license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_mnli_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 1.0256 - Accuracy: 0.5004 | 872ca29f329f21143392f0a1841a067b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0987 | 1.0 | 1534 | 1.0980 | 0.3545 | | 1.0979 | 2.0 | 3068 | 1.0942 | 0.3580 | | 1.0897 | 3.0 | 4602 | 1.0896 ... | ed5ff1c7e00b67d57bc2940d4e2021c9 |
apache-2.0 | ['translation'] | false | heb-ukr * source group: Hebrew * target group: Ukrainian * OPUS readme: [heb-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-ukr/README.md) * model: transformer-align * source language(s): heb * target language(s): ukr * model: transformer-align * pre-processing: normalization + Sen... | 7e9e63c831d8eef27ea697e76917a987 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: heb-ukr - source_languages: heb - target_languages: ukr - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-ukr/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['he', 'uk'] - src_constituents: {'heb'} - tgt_const... | 94b54450e3b4b8728a67b98d4908c873 |
cc-by-4.0 | ['generated_from_trainer'] | false | pko-t5-small-finetuned-paper-4564652 This model is a fine-tuned version of [paust/pko-t5-small](https://huggingface.co/paust/pko-t5-small) on the aihub_paper_summarization dataset. It achieves the following results on the evaluation set: - Loss: 0.4922 - Rouge1: 4.874 - Rouge2: 1.0497 - Rougel: 4.8599 - Rougelsum: 4.... | c6ef589b0477477a645f2983121bd7e1 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_ep... | 520dd46aa520b6a3385499a4c3c8de32 |
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: 0.1728 - Accuracy: 0.934 - F1: 0.9342 | bde0a6d41a231e0b6469ed413825c455 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8524 | 1.0 | 250 | 0.2967 | 0.908 | 0.9053 | | 0.2319 | 2.0 | 500 | 0.1936 | 0.9245 | 0.9246 | | 0.154 |... | 57dcdc150d3bcda4d38165738fdf0730 |
apache-2.0 | ['generated_from_keras_callback'] | false | itsGanni/Adult_contemporary_music-clustered This model is a fine-tuned version of [nandysoham/15-clustered](https://huggingface.co/nandysoham/15-clustered) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4347 - Train End Logits Accuracy: 0.9062 - Train Start Logits Accu... | 96d8cae587a8004b4ae45389034fb7e5 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | e20db30873fd6cba04ed5c3b7ab9287f |
apache-2.0 | ['generated_from_keras_callback'] | false | Laikokwei/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: 0.4662 - Epoch: 2 | 329f874e66a286b87d431f3ebdc06a42 |
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': 44364, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | 0c2cdf2b524fd81ed68efb8a1a74a465 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 28 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | f050dd5dac49b46e9d2dfb40a8a88dfc |
apache-2.0 | ['generated_from_trainer'] | false | bert-nlp-project-ft-imdb-ds-news This model is a fine-tuned version of [jestemleon/bert-nlp-project-imdb](https://huggingface.co/jestemleon/bert-nlp-project-imdb) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4268 - Accuracy: 0.8984 - F1: 0.8780 | 971c1cdc7abf694721ab3aec46a83686 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4318 | 0.37 | 120 | 0.3162 | 0.8828 | 0.8598 | | 0.3094 | 0.75 | 240 | 0.2958 | 0.8891 | 0.8678 | | 0.2667 |... | 981463fd382ec19fa8838bea5e95e461 |
apache-2.0 | ['generated_from_keras_callback'] | false | Lunage/my_distilbert-finetuned-vaolo 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: 3.4576 - Validation Loss: 3.1231 - Epoch: 0 | 2c5f218a64eff6a8aae5783d182bf2c9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | 17b2310d3527a7f2f5f5fc4e217a6c56 |
mit | [] | false | mtl-longsky on Stable Diffusion This is the `<mtl-longsky>` 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 al... | 8ea3f2cd8d65d7d30e4b48cb2d5d7ae8 |
apache-2.0 | ['generated_from_trainer'] | false | whisper-tiny-ASR This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8363 - Wer: 60.1936 | ea140c41cc7c0956c0617ca5b8575199 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precis... | 1489364ad717833a916578e32f0d97e6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1494 | 6.8 | 1000 | 0.4732 | 50.4342 | | 0.0113 | 13.61 | 2000 | 0.6944 | 62.9461 | | 0.0024 | 20.41 | 3000 | 0.8042 | 59.803... | 392fcfc63b0a6897dba002f3cb65b432 |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2208 - Accuracy: 0.9205 - F1: 0.9207 | 91b5e771d4d9d78ddf6665851e372490 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8134 | 1.0 | 250 | 0.3103 | 0.9055 | 0.9035 | | 0.2419 | 2.0 | 500 | 0.2208 | 0.9205 | 0.9207 | | ab816da6aa4e3af293561cc59370b672 |
afl-3.0 | ['translation'] | false | This is a BART-large model finetuned on roughly 58000 aligned sentence pairs in English and Middle English, collected from the works of Geoffrey Chaucer, John Wycliffe, and the Gawain Poet. <br> It includes special characters such as þ. <br> This model reflects the spelling inconsistencies characteristic of Middle En... | 5a974ac32b87d9c64388a9eafb26991a |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_gender_male-0_female-10_s412 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure ... | 1ae39118062d42e8ed755ba7d3c16cfc |
apache-2.0 | ['generated_from_trainer'] | false | model_trained_by_me2 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: 0.4258 - Accuracy: 0.7983 - F1: 0.7888 | e65b8dc7144be87b42549b28df403912 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ft1500_class This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9779 - Accuracy: 0.2357 - F1: 0.2352 | eba5aa431e60cda40d49e4b1eded3780 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 2.034 | 1.0 | 3122 | 1.9454 | 0.2351 | 0.1964 | | 1.8558 | 2.0 | 6244 | 1.9235 | 0.2377 | 0.2300 | | 1.6754 |... | a5d724b925a3a7f61e40b34aec03bb68 |
mit | ['generated_from_trainer'] | false | outputs This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0926 - Accuracy: 0.8780 - F1: 0.3881 - Precision: 0.5417 - Recall: 0.3023 | fb9e3158db68b1d455462d101a68a245 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 256 - eval_batch_size: 512 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 - mixed_precision_t... | 8dbd6d6e5f59179be5c8eab583eb734b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 6 | 0.0874 | 0.8810 | 0.4118 | 0.56 | 0.3256 | | No log | 2.0 |... | 9666b5a23f776c0e9544b970d8b9a881 |
apache-2.0 | ['generated_from_keras_callback'] | false | whisper_final_nosp This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0732 - Train Accuracy: 0.0234 - Validation Loss: 0.8512 - Validation Accuracy: 0.0203 - Epoch: 24 | bb088e46e8976f77997198c6bba30b45 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 7.5559 | 0.0010 | 6.3853 | 0.0013 | 0 | | 6.3227 | 0.0021 | 5.7023 | 0.0038 ... | 69189293ee003532168770b890ac7f97 |
apache-2.0 | ['translation'] | false | eng-euq * source group: English * target group: Basque (family) * OPUS readme: [eng-euq](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-euq/README.md) * model: transformer * source language(s): eng * target language(s): eus * model: transformer * pre-processing: normalization + Sentence... | 0b4dd03939e80960a4d5df0cdcccf077 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-euq - source_languages: eng - target_languages: euq - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-euq/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'euq'] - src_constituents: {'eng'} - tgt_cons... | 28dd6f271d7d1ed42bbe7631569ee710 |
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: 0.2148 - Accuracy: 0.9275 - F1: 0.9274 | 1b5fa2cf207dab503f23f56cce5b47b9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8308 | 1.0 | 250 | 0.3053 | 0.9075 | 0.9053 | | 0.2421 | 2.0 | 500 | 0.2148 | 0.9275 | 0.9274 | | 059e6c80f4eab44194fd7a615322077a |
apache-2.0 | ['translation'] | false | opus-mt-loz-sv * source languages: loz * target languages: sv * OPUS readme: [loz-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/loz-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | 74700c8be5b6f6293c21fd49eda6c235 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_2_binary This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4724 - F1: 0.7604 | ad0ad6f6b718aa11d868659b3564baff |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.4280 | 0.7515 | | 0.4018 | 2.0 | 580 | 0.4724 | 0.7604 | | 0.4018 | 3.0 | 870 | 0.5336 | 0.7428 | |... | b9ceda7dfc125167438f5b7e7837d560 |
apache-2.0 | ['generated_from_trainer', 'distilgpt2', 'email generation', 'email'] | false | distilgpt2-emailgen Why write the rest of your email when you can generate it? ```python from transformers import pipeline model_tag = "postbot/distilgpt2-emailgen" generator = pipeline( 'text-generation', model=model_tag, ) prompt = """ Hello, Following up o... | 2bd4829cd426ab1d80872a244cf148d2 |
apache-2.0 | ['generated_from_trainer', 'distilgpt2', 'email generation', 'email'] | false | generate print(result[0]['generated_text']) ``` - try it in a [Google Colab](https://colab.research.google.com/gist/pszemraj/91df57e0c2caf1d5273b78576ad2853e/postbot-distilgpt2-emailgen-demo.ipynb) notebook - Use it in bash/cmd [with this gist](https://gist.github.com/pszemraj/c1b0a76445418b6bbddd5f9633d1bb7f) :) >... | 8d47377b63d0d080d20b6fbb8186ac24 |
apache-2.0 | ['generated_from_trainer', 'distilgpt2', 'email generation', 'email'] | false | Model description This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on a dataset of 50k emails, including the classic `aeslc` dataset. It achieves the following results on the evaluation set: - Loss: 2.6247 | 238f1fb348a70db024b3f05b7cf1414f |
apache-2.0 | ['generated_from_trainer', 'distilgpt2', 'email generation', 'email'] | false | Intended uses & limitations The intended use of this model is to provide suggestions to "autocomplete" the rest of your email. Said another way, it should serve as a **tool to write predictable emails faster**. It is not intended to write entire emails; at least **some input** is required to guide the direction of th... | dc602626995b3311f4f31a7f02ebefe4 |
apache-2.0 | ['generated_from_trainer', 'distilgpt2', 'email generation', 'email'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_s... | fd29b6b42d50664c1ab3f67bf2277682 |
apache-2.0 | ['generated_from_trainer', 'distilgpt2', 'email generation', 'email'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.8299 | 1.0 | 248 | 2.7971 | | 2.6984 | 2.0 | 496 | 2.6826 | | 2.7022 | 3.0 | 744 | 2.6361 | | 2.6436 | 4.0 | 992 | 2.6245 ... | 9dc4a2bbc25585e4c5485f25142f98f9 |
mit | ['generated_from_trainer'] | false | kobart_8_6e-5_datav2_min30_lp5.0_temperature1.0 This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8935 - Rouge1: 35.9396 - Rouge2: 12.7251 - Rougel: 23.4072 - Bleu1: 29.8... | 799ad45a32730d387372453483fef739 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 | 68de7c73ee5b9bdf810083672894c9db |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:| | 2.5006 | 0.19 | 1000 | 2.974... | 78e4d3db5ce51b4e3e86fab05faec12a |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Taran-Protogen-3.4 Dreambooth model trained by taranarora 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/fas... | c8a308644b79e716fcbf19e48118fc84 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_100k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 100k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different ... | 4adc1b6a42cf253a3ee5050503f79e53 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_100k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_100k') model = TFBertModel.from_pretrained("google/multibe... | 7cb0cffb56c068f012fc9afe63682ee1 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'diffusers'] | false | Civilizations 6 Diffusion This model was trained on the leaders portraits/loading screens, and the splash arts. However, it was not trained on the gameplay map at all. To reference the art style, use the token: civsix style | bced06b920c13c40d459e0e0b92912e3 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'diffusers'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run Civilizations_6_Diffusion: [ notebook. You can als... | 29a062c93badd0b27badcffd0c5dff43 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Japanese This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 ja dataset. It achieves the following results on the evaluation set: - Loss: 0.2543 - Wer: 13.7687 | 5d655fa176fcfe9c7cee2eaa5d934a51 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2515 | 1.06 | 200 | 0.2881 | 16.9442 | | 0.2212 | 2.12 | 400 | 0.2616 | 14.6884 | | 0.0774 | 4.04 | 600 | 0.2543 | 13.768... | e376e37316a4fd66f7ce3a39e2715f84 |
apache-2.0 | ['automatic-speech-recognition', 'fa'] | false | exp_w2v2t_fa_vp-nl_s339 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 517fd04cc766682e0c365dcf5df86145 |
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: 0.1698 - Accuracy : 0.933 - F1: 0.9335 | 608a965fc848393af98f8bf5380b9316 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 0.6265 | 1.0 | 500 | 0.2137 | 0.926 | 0.9256 | | 0.1795 | 2.0 | 1000 | 0.1698 | 0.933 | 0.9335 | | c2aeedb83922e24cf59fe0c22f610ebf |
apache-2.0 | ['translation'] | false | opus-mt-luo-en * source languages: luo * target languages: en * OPUS readme: [luo-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/luo-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](http... | 4e829c817321dc93711e33d124e7192b |
apache-2.0 | ['generated_from_trainer'] | false | sentiment_analysis_on_covid_tweets 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: - eval_loss: 0.5883 - eval_accuracy: 0.771 - eval_runtime: 33.4887 - eval_samples_per_secon... | 47bb16bf94c55e7f7367d991d4cb733c |
other | [] | false | Tile Grout Cleaning Mesquite TX http://mesquitecarpetcleaningtx.com/tile-grout-cleaning.html (469) 213-8132 Your home is your very own castle, and you make every effort to keep it spotless and inviting at all times.However, you will discover that many tasks, including tile and grout cleaning, take up too much of your t... | 4160a5c015dc76b35841e72420237686 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4094 - Wer: 0.2825 | efac891927ff958607953dbba29d0bf7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.5419 | 3.45 | 500 | 1.2376 | 0.8772 | | 0.5393 | 6.9 | 1000 | 0.4489 | 0.3894 | | 0.1916 | 10.34 | 1500 | 0.3777 | 0.3185 | |... | f6c1a834a2a242cdf48937d5518e43d0 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'pytorch', 'diffusers', 'dreambooth-hackathon', 'food'] | false | Ice_cream_animals Dreambooth Model for Food trained on a custom dataset. This is a Stable Diffusion **2.1 768px** model fine-tuned on the food concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a butterfly ice cream, icenimal** This model was created as part of the DreamBooth Hackathon 🔥. ... | 2e4ec0e09e5f3b2267e637b0e0fefe08 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_no-pretraining_s445 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (es)](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 bee... | 9da50bd960f8d12ecde500f8ca610abe |
gpl-3.0 | ['gpt2', 'tagalog', 'filipino'] | false | GPT-2 Tagalog The Tagalog GPT-2 model used to benchmark our fake news detection system Cruz et al. (2020). We make available an improved version of our GPT-2 model trained with NewsPH in addition to WikiText-TL-39. | ef06ad220bb753d0ace1473a18948290 |
gpl-3.0 | ['gpt2', 'tagalog', 'filipino'] | false | Limitations and Bias The model was trained with two language modeling datasets for Tagalog: * **WikiText-TL-39**, which is sourced from a dump of Tagalog WikiPedia. * **NewsPH**, which is a dump of news articles from all available mainstream news outlets in the Philippines. Due to the source of the training data, gen... | faea49e8f7199c58067d7419c5647ab0 |
gpl-3.0 | ['gpt2', 'tagalog', 'filipino'] | false | Citations ```bibtex @inproceedings{localization2020cruz, title={{Localization of Fake News Detection via Multitask Transfer Learning}}, author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth}, booktitle={Proceedings of The 12th Language Resources and Evaluation Conference}, pages={25... | 1e47732402b3ef9b4d04c6b1dfe732fd |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | Model description This is a ported version of [S3PRL's Hubert for the SUPERB Emotion Recognition task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/emotion). The base model is [hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k), which is pretrained on 16kHz sampled speech audio.... | 59803c3853b1c9706fdcf15c45199cfc |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | Task and dataset description Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset [IEMOCAP](https://sail.usc.edu/iemocap/) is adopted, and we follow the conventional evaluation protocol: we drop the unbalanced emotion classes to leave the final four classes with a si... | 0004317f147a10c4f4a898ef7933b55b |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | Usage examples You can use the model via the Audio Classification pipeline: ```python from datasets import load_dataset from transformers import pipeline dataset = load_dataset("anton-l/superb_demo", "er", split="session1") classifier = pipeline("audio-classification", model="superb/hubert-large-superb-er") labels ... | 6ba80cd0bd2e4eed1111ec589be385fa |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | load a demo dataset and read audio files dataset = load_dataset("anton-l/superb_demo", "er", split="session1") dataset = dataset.map(map_to_array) model = HubertForSequenceClassification.from_pretrained("superb/hubert-large-superb-er") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-large-... | 9243ffcd6bae9256badcc5cb506ddd4e |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | compute attention masks and normalize the waveform if needed inputs = feature_extractor(dataset[:4]["speech"], sampling_rate=16000, padding=True, return_tensors="pt") logits = model(**inputs).logits predicted_ids = torch.argmax(logits, dim=-1) labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()] ``... | 6109b0948dfd02d18a67f665d73f4764 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: tpu - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | c00c1e8059b7e1951c101ae09a1ef105 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | 『Ginger Mix』    - "Ginger Mix" is a merged model based on "LimeMixV2"(https://huggingface.co/Hemlok/LimeMix) with improved expression and color tone. --- | bec68036841d3257b641619372c30f17 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | 『Ginger MixR』   - "Ginger MixR" is a model based on "Ginger Mix" with improved composition and background, and is as close to "LimeMixV2" as possible. --- | 95f5aa8cd336915cdd5c45c073cec84b |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | ◆About - Sampler: DDIM or DPM++ SDE Karras - Steps: 20~ - Clipskip: 2 - CFG Scale: 5-8 - Denoise strength: 0.5-0.65 - Negative prompts should be as few as possible. - vae: Use "Any" or similar. ---- | 69c69d4cbd16f980d002001aac265e80 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_itr0_3e-05_editorials_16_02_2022-21_06_22 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: 0.1060 - Preci... | 3df670b418450f664cc4ccec31bc3d03 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 15 | 0.0897 | 0.08 | 0.0110 | 0.0193 | 0.9801 | | No log | 2.0 |... | 7428e0a78a567668fd9d1b99401595fd |
apache-2.0 | [] | false | This is a pretrained [MT5](https://github.com/google-research/multilingual-t5) base model (**390M** parameters). Training was performed with the span corruption task on a clean 80GB Romanian text corpus for 4M total steps with these [scripts](https://github.com/dumitrescustefan/t5x_models), starting from the 1M publi... | 5804174fdae5e0eddf634d3dd657837a |
apache-2.0 | [] | false | How to load an mt5x model ```python from transformers import MT5Model, T5Tokenizer model = MT5Model.from_pretrained('dumitrescustefan/mt5-base-romanian') tokenizer = T5Tokenizer.from_pretrained('dumitrescustefan/mt5-base-romanian') input_text = "Acesta este un test." target_text = "Acesta este" inputs = tokenizer(in... | 53bf40f56e270233bacd81e24106d7f0 |
apache-2.0 | [] | false | this will print [1, 4, 768] ``` Remember to always sanitize your text! Replace ``ş`` and ``ţ`` cedilla-letters to comma-letters with : ```python text = text.replace("ţ", "ț").replace("ş", "ș").replace("Ţ", "Ț").replace("Ş", "Ș") ``` because the model was **not** trained on cedilla ``ş`` and ``ţ``s. If you don't, you ... | 5038209cd25aae7b848b0498404d475e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Maori This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs mi_nz dataset. It achieves the following results on the evaluation set: - Loss: 0.7756 - Wer: 30.4816 | 3a951c746c6d5ede9c0177143c602e30 |
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: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 500 - mixed_precisi... | db32bf16781220dc6598ea3e5572038b |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2693 | 7.02 | 100 | 0.6741 | 35.4845 | | 0.0084 | 15.01 | 200 | 0.7756 | 30.4816 | | 0.0029 | 23.0 | 300 | 0.8154 | 31.474... | eb55c14ab91c2740e830999db7bd8328 |
apache-2.0 | ['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_2000k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 4, Step 2000k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different... | 8aee92b4ed1703396aa20fb676f24283 |
apache-2.0 | ['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_2000k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_2000k') model = TFBertModel.from_pretrained("google/multib... | 71a4ee63a9883dc6bbdbd3a1685b34ba |
apache-2.0 | [] | false | General Information:
Used Dataset: cats_vs_dogs (https://huggingface.co/datasets/cats_vs_dogs)
Used Label: Randomly Images are flipped and labels for the flipped images are 1, otherwise 0.
Used Library: Pytorch
Used Model: ResNet18 from torchvision
Number of classes: 2 (0 means No flip and 1 means Flippe... | 7d94bb645fdc661287ea7d724277cb3b |
apache-2.0 | [] | false | Specific information about the Dataset:
The following files from the dataset were removed during the training because those files are broken/ corrupted.
- ./kagglecatsanddogs_3367a/PetImages/Cat/666.jpg
- ./kagglecatsanddogs_3367a/PetImages/Cat/10404.jpg
- ./kagglecatsanddogs_3367a/PetImages/Dog/11702.jpg
... | e9bfa003745250355ee29bbed59a8b5d |
apache-2.0 | [] | false | Training Information:
Total Epoch: 5
Pretrained: True (ImageNet weight) (Every layer is trainable)
Image Size: 224 x 224
Batch Size: 128
Optimizer: SGD
Learning Rate: 0.001 (Constant throughout the training)
Momentum: 0.9
Loss: CrossEntropy Loss
| 330600ceac51a6d2ef88b3cc3997c98e |
apache-2.0 | [] | false | Some possible improvements:
- Most of the misclassified images are occluded by some other objects or partly visible. One possible improvement could be to improve this type of image in the training dataset.
- Hyperparameter tuning is another option, we could try to see whether the performance improves or not. For exa... | 0d059ef45a5389bc1d3f1febd4d6c83b |
other | ['diffusion', 'point-cloud', 'airplane', '3D'] | false | Model Description – Luo, Shitong and Hu, Wei – 2021 Proposed a probabilistic generative model for point clouds inspired by non-equilibrium thermodynamics, exploiting the reverse diffusion process to learn the point distribution. All models are available on the original [***Github repo Link***](https://github.com/luos... | 436668af49281e12895f8c081f3eb919 |
other | ['diffusion', 'point-cloud', 'airplane', '3D'] | false | Datasets ShapeNet is a comprehensive 3D shape dataset created for research in computer graphics, computer vision, robotics and related diciplines. - [Offical Dataset of ShapeNet](https://shapenet.org/) - [author's training dataset](https://drive.google.com/drive/folders/1SRJdYDkVDU9Li5oNFVPOutJzbrW7KQ-b?usp=share_lin... | 4d7cbffad609baae2608f8d8c24897e7 |
other | ['diffusion', 'point-cloud', 'airplane', '3D'] | false | BibTeX Entry and Citation Info ``` @inproceedings{luo2021diffusion, author = {Luo, Shitong and Hu, Wei}, title = {Diffusion Probabilistic Models for 3D Point Cloud Generation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {202... | 99dec608b0eff06d3ea1cecfee466599 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'style-transfer', 'formality-style-transfer'] | false | mT5 Base for Formal-to-informal Style Transfer 🤗 This repository contains the checkpoint for the [mT5 Base](https://huggingface.co/google/mt5-base) model fine-tuned on Formal-to-informal style transfer on the Italian subset of the XFORMAL dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text... | e63ad30bd7d3bff4a0aafa83331cff5f |
apache-2.0 | ['italian', 'sequence-to-sequence', 'style-transfer', 'formality-style-transfer'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines f2i = pipeline("text2text-generation", model='it5/mt5-base-formal-to-informal') f2i("Vi ringrazio infinitamente per vostra disponibilità... | ce85ce05531b37d1d8b6451af9183f85 |
cc-by-4.0 | ['bert'] | false | bert-fc-small A small-size BERT Language Model with a **first character** prediction pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties?]... | c38b91f984a42e464aec461f5289202b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0610 - Precision: 0.9244 - Recall: 0.9367 - F1: 0.9305 - Accuracy: 0.9838 | 0adb0455f5294c3ec26d2fea377d661e |
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