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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apache-2.0 | ['translation'] | false | tur-ukr * source group: Turkish * target group: Ukrainian * OPUS readme: [tur-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-ukr/README.md) * model: transformer-align * source language(s): tur * target language(s): ukr * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-ukr/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-ukr/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-ukr/opus-2020-06-17.eval.txt) | 13f08a56320ebed29499b5e922ec075a |
apache-2.0 | ['translation'] | false | System Info: - hf_name: tur-ukr - source_languages: tur - target_languages: ukr - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-ukr/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['tr', 'uk'] - src_constituents: {'tur'} - tgt_constituents: {'ukr'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/tur-ukr/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/tur-ukr/opus-2020-06-17.test.txt - src_alpha3: tur - tgt_alpha3: ukr - short_pair: tr-uk - chrF2_score: 0.624 - bleu: 42.5 - brevity_penalty: 0.983 - ref_len: 12988.0 - src_name: Turkish - tgt_name: Ukrainian - train_date: 2020-06-17 - src_alpha2: tr - tgt_alpha2: uk - prefer_old: False - long_pair: tur-ukr - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 52b7254db62b2ad1f7a3605207b1c7fb |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `lmqg/bart-large-tweetqa-qag` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question & answer pair generation task on the [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 18b1fa9a7adcc5e36fa1e54803dfead8 |
cc-by-4.0 | ['questions and answers generation'] | false | Overview - **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large) - **Language:** en - **Training data:** [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) | 35580d170d548cf9d5254b613913d9b9 |
cc-by-4.0 | ['questions and answers generation'] | false | model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/bart-large-tweetqa-qag") output = pipe("Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | e797b3e863d45e41fb04127031a768db |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-large-tweetqa-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:---------------------------------------------------------------------| | BERTScore | 91.27 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | Bleu_1 | 44.55 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | Bleu_2 | 31.15 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | Bleu_3 | 21.58 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | Bleu_4 | 15.18 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | METEOR | 27.91 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | MoverScore | 62.25 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | QAAlignedF1Score (BERTScore) | 92.47 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | QAAlignedF1Score (MoverScore) | 64.66 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | QAAlignedPrecision (BERTScore) | 92.74 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | QAAlignedPrecision (MoverScore) | 65.39 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | QAAlignedRecall (BERTScore) | 92.21 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | QAAlignedRecall (MoverScore) | 64.03 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | ROUGE_L | 34.99 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | 9d870d1e8b9dd8bc4563a4fda6c3ba0e |
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_tweetqa - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: facebook/bart-large - max_length: 256 - max_length_output: 128 - epoch: 14 - batch: 32 - lr: 5e-05 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 8 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/bart-large-tweetqa-qag/raw/main/trainer_config.json). | c674505a44dda10dc09cfab2b5b8ec60 |
apache-2.0 | ['generated_from_trainer'] | false | Sentiment140_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the sentiment140 dataset. It achieves the following results on the evaluation set: - Loss: 0.6103 - Accuracy: 0.8533 | 19294a82439a37bacd66a3ed9de6ef82 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6713 | 0.08 | 50 | 0.5704 | 0.7333 | | 0.5742 | 0.16 | 100 | 0.4620 | 0.8 | | 0.5104 | 0.24 | 150 | 0.5536 | 0.74 | | 0.5313 | 0.32 | 200 | 0.5198 | 0.76 | | 0.5023 | 0.4 | 250 | 0.4286 | 0.8 | | 0.4871 | 0.48 | 300 | 0.4294 | 0.8267 | | 0.4513 | 0.56 | 350 | 0.4349 | 0.8133 | | 0.4647 | 0.64 | 400 | 0.4046 | 0.8333 | | 0.4827 | 0.72 | 450 | 0.4218 | 0.8333 | | 0.4517 | 0.8 | 500 | 0.4093 | 0.82 | | 0.4417 | 0.88 | 550 | 0.3999 | 0.84 | | 0.4701 | 0.96 | 600 | 0.3779 | 0.8867 | | 0.397 | 1.04 | 650 | 0.3730 | 0.8667 | | 0.3377 | 1.12 | 700 | 0.3833 | 0.8333 | | 0.411 | 1.2 | 750 | 0.3704 | 0.84 | | 0.3796 | 1.28 | 800 | 0.3472 | 0.86 | | 0.3523 | 1.36 | 850 | 0.3512 | 0.8733 | | 0.3992 | 1.44 | 900 | 0.3712 | 0.84 | | 0.3641 | 1.52 | 950 | 0.3718 | 0.82 | | 0.3973 | 1.6 | 1000 | 0.3508 | 0.84 | | 0.3576 | 1.68 | 1050 | 0.3600 | 0.86 | | 0.3701 | 1.76 | 1100 | 0.3287 | 0.8667 | | 0.3721 | 1.84 | 1150 | 0.3794 | 0.82 | | 0.3673 | 1.92 | 1200 | 0.3378 | 0.8733 | | 0.4223 | 2.0 | 1250 | 0.3508 | 0.86 | | 0.2745 | 2.08 | 1300 | 0.3835 | 0.86 | | 0.283 | 2.16 | 1350 | 0.3500 | 0.8533 | | 0.2769 | 2.24 | 1400 | 0.3334 | 0.8733 | | 0.2491 | 2.32 | 1450 | 0.3519 | 0.8867 | | 0.3237 | 2.4 | 1500 | 0.3438 | 0.86 | | 0.2662 | 2.48 | 1550 | 0.3513 | 0.8667 | | 0.2423 | 2.56 | 1600 | 0.3413 | 0.8867 | | 0.2655 | 2.64 | 1650 | 0.3126 | 0.8933 | | 0.2516 | 2.72 | 1700 | 0.3333 | 0.8733 | | 0.252 | 2.8 | 1750 | 0.3316 | 0.88 | | 0.2872 | 2.88 | 1800 | 0.3227 | 0.9 | | 0.306 | 2.96 | 1850 | 0.3383 | 0.8733 | | 0.248 | 3.04 | 1900 | 0.3474 | 0.8733 | | 0.1507 | 3.12 | 1950 | 0.4140 | 0.8667 | | 0.1994 | 3.2 | 2000 | 0.3729 | 0.8533 | | 0.167 | 3.28 | 2050 | 0.3782 | 0.8867 | | 0.1872 | 3.36 | 2100 | 0.4352 | 0.8867 | | 0.1611 | 3.44 | 2150 | 0.4511 | 0.8667 | | 0.2338 | 3.52 | 2200 | 0.4244 | 0.8533 | | 0.1538 | 3.6 | 2250 | 0.4226 | 0.8733 | | 0.1561 | 3.68 | 2300 | 0.4126 | 0.88 | | 0.2156 | 3.76 | 2350 | 0.4382 | 0.86 | | 0.1684 | 3.84 | 2400 | 0.4969 | 0.86 | | 0.1917 | 3.92 | 2450 | 0.4439 | 0.8667 | | 0.1584 | 4.0 | 2500 | 0.4759 | 0.86 | | 0.1038 | 4.08 | 2550 | 0.5042 | 0.8667 | | 0.0983 | 4.16 | 2600 | 0.5527 | 0.8533 | | 0.1404 | 4.24 | 2650 | 0.5801 | 0.84 | | 0.0844 | 4.32 | 2700 | 0.5884 | 0.86 | | 0.1347 | 4.4 | 2750 | 0.5865 | 0.8467 | | 0.1373 | 4.48 | 2800 | 0.5915 | 0.8533 | | 0.1506 | 4.56 | 2850 | 0.5976 | 0.8467 | | 0.1007 | 4.64 | 2900 | 0.6678 | 0.82 | | 0.1311 | 4.72 | 2950 | 0.6082 | 0.8533 | | 0.1402 | 4.8 | 3000 | 0.6180 | 0.8467 | | 0.1363 | 4.88 | 3050 | 0.6107 | 0.8533 | | 0.0995 | 4.96 | 3100 | 0.6103 | 0.8533 | | f44d0b89ccee2ea3a91b22bf72e67e6a |
cc-by-4.0 | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sarnikowski/electra-small-discriminator-da-256-cased") model = AutoModel.from_pretrained("sarnikowski/electra-small-discriminator-da-256-cased") ``` | cb5090b6a90ac936d7c1d2c41305de38 |
cc-by-4.0 | [] | false | Questions? If you have any questions feel free to open an issue on the [danish_transformers](https://github.com/sarnikowski/danish_transformers) repository, or send an email to p.sarnikowski@gmail.com | 494a2a94ba6d07d842c31ad02a963917 |
apache-2.0 | ['translation'] | false | opus-mt-gil-fr * source languages: gil * target languages: fr * OPUS readme: [gil-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/gil-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/gil-fr/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/gil-fr/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/gil-fr/opus-2020-01-09.eval.txt) | e1f662e5d01d678a6b5721513d95671d |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-entailement-Writer-T5-small This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5628 | ccdcaefa193a6bd061647fe6c4ddd51e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 83 | 1.2943 | | No log | 2.0 | 166 | 0.9323 | | No log | 3.0 | 249 | 0.8443 | | No log | 4.0 | 332 | 0.7884 | | No log | 5.0 | 415 | 0.7582 | | No log | 6.0 | 498 | 0.7355 | | 1.2761 | 7.0 | 581 | 0.7178 | | 1.2761 | 8.0 | 664 | 0.7105 | | 1.2761 | 9.0 | 747 | 0.6972 | | 1.2761 | 10.0 | 830 | 0.6847 | | 1.2761 | 11.0 | 913 | 0.6774 | | 1.2761 | 12.0 | 996 | 0.6708 | | 0.7765 | 13.0 | 1079 | 0.6609 | | 0.7765 | 14.0 | 1162 | 0.6566 | | 0.7765 | 15.0 | 1245 | 0.6507 | | 0.7765 | 16.0 | 1328 | 0.6454 | | 0.7765 | 17.0 | 1411 | 0.6438 | | 0.7765 | 18.0 | 1494 | 0.6384 | | 0.693 | 19.0 | 1577 | 0.6347 | | 0.693 | 20.0 | 1660 | 0.6321 | | 0.693 | 21.0 | 1743 | 0.6254 | | 0.693 | 22.0 | 1826 | 0.6237 | | 0.693 | 23.0 | 1909 | 0.6215 | | 0.693 | 24.0 | 1992 | 0.6167 | | 0.6504 | 25.0 | 2075 | 0.6167 | | 0.6504 | 26.0 | 2158 | 0.6131 | | 0.6504 | 27.0 | 2241 | 0.6120 | | 0.6504 | 28.0 | 2324 | 0.6091 | | 0.6504 | 29.0 | 2407 | 0.6076 | | 0.6504 | 30.0 | 2490 | 0.6058 | | 0.615 | 31.0 | 2573 | 0.6031 | | 0.615 | 32.0 | 2656 | 0.6015 | | 0.615 | 33.0 | 2739 | 0.6015 | | 0.615 | 34.0 | 2822 | 0.6000 | | 0.615 | 35.0 | 2905 | 0.5998 | | 0.615 | 36.0 | 2988 | 0.5969 | | 0.586 | 37.0 | 3071 | 0.5959 | | 0.586 | 38.0 | 3154 | 0.5941 | | 0.586 | 39.0 | 3237 | 0.5923 | | 0.586 | 40.0 | 3320 | 0.5936 | | 0.586 | 41.0 | 3403 | 0.5929 | | 0.586 | 42.0 | 3486 | 0.5922 | | 0.5618 | 43.0 | 3569 | 0.5910 | | 0.5618 | 44.0 | 3652 | 0.5885 | | 0.5618 | 45.0 | 3735 | 0.5879 | | 0.5618 | 46.0 | 3818 | 0.5873 | | 0.5618 | 47.0 | 3901 | 0.5877 | | 0.5618 | 48.0 | 3984 | 0.5878 | | 0.5418 | 49.0 | 4067 | 0.5881 | | 0.5418 | 50.0 | 4150 | 0.5858 | | 0.5418 | 51.0 | 4233 | 0.5847 | | 0.5418 | 52.0 | 4316 | 0.5839 | | 0.5418 | 53.0 | 4399 | 0.5843 | | 0.5418 | 54.0 | 4482 | 0.5826 | | 0.5283 | 55.0 | 4565 | 0.5843 | | 0.5283 | 56.0 | 4648 | 0.5833 | | 0.5283 | 57.0 | 4731 | 0.5825 | | 0.5283 | 58.0 | 4814 | 0.5827 | | 0.5283 | 59.0 | 4897 | 0.5830 | | 0.5283 | 60.0 | 4980 | 0.5806 | | 0.5135 | 61.0 | 5063 | 0.5808 | | 0.5135 | 62.0 | 5146 | 0.5806 | | 0.5135 | 63.0 | 5229 | 0.5807 | | 0.5135 | 64.0 | 5312 | 0.5823 | | 0.5135 | 65.0 | 5395 | 0.5801 | | 0.5135 | 66.0 | 5478 | 0.5799 | | 0.5053 | 67.0 | 5561 | 0.5808 | | 0.5053 | 68.0 | 5644 | 0.5796 | | 0.5053 | 69.0 | 5727 | 0.5793 | | 0.5053 | 70.0 | 5810 | 0.5785 | | 0.5053 | 71.0 | 5893 | 0.5790 | | 0.5053 | 72.0 | 5976 | 0.5775 | | 0.4985 | 73.0 | 6059 | 0.5770 | | 0.4985 | 74.0 | 6142 | 0.5777 | | 0.4985 | 75.0 | 6225 | 0.5780 | | 0.4985 | 76.0 | 6308 | 0.5779 | | 0.4985 | 77.0 | 6391 | 0.5782 | | 0.4985 | 78.0 | 6474 | 0.5773 | | 0.4889 | 79.0 | 6557 | 0.5787 | | 0.4889 | 80.0 | 6640 | 0.5787 | | 0.4889 | 81.0 | 6723 | 0.5773 | | 0.4889 | 82.0 | 6806 | 0.5777 | | 0.4889 | 83.0 | 6889 | 0.5759 | | 0.4889 | 84.0 | 6972 | 0.5765 | | 0.4806 | 85.0 | 7055 | 0.5758 | | 0.4806 | 86.0 | 7138 | 0.5760 | | 0.4806 | 87.0 | 7221 | 0.5758 | | 0.4806 | 88.0 | 7304 | 0.5760 | | 0.4806 | 89.0 | 7387 | 0.5759 | | 0.4806 | 90.0 | 7470 | 0.5758 | | 0.4817 | 91.0 | 7553 | 0.5753 | | 0.4817 | 92.0 | 7636 | 0.5757 | | 0.4817 | 93.0 | 7719 | 0.5754 | | 0.4817 | 94.0 | 7802 | 0.5750 | | 0.4817 | 95.0 | 7885 | 0.5753 | | 0.4817 | 96.0 | 7968 | 0.5752 | | 0.4767 | 97.0 | 8051 | 0.5754 | | 0.4767 | 98.0 | 8134 | 0.5756 | | 0.4767 | 99.0 | 8217 | 0.5755 | | 0.4767 | 100.0 | 8300 | 0.5755 | | 3ab786bbe90c5d7d2766289984c2e6bb |
creativeml-openrail-m | [] | false | Use 'wewulzkz' as the keyword. A bit overcooked but gets the job done, I wanted a model that could create a good base for werewolves that I could then paint over, and this serves that purpose well. Based on SD 1.5.  It is also pretty good at applying the werewolf concept to other things. For example, below the prompt is just "A spooky cat" and "a spooky mouse"  Or this example is a not cherrypicked (first results) for just the prompt "An alligator"  | 045f36734b34de76e2d6a5d9628dd98d |
mit | ['summarization'] | false | Pre-trained BART Model fine-tune on WikiLingua dataset The repository for the fine-tuned BART model (by sshleifer) using the **wiki_lingua** dataset (English) **Purpose:** Examine the performance of a fine-tuned model research purposes **Observation:** - Pre-trained model was trained on the XSum dataset, which summarize a not-too-long documents into one-liner summary - Fine-tuning this model using WikiLingua is appropriate since the summaries for that dataset are also short - In the end, however, the model cannot capture much clearer key points, but instead it mostly extracts the opening sentence - Some data pre-processing and models' hyperparameter are also need to be tuned more properly. | edf63d520d044708b74a9136ded75f8d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 24 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 | bd7c94e2b0acc80ba41428b481456ae4 |
apache-2.0 | ['translation'] | false | opus-mt-fi-ts * source languages: fi * target languages: ts * OPUS readme: [fi-ts](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-ts/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-ts/opus-2020-01-24.zip) * test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-ts/opus-2020-01-24.test.txt) * test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-ts/opus-2020-01-24.eval.txt) | 9d63c9dacb513b75e4ab58739edb2ef8 |
cc-by-4.0 | ['answer extraction'] | false | Model Card of `lmqg/mt5-base-koquad-ae` This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for answer extraction on the [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 759bc5bf28a58f2b9da5245cd2d27a02 |
cc-by-4.0 | ['answer extraction'] | false | model prediction answers = model.generate_a("1990๋
์ํ ใ ๋จ๋ถ๊ตฐ ใ์์ ๋จ์ญ์ผ๋ก ์ํ๋ฐฐ์ฐ ์ฒซ ๋ฐ๋ท์ ์ด์ด ๊ฐ์ ํด KBS ๋๋ผ๋ง ใ์ง๊ตฌ์ธใ์์ ๋จ์ญ์ผ๋ก ์ถ์ฐํ์๊ณ ์ด๋ฌํด MBC ใ์ฌ๋ช
์ ๋๋์ใ๋ฅผ ํตํด ๋จ์ญ์ผ๋ก ์ถ์ฐํ์๋ค.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-base-koquad-ae") output = pipe("๋ํ ์คํผ์ด์ค๋ ๋ง์ ์๋ก์ด ์ฌ์ฑ ์ํฐ์คํธ๋ค์๊ฒ ์ํฅ์ ๋ผ์ณค๋๋ฐ, ๋ํ์ ์ผ๋ก ๋ฐ๋ฏธ ๋ก๋ฐํ , ์ผ์ดํฐ ํ๋ฆฌ, ํฌ๋ฆฌ์คํฐ๋์ ๋๋ฐ์ง, ๋ ์ด๋ ๊ฐ๊ฐ, ๋ฆฌํ ๋ถ์ธ , ์
๋ ๋ ๊ณ ๋ฉ์ฆ & ๋์ฌ, ํฝ์ ๋กํธ ์ด ์๋ค. 2007๋
๋น์์ธ ๋์ค๋ Total Request Live์์ ์ธํฐ๋ทฐ์์ '๋๋ ๋ธ๋ฆฌํธ๋๋ฅผ ์ฌ๋ํ๊ณ ํฌ์ด์์. ํนํ ์ ์จ๋ฒ Blackout์ ์ข์ํด์'๋ผ๊ณ ๋งํ๋ค. ๋ฆฐ์ ์ด ๋กํ์ '์ธ์ ๋ ๋ธ๋ฆฌํธ๋ ์คํผ์ด์ค์๊ฒ ์๊ฐ์ ๋ฐ๋๋ค. ํ์ฐฝ์์ ๊ทธ๋
์ฒ๋ผ ํ๋ธ๋ก์ด๋์ ์ค๋ฅด๊ธฐ๋ฅผ ๊ฟ๊ฟ์๋ค'๊ณ ๋งํ๋ฉฐ ๋กค ๋ชจ๋ธ๋ก ๊ผฝ์๋ค. ์คํผ์ด์ค๋ ํ๋ ์์
๊ฐ๋ค์๊ฒ ์์
์ ์๊ฐ์ผ๋ก ์ธ๊ธ๋๊ธฐ๋ ํ๋ค. <hl> ๋ง์ผ๋ฆฌ ์ฌ์ด๋ฌ์ค๋ ์์ ์ ํํธ๊ณก Party in the U.S.A. ๊ฐ ๋ธ๋ฆฌํธ๋์๊ฒ ์๊ฐ๊ณผ ์ํฅ์ ๋ฐ์ ๊ณก์ด๋ผ๊ณ ๋ฐํ๋ค. <hl> ๋ฒ ๋ฆฌ ๋งค๋๋ก์ฐ์ ์จ๋ฒ 15 Minutes ์ญ์ ๋ธ๋ฆฌํธ๋์๊ฒ ์๊ฐ์ ์ป์๋ค๊ณ ์ธ๊ธ๋์๋ค.") ``` | 21506eb71cf9cef907fa50e670ae5fbf |
cc-by-4.0 | ['answer extraction'] | false | Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-koquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_koquad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:-----------------------------------------------------------------| | AnswerExactMatch | 69.49 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | AnswerF1Score | 77.32 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | BERTScore | 91.76 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | Bleu_1 | 59.38 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | Bleu_2 | 48.34 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | Bleu_3 | 34.11 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | Bleu_4 | 20.6 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | METEOR | 51.78 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | MoverScore | 90.78 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | ROUGE_L | 72.57 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | | b074cddb3783e49adb8d26e6fbe50bc9 |
cc-by-4.0 | ['answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_koquad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: None - model: google/mt5-base - max_length: 512 - max_length_output: 32 - epoch: 5 - batch: 8 - lr: 0.0005 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 8 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-koquad-ae/raw/main/trainer_config.json). | 39b193c76eb117719e20ee0559a4b6a3 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-LARGE-KV128 (Deep-Narrow version) T5-Efficient-LARGE-KV128 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 modelโs 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. | 7f8d1ad91be9f189d2ec884d5e2cb8ce |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-large-kv128** - is of model type **Large** with the following variations: - **kv** is **128** It has **1039.71** million parameters and thus requires *ca.* **4158.86 MB** of memory in full precision (*fp32*) or **2079.43 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 | | 1c1dd5262e731ab918ac3bcbf5168cfe |
mit | ['conversational'] | false | personachat-arabic (conversational AI) This is personachat-arabic, using a subset from the persona-chat validation dataset, machine translated to Arabic (from English) and fine-tuned from [akhooli/gpt2-small-arabic](https://huggingface.co/akhooli/gpt2-small-arabic) which is a limited text generation model. Usage: see the last section of this [example notebook](https://colab.research.google.com/drive/1I6RFOWMaTpPBX7saJYjnSTddW0TD6H1t?usp=sharing) Note: model has limited training set which was machine translated (do not use for production). | 355a4d63a87db4472f2bd1d89d8671de |
apache-2.0 | ['generated_from_trainer'] | false | insertion-prop05-ls01 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2120 - Precision: 0.9800 - Recall: 0.9776 - F1: 0.9788 - Accuracy: 0.9924 | fa069a87439443bba792e18db6f0b00e |
apache-2.0 | ['generated_from_trainer'] | false | 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: 1 - label_smoothing_factor: 0.1 | 5c055301ae95d3be80c68db0033865e0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2462 | 0.32 | 500 | 0.2160 | 0.9754 | 0.9697 | 0.9725 | 0.9902 | | 0.2194 | 0.64 | 1000 | 0.2128 | 0.9784 | 0.9763 | 0.9773 | 0.9919 | | 0.2171 | 0.96 | 1500 | 0.2120 | 0.9800 | 0.9776 | 0.9788 | 0.9924 | | 2635c79814db5dc51e4bd44d66114a26 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-NL24 (Deep-Narrow version) T5-Efficient-BASE-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 modelโs 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. | bba1ba0e232b0c42d458505aeb2a013f |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-nl24** - is of model type **Base** with the following variations: - **nl** is **24** It has **421.19** million parameters and thus requires *ca.* **1684.75 MB** of memory in full precision (*fp32*) or **842.37 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 | | ba505c7ce97259864199ec4c12932980 |
cc-by-4.0 | ['hi', 'en', 'codemix'] | false | HingRoBERTa-Mixed HingRoBERTa-Mixed is a Hindi-English code-mixed BERT model trained on roman + devanagari text. It is a xlm-RoBERTa model fine-tuned on mixed script L3Cube-HingCorpus. <br> [dataset link] (https://github.com/l3cube-pune/code-mixed-nlp) More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2204.08398) ``` @inproceedings{nayak-joshi-2022-l3cube, title = "{L}3{C}ube-{H}ing{C}orpus and {H}ing{BERT}: A Code Mixed {H}indi-{E}nglish Dataset and {BERT} Language Models", author = "Nayak, Ravindra and Joshi, Raviraj", booktitle = "Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.wildre-1.2", pages = "7--12", } ``` | 6acab6cf9cd88d9ca338258bc25957b7 |
apache-2.0 | ['translation'] | false | opus-mt-fr-srn * source languages: fr * target languages: srn * OPUS readme: [fr-srn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-srn/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-srn/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-srn/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-srn/opus-2020-01-16.eval.txt) | bf8e195d3f10482559986697d226a33f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Vi v1 - Shiv Kumar Ganesh This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 1.0641 - Wer: 34.0974 | e313cbe619ffde57d0f2148edff17f3e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0005 | 31.0 | 500 | 0.7179 | 33.7464 | | 0.0002 | 62.0 | 1000 | 0.7837 | 32.4742 | | 0.0001 | 93.0 | 1500 | 0.8267 | 34.2729 | | 0.0001 | 124.0 | 2000 | 0.8677 | 35.1722 | | 0.0 | 156.0 | 2500 | 0.9045 | 35.3257 | | 0.0 | 187.0 | 3000 | 0.9316 | 33.9877 | | 0.0 | 218.0 | 3500 | 0.9585 | 34.0097 | | 0.0 | 249.0 | 4000 | 0.9846 | 33.3626 | | 0.0 | 281.0 | 4500 | 1.0082 | 33.4832 | | 0.0 | 312.0 | 5000 | 1.0247 | 33.7026 | | 0.0 | 343.0 | 5500 | 1.0391 | 32.8691 | | 0.0 | 374.0 | 6000 | 1.0516 | 32.9020 | | 0.0 | 406.0 | 6500 | 1.0606 | 33.6477 | | 0.0 | 437.0 | 7000 | 1.0641 | 34.0974 | | d3d7098c4bcdf6ceaafcb74fe33e9109 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0008 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP | 4c8f13f32fd0014f5659cbb3321a7c88 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 128, 'prefix': '<|aligned|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 128, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'debug-pt-conditional', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 8, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 10, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | 84db4350c2c7930d866da99713e90eba |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.6380 - F1: 0.5542 | e80f4f48c6e33850c2ca4ed6b93de741 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 13 | 1.0388 | 0.1801 | | No log | 2.0 | 26 | 0.7545 | 0.5053 | | No log | 3.0 | 39 | 0.6380 | 0.5542 | | f870f161a9d27bd08ce8c7af7a52337e |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.3892 - F1: 0.6859 | 4648e6bb0f6df532ecd9b76de4686033 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1135 | 1.0 | 50 | 0.5347 | 0.5463 | | 0.4935 | 2.0 | 100 | 0.4424 | 0.6338 | | 0.3732 | 3.0 | 150 | 0.3892 | 0.6859 | | 2b131da1864eaf7fbca6392b7dbec384 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-German Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | e1d7bd7ae1fab90f4546da07da6ba628 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | 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", "de", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") resampler = torchaudio.transforms.Resample(48_000, 16_000) | 4c0d7fbd20f4a7764d87dd39f5a32739 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "de", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\โ\%\โ\๏ฟฝ\ใซ\รฆ\็ก\เฝ\ใซ\่ฃ\ัน\โฆ\ยซ\ยป\รฐ\ฤฑ\โ\ๅนบ\ื\ื\ๆฏ\ั\ืข\)\แปฉ\ะฒ\ล\ั\+\โ\ั\โ\ื \ะผ\ล\ไนก\$\=\ืฉ\ั\ๆฏ\(\ยฐ\ะธ\ะบ\ฬ]' substitutions = { 'e' : '[\ษ\รฉ\ฤ\ฤ\รช\แบฟ\แบฟ\รซ\ฤ\ะต]', 'o' : '[\ล\รด\รด\รณ\รฒ\รธ\แป\ล\รต\ล\ะพ]', 'a' : '[\รก\ฤ\ฤ\ฤ\รฃ\รฅ\รข\ร \ฤ
\ะฐ]', 'c' : '[\ฤ\ฤ\รง\ั]', 'l' : '[\ล]', 'u' : '[\รบ\ลซ\แปฉ\ลฏ]', 'und' : '[\&]', 'r' : '[\ล]', 'y' : '[\รฝ]', 's' : '[\ล\ลก\ศ\ล]', 'i' : '[\ฤซ\ว\รญ\รฏ\รฎ\รฏ]', 'z' : '[\ลบ\ลพ\ลบ\ลผ]', 'n' : '[\รฑ\ล\ล]', 'g' : '[\ฤ]', 'ss' : '[\ร]', 't' : '[\ศ\ลฅ]', 'd' : '[\ฤ\ฤ]', "'": '[\สฟ\เผ\โ\`\ยด\สป\`\โ]', 'p': '\ั' } resampler = torchaudio.transforms.Resample(48_000, 16_000) | db1417632630bef467230b0b3616981a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` The model can also be evaluated with in 10% chunks which needs less ressources (to be tested). ``` import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re import jiwer lang_id = "de" processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\โ\%\โ\๏ฟฝ\ใซ\รฆ\็ก\เฝ\ใซ\่ฃ\ัน\โฆ\ยซ\ยป\รฐ\ฤฑ\โ\ๅนบ\ื\ื\ๆฏ\ั\ืข\)\แปฉ\ะฒ\ล\ั\+\โ\ั\โ\ื \ะผ\ล\ไนก\$\=\ืฉ\ั\ๆฏ\(\ยฐ\ะธ\ะบ\ฬ]' substitutions = { 'e' : '[\ษ\รฉ\ฤ\ฤ\รช\แบฟ\แบฟ\รซ\ฤ\ะต]', 'o' : '[\ล\รด\รด\รณ\รฒ\รธ\แป\ล\รต\ล\ะพ]', 'a' : '[\รก\ฤ\ฤ\ฤ\รฃ\รฅ\รข\ร \ฤ
\ะฐ]', 'c' : '[\ฤ\ฤ\รง\ั]', 'l' : '[\ล]', 'u' : '[\รบ\ลซ\แปฉ\ลฏ]', 'und' : '[\&]', 'r' : '[\ล]', 'y' : '[\รฝ]', 's' : '[\ล\ลก\ศ\ล]', 'i' : '[\ฤซ\ว\รญ\รฏ\รฎ\รฏ]', 'z' : '[\ลบ\ลพ\ลบ\ลผ]', 'n' : '[\รฑ\ล\ล]', 'g' : '[\ฤ]', 'ss' : '[\ร]', 't' : '[\ศ\ลฅ]', 'd' : '[\ฤ\ฤ]', "'": '[\สฟ\เผ\โ\`\ยด\สป\`\โ]', 'p': '\ั' } resampler = torchaudio.transforms.Resample(48_000, 16_000) | 4986e8c944e48228a5b25b4b872423fd |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | 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() for x in substitutions: batch["sentence"] = re.sub(substitutions[x], x, batch["sentence"]) speech_array, sampling_rate = torchaudio.load(batch["path"]) speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch | 06502ad49c22fdd9cb90dea3afe3d493 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch H, S, D, I = 0, 0, 0, 0 for i in range(10): print("test["+str(10*i)+"%:"+str(10*(i+1))+"%]") test_dataset = load_dataset("common_voice", "de", split="test["+str(10*i)+"%:"+str(10*(i+1))+"%]") test_dataset = test_dataset.map(speech_file_to_array_fn) result = test_dataset.map(evaluate, batched=True, batch_size=8) predictions = result["pred_strings"] targets = result["sentence"] chunk_metrics = jiwer.compute_measures(targets, predictions) H = H + chunk_metrics["hits"] S = S + chunk_metrics["substitutions"] D = D + chunk_metrics["deletions"] I = I + chunk_metrics["insertions"] WER = float(S + D + I) / float(H + S + D) print("WER: {:2f}".format(WER*100)) ``` **Test Result**: 15.80 % | 17f552d3c0a21f21c1d0915cd58c5e18 |
apache-2.0 | ['generated_from_keras_callback'] | false | bert-finetuned-ner-per-v7 This model is a fine-tuned version of [BeardedJohn/bert-finetuned-ner-ubb-conll-endava-only-misc-v2](https://huggingface.co/BeardedJohn/bert-finetuned-ner-ubb-conll-endava-only-misc-v2) on an unknown dataset. It achieves the following results on the evaluation set: | 2331e0ee6c5e4ece97fffbc7fd8cf0a5 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 313, '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: mixed_float16 | 3a4ccaf696d0ba6e367529b41ce67d3e |
apache-2.0 | ['mobile', 'vison', 'image-classification'] | false | Model Details <!-- Give an overview of your model, the relevant research paper, who trained it, etc. --> EfficientFormer-L3, developed by [Snap Research](https://github.com/snap-research), is one of three EfficientFormer models. The EfficientFormer models were released as part of an effort to prove that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance. This checkpoint of EfficientFormer-L3 was trained for 300 epochs. - Developed by: Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren - Language(s): English - License: This model is licensed under the apache-2.0 license - Resources for more information: - [Research Paper](https://arxiv.org/abs/2206.01191) - [GitHub Repo](https://github.com/snap-research/EfficientFormer/) </model_details> <how_to_start> | 188d23c753f854712a17c90160635430 |
apache-2.0 | ['generated_from_trainer'] | false | t5-large-finetune-keyword-to-text-generation This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1471 - Rouge1: 2.175 - Rouge2: 0.3661 - Rougel: 1.7927 - Rougelsum: 1.7951 - Gen Len: 15.3252 | 4d0a905c5ba04aff73bef44983a20b24 |
apache-2.0 | ['generated_from_trainer'] | false | Model description This model is designed to generate text from a single keyword. This project is intended to be used for generating vocabulary questions for ed-tech applications. NOTE!: Be sure to use the 'summarize: ' prefix before the word that you would like to un-summarize. | 2e260999e88eee3d755241d90b7abc2e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 3.3083 | 1.0 | 3000 | 3.1706 | 2.1498 | 0.331 | 1.7579 | 1.761 | 16.6826 | | 3.2121 | 2.0 | 6000 | 3.1403 | 2.1555 | 0.3409 | 1.7659 | 1.769 | 16.208 | | 3.1286 | 3.0 | 9000 | 3.1300 | 2.1577 | 0.3511 | 1.7703 | 1.7733 | 15.9009 | | 3.0567 | 4.0 | 12000 | 3.1282 | 2.183 | 0.3584 | 1.7895 | 1.7909 | 15.7135 | | 2.9953 | 5.0 | 15000 | 3.1293 | 2.1589 | 0.3525 | 1.776 | 1.7781 | 15.678 | | 2.9483 | 6.0 | 18000 | 3.1308 | 2.1645 | 0.3556 | 1.7824 | 1.784 | 15.425 | | 2.9009 | 7.0 | 21000 | 3.1358 | 2.1622 | 0.3622 | 1.7848 | 1.7877 | 15.3348 | | 2.8752 | 8.0 | 24000 | 3.1387 | 2.1716 | 0.36 | 1.7936 | 1.7963 | 15.5296 | | 2.835 | 9.0 | 27000 | 3.1454 | 2.1806 | 0.3658 | 1.7941 | 1.7966 | 15.4625 | | 2.8352 | 10.0 | 30000 | 3.1471 | 2.175 | 0.3661 | 1.7927 | 1.7951 | 15.3252 | | 0f71377a9d3f53d63c875bcdb7d42129 |
apache-2.0 | ['generated_from_keras_callback'] | false | DamianCummins/distilbert-base-uncased-finetuned-ner 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: 0.0556 - Validation Loss: 0.0608 - Train Precision: 0.9196 - Train Recall: 0.9304 - Train F1: 0.9250 - Train Accuracy: 0.9820 - Epoch: 0 | f9a71230bcbe19ce15830144a0ef8fcb |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.0556 | 0.0608 | 0.9196 | 0.9304 | 0.9250 | 0.9820 | 0 | | 4e1fde11fe3e0abcd0fab01095d13b42 |
apache-2.0 | ['generated_from_trainer'] | false | M6_cross 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: 0.0084 - Pearson: 0.9811 - Spearmanr: 0.9075 | 1fd26518dfa35334a87d747f4fd79791 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 20 - eval_batch_size: 20 - seed: 25 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 6.0 - num_epochs: 5 - mixed_precision_training: Native AMP | 1b3d344de76796529b2b6a97f2594a13 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | 0.0059 | 1.0 | 105 | 0.0158 | 0.9633 | 0.9054 | | 0.001 | 2.0 | 210 | 0.0102 | 0.9770 | 0.9103 | | 0.0008 | 3.0 | 315 | 0.0083 | 0.9805 | 0.9052 | | 0.0011 | 4.0 | 420 | 0.0075 | 0.9812 | 0.9082 | | 0.0017 | 5.0 | 525 | 0.0084 | 0.9811 | 0.9075 | | 83bdf850dad73cb49811332ddd716a7f |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `Shinji_Watanabe/laborotv_asr_train_asr_conformer2_latest33_raw_char_sp_valid.acc.ave` โป๏ธ Imported from https://zenodo.org/record/4304245/ This model was trained by Shinji Watanabe using laborotv/asr1 recipe in [espnet](https://github.com/espnet/espnet/). | 72ec830019d037a92f4329874acce30b |
apache-2.0 | ['thai', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-base-thai-syllable-upos](https://huggingface.co/KoichiYasuoka/roberta-base-thai-syllable-upos). | 9083688c8a785b31c2442188b495c07a |
apache-2.0 | ['thai', 'token-classification', 'pos', 'dependency-parsing'] | false | text = "+text+"\n" v=[(s,e) for s,e in w["offset_mapping"] if s<e] for i,(s,e) in enumerate(v,1): q=self.model.config.id2label[p[i,h[i]]].split("|") u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=UDgoeswith("KoichiYasuoka/roberta-base-thai-syllable-ud-goeswith") print(nlp("เธซเธฅเธฒเธขเธซเธฑเธงเธเธตเธเธงเนเธฒเธซเธฑเธงเนเธเธตเธขเธง")) ``` with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/). Or without ufal.chu-liu-edmonds: ``` from transformers import pipeline nlp=pipeline("universal-dependencies","KoichiYasuoka/roberta-base-thai-syllable-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple") print(nlp("เธซเธฅเธฒเธขเธซเธฑเธงเธเธตเธเธงเนเธฒเธซเธฑเธงเนเธเธตเธขเธง")) ``` | 4ed21be46d89a7e40b7254645cfab143 |
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: 0.2238 - Accuracy: 0.922 - F1: 0.9221 | 9c61ea197f9a9720c1cc1ce743ad7425 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.829 | 1.0 | 250 | 0.3173 | 0.9005 | 0.8980 | | 0.247 | 2.0 | 500 | 0.2238 | 0.922 | 0.9221 | | 36012314e2698f4243a1a1f17ae417ad |
apache-2.0 | ['generated_from_keras_callback'] | false | amitjohn007/bert-finetuned-squad 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: 0.5685 - Epoch: 2 | c6ab106f6cf2ce19c0dcc6f2db4d70b6 |
apache-2.0 | ['generated_from_keras_callback'] | false | long-t5-local-base This model is a fine-tuned version of [google/long-t5-local-base](https://huggingface.co/google/long-t5-local-base) on an unknown dataset. It achieves the following results on the evaluation set: | 75b574f1278b48ec96c43e0aa75d1008 |
apache-2.0 | ['audio', 'TTS'] | false | load the model and tokenizer from fastspeech2_hf.modeling_fastspeech2 import FastSpeech2ForPretraining, FastSpeech2Tokenizer model = FastSpeech2ForPretraining.from_pretrained("ontocord/fastspeech2-en") tokenizer = FastSpeech2Tokenizer.from_pretrained("ontocord/fastspeech2-en") | e5d4a10ff71e6a43f6a59fa6e8548196 |
apache-2.0 | ['audio', 'TTS'] | false | some helper routines from IPython.display import Audio as IPAudio, display as IPdisplay import torch import torchaudio def play_audio(waveform, sample_rate): waveform = waveform.numpy() if len(waveform.shape)==1: IPdisplay(IPAudio(waveform, rate=sample_rate)) return num_channels, num_frames = waveform.shape if num_channels <= 1: IPdisplay(IPAudio(waveform[0], rate=sample_rate)) elif num_channels == 2: IPdisplay(IPAudio((waveform[0], waveform[1]), rate=sample_rate)) else: raise ValueError("Waveform with more than 2 channels are not supported.") | 2b6d1ecd6939a08150d0146f21310313 |
apache-2.0 | ['audio', 'TTS'] | false | you can run in half mode on gpu. model = model.cuda().half() sentences = [ "Advanced text to speech models such as Fast Speech can synthesize speech significantly faster than previous auto regressive models with comparable quality. The training of Fast Speech model relies on an auto regressive teacher model for duration prediction and knowledge distillation, which can ease the one to many mapping problem in T T S. However, Fast Speech has several disadvantages, 1, the teacher student distillation pipeline is complicated, 2, the duration extracted from the teacher model is not accurate enough, and the target mel spectrograms distilled from teacher model suffer from information loss due to data simplification, both of which limit the voice quality. ", "Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition " "in being comparatively modern. ", "For although the Chinese took impressions from wood blocks engraved in relief for centuries before the woodcutters of the Netherlands, by a similar process " "produced the block books, which were the immediate predecessors of the true printed book, " "the invention of movable metal letters in the middle of the fifteenth century may justly be considered as the invention of the art of printing. ", "And it is worth mention in passing that, as an example of fine typography, " "the earliest book printed with movable types, the Gutenberg, or \"forty-two line Bible\" of about 1455, " "has never been surpassed. ", "Printing, then, for our purpose, may be considered as the art of making books by means of movable types. " "Now, as all books not primarily intended as picture-books consist principally of types composed to form letterpress,", ] batch = tokenizer(sentences, return_tensors="pt", padding=True) model.eval() with torch.no_grad(): out = model(use_postnet=False, **batch) wav =out[-2] for line, phone, w in zip(sentences, tokenizer.batch_decode(batch['input_ids']), wav): print ("txt:", line) print ("phoneme:", phone) play_audio(w.type(torch.FloatTensor), model.config.sampling_rate) ``` | d4a420df4a682555cc9dfac15146ea7a |
apache-2.0 | ['audio', 'TTS'] | false | Github Code Repo Current code for this model can be found [here](https://github.com/ontocord/fastspeech2_hf) This is a work in progress (WIP) port of the model and code from [this repo] (https://github.com/ming024/FastSpeech2). The datasets on which this model was trained: - LJSpeech: a single-speaker English dataset consists of 13100 short audio clips of a female speaker reading passages from 7 non-fiction books, approximately 24 hours in total. - LibriTTS: a multi-speaker English dataset containing 585 hours of speech by 2456 speakers. | 1487ceec1a820cd45b7950c00cb45b16 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Model Dreambooth concept any-ely-wd-Noah_Titan-3500 ฤฦฐแปฃc train bแปi hr16 bแบฑng [Shinja Zero SoTA DreamBooth_Stable_Diffusion](https://colab.research.google.com/drive/1G7qx6M_S1PDDlsWIMdbZXwdZik6sUlEh) notebook <br> Test concept bแบฑng [Shinja Zero no Notebook](https://colab.research.google.com/drive/1Hp1ZIjPbsZKlCtomJVmt2oX7733W44b0) <br> Hoแบทc test bแบฑng `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) แบขnh mแบซu cแปงa concept: WIP | cea4730f7d849b588bd5e96a1c4bf3d3 |
apache-2.0 | ['sexism detector'] | false | twitter_sexismo-finetuned-exist2021 This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) on the EXIST dataset It achieves the following results on the evaluation set: - Loss: 0.47 - Accuracy: 0.80 - F1: 0.83 - F2: 0.89 | 060be7796453a7be86485f87ad92df78 |
apache-2.0 | ['sexism detector'] | false | Training procedure The model has been trained to get the best F2 score.The F-measure is calculated as the harmonic mean of precision and recall, giving each the same weighting. It allows a model to be evaluated taking both the precision and recall into account using a single score, which is helpful when describing the performance of the model and in comparing models. The Fbeta-measure is a generalization of the F-measure that adds a configuration parameter called beta. A default beta value is 1.0, which is the same as the F-measure. A smaller beta value, such as 0.5, gives more weight to precision and less to recall, whereas a larger beta value, such as 2.0, gives less weight to precision and more weight to recall in the calculation of the score. It is a useful metric to use when both precision and recall are important but slightly more attention is needed on one or the other, such as when false negatives are more important than false positives, or the reverse.F2-measure puts more attention on minimizing false negatives. We want to detect the sexist comments. | 610cc83b4e136efb78ca3d22000251b7 |
apache-2.0 | ['sexism detector'] | false | Training hyperparameters The following hyperparameters were used during training: - my_learning_rate = 5E-5 - my_adam_epsilon = 1E-8 - my_number_of_epochs = 8 - my_warmup = 3 - my_mini_batch_size = 32 - optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | d9ae1ae9d1d1469f064a523a4830d557 |
apache-2.0 | ['sexism detector'] | false | Training results Epoch T. Loss V. Loss Accuracy F1 Precision Recall F2 1 0.478700 0.443148 0.804386 0.830160 0.750689 0.928450 0.886467 2 0.298000 0.460549 0.823684 0.841107 0.784661 0.906303 0.879048 3 0.063600 0.706177 0.817544 0.829508 0.799368 0.862010 0.848708 4 0.078700 1.060862 0.816667 0.836078 0.774709 0.908007 0.877800 5 0.005900 1.069239 0.808772 0.821604 0.790551 0.855196 0.841435 6 0.008300 1.184729 0.808772 0.821604 0.790551 0.855196 0.841435 7 0.001400 1.238865 0.816667 0.829388 0.796238 0.865417 0.850636 8 0.000100 1.267197 0.815789 0.827303 0.799682 0.856899 0.844810 9 0.000100 1.267815 0.808772 0.818937 0.799028 0.839864 0.831366 10 0.000300 1.275827 0.807895 0.818257 0.797735 0.839864 0.831086 | e8689a542ec96253b782f970fc35eb2f |
apache-2.0 | ['sexism detector'] | false | usage pipelines model_checkpoint = "robertou2/twitter_sexismo-finetuned-robertuito-exist2021" pipeline_nlp = pipeline("text-classification", model=model_checkpoint) pipeline_nlp("mujer al volante peligro!") | 76f95e6ad0566175e1be7214804f30c2 |
apache-2.0 | ['sexism detector'] | false | Retos Uno de los principales retos que se encontrรณ en este proceso ha sido disponer de un dataset en espaรฑol. Se ha logrado conseguir (previa solicitud) el dataset utilizado en [EXIST:sEXism Identification in Social neTworks](http://nlp.uned.es/exist2021/), el cual fue un gran punto de partida para comenzar con el modelo. Lamentablemente este un dataset presenta limitaciones debido a licencias y polรญticas para ser compartido libremente. Este dataset incorpora cualquier tipo de expresiรณn sexista o fenรณmenos relacionados, incluidas las afirmaciones descriptivas o informadas donde el mensaje sexista es un informe o una descripciรณn de un comportamiento sexista. se han utilizado los 3,541 tweets etiquetados en espaรฑol. Luego se logrรณ disponer de otro dataset en espaรฑol [MeTwo: Machismo and Sexism Twitter Identification dataset](https://github.com/franciscorodriguez92/MeTwo). Este dataset contiene los id de cada tweet con su etiqueta respectiva, lo que nos permitiรณ obtener el texto del tweet e incrementar el dataset original. Un desafรญo ha sido iniciar los procesos de finetuned en las prueba, esto pues se dispone de diversas variables para validar y testear (desde modelos como: BETO o Roberta, hasta hiperparรกmetros: como learning rate), y solo se disponede un plazo acotado de dos semanas, ademรกs de la curva de aprendizaje. Para este desafรญo, se han basado las primeras pruebas en los parรกmetros presentados por de Paula et al. (2021), lo cual brindรณ un punto de partida y un reto a vencer, el **_0.790 de accuracy_** obtenidos por el trabajo previo en la identificaciรณn de tweets sexistas en espaรฑol. En este รกmbito se realizaron diversas pruebas en paralelo para encontrar el mejor modelo. Luego de un proceso colaborativo de finetuned se ha logrado obtener un **83% de accuracy**. | 52a531da48d3b85b3fefc0442269592b |
apache-2.0 | ['sexism detector'] | false | Trabajos Futuros Se propone incrementar el dataset desarrollado. Para esto es posible descargar cantidades superiores de tweets en espaรฑol y aplicar tรฉcnicas de active learning para obtener un grupo reducido de tweets a etiquetar vรญa crowdsourcing, y en donde estos datos etiquetados puedan servir para etiquetar el resto. Tambiรฉn se pueden utilizar tรฉcnicas de Data Augmentation, para duplicar y extender el dataset. Realizar mรกs pruebas con otros modelos y mejorar el modelo es otro reto que se propone como trabajos futuros. | 1bba0ffb93679072cd1eae080913804e |
apache-2.0 | ['sexism detector'] | false | Posibles Aplicaciones Primero es sumamente importante dar mayor visibilidad al problema de _sexismo en redes sociales_, principalmente en espaรฑol. El proceso de Transfer Learning logra reutilizar y aprovechar modelos previamente entrenados, y lo que se desea es que nuevos grupos de investigaciรณn, estudiantes, etc. utilicen la base del actual modelo para desarrollar los propios y crear un mejor modelo. De esta manera, se podrรญa construir una herramienta que pueda identificar en tiempo real los tweets sexistas y eliminarlos antes de su propagaciรณn. | a4157484f8ee5dda7f78325f07e7fab7 |
apache-2.0 | ['sexism detector'] | false | Referencias 1 de Paula, A. F. M., da Silva, R. F., & Schlicht, I. B. (2021). Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models. arXiv preprint arXiv:2111.04551. Rodrรญguez-Sรกnchez, F., Carrillo-de-Albornoz, J., Plaza, L., Gonzalo, J., Rosso, P., Comet, M., & Donoso, T. (2021). Overview of exist 2021: sexism identification in social networks. Procesamiento del Lenguaje Natural, 67, 195-207. | 9b2ce060b8ba9bb9b3956e7f9178604b |
creativeml-openrail-m | [] | false | isopixel-diffusion-v1 Stable Diffusion v2-768 model trained on to generate isometric pixel art <div style="display: flex; flex-direction: row; flex-wrap: wrap"> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669957996471-6303f37c3926de1f7ec42d3e.png" width="256"> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669958023998-6303f37c3926de1f7ec42d3e.png" width="256"> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669958037455-6303f37c3926de1f7ec42d3e.png" width="256"> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669958067857-6303f37c3926de1f7ec42d3e.png" width="256"> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669958100092-6303f37c3926de1f7ec42d3e.png" width="256"> </div> | 5563657a24e8483791a5894c5e037b82 |
creativeml-openrail-m | [] | false | How to use - Download the model and use it on your desired UI (Tested on AUTOMATIC1111's) Currently only .ckpt version is supported - Trigger the style in your prompt with the **isopixel** token, look at the next section for more examples | afa77374083d546210580eec57559042 |
creativeml-openrail-m | [] | false | Examples **isometric bedroom, isopixel style** Steps: 50, Sampler: Euler a, CFG scale: 7.5, Size: 768x768 <img src="https://s3.amazonaws.com/moonup/production/uploads/1669958684775-6303f37c3926de1f7ec42d3e.png" width="512"/> **isometric sushi store, isopixel style** Steps: 50, Sampler: Euler a, CFG scale: 7.5, Size: 768x768 <img src="https://s3.amazonaws.com/moonup/production/uploads/1669958822683-6303f37c3926de1f7ec42d3e.png" width="512"/> **isometric gas station, isopixel style** Steps: 50, Sampler: Euler a, CFG scale: 7.5, Size: 768x768 <img src="https://s3.amazonaws.com/moonup/production/uploads/1669958976478-6303f37c3926de1f7ec42d3e.png" width="512"/> **isometric magical forest, isopixel style** Steps: 50, Sampler: Euler a, CFG scale: 7.5, Size: 768x768 <img src="https://s3.amazonaws.com/moonup/production/uploads/1669959188129-6303f37c3926de1f7ec42d3e.png" width="512"/> | b2a2f1457527fe6845a908281ad34a3b |
creativeml-openrail-m | [] | false | Tips - Always use 768x768 - High step count on Euler_a gives the best results - Low CFG scale outputs great results - You can use a tool like Pixelator to achieve a better effect. This model **isn't pixel perfect** (yet ๐) Please consider supporting further research on my Patreon: <a href="https://www.patreon.com/user?u=29466374" target="_blank"> <img src="https://img.shields.io/badge/Patreon-F96854?style=for-the-badge&logo=patreon&logoColor=white" alt="Patreon"/> </a> If you have any question, suggestion for new models or need help in general with SD related stuff, don't hesistate to reach out on Twitter: <a href="https://twitter.com/nerijs" target="_blank"> <img src="https://img.shields.io/badge/Twitter-1DA1F2?style=for-the-badge&logo=twitter&logoColor=white" alt="Twitter"/> </a> | 5f446b2d5edca98c61dc6b2636f4b573 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-patch16-224-in21k-finetuned-lora-food101 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 0.1448 - Accuracy: 0.96 | 2cbd31d000b72c4b3dec89997d3eff4b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP | 02427c5c458bd7774e7d1c4fe8e1488d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 9 | 0.5069 | 0.896 | | 2.1627 | 2.0 | 18 | 0.1891 | 0.946 | | 0.3451 | 3.0 | 27 | 0.1448 | 0.96 | | 0.2116 | 4.0 | 36 | 0.1509 | 0.958 | | 0.1711 | 5.0 | 45 | 0.1498 | 0.958 | | 4fc4937de2d3ea7ddeb18328c4de2f14 |
apache-2.0 | ['generated_from_trainer'] | false | insertion-prop05-vocab This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0209 - Precision: 0.9815 - Recall: 0.9787 - F1: 0.9801 - Accuracy: 0.9929 | 6ce7bc6678465c52ef0808373e8f8c7a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0687 | 0.32 | 500 | 0.0275 | 0.9770 | 0.9694 | 0.9732 | 0.9904 | | 0.0327 | 0.64 | 1000 | 0.0221 | 0.9791 | 0.9783 | 0.9787 | 0.9924 | | 0.0289 | 0.96 | 1500 | 0.0209 | 0.9815 | 0.9787 | 0.9801 | 0.9929 | | d716aa5282409d325a311ad4936b62df |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_cola_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6839 - Matthews Correlation: 0.0 | e3e7fdbf8af18227ae9f22117745657b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.82 | 1.0 | 34 | 0.6841 | 0.0 | | 0.7971 | 2.0 | 68 | 0.6840 | 0.0 | | 0.7966 | 3.0 | 102 | 0.6841 | 0.0 | | 0.7953 | 4.0 | 136 | 0.6840 | 0.0 | | 0.7977 | 5.0 | 170 | 0.6839 | 0.0 | | 0.7955 | 6.0 | 204 | 0.6839 | 0.0 | | 0.7978 | 7.0 | 238 | 0.6841 | 0.0 | | 0.7974 | 8.0 | 272 | 0.6840 | 0.0 | | 0.7949 | 9.0 | 306 | 0.6847 | 0.0 | | 0.7978 | 10.0 | 340 | 0.6840 | 0.0 | | 0.7962 | 11.0 | 374 | 0.6841 | 0.0 | | ba6ec6b39e2131446300b053f9cf4fde |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper medium Serbian El Greco This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0,google/fleurs sr,sr_rs dataset. It achieves the following results on the evaluation set: - Loss: 0.4868 - Wer: 12.1408 | 335419246c0708623997c6cbd4e821b0 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.0222 | 2.72 | 1000 | 0.3442 | 14.0834 | | 0.0032 | 5.43 | 2000 | 0.4106 | 14.5285 | | 0.0011 | 8.15 | 3000 | 0.4331 | 12.8693 | | 0.0029 | 10.87 | 4000 | 0.3948 | 12.6265 | | 0.0012 | 13.59 | 5000 | 0.4512 | 12.6669 | | 0.0009 | 16.3 | 6000 | 0.4890 | 12.7479 | | 0.001 | 19.02 | 7000 | 0.4868 | 12.1408 | | 0.0016 | 21.74 | 8000 | 0.4780 | 12.7074 | | 0.0002 | 24.46 | 9000 | 0.4902 | 12.2218 | | 0.0012 | 27.17 | 10000 | 0.5059 | 12.6669 | | f8d80624927cb46e62a22ba94f7047fb |
apache-2.0 | ['generated_from_trainer'] | false | distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_12_47 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: 0.1194 - Precision: 0.0637 - Recall: 0.0080 - F1: 0.0141 - Accuracy: 0.9707 | 65cc386090d875c80f5355e4dcbad880 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 15 | 0.0877 | 0.12 | 0.0194 | 0.0333 | 0.9830 | | No log | 2.0 | 30 | 0.0806 | 0.12 | 0.0194 | 0.0333 | 0.9830 | | No log | 3.0 | 45 | 0.0758 | 0.12 | 0.0194 | 0.0333 | 0.9830 | | No log | 4.0 | 60 | 0.0741 | 0.12 | 0.0194 | 0.0333 | 0.9830 | | No log | 5.0 | 75 | 0.0741 | 0.12 | 0.0194 | 0.0333 | 0.9830 | | 19f86528d7bc13081c652e3b1783eb69 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-common_voice-ur-demo-dist This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 0.5252 | 80b0019a9d9c0ba4722b5643f5e6ed8d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 8 - total_eval_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: 15.0 - mixed_precision_training: Native AMP | aa46ceb4c8be2acbff8236f5d648bc12 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 6.0956 | 0.11 | 100 | inf | 1.0 | | 3.4569 | 0.22 | 200 | inf | 1.0 | | 3.0492 | 0.32 | 300 | inf | 0.9973 | | 3.0042 | 0.43 | 400 | inf | 0.9993 | | 1.7725 | 0.54 | 500 | inf | 0.9112 | | 1.875 | 0.65 | 600 | inf | 0.8314 | | 1.2135 | 0.75 | 700 | inf | 0.8312 | | 1.0577 | 0.86 | 800 | inf | 0.7337 | | 1.4374 | 0.97 | 900 | inf | 0.7513 | | 1.0388 | 1.08 | 1000 | inf | 0.7077 | | 0.8839 | 1.18 | 1100 | inf | 0.6833 | | 0.8233 | 1.29 | 1200 | inf | 0.6503 | | 0.7636 | 1.4 | 1300 | inf | 0.6851 | | 0.8722 | 1.51 | 1400 | inf | 0.6185 | | 0.6055 | 1.61 | 1500 | inf | 0.6085 | | 0.7535 | 1.72 | 1600 | inf | 0.6130 | | 0.4796 | 1.83 | 1700 | inf | 0.5901 | | 0.739 | 1.94 | 1800 | inf | 0.5643 | | 0.4362 | 2.05 | 1900 | inf | 0.5895 | | 0.66 | 2.15 | 2000 | inf | 0.5468 | | 0.5365 | 2.26 | 2100 | inf | 0.5337 | | 0.4798 | 2.37 | 2200 | inf | 0.5412 | | 0.5259 | 2.48 | 2300 | inf | 0.5764 | | 0.5697 | 2.58 | 2400 | inf | 0.5408 | | 0.7113 | 2.69 | 2500 | inf | 0.6217 | | 0.6562 | 2.8 | 2600 | inf | 0.5362 | | 0.3337 | 2.91 | 2700 | inf | 0.5397 | | 0.392 | 3.01 | 2800 | inf | 0.5299 | | 0.4472 | 3.12 | 2900 | inf | 0.5332 | | 0.3124 | 3.23 | 3000 | inf | 0.5202 | | 0.6489 | 3.34 | 3100 | inf | 0.5360 | | 0.2983 | 3.44 | 3200 | inf | 0.5146 | | 0.287 | 3.55 | 3300 | inf | 0.5170 | | 0.5538 | 3.66 | 3400 | inf | 0.5304 | | 0.3668 | 3.77 | 3500 | inf | 0.4904 | | 0.6103 | 3.88 | 3600 | inf | 0.5044 | | 0.2878 | 3.98 | 3700 | inf | 0.5208 | | 0.4004 | 4.09 | 3800 | inf | 0.5121 | | 0.3397 | 4.2 | 3900 | inf | 0.5394 | | 0.3226 | 4.31 | 4000 | inf | 0.4968 | | 0.2259 | 4.41 | 4100 | inf | 0.4905 | | 0.254 | 4.52 | 4200 | inf | 0.4879 | | 0.3353 | 4.63 | 4300 | inf | 0.4836 | | 0.3923 | 4.74 | 4400 | inf | 0.4746 | | 0.3685 | 4.84 | 4500 | inf | 0.4933 | | 0.2538 | 4.95 | 4600 | inf | 0.4721 | | 0.2082 | 5.06 | 4700 | inf | 0.4727 | | 0.3077 | 5.17 | 4800 | inf | 0.4689 | | 0.2114 | 5.27 | 4900 | inf | 0.4725 | | 0.2047 | 5.38 | 5000 | inf | 0.4756 | | 0.1977 | 5.49 | 5100 | inf | 0.4716 | | 0.2005 | 5.6 | 5200 | inf | 0.4675 | | 0.1636 | 5.71 | 5300 | inf | 0.4673 | | 0.3709 | 5.81 | 5400 | inf | 0.4767 | | 0.2338 | 5.92 | 5500 | inf | 0.4543 | | 0.172 | 6.03 | 5600 | inf | 0.4607 | | 0.2413 | 6.14 | 5700 | inf | 0.4639 | | 0.1997 | 6.24 | 5800 | inf | 0.4640 | | 0.2536 | 6.35 | 5900 | inf | 0.4840 | | 0.3206 | 6.46 | 6000 | inf | 0.4685 | | 0.2491 | 6.57 | 6100 | inf | 0.4666 | | 0.2215 | 6.67 | 6200 | inf | 0.4498 | | 0.245 | 6.78 | 6300 | inf | 0.4534 | | 0.2336 | 6.89 | 6400 | inf | 0.4520 | | 0.2885 | 7.0 | 6500 | inf | 0.4550 | | 0.5927 | 7.1 | 6600 | inf | 0.4602 | | 0.124 | 7.21 | 6700 | inf | 0.4706 | | 0.2169 | 7.32 | 6800 | inf | 0.4498 | | 0.3245 | 7.43 | 6900 | inf | 0.4544 | | 0.3848 | 7.53 | 7000 | inf | 0.4411 | | 0.2226 | 7.64 | 7100 | inf | 0.4518 | | 0.286 | 7.75 | 7200 | inf | 0.4503 | | 0.2474 | 7.86 | 7300 | inf | 0.4433 | | 0.1786 | 7.97 | 7400 | inf | 0.4507 | | 0.1477 | 8.07 | 7500 | inf | 0.4494 | | 0.1193 | 8.18 | 7600 | inf | 0.4501 | | 0.1709 | 8.29 | 7700 | inf | 0.4656 | | 0.1695 | 8.4 | 7800 | inf | 0.4525 | | 0.2417 | 8.5 | 7900 | inf | 0.4437 | | 0.2656 | 8.61 | 8000 | inf | 0.4434 | | 0.1599 | 8.72 | 8100 | inf | 0.4418 | | 0.1847 | 8.83 | 8200 | inf | 0.4451 | | 0.2093 | 8.93 | 8300 | inf | 0.4441 | | 0.0869 | 9.04 | 8400 | inf | 0.4410 | | 0.2049 | 9.15 | 8500 | inf | 0.4402 | | 0.1679 | 9.26 | 8600 | inf | 0.4320 | | 0.0796 | 9.36 | 8700 | inf | 0.4427 | | 0.1241 | 9.47 | 8800 | inf | 0.4372 | | 0.1841 | 9.58 | 8900 | inf | 0.4408 | | 0.0661 | 9.69 | 9000 | inf | 0.4362 | | 0.1172 | 9.8 | 9100 | inf | 0.4370 | | 0.0539 | 9.9 | 9200 | inf | 0.4369 | | 0.1262 | 10.01 | 9300 | inf | 0.4313 | | 0.1006 | 10.12 | 9400 | inf | 0.4379 | | 0.0892 | 10.23 | 9500 | inf | 0.4434 | | 0.1302 | 10.33 | 9600 | inf | 0.4431 | | 0.2019 | 10.44 | 9700 | inf | 0.4403 | | 0.0934 | 10.55 | 9800 | inf | 0.4392 | | 0.1628 | 10.66 | 9900 | inf | 0.4407 | | 0.1419 | 10.76 | 10000 | inf | 0.4379 | | 0.1327 | 10.87 | 10100 | inf | 0.4458 | | 0.1889 | 10.98 | 10200 | inf | 0.4682 | | 0.1053 | 11.09 | 10300 | inf | 0.4532 | | 0.0761 | 11.19 | 10400 | inf | 0.4572 | | 0.1382 | 11.3 | 10500 | inf | 0.4374 | | 0.1336 | 11.41 | 10600 | inf | 0.4326 | | 0.1427 | 11.52 | 10700 | inf | 0.4340 | | 0.1167 | 11.63 | 10800 | inf | 0.4336 | | 0.1042 | 11.73 | 10900 | inf | 0.4379 | | 0.1159 | 11.84 | 11000 | inf | 0.4766 | | 0.1872 | 11.95 | 11100 | inf | 0.4931 | | 0.2099 | 12.06 | 11200 | inf | 0.5170 | | 0.2515 | 12.16 | 11300 | inf | 0.5017 | | 0.1527 | 12.27 | 11400 | inf | 0.4959 | | 0.2435 | 12.38 | 11500 | inf | 0.5174 | | 0.2271 | 12.49 | 11600 | inf | 0.5045 | | 0.3953 | 12.59 | 11700 | inf | 0.5567 | | 0.2862 | 12.7 | 11800 | inf | 0.5608 | | 0.3511 | 12.81 | 11900 | inf | 0.5612 | | 0.2356 | 12.92 | 12000 | inf | 0.5421 | | 0.1181 | 13.02 | 12100 | inf | 0.5096 | | 0.3625 | 13.13 | 12200 | inf | 0.5252 | | 0.3627 | 13.24 | 12300 | inf | 0.5340 | | 0.2822 | 13.35 | 12400 | inf | 0.5579 | | 0.3136 | 13.46 | 12500 | inf | 0.5314 | | 0.3516 | 13.56 | 12600 | inf | 0.5411 | | 0.4331 | 13.67 | 12700 | inf | 0.5514 | | 0.5406 | 13.78 | 12800 | inf | 0.5441 | | 0.5346 | 13.89 | 12900 | inf | 0.5311 | | 0.3645 | 13.99 | 13000 | inf | 0.5354 | | 0.3339 | 14.1 | 13100 | inf | 0.5292 | | 0.3335 | 14.21 | 13200 | inf | 0.5577 | | 0.3436 | 14.32 | 13300 | inf | 0.5475 | | 0.1934 | 14.42 | 13400 | inf | 0.5255 | | 0.3422 | 14.53 | 13500 | inf | 0.5302 | | 0.4293 | 14.64 | 13600 | inf | 0.5368 | | 0.363 | 14.75 | 13700 | inf | 0.5325 | | 0.2851 | 14.85 | 13800 | inf | 0.5260 | | 0.3106 | 14.96 | 13900 | inf | 0.5252 | | 13801f3bbf624bf413d12efeb40095d6 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3568 - Accuracy: 0.86 - F1: 0.8679 | e4221e384b4b1740849e38fd5d1e95d1 |
apache-2.0 | ['translation'] | false | swe-epo * source group: Swedish * target group: Esperanto * OPUS readme: [swe-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/swe-epo/README.md) * model: transformer-align * source language(s): swe * target language(s): epo * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/swe-epo/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/swe-epo/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/swe-epo/opus-2020-06-16.eval.txt) | 4e9432bd4657991b31a54552148b478d |
apache-2.0 | ['translation'] | false | System Info: - hf_name: swe-epo - source_languages: swe - target_languages: epo - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/swe-epo/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['sv', 'eo'] - src_constituents: {'swe'} - tgt_constituents: {'epo'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/swe-epo/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/swe-epo/opus-2020-06-16.test.txt - src_alpha3: swe - tgt_alpha3: epo - short_pair: sv-eo - chrF2_score: 0.498 - bleu: 29.7 - brevity_penalty: 0.958 - ref_len: 10987.0 - src_name: Swedish - tgt_name: Esperanto - train_date: 2020-06-16 - src_alpha2: sv - tgt_alpha2: eo - prefer_old: False - long_pair: swe-epo - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | cca8d06f54f088a26214439aa24458b7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.2469 - Accuracy: 0.9458 | 244ac8b9906a48816c36e39486b88f25 |
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