetuned-data3 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5058 - Accuracy: 0.86 - Precision: 0.875 - Recall: 0.9265 - F1: 0.9 | 6cdd4c64352e30f7420ca3f93da5bd6d |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP | f9596d239b1e38c9f37fa873ce32a409 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_unispeech-ml_s784 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 53bdda7f21af09831aa3202bd99f34ba |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Woolly Dreambooth model trained by LaCambre with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Il faut écrire : "A woolly style blablabla" Sample pictures of this concept: .jpg) Il faut écrire : "A woolly style blablabla" | 671b4a9f25280c9925ad7dfc3f155de8 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-FF9000 (Deep-Narrow version) T5-Efficient-BASE-FF9000 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. | fb44774e3c6435ad9c6fbec4ce6e435b |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-ff9000** - is of model type **Base** with the following variations: - **ff** is **9000** It has **449.42** million parameters and thus requires *ca.* **1797.7 MB** of memory in full precision (*fp32*) or **898.85 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | | cecf2e9361cf991511f5e4389a828439 |
apache-2.0 | ['generated_from_trainer'] | false | depression_suggestion This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3740 | a516b767a22819f313cc5189a4e5a85a |
apache-2.0 | ['generated_from_trainer'] | false | 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: 70 | 39d9ed5b45dbf0e61ccf9ac589db03d8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 3 | 60.7965 | | No log | 2.0 | 6 | 60.5778 | | No log | 3.0 | 9 | 60.1954 | | No log | 4.0 | 12 | 59.6487 | | No log | 5.0 | 15 | 58.9372 | | No log | 6.0 | 18 | 58.0582 | | No log | 7.0 | 21 | 57.0106 | | No log | 8.0 | 24 | 55.7910 | | No log | 9.0 | 27 | 54.3934 | | No log | 10.0 | 30 | 52.8099 | | No log | 11.0 | 33 | 51.0219 | | No log | 12.0 | 36 | 49.0127 | | No log | 13.0 | 39 | 46.7522 | | No log | 14.0 | 42 | 44.2033 | | No log | 15.0 | 45 | 41.3146 | | No log | 16.0 | 48 | 37.9982 | | No log | 17.0 | 51 | 34.2236 | | No log | 18.0 | 54 | 29.8068 | | No log | 19.0 | 57 | 24.9750 | | No log | 20.0 | 60 | 20.0707 | | No log | 21.0 | 63 | 15.5166 | | No log | 22.0 | 66 | 12.0328 | | No log | 23.0 | 69 | 9.1012 | | No log | 24.0 | 72 | 7.2116 | | No log | 25.0 | 75 | 6.3149 | | No log | 26.0 | 78 | 5.8127 | | No log | 27.0 | 81 | 5.4548 | | No log | 28.0 | 84 | 5.1684 | | No log | 29.0 | 87 | 4.8927 | | No log | 30.0 | 90 | 4.6128 | | No log | 31.0 | 93 | 4.3782 | | No log | 32.0 | 96 | 4.1996 | | No log | 33.0 | 99 | 4.0981 | | No log | 34.0 | 102 | 4.0022 | | No log | 35.0 | 105 | 3.9224 | | No log | 36.0 | 108 | 3.8381 | | No log | 37.0 | 111 | 3.7660 | | No log | 38.0 | 114 | 3.6887 | | No log | 39.0 | 117 | 3.6483 | | No log | 40.0 | 120 | 3.6020 | | No log | 41.0 | 123 | 3.5590 | | No log | 42.0 | 126 | 3.5199 | | No log | 43.0 | 129 | 3.4646 | | No log | 44.0 | 132 | 3.4098 | | No log | 45.0 | 135 | 3.3684 | | No log | 46.0 | 138 | 3.3290 | | No log | 47.0 | 141 | 3.3113 | | No log | 48.0 | 144 | 3.3033 | | No log | 49.0 | 147 | 3.2928 | | No log | 50.0 | 150 | 3.2776 | | No log | 51.0 | 153 | 3.2587 | | No log | 52.0 | 156 | 3.2487 | | No log | 53.0 | 159 | 3.2390 | | No log | 54.0 | 162 | 3.2318 | | No log | 55.0 | 165 | 3.2311 | | No log | 56.0 | 168 | 3.2377 | | No log | 57.0 | 171 | 3.2554 | | No log | 58.0 | 174 | 3.2720 | | No log | 59.0 | 177 | 3.2781 | | No log | 60.0 | 180 | 3.2882 | | No log | 61.0 | 183 | 3.3089 | | No log | 62.0 | 186 | 3.3352 | | No log | 63.0 | 189 | 3.3519 | | No log | 64.0 | 192 | 3.3233 | | No log | 65.0 | 195 | 3.3028 | | No log | 66.0 | 198 | 3.3153 | | No log | 67.0 | 201 | 3.3422 | | No log | 68.0 | 204 | 3.3753 | | No log | 69.0 | 207 | 3.4003 | | No log | 70.0 | 210 | 3.3740 | | 07f177fe3cece1c4c67db341b3531e74 |
mit | ['translation'] | false | mBART 25 SentencePiece tokenizer This tokenizer is used for Mideind's mBART translation models. It is based on Facebooks mBART-25 SentencePiece model. A language token from the original model has been replaced with "is_IS". Usage example (for debugging): ```python import sys from transformers.models import mbart MODEL_DIR = sys.argv[1] tokenizer: mbart.MBartTokenizerFast = mbart.MBartTokenizerFast.from_pretrained( MODEL_DIR, src_lang="en_XX" ) is_lang_idx = tokenizer.convert_tokens_to_ids("is_IS") model = mbart.MBartForConditionalGeneration.from_pretrained(MODEL_DIR) test_sentence = "This is a test." input_ids = tokenizer(test_sentence, return_tensors="pt") print(input_ids) outputs = model.generate( **input_ids, decoder_start_token_id=is_lang_idx ) print(outputs) print(tokenizer.batch_decode(outputs)) ``` | a225d1438bfee7cdaa9569186c3db6c0 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-small-squad-qg-no-paragraph` This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). This model is fine-tuned without pargraph information but only the sentence that contains the answer. | 033305e8c27086bad333c02151fba583 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-small-squad-qg-no-paragraph") output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | 5799be9bce024b58c4e41c3709d826df |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-squad-qg-no-paragraph/raw/main/eval/metric.first.sentence.sentence_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:---------------------------------------------------------------| | BERTScore | 90.36 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 55.39 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 39.1 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 29.7 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 23.23 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 24.8 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 63.18 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 50.18 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | 02442bc0d9250a57db9741dda7f52943 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['sentence_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-small - max_length: 128 - max_length_output: 32 - epoch: 8 - batch: 64 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 1 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/t5-small-squad-qg-no-paragraph/raw/main/trainer_config.json). | 3860406e127067a6fc372edab2537edc |
cc-by-4.0 | [] | false | FinEst BERT FinEst BERT is a trilingual model, using bert-base architecture, trained on Finnish, Estonian, and English corpora. Focusing on three languages, the model performs better than [multilingual BERT](https://huggingface.co/bert-base-multilingual-cased), while still offering an option for cross-lingual knowledge transfer, which a monolingual model wouldn't. Evaluation is presented in our article: ``` @Inproceedings{ulcar-robnik2020finest, author = "Ulčar, M. and Robnik-Šikonja, M.", year = 2020, title = "{FinEst BERT} and {CroSloEngual BERT}: less is more in multilingual models", editor = "Sojka, P and Kopeček, I and Pala, K and Horák, A", booktitle = "Text, Speech, and Dialogue {TSD 2020}", series = "Lecture Notes in Computer Science", volume = 12284, publisher = "Springer", url = "https://doi.org/10.1007/978-3-030-58323-1_11", } ``` The preprint is available at [arxiv.org/abs/2006.07890](https://arxiv.org/abs/2006.07890). | 4aa12f02d3228f3203d0c73ebeb62a2d |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-squad2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9325 | f221a98704774d4ea505889864743eb0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.88 | 1.0 | 8160 | 0.8129 | | 0.6643 | 2.0 | 16320 | 0.8567 | | 0.5096 | 3.0 | 24480 | 0.9325 | | ec168572935034e0068c1ad01fbd359b |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-NL24 (Deep-Narrow version) T5-Efficient-SMALL-NL24 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. | f50f4d7f789ac3caf53c3c1f424cb49a |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-nl24** - is of model type **Small** with the following variations: - **nl** is **24** It has **192.73** million parameters and thus requires *ca.* **770.92 MB** of memory in full precision (*fp32*) or **385.46 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | | 295b5e3e4f6f85a36b86ceb1ca52a88a |
mit | ['T5', 'Seq2Seq', 'EconderDecoder', 'Spanish'] | false | Spanish T5 (small) trained on [large_spanish_corpus](https://huggingface.co/datasets/viewer/?dataset=large_spanish_corpus). This is a Spanish **T5** (small arch) trained from scratch on the [large_spanish_corpus](https://huggingface.co/datasets/viewer/?dataset=large_spanish_corpus) aka BETO's corpus with [Flax](https://github.com/google/flax) This is part of the [Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google. | 4b033f2c63be844d2a9d0bf42dd1edbf |
mit | ['T5', 'Seq2Seq', 'EconderDecoder', 'Spanish'] | false | Citation If you want to cite this model you can use this: ```bibtex @misc{mromero2021spanish-t5-small, title={Spanish T5 (small) by Manuel Romero}, author={Romero, Manuel}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/flax-community/spanish-t5-small}}, year={2021} } ``` | a41a4b6064939a4bd65b488ec04f8481 |
apache-2.0 | ['fill-mask', 'korean', 'lassl'] | false | How to use
```python
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("lassl/bert-ko-base")
tokenizer = AutoTokenizer.from_pretrained("lassl/bert-ko-base")
```
| 33b19209ed361436f1b8c069a8b6d627 |
apache-2.0 | ['fill-mask', 'korean', 'lassl'] | false | Corpora
This model was trained from 702,437 examples (whose have 3,596,465,664 tokens). 702,437 examples are extracted from below corpora. If you want to get information for training, you should see `config.json`.
```bash
corpora/
├── [707M] kowiki_latest.txt
├── [ 26M] modu_dialogue_v1.2.txt
├── [1.3G] modu_news_v1.1.txt
├── [9.7G] modu_news_v2.0.txt
├── [ 15M] modu_np_v1.1.txt
├── [1008M] modu_spoken_v1.2.txt
├── [6.5G] modu_written_v1.0.txt
└── [413M] petition.txt
```
| a7a1403a6133f771aa55cf44b843377b |
apache-2.0 | ['generated_from_trainer'] | false | test-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1014 - Precision: 0.9609 - Recall: 0.9574 - F1: 0.9591 - Accuracy: 0.9732 | 707195b849270bf76fc32a2f22019731 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 151 | 0.1848 | 0.9060 | 0.9184 | 0.9122 | 0.9490 | | No log | 2.0 | 302 | 0.1137 | 0.9548 | 0.9529 | 0.9538 | 0.9705 | | No log | 3.0 | 453 | 0.1014 | 0.9609 | 0.9574 | 0.9591 | 0.9732 | | 3082f3aeeec5bb41e2040b9269f1dfe6 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-bne-finetuned-detests-02-11-2022 This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8124 - F1: 0.6381 | 6d82e063df6eaf864615e560a8e1afbb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.379 | 0.64 | 25 | 0.4136 | 0.0 | | 0.315 | 1.28 | 50 | 0.3663 | 0.6343 | | 0.3228 | 1.92 | 75 | 0.3424 | 0.6386 | | 0.1657 | 2.56 | 100 | 0.5133 | 0.5385 | | 0.108 | 3.21 | 125 | 0.4766 | 0.6452 | | 0.0631 | 3.85 | 150 | 0.6063 | 0.6083 | | 0.0083 | 4.49 | 175 | 0.6200 | 0.6198 | | 0.0032 | 5.13 | 200 | 0.6508 | 0.6335 | | 0.0047 | 5.77 | 225 | 0.6877 | 0.6269 | | 0.0018 | 6.41 | 250 | 0.7745 | 0.6148 | | 0.0014 | 7.05 | 275 | 0.7741 | 0.6299 | | 0.001 | 7.69 | 300 | 0.7896 | 0.6381 | | 0.0011 | 8.33 | 325 | 0.8008 | 0.6381 | | 0.0008 | 8.97 | 350 | 0.8086 | 0.6381 | | 0.0009 | 9.62 | 375 | 0.8124 | 0.6381 | | 4e4f8aa96c53cbef8f29bcff9b66bf57 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples-new This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3103 - Accuracy: 0.8667 - F1: 0.8667 | 1b98f96ff37316b096009523f417cdfc |
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