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 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8403 | 6.94 | 500 | 1.1345 | 0.4657 | | 0.5795 | 13.88 | 1000 | 0.3579 | 0.1169 | | 0.3567 | 20.83 | 1500 | 0.3866 | 0.1174 | | 0.2717 | 27.77 | 2000 | 0.4219 | 0.1169 | | 0.2135 | 34.72 | 2500 | 0.4861 | 0.1199 | | 0.1664 | 41.66 | 3000 | 0.5490 | 0.1179 | | 0.1375 | 48.61 | 3500 | 0.5783 | 0.1178 | | 70c22a37f62b1a6a9841f768ffee7a03 |
apache-2.0 | ['generated_from_trainer'] | false | model_for_inca This model is a fine-tuned version of [marcus2000/finetuning-sentiment-model-3000-samples](https://huggingface.co/marcus2000/finetuning-sentiment-model-3000-samples) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3349 - F1: 0.9281 | 8b499bfdb61f6cb1f19c143a968c3807 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event', 'uk'] | false | Ukrainian STT model (with Language Model) 🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk ⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UK dataset. It achieves the following results on the evaluation set without the language model: - Loss: 0.1875 - Wer: 0.2033 - Cer: 0.0384 | 19e198c3ea41d3ec32470fccd1eec1b9 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event', 'uk'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 20 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100.0 - mixed_precision_training: Native AMP | 1b3a5218475d1661be04d9ac543586e8 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event', 'uk'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 1.2815 | 7.93 | 500 | 0.3536 | 0.4753 | 0.1009 | | 1.0869 | 15.86 | 1000 | 0.2317 | 0.3111 | 0.0614 | | 0.9984 | 23.8 | 1500 | 0.2022 | 0.2676 | 0.0521 | | 0.975 | 31.74 | 2000 | 0.1948 | 0.2469 | 0.0487 | | 0.9306 | 39.67 | 2500 | 0.1916 | 0.2377 | 0.0464 | | 0.8868 | 47.61 | 3000 | 0.1903 | 0.2257 | 0.0439 | | 0.8424 | 55.55 | 3500 | 0.1786 | 0.2206 | 0.0423 | | 0.8126 | 63.49 | 4000 | 0.1849 | 0.2160 | 0.0416 | | 0.7901 | 71.42 | 4500 | 0.1869 | 0.2138 | 0.0413 | | 0.7671 | 79.36 | 5000 | 0.1855 | 0.2075 | 0.0394 | | 0.7467 | 87.3 | 5500 | 0.1884 | 0.2049 | 0.0389 | | 0.731 | 95.24 | 6000 | 0.1877 | 0.2060 | 0.0387 | | 9c63db4139f91635be6ce972e3850593 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event', 'uk'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test` ```bash python eval.py --model_id Yehor/wav2vec2-xls-r-1b-uk-with-lm --dataset mozilla-foundation/common_voice_7_0 --config uk --split test ``` | 0cb3c74df6dd17dced676a8de054cfab |
mit | [] | false | model by homanp This your the Stable Diffusion model fine-tuned the Backpack concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks backpack** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). Here are the images used for training this concept:   | cfc08e33624cfb86884da2cc6acb0a08 |
mit | ['spacy', 'token-classification'] | false | de_core_news_md German pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner. | Feature | Description | | --- | --- | | **Name** | `de_core_news_md` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` | | **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` | | **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) | | **Sources** | [TIGER Corpus](https://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/tiger.html) (Brants, Sabine, Stefanie Dipper, Peter Eisenberg, Silvia Hansen, Esther König, Wolfgang Lezius, Christian Rohrer, George Smith, and Hans Uszkoreit)<br />[Tiger2Dep](https://www.ims.uni-stuttgart.de/forschung/ressourcen/werkzeuge/tiger2dep/) (Wolfgang Seeker)<br />[WikiNER](https://figshare.com/articles/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500) (Joel Nothman, Nicky Ringland, Will Radford, Tara Murphy, James R Curran)<br />[Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia)](https://spacy.io) (Explosion) | | **License** | `MIT` | | **Author** | [Explosion](https://explosion.ai) | | 242ffda10096646f70b94771aa9fa5d5 |
mit | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.96 | | `TOKEN_P` | 99.92 | | `TOKEN_R` | 99.90 | | `TOKEN_F` | 99.91 | | `TAG_ACC` | 97.81 | | `POS_ACC` | 98.29 | | `MORPH_ACC` | 91.51 | | `MORPH_MICRO_P` | 95.69 | | `MORPH_MICRO_R` | 95.61 | | `MORPH_MICRO_F` | 95.65 | | `SENTS_P` | 95.41 | | `SENTS_R` | 96.22 | | `SENTS_F` | 95.08 | | `DEP_UAS` | 92.54 | | `DEP_LAS` | 90.57 | | `LEMMA_ACC` | 97.70 | | `ENTS_P` | 84.39 | | `ENTS_R` | 83.43 | | `ENTS_F` | 83.91 | | 31c12d17b34a243bfe0f95d21a8edf90 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small dysarthric Dutch This model is a fine-tuned version of [qmeeus/whisper-small-nl](https://huggingface.co/qmeeus/whisper-small-nl) on the data/copas copas-full dataset. It achieves the following results on the evaluation set: - Loss: 0.4242 - Wer: 24.5560 | 9f72d1967c3b2b86ced1f7e733195946 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - 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: 500 - training_steps: 10000 - mixed_precision_training: Native AMP | 8470ae9c6d94be3444625e5cf732ab24 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.3363 | 2.02 | 500 | 0.3762 | 29.7934 | | 0.0945 | 5.02 | 1000 | 0.3418 | 27.6912 | | 0.0332 | 8.01 | 1500 | 0.3353 | 26.1689 | | 0.0147 | 11.01 | 2000 | 0.3476 | 26.1327 | | 0.0071 | 14.01 | 2500 | 0.3623 | 25.9333 | | 0.0034 | 17.01 | 3000 | 0.3789 | 25.2084 | | 0.0024 | 20.01 | 3500 | 0.3827 | 24.8641 | | 0.0026 | 23.01 | 4000 | 0.3877 | 25.3171 | | 0.0021 | 26.01 | 4500 | 0.3933 | 25.4259 | | 0.0014 | 29.01 | 5000 | 0.3941 | 25.0997 | | 0.0008 | 32.01 | 5500 | 0.4014 | 25.0997 | | 0.0004 | 35.01 | 6000 | 0.4035 | 24.8278 | | 0.0003 | 38.01 | 6500 | 0.4080 | 24.9184 | | 0.0003 | 41.01 | 7000 | 0.4120 | 24.8097 | | 0.0002 | 44.01 | 7500 | 0.4151 | 24.6104 | | 0.0002 | 47.01 | 8000 | 0.4176 | 24.3929 | | 0.0002 | 50.01 | 8500 | 0.4200 | 24.5198 | | 0.0001 | 53.0 | 9000 | 0.4230 | 24.5198 | | 0.0001 | 56.0 | 9500 | 0.4252 | 24.4291 | | 0.0001 | 59.0 | 10000 | 0.4242 | 24.5560 | | 5e817ecf81124824a21e5c76293c6b22 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | new_exper3 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 sudo-s/herbier_mesuem1 dataset. It achieves the following results on the evaluation set: - Loss: 0.3000 - Accuracy: 0.9298 | 25a35f4a25800c8da5e8969b2d0a083b |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Apex, opt level O1 | f4dd97e57c78ba7ada893ae7dd613ccb |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.093 | 0.16 | 100 | 4.1045 | 0.1885 | | 3.5057 | 0.31 | 200 | 3.4448 | 0.3231 | | 2.9116 | 0.47 | 300 | 2.9483 | 0.4537 | | 2.561 | 0.63 | 400 | 2.5700 | 0.5258 | | 2.1611 | 0.78 | 500 | 2.1721 | 0.6145 | | 1.715 | 0.94 | 600 | 1.8255 | 0.6407 | | 1.2752 | 1.1 | 700 | 1.5340 | 0.7051 | | 1.2487 | 1.25 | 800 | 1.3533 | 0.7201 | | 1.0333 | 1.41 | 900 | 1.1474 | 0.7826 | | 0.8856 | 1.56 | 1000 | 1.0914 | 0.7645 | | 0.7512 | 1.72 | 1100 | 0.8893 | 0.8119 | | 0.747 | 1.88 | 1200 | 0.8370 | 0.8304 | | 0.5082 | 2.03 | 1300 | 0.7131 | 0.8566 | | 0.4449 | 2.19 | 1400 | 0.6573 | 0.8547 | | 0.2912 | 2.35 | 1500 | 0.6184 | 0.8597 | | 0.285 | 2.5 | 1600 | 0.5974 | 0.8570 | | 0.2267 | 2.66 | 1700 | 0.5621 | 0.8647 | | 0.2553 | 2.82 | 1800 | 0.5044 | 0.8816 | | 0.2029 | 2.97 | 1900 | 0.4342 | 0.8955 | | 0.1763 | 3.13 | 2000 | 0.4487 | 0.8905 | | 0.1418 | 3.29 | 2100 | 0.4173 | 0.9005 | | 0.0563 | 3.44 | 2200 | 0.3870 | 0.9048 | | 0.0579 | 3.6 | 2300 | 0.3849 | 0.9036 | | 0.166 | 3.76 | 2400 | 0.3933 | 0.9025 | | 0.11 | 3.91 | 2500 | 0.3918 | 0.9056 | | 0.0356 | 4.07 | 2600 | 0.3298 | 0.9202 | | 0.0513 | 4.23 | 2700 | 0.3371 | 0.9210 | | 0.0762 | 4.38 | 2800 | 0.3253 | 0.9225 | | 0.018 | 4.54 | 2900 | 0.3467 | 0.9148 | | 0.0263 | 4.69 | 3000 | 0.3544 | 0.9144 | | 0.0205 | 4.85 | 3100 | 0.3340 | 0.9221 | | 0.0237 | 5.01 | 3200 | 0.3353 | 0.9144 | | 0.013 | 5.16 | 3300 | 0.3218 | 0.9229 | | 0.0116 | 5.32 | 3400 | 0.3088 | 0.9291 | | 0.0119 | 5.48 | 3500 | 0.3047 | 0.9279 | | 0.0098 | 5.63 | 3600 | 0.3063 | 0.9283 | | 0.0086 | 5.79 | 3700 | 0.3074 | 0.9268 | | 0.0081 | 5.95 | 3800 | 0.3220 | 0.9237 | | 0.0078 | 6.1 | 3900 | 0.3064 | 0.9268 | | 0.0074 | 6.26 | 4000 | 0.3062 | 0.9279 | | 0.0068 | 6.42 | 4100 | 0.3051 | 0.9291 | | 0.006 | 6.57 | 4200 | 0.3000 | 0.9298 | | 0.0075 | 6.73 | 4300 | 0.3010 | 0.9310 | | 0.0057 | 6.89 | 4400 | 0.3037 | 0.9298 | | 0.0058 | 7.04 | 4500 | 0.3071 | 0.9279 | | 0.0075 | 7.2 | 4600 | 0.3075 | 0.9283 | | 0.0066 | 7.36 | 4700 | 0.3077 | 0.9295 | | 0.0056 | 7.51 | 4800 | 0.3084 | 0.9295 | | 0.0053 | 7.67 | 4900 | 0.3064 | 0.9310 | | 0.0057 | 7.82 | 5000 | 0.3068 | 0.9318 | | 0.0055 | 7.98 | 5100 | 0.3068 | 0.9318 | | 013468799f0774e9c82e3b313c082c39 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-turkish-demo-colab 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 dataset. It achieves the following results on the evaluation set: - Loss: 0.4055 - Wer: 0.4800 | c86acdf1845498ace23078893471b23a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.0179 | 4.21 | 400 | 1.4935 | 1.0249 | | 0.7075 | 8.42 | 800 | 0.4546 | 0.6071 | | 0.3072 | 12.63 | 1200 | 0.3947 | 0.5401 | | 0.2145 | 16.84 | 1600 | 0.4049 | 0.5194 | | 0.1647 | 21.05 | 2000 | 0.4199 | 0.5003 | | 0.1338 | 25.26 | 2400 | 0.4144 | 0.4859 | | 0.116 | 29.47 | 2800 | 0.4055 | 0.4800 | | bed53643fdfc54a3037535599a6b330b |
apache-2.0 | ['generated_from_trainer'] | false | test-trainer-init This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6581 - Accuracy: 0.8603 - F1: 0.9042 | 2301c7d6bdf5fd2d0555b91bc4e03d78 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 459 | 0.3660 | 0.8505 | 0.8893 | | 0.5003 | 2.0 | 918 | 0.5355 | 0.8407 | 0.8922 | | 0.2654 | 3.0 | 1377 | 0.6581 | 0.8603 | 0.9042 | | 74f349d222591fa2c1a795f34153ed6b |
creativeml-openrail-m | ['text-to-image'] | false | Persona-5-Shigenori-Style Dreambooth model trained by Allenbv 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) Sample pictures of this concept: 3200 Steps, 20% text encoder, 23 images "Shigenori Style" on your prompt .png) .png) .png) .png) .png) .png) .png) | d638aee99d6fe4598a72bf681990c0a4 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4381100/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 463915fe4ac0b4e48d78902aea672752 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 1.7601 - Accuracy: 0.8532 | a20cc764ff4a6416128a75d72c64e077 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 168f1c8ed96e78ffc29021586c70eb4d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 159 | 3.9593 | 0.6442 | | 4.0539 | 2.0 | 318 | 2.9237 | 0.7606 | | 4.0539 | 3.0 | 477 | 2.2412 | 0.8174 | | 2.3862 | 4.0 | 636 | 1.8768 | 0.8397 | | 2.3862 | 5.0 | 795 | 1.7601 | 0.8532 | | f1f653c5646d327319c15699a2d886c7 |
apache-2.0 | ['argumentation'] | false | Generate the conclusion of an argument This model has the same model parameters as [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), but with an additional soft prompt which has been optimized on the task of generating the conclusion of an argument given its premises. It was trained as part of a University of Melbourne [research project](https://github.com/Hunt-Laboratory/language-model-optimization) evaluating how large language models can best be optimized to perform argumentative reasoning tasks. Code used for optimization and evaluation can be found in the project [GitHub repository](https://github.com/Hunt-Laboratory/language-model-optimization). A paper reporting on model evaluation is currently under review. | 0e672beb96a95d50a950a6bd923df6a0 |
apache-2.0 | ['argumentation'] | false | Limitations and Biases The model is a finetuned version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), so likely has many of the same limitations and biases. Additionally, note that while the goal of the model is to produce coherent and valid reasoning, many generated model outputs will be illogical or nonsensical and should not be relied upon. | 2796e59c1cb2979b0deb8a4245d00783 |
apache-2.0 | ['argumentation'] | false | Acknowledgements This research was funded by the Australian Department of Defence and the Office of National Intelligence under the AI for Decision Making Program, delivered in partnership with the Defence Science Institute in Victoria, Australia. | a0f7dcc429b984ea9a35c287ba2ce2b7 |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_r-wav2vec2_s732 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (et)](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. | 1679c353489f540b0fb0abbadcf54472 |
mit | [] | false | vb-mox on Stable Diffusion This is the `<vb-mox>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:         | 16dd8255f1acf07a5c487bd8039f4fc4 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1644 - F1: 0.8617 | 5ad498c4813e8243ca3b51d2d7d6781c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2891 | 1.0 | 715 | 0.1780 | 0.8288 | | 0.1471 | 2.0 | 1430 | 0.1627 | 0.8509 | | 0.0947 | 3.0 | 2145 | 0.1644 | 0.8617 | | dd202f0debf44b1abe916152454c3621 |
mit | ['generated_from_trainer'] | false | predict-perception-bert-focus-assassin This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2964 - Rmse: 0.8992 - Rmse Focus::a Sull'assassino: 0.8992 - Mae: 0.7331 - Mae Focus::a Sull'assassino: 0.7331 - R2: 0.6500 - R2 Focus::a Sull'assassino: 0.6500 - Cos: 0.7391 - Pair: 0.0 - Rank: 0.5 - Neighbors: 0.6131 - Rsa: nan | 8f80f926f3b7ccef8360b845915fcab4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Sull'assassino | Mae | Mae Focus::a Sull'assassino | R2 | R2 Focus::a Sull'assassino | Cos | Pair | Rank | Neighbors | Rsa | |:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------:|:------:|:---------------------------:|:-------:|:--------------------------:|:------:|:----:|:----:|:---------:|:---:| | 1.0674 | 1.0 | 15 | 0.9851 | 1.6393 | 1.6393 | 1.5316 | 1.5316 | -0.1633 | -0.1633 | 0.1304 | 0.0 | 0.5 | 0.2457 | nan | | 1.0099 | 2.0 | 30 | 0.8921 | 1.5601 | 1.5601 | 1.4317 | 1.4317 | -0.0535 | -0.0535 | 0.5652 | 0.0 | 0.5 | 0.4734 | nan | | 0.9295 | 3.0 | 45 | 0.7345 | 1.4155 | 1.4155 | 1.3113 | 1.3113 | 0.1327 | 0.1327 | 0.5652 | 0.0 | 0.5 | 0.3596 | nan | | 0.8485 | 4.0 | 60 | 0.7282 | 1.4094 | 1.4094 | 1.2678 | 1.2678 | 0.1401 | 0.1401 | 0.7391 | 0.0 | 0.5 | 0.5367 | nan | | 0.7551 | 5.0 | 75 | 0.5966 | 1.2758 | 1.2758 | 1.1144 | 1.1144 | 0.2955 | 0.2955 | 0.6522 | 0.0 | 0.5 | 0.3911 | nan | | 0.5563 | 6.0 | 90 | 0.4578 | 1.1175 | 1.1175 | 0.9105 | 0.9105 | 0.4594 | 0.4594 | 0.6522 | 0.0 | 0.5 | 0.3911 | nan | | 0.4048 | 7.0 | 105 | 0.3539 | 0.9826 | 0.9826 | 0.7770 | 0.7770 | 0.5821 | 0.5821 | 0.6522 | 0.0 | 0.5 | 0.5522 | nan | | 0.3319 | 8.0 | 120 | 0.2938 | 0.8953 | 0.8953 | 0.7110 | 0.7110 | 0.6530 | 0.6530 | 0.6522 | 0.0 | 0.5 | 0.6021 | nan | | 0.2224 | 9.0 | 135 | 0.3455 | 0.9708 | 0.9708 | 0.7607 | 0.7607 | 0.5921 | 0.5921 | 0.6522 | 0.0 | 0.5 | 0.3911 | nan | | 0.1794 | 10.0 | 150 | 0.2719 | 0.8612 | 0.8612 | 0.6768 | 0.6768 | 0.6790 | 0.6790 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.1553 | 11.0 | 165 | 0.2855 | 0.8826 | 0.8826 | 0.7053 | 0.7053 | 0.6628 | 0.6628 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.1008 | 12.0 | 180 | 0.3000 | 0.9046 | 0.9046 | 0.7255 | 0.7255 | 0.6458 | 0.6458 | 0.6522 | 0.0 | 0.5 | 0.5261 | nan | | 0.1121 | 13.0 | 195 | 0.2817 | 0.8766 | 0.8766 | 0.7236 | 0.7236 | 0.6674 | 0.6674 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.08 | 14.0 | 210 | 0.3504 | 0.9777 | 0.9777 | 0.7631 | 0.7631 | 0.5863 | 0.5863 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.0802 | 15.0 | 225 | 0.3031 | 0.9094 | 0.9094 | 0.7565 | 0.7565 | 0.6420 | 0.6420 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.0685 | 16.0 | 240 | 0.3041 | 0.9109 | 0.9109 | 0.7409 | 0.7409 | 0.6408 | 0.6408 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.0592 | 17.0 | 255 | 0.3496 | 0.9767 | 0.9767 | 0.7812 | 0.7812 | 0.5871 | 0.5871 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.0625 | 18.0 | 270 | 0.3260 | 0.9430 | 0.9430 | 0.7757 | 0.7757 | 0.6151 | 0.6151 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.0589 | 19.0 | 285 | 0.3118 | 0.9222 | 0.9222 | 0.7442 | 0.7442 | 0.6318 | 0.6318 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.0518 | 20.0 | 300 | 0.3062 | 0.9140 | 0.9140 | 0.7459 | 0.7459 | 0.6384 | 0.6384 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.0456 | 21.0 | 315 | 0.3200 | 0.9344 | 0.9344 | 0.7592 | 0.7592 | 0.6221 | 0.6221 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.0477 | 22.0 | 330 | 0.3132 | 0.9244 | 0.9244 | 0.7532 | 0.7532 | 0.6301 | 0.6301 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.0448 | 23.0 | 345 | 0.3006 | 0.9056 | 0.9056 | 0.7321 | 0.7321 | 0.6450 | 0.6450 | 0.6522 | 0.0 | 0.5 | 0.5261 | nan | | 0.0494 | 24.0 | 360 | 0.2985 | 0.9024 | 0.9024 | 0.7463 | 0.7463 | 0.6475 | 0.6475 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.0369 | 25.0 | 375 | 0.3039 | 0.9105 | 0.9105 | 0.7359 | 0.7359 | 0.6412 | 0.6412 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.0456 | 26.0 | 390 | 0.2989 | 0.9030 | 0.9030 | 0.7210 | 0.7210 | 0.6471 | 0.6471 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.044 | 27.0 | 405 | 0.2997 | 0.9042 | 0.9042 | 0.7418 | 0.7418 | 0.6461 | 0.6461 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.0352 | 28.0 | 420 | 0.2970 | 0.9001 | 0.9001 | 0.7346 | 0.7346 | 0.6493 | 0.6493 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.0429 | 29.0 | 435 | 0.2970 | 0.9001 | 0.9001 | 0.7281 | 0.7281 | 0.6493 | 0.6493 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 0.0378 | 30.0 | 450 | 0.2964 | 0.8992 | 0.8992 | 0.7331 | 0.7331 | 0.6500 | 0.6500 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan | | 61648a5331166f4aa2a55b9d67eb4789 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_ES This model is a fine-tuned version of [StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES](https://huggingface.co/StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES) on the CRAFT dataset. It achieves the following results on the evaluation set: - Loss: 0.2043 - Precision: 0.8666 - Recall: 0.8614 - F1: 0.8639 - Accuracy: 0.9734 | 572fb6db7f2514e20d096463678ce6c7 |
apache-2.0 | ['generated_from_trainer'] | false | Model description This model performs Named Entity Recognition for 6 entity tags: Sequence, Cell, Protein, Gene, Taxon, and Chemical from the CRAFT(Colorado Richly Annotated Full Text) Corpus in Spanish (MT translated) and English. Entity tags have been normalized and replaced from the original three letter code to a full name e.g. B-Protein, I-Chemical. This model is trained on augmented data created using Entity Replacement. 20% of the entities were replaced using a list of entities for each entity tag obtained from the official ontologies for each entity class. Three datasets (original, augmented, MT translated CRAFT) were concatenated. To improve F1 score the transfer learning was completed in two steps. Using [StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES](https://huggingface.co/StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES) as a base model, I finetuned once more on the original CRAFT dataset in English. Biobert --> Augmented CRAFT --> CRAFT ES (MT translated) | 4e38e58eb93329953a4e8004c6f0163d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0088 | 1.0 | 1360 | 0.1793 | 0.8616 | 0.8487 | 0.8551 | 0.9721 | | 0.0046 | 2.0 | 2720 | 0.1925 | 0.8618 | 0.8426 | 0.8521 | 0.9713 | | 0.0032 | 3.0 | 4080 | 0.1926 | 0.8558 | 0.8630 | 0.8594 | 0.9725 | | 0.0011 | 4.0 | 5440 | 0.2043 | 0.8666 | 0.8614 | 0.8639 | 0.9734 | | 0a31625c2948b6e7bbd1f5f7b70c8fa5 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `Shinji_Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave` ♻️ Imported from https://zenodo.org/record/4585546/ This model was trained by Shinji Watanabe using spgispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/). | 03d43838635e613fe7be0ebd5d777b2e |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-wikisql 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: 0.2640 - Rouge2 Precision: 0.8471 - Rouge2 Recall: 0.3841 - Rouge2 Fmeasure: 0.5064 | a6b5f55e98c729757fc49efba6ebd466 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 | 103178689c53b320978abcf28576831d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | No log | 1.0 | 11 | 2.7587 | 0.098 | 0.0305 | 0.045 | | No log | 2.0 | 22 | 2.0056 | 0.0969 | 0.0284 | 0.0422 | | No log | 3.0 | 33 | 1.4456 | 0.1046 | 0.0349 | 0.0503 | | No log | 4.0 | 44 | 1.0317 | 0.1054 | 0.0337 | 0.0482 | | No log | 5.0 | 55 | 0.7603 | 0.2749 | 0.1299 | 0.1724 | | No log | 6.0 | 66 | 0.5722 | 0.7115 | 0.352 | 0.4552 | | No log | 7.0 | 77 | 0.4751 | 0.6872 | 0.337 | 0.436 | | No log | 8.0 | 88 | 0.4253 | 0.7256 | 0.3439 | 0.4462 | | No log | 9.0 | 99 | 0.3805 | 0.7335 | 0.3204 | 0.4308 | | No log | 10.0 | 110 | 0.3562 | 0.7342 | 0.3239 | 0.433 | | No log | 11.0 | 121 | 0.3275 | 0.7906 | 0.355 | 0.471 | | No log | 12.0 | 132 | 0.3133 | 0.8382 | 0.3838 | 0.5061 | | No log | 13.0 | 143 | 0.2996 | 0.8409 | 0.3841 | 0.5062 | | No log | 14.0 | 154 | 0.2903 | 0.8304 | 0.3763 | 0.4978 | | No log | 15.0 | 165 | 0.2867 | 0.8409 | 0.3841 | 0.5062 | | No log | 16.0 | 176 | 0.2786 | 0.8409 | 0.3841 | 0.5062 | | No log | 17.0 | 187 | 0.2711 | 0.8409 | 0.3841 | 0.5062 | | No log | 18.0 | 198 | 0.2673 | 0.8409 | 0.3841 | 0.5062 | | No log | 19.0 | 209 | 0.2643 | 0.8471 | 0.3841 | 0.5064 | | No log | 20.0 | 220 | 0.2640 | 0.8471 | 0.3841 | 0.5064 | | 993ba7fc5641c7e81deed382da81b8d9 |
apache-2.0 | ['automatic-speech-recognition', 'th'] | false | exp_w2v2t_th_vp-100k_s497 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition on Thai using the train split of [Common Voice 7.0](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. | 7113214f396f7496a2a85f7bd6f48b93 |
apache-2.0 | ['translation'] | false | opus-mt-en-tvl * source languages: en * target languages: tvl * OPUS readme: [en-tvl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-tvl/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-tvl/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-tvl/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-tvl/opus-2020-01-20.eval.txt) | a33893c9c609f69bdff45c586c48caf0 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-patch16-224-in21k 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 None dataset. It achieves the following results on the evaluation set: - Loss: 0.1026 - Accuracy: 0.982 | 807c280f971e6076689c7bc1ffe2e09c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | b2850832961164ca02f85d77823e8398 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.177 | 0.5 | 500 | 0.2100 | 0.9435 | | 0.1515 | 1.0 | 1000 | 0.0710 | 0.975 | | 0.0443 | 1.5 | 1500 | 0.2043 | 0.9535 | | 0.0625 | 2.0 | 2000 | 0.0898 | 0.9745 | | 0.0181 | 2.5 | 2500 | 0.0961 | 0.9805 | | 0.0091 | 3.0 | 3000 | 0.1049 | 0.982 | | 0.0016 | 3.5 | 3500 | 0.1066 | 0.981 | | 0.0015 | 4.0 | 4000 | 0.1026 | 0.982 | | 2ad1b427253a6854ccd6581656ed7e63 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 282df92c3a5fe02031a8eaa98a5b59c1 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens') embeddings = model.encode(sentences) print(embeddings) ``` | e59bbb1558269bacbb9e87ddfac86c37 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens') model = AutoModel.from_pretrained('sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens') | b4f602635e7a9d94cf0ec5e4e9cc5ce1 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens) | 1e72c1b6a6d19a6b02fe7761f513c935 |
apache-2.0 | ['generated_from_trainer', 'CV', 'ConvNeXT', 'satellite', 'EuroSAT'] | false | ConvNeXT (tiny) fine-tuned on EuroSAT This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the [EuroSAT](https://github.com/phelber/eurosat) dataset. It achieves the following results on the evaluation set: - Loss: 0.0549 - Accuracy: 0.9805 | 9e611f4bb453e5a5793bfcc89668c640 |
apache-2.0 | ['generated_from_trainer', 'CV', 'ConvNeXT', 'satellite', 'EuroSAT'] | false | Drag and drop the following pics in the right widget to test the model   | 86283ed644286adda34258df2ac334ab |
apache-2.0 | ['generated_from_trainer', 'CV', 'ConvNeXT', 'satellite', 'EuroSAT'] | false | Model description ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration. | e203a68e17b4c738839df5204004d368 |
apache-2.0 | ['generated_from_trainer', 'CV', 'ConvNeXT', 'satellite', 'EuroSAT'] | false | Dataset information **EuroSAT : Land Use and Land Cover Classification with Sentinel-2** In this study, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible provided in the Earth observation program Copernicus. We present a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27,000 labeled and geo-referenced images. We provide benchmarks for this novel dataset with its spectral bands using state-of-the-art deep Convolutional Neural Network (CNNs). With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The resulting classification system opens a gate towards a number of Earth observation applications. We demonstrate how this classification system can be used for detecting land use and land cover changes and how it can assist in improving geographical maps. | 963e7bad369db2c9afc5b240595b9c0b |
apache-2.0 | ['generated_from_trainer', 'CV', 'ConvNeXT', 'satellite', 'EuroSAT'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 7171 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | ca89a5e4f2ebcffa8e76cde9b8374452 |
apache-2.0 | ['generated_from_trainer', 'CV', 'ConvNeXT', 'satellite', 'EuroSAT'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2082 | 1.0 | 718 | 0.1057 | 0.9654 | | 0.1598 | 2.0 | 1436 | 0.0712 | 0.9775 | | 0.1435 | 3.0 | 2154 | 0.0549 | 0.9805 | | 5fc1fa10bb57d9f1b2b2c5e616dda6d3 |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/mt5-base-itquad-qg` This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | d0164e1f8746275de753066515d5f580 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.", list_answer="Dopo il 1971") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-base-itquad-qg") output = pipe("<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.") ``` | cfb2f4fd9fcaf4e109aeb97280cf490c |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-itquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 81.16 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_1 | 23.29 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_2 | 15.37 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_3 | 10.72 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_4 | 7.7 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | METEOR | 18 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | MoverScore | 57.11 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | ROUGE_L | 22.51 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | - ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mt5-base-itquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_itquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 87.93 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedF1Score (MoverScore) | 61.91 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedPrecision (BERTScore) | 88.02 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedPrecision (MoverScore) | 62.04 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedRecall (BERTScore) | 87.84 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedRecall (MoverScore) | 61.78 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | - ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mt5-base-itquad-ae`](https://huggingface.co/lmqg/mt5-base-itquad-ae). [raw metric file](https://huggingface.co/lmqg/mt5-base-itquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_itquad.default.lmqg_mt5-base-itquad-ae.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 81.68 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedF1Score (MoverScore) | 55.83 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedPrecision (BERTScore) | 81.25 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedPrecision (MoverScore) | 55.68 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedRecall (BERTScore) | 82.16 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedRecall (MoverScore) | 56.01 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | 266a377c9dcfec764b679b5d058a27d0 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_itquad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: google/mt5-base - max_length: 512 - max_length_output: 32 - epoch: 11 - batch: 4 - lr: 0.001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 16 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-itquad-qg/raw/main/trainer_config.json). | 25a24289a616cb7ecdaf79bb05600c46 |
creativeml-openrail-m | ['text-to-image'] | false | Duskfall Ani Backgrounds Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You 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). Don't forget to use the concept prompts! If you want to donate towards costs and don't want to subscribe: https://ko-fi.com/DUSKFALLcrew If you want to monthly support the EARTH & DUSK media projects and not just AI: https://www.patreon.com/earthndusk BgAniDusk (use that on your prompt) | 340f2e5562d26f0c9e7a574959aa3c4a |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Tiny it 7 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 2.137834 - Wer: 97.566556 | 638441ce431d6d26238aee9883308037 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Model description This model is the openai whisper small transformer adapted for Italian audio to text transcription. As part of the hyperparameter tuning process weight decay set to 0.1, attention dropout, encoder dropout and decoder dropout have been set to 0.1, the learning rate has been set to 1e-6, the number of decoder attention heads and encoder attention heads have been set to 8 however, it did not improved the performance on the evaluation set. | 71c1b9e132ef41ed338f76b5d17fab2a |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP | 8e0afd02eb7a3792b23eda98cece4853 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 1.7353 | 3.82 | 4000 | 2.1378 | 97.5666 | | 8fc2911c34536f001d4ae583e00544d5 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-hindi This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8814 - Wer: 1.0 | 6ef52a346cf421fac3e40fb5fc9b7050 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 40 - mixed_precision_training: Native AMP | 028c03a05ac100e8f11eadfdd7dc338c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 23.6834 | 6.25 | 100 | 13.5748 | 1.0 | | 8.2358 | 12.5 | 200 | 3.9834 | 1.0 | | 3.6953 | 18.75 | 300 | 3.7861 | 1.0 | | 3.4186 | 25.0 | 400 | 3.8232 | 1.0 | | 3.2462 | 31.25 | 500 | 3.4688 | 1.0 | | 2.8108 | 37.5 | 600 | 2.8814 | 1.0 | | 2b032f122c3add849d91b28d57b6c23f |
apache-2.0 | [] | false | Funnel Transformer large model (B8-8-8 without decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. | 597c8423309139d0c47c72e6a8b7e69f |
apache-2.0 | [] | false | Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. **Note:** This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if you need one input per initial token. You should use the `large` model in that case. | 7640eee2766c17258f1243c72c0bb241 |
apache-2.0 | [] | false | Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. | 33e756f0272020cf56947379f782a5b8 |
apache-2.0 | [] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large-base") model = FunnelBaseModel.from_pretrained("funnel-transformer/large-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large-base") model = TFFunnelBaseModel.from_pretrained("funnel-transformer/large-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` | f777a3f7b2e6c1dcad10ef4f8e05e318 |
apache-2.0 | [] | false | Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. | 7797c65b77d25d57a122d8a1f0fabdba |
apache-2.0 | [] | false | BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` | 7b57d04fc06be175107602a9be024f02 |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_no-pretraining_s399 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (nl)](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. | 86217cac8a0750abd39d93af87e01c01 |
apache-2.0 | ['generated_from_keras_callback'] | false | transformers-qa This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.3199 - Validation Loss: 3.2826 - Train Rougel: tf.Tensor(0.3922559, shape=(), dtype=float32) - Epoch: 0 | 9f28e368f6580fc090f3c3358786bc38 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rougel | Epoch | |:----------:|:---------------:|:---------------------------------------------:|:-----:| | 2.3199 | 3.2826 | tf.Tensor(0.3922559, shape=(), dtype=float32) | 0 | | 3e7c5e9ed14b82a9cab80e3184ebc33e |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-DL8 (Deep-Narrow version) T5-Efficient-SMALL-DL8 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. | fe3fb8d947163ac4b7b665cd37bf8bbc |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-dl8** - is of model type **Small** with the following variations: - **dl** is **8** It has **68.92** million parameters and thus requires *ca.* **275.66 MB** of memory in full precision (*fp32*) or **137.83 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 | | 893844c5b94e47d3a0cc0e9429bb949f |
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(https://huggingface.co/datasets/emotion) dataset for in the dataset in HG. It achieves the following results on the evaluation set: - Loss: 0.2033 - Accuracy: 0.9275 - F1: 0.9273 | 83429eda879166967fb25118f9edcf8f |
apache-2.0 | ['generated_from_trainer'] | false | Model description This model is a copy of the model found in the book [Natural Language Processing with Transformers](https://github.com/nlp-with-transformers/notebooks/blob/main/02_classification.ipynb). | 5247d020961f0b23c6d545ec34d0322c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.806 | 1.0 | 250 | 0.2954 | 0.908 | 0.9062 | | 0.2361 | 2.0 | 500 | 0.2033 | 0.9275 | 0.9273 | | dfe5d97d10c0af88466587aceb28ed8b |
cc-by-4.0 | ['spanish', 'roberta', 'bertin'] | false | Readability ES Sentences for three classes Model based on the Roberta architecture finetuned on [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for readability assessment of Spanish texts. | 7a34f7b0bf0eb83ad79eb1e062de4fe9 |
cc-by-4.0 | ['spanish', 'roberta', 'bertin'] | false | Description and performance This version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model performs classification among three complexity levels: - Basic. - Intermediate. - Advanced. The relationship of these categories with the Common European Framework of Reference for Languages is described in [our report](https://wandb.ai/readability-es/readability-es/reports/Texts-Readability-Analysis-for-Spanish--VmlldzoxNzU2MDUx). This model achieves a F1 macro average score of 0.6951, measured on the validation set. | 37cc663438dba41a3e2fa81a8112cfd6 |
cc-by-4.0 | ['spanish', 'roberta', 'bertin'] | false | Model variants - [`readability-es-sentences`](https://huggingface.co/hackathon-pln-es/readability-es-sentences). Two classes, sentence-based dataset. - [`readability-es-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-paragraphs). Two classes, paragraph-based dataset. - `readability-es-3class-sentences` (this model). Three classes, sentence-based dataset. - [`readability-es-3class-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-3class-paragraphs). Three classes, paragraph-based dataset. | 0da37dfcfe34b0d9fce81a976e4f0257 |
cc-by-4.0 | ['spanish', 'roberta', 'bertin'] | false | Datasets - [`readability-es-hackathon-pln-public`](https://huggingface.co/datasets/hackathon-pln-es/readability-es-hackathon-pln-public), composed of: * coh-metrix-esp corpus. * Various text resources scraped from websites. - Other non-public datasets: newsela-es, simplext. | e524aea7f6f7c63f69a88f43c3cd2f75 |
cc-by-4.0 | ['spanish', 'roberta', 'bertin'] | false | Biases and Limitations - Due to the scarcity of data and the lack of a reliable gold test set, performance metrics are reported on the validation set. - One of the datasets involved is the Spanish version of newsela, which is frequently used as a reference. However, it was created by translating previous datasets, and therefore it may contain somewhat unnatural phrases. - Some of the datasets used cannot be publicly disseminated, making it more difficult to assess the existence of biases or mistakes. - Language might be biased towards the Spanish dialect spoken in Spain. Other regional variants might be sub-represented. - No effort has been performed to alleviate the shortcomings and biases described in the [original implementation of BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish | 2749936f3dd48cb97ba83c883c797dd5 |
cc-by-4.0 | ['spanish', 'roberta', 'bertin'] | false | Authors - [Laura Vásquez-Rodríguez](https://lmvasque.github.io/) - [Pedro Cuenca](https://twitter.com/pcuenq) - [Sergio Morales](https://www.fireblend.com/) - [Fernando Alva-Manchego](https://feralvam.github.io/) | 0a6122171297c108e1e3ca2deea618ed |
cc | [] | false | Installation To install `image-classifier` first [install and configure awesome-bash-cli](https://github.com/kamangir/awesome-bash-cli) then run: ``` abcli huggingface clone image-classifier ``` To see the list of `image-classifier` saved models type in ``` image_classifier list ``` You should see the following items: 1. [fashion-mnist]( | 72dc53626c552516c53391fb78c80715 |
cc | [] | false | fashion-mnist  `fashion-mnist` is an `image-classifier` trained on [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist). To retrain `fashion-mnist` type in: ``` abcli select fashion_mnist train abcli upload image_classifier list . browser=1,model=object ``` You should now see the structure of the network (left) and the [content of the model](https://github.com/kamangir/browser) (right). |  |  | |---|---| You can save this model under a new name by typing in: ``` fashion_mnist save new_name_1 ``` / END | 1a114dd240432c4bad5df54db296bf65 |
apache-2.0 | ['automatic-speech-recognition', 'th'] | false | exp_w2v2t_th_xlsr-53_s711 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition on Thai using the train split of [Common Voice 7.0](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. | 3cd348d9ecbe0cead2c781fcab720794 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-sst2-target-glue-stsb This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-sst2](https://huggingface.co/muhtasham/tiny-mlm-glue-sst2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9195 - Pearson: 0.8130 - Spearmanr: 0.8114 | 5c42a4aa483f13c8ea6fb8c72e780e2b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | 2.7776 | 2.78 | 500 | 1.1238 | 0.7313 | 0.7669 | | 0.932 | 5.56 | 1000 | 1.0628 | 0.7833 | 0.8086 | | 0.737 | 8.33 | 1500 | 1.0050 | 0.8025 | 0.8208 | | 0.6099 | 11.11 | 2000 | 0.8592 | 0.8165 | 0.8220 | | 0.5164 | 13.89 | 2500 | 0.8875 | 0.8158 | 0.8181 | | 0.4659 | 16.67 | 3000 | 0.9524 | 0.8155 | 0.8198 | | 0.4114 | 19.44 | 3500 | 0.8872 | 0.8173 | 0.8174 | | 0.3728 | 22.22 | 4000 | 0.9423 | 0.8163 | 0.8166 | | 0.3396 | 25.0 | 4500 | 0.9953 | 0.8197 | 0.8202 | | 0.321 | 27.78 | 5000 | 0.9409 | 0.8160 | 0.8160 | | 0.3034 | 30.56 | 5500 | 0.9273 | 0.8142 | 0.8139 | | 0.2811 | 33.33 | 6000 | 0.9195 | 0.8130 | 0.8114 | | f18d3ee416e777bc3f8dca8dc64b2749 |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_food_model 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: 1.2335 - Accuracy: 0.985 | 16b7db5b19675530218d5e7da89f6a76 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 | f2dc6e130e3ab2e35e06868e0dd5a166 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.0523 | 1.0 | 50 | 1.9226 | 0.935 | | 1.3718 | 2.0 | 100 | 1.3422 | 0.995 | | 1.2298 | 3.0 | 150 | 1.2335 | 0.985 | | a63721fb8b103b42eb38ff300e1d6e89 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny Bengali This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0 bn dataset. It achieves the following results on the evaluation set: - Loss: 0.2314 - Wer: 32.8977 | eb79e5d5617fc1907328bbcd6d04d31f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3362 | 0.96 | 1000 | 0.3536 | 45.0860 | | 0.2395 | 1.91 | 2000 | 0.2745 | 37.1714 | | 0.205 | 2.87 | 3000 | 0.2485 | 34.7353 | | 0.1795 | 3.83 | 4000 | 0.2352 | 33.2469 | | 0.1578 | 4.78 | 5000 | 0.2314 | 32.8977 | | 6361749b35d2d616faae11963bcc1c43 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-XL-NL8 (Deep-Narrow version) T5-Efficient-XL-NL8 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. | d531038a8a883ad0a87b679546c3d887 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-xl-nl8** - is of model type **Xl** with the following variations: - **nl** is **8** It has **972.49** million parameters and thus requires *ca.* **3889.95 MB** of memory in full precision (*fp32*) or **1944.97 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 | | a3547dc24e1bb9310ebd4d3be6f7bb8a |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-ko-en-finetuned-ko-to-en-2780616 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Helsinki-NLP/opus-mt-ko-en) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8435 | eaa6aba8783687222d33fa1883da70b0 |
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