license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.7082 ... | db1dae30cf76611685ef9ffb70b89729 |
apache-2.0 | ['generated_from_keras_callback'] | false | mak109/distilgpt2-finetuned-lyrics This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.0226 - Validation Loss: 3.0275 - Epoch: 4 | 972bf5d2c502e5d951617cb955779c96 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.2907 | 3.1500 | 0 | | 3.1607 | 3.0962 | 1 | | 3.1005 | 3.0664 | 2 | | 3.0573 | 3.0430 | 3 | | 3.0226 | 3.0275 | 4 | | 855ff55c12ac51c54c1abde3e3c1fae5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.6627 | 0.05 | 5000 | 2.1507 | | 2.135 | 0.1 | 10000 | 2.0032 | | 2.0378 | 0.15 | 15000 | 1.9287 | | 1.9804 | 0.2 | 20000 | 1.8809 ... | fde09b2d8731748be57e2343e860e471 |
apache-2.0 | ['translation'] | false | vie-epo * source group: Vietnamese * target group: Esperanto * OPUS readme: [vie-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-epo/README.md) * model: transformer-align * source language(s): vie * target language(s): epo * model: transformer-align * pre-processing: normalization +... | 3c92ae9bba1ad3299bf8a1450c5a858c |
apache-2.0 | ['translation'] | false | System Info: - hf_name: vie-epo - source_languages: vie - target_languages: epo - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-epo/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['vi', 'eo'] - src_constituents: {'vie', 'vie_Hani'} ... | bf374b6c17b70812cf73e25fcea49ac9 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Italian This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2421 - Wer: 17.3709 | f04bf8da8da569b4a750a6093324123c |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 - mixed_precision_tra... | 20181c2bf44e08b8567ee05626343ae1 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4521 | 0.1 | 100 | 1.3771 | 120.3480 | | 0.7526 | 0.21 | 200 | 0.9120 | 83.8949 | | 0.3023 | 0.31 | 300 | 0.4427 | 26... | e0c34e22241677d14440f29730210dae |
mit | ['generated_from_trainer', 'medical'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 216 - num_epochs: 5 | 6a0d2aa997f634bed9074006b17e61b6 |
mit | ['generated_from_trainer'] | false | minilm-finetuned-emotionclassification This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0554 - F1 Score: 0.6732 | f621d6f2f7eafa3629ad55073a3d817b |
mit | ['generated_from_trainer'] | false | Model description The base model used is Microsoft MiniLM-L12-H384-uncased which is finetuned on [GoEmotions dataset](https://huggingface.co/datasets/go_emotions) available on huggingface. With this model, you can classify emotions in English text data. The model predicts 10 basic emotions: 1) anger 🤬 2) love ❤️ 3... | 12d1805784b27f983ad1742b9a73ccc7 |
mit | ['generated_from_trainer'] | false | Training and evaluation data The dataset used for Training and Evaluation is [GoEmotions dataset](https://huggingface.co/datasets/go_emotions) and in this, we have used 10 emotion variables. {0:'sadness',1:'joy',2:'love',3:'anger',4:'fear',5:'surprise',6:'excitement',7:'gratitude',8:'curiosity',9:'caring'} | 951d243f520d77cb588a7a0a7ffe23b1 |
mit | ['generated_from_trainer'] | false | How to use the model Here is how to use this model to extract the emotions from the given text in PyTorch: ```python >>> from transformers import pipeline >>> model_ckpt ="sid321axn/minilm-finetuned-emotionclassification" >>> pipe = pipeline("text-classification",model=model_ckpt) >>> pipe("I am really excited about ... | 1b0c3756ac61e6d52b8e4706d7c6590c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1659 | 1.0 | 539 | 1.1419 | 0.6347 | | 1.0719 | 2.0 | 1078 | 1.0789 | 0.6589 | | 0.9893 | 3.0 | 1617 | 1.0537 | 0.... | 93e7686431b9108ea228593f3edfaa1a |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-qnli This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7933 | 3405f938f9a429d33b4055884c8710c7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.1842 | 0.4 | 500 | 3.7675 | | 4.1114 | 0.8 | 1000 | 3.7501 | | 4.1321 | 1.2 | 1500 | 3.7769 | | 4.0487 | 1.6 | 2000 | 3.7642 ... | a64a1fe2f067f128ba59d306c21a3064 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-tweet-bert 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: 0.8777 - Accuracy: 0.8233 - F1: 0.8233 | 219d10670f86c935d9f762fcd6088863 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_wnli_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 1.2471 - Accuracy: 0.1268 | e02b3e7eb25bead5e97e0a7c128d20a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6786 | 1.0 | 218 | 1.2471 | 0.1268 | | 0.5294 | 2.0 | 436 | 1.9112 | 0.1268 | | 0.4476 | 3.0 | 654 | 2.6565 | 0.... | 37288c8b1324ccf1d718b63500fb95ca |
creativeml-openrail-m | ['text-to-image'] | false | enAttendantColine-v2-512-DreamBooth Dreambooth model trained by eBoreal with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-512 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/gi... | 87b1cc236f6dbd260bf128eec94cef58 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 80d27bbb3d520496ce1ba2cee0c1c947 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | reasonableDrinkV2 This is an update of my own SD trainee with a many lot of dreaming (or nightmare) illustrations as a style to SD 2.1. If you wanna test it, you can put this word on the prompt: reasonableDrink If you enjoy my work, please consider supporting me: [ on the emo dataset. It achieves the following results on the evaluation set: - Loss: 0.3616 - Accuracy: 0.8708 - F1: 0.8824 | 5f6ba79f38617f2bb54f1d353e93eb17 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4841 | 1.0 | 472 | 0.3516 | 0.8695 | 0.8812 | | 0.2767 | 2.0 | 944 | 0.3616 | 0.8708 | 0.8824 | | 57d675b42269b15b077acaca042f4b9f |
apache-2.0 | ['translation'] | false | opus-mt-af-fi * source languages: af * target languages: fi * OPUS readme: [af-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/af-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | d222d07b5f9bc57564329598075c273b |
mit | [] | false | Jinjoon Lee, They on Stable Diffusion This is the `<jinjoon_lee_they>` 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.... | 187270eb3e6512a2c757a6611dc2838d |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4785 - Rouge1: 28.3127 - Rouge2: 7.7376 - Rougel: 22.2445 - Rougelsum: 22.2505 - Gen Len: 18.8304 | 28a17be90b81c3dc7ac0bf060ce57de6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.7174 | 1.0 | 12753 | 2.4785 | 28.3127 | 7.7376 | 22.2445 | 22.2505 | 18... | 320099ce27a0a24282080314ac890c54 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-sngp-for-qa-squad-seed-9990 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.9271 | c9775a25bb6ea3e0a3e00345cc80c210 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.4046 | 1.0 | 8248 | 2.0792 | | 2.0132 | 2.0 | 16496 | 1.8262 | | 1.7824 | 3.0 | 24744 | 1.9271 | | ba81a9c3e4d7435b7001b70793f2d0dd |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__hate_speech_offensive__train-16-7 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: 0.9011 - Accuracy: 0.578 | 781c1194c3b43360e88f8cc6b6f169b9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0968 | 1.0 | 10 | 1.1309 | 0.0 | | 1.0709 | 2.0 | 20 | 1.1237 | 0.1 | | 0.9929 | 3.0 | 30 | 1.1254 | 0.... | fd7bcca8cd70a528b53ac9a7e0b14b6f |
apache-2.0 | ['translation'] | false | opus-mt-sv-st * source languages: sv * target languages: st * OPUS readme: [sv-st](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-st/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | ffff4501ceb0423f03fd8e26edbb793e |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb 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: 4.6022 | aa446dfbc609ed11be72f606815d98ed |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.414 | 1.0 | 10 | 4.7780 | | 4.8623 | 2.0 | 20 | 4.7064 | | 4.6726 | 3.0 | 30 | 4.5646 | | 18709b9b4542d709489dec835b453478 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-misogyny-sexism-4tweets-2e-05-0.025 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: 0.9846 - Accuracy: 0.6673 - F1: 0.6700 - Precision: 0.6172 -... | 347fba8db542df321e9f6f001d487021 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:---:|:---:|:---:|:---:| | 0.4767 | 1.0 | 1346 | 0.6746 | ... | a1877f06430b5b585658fc0802c2893a |
apache-2.0 | ['translation'] | false | ces-epo * source group: Czech * target group: Esperanto * OPUS readme: [ces-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ces-epo/README.md) * model: transformer-align * source language(s): ces * target language(s): epo * model: transformer-align * pre-processing: normalization + Sent... | 0cd1c1b90c0e3f1310a920340e5576e6 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ces-epo - source_languages: ces - target_languages: epo - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ces-epo/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['cs', 'eo'] - src_constituents: {'ces'} - tgt_const... | f1920e71bb737abe515820b393889145 |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_vp-it_s149 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) 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 you... | d2bd199c14a7a1766510a9548b49d6f6 |
apache-2.0 | ['translation'] | false | opus-mt-ht-en * source languages: ht * target languages: en * OPUS readme: [ht-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ht-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | 14bd1ec34f4d8f7938c10938fbe79029 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Informations Fine-tuned SD v1-5 model, 61320 steps, 7 epochs Aspect Ratio Bucketing centered at 768 resolution <img alt="Showcase" src="https://huggingface.co/FloydianSound/Wlop_Diffusion/resolve/main/WLOP_Artstyle_AR_Chart.png"/> Made with 876 pictures of the artist WLOP If you like the artist support their work ... | fae5bb1fab40b85989c45f36e9c063da |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Samples <img alt="Showcase" src="https://huggingface.co/FloydianSound/Wlop_Diffusion/resolve/main/00000-souryuu%20asuka%20langley%20red%20hairs%20green%20eyes%20wlop-2961790964-WLOP_Artstyle_wlop_artstyle_768_e7.png"/> <img alt="Showcase" src="https://huggingface.co/FloydianSound/Wlop_Diffusion/resolve/main/00000-prin... | b1c52487dac400b92efc81a29da06e61 |
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: 0.2157 - Accuracy: 0.9255 - F1: 0.9258 | 3ae1b998c0e8e8e1fb93a7cedb99cf15 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8145 | 1.0 | 250 | 0.3093 | 0.91 | 0.9081 | | 0.2461 | 2.0 | 500 | 0.2157 | 0.9255 | 0.9258 | | d6f05ac837112fca6f992b2fb8f443de |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 | 29e59a55cafd58ab1292540a2ca8a950 |
mit | ['generated_from_trainer'] | false | Bio_ClinicalBERT-finetuned-medicalcondition This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7201 - F1 Score: 0.8254 | a6f44711a67712df11368cc296e7e47c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.8002 | 1.0 | 1772 | 0.6327 | 0.7759 | | 0.5933 | 2.0 | 3544 | 0.5906 | 0.7934 | | 0.5015 | 3.0 | 5316 | 0.5768 ... | 6327944bf0d178c41db031f3a4557d22 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-finetuned-weaksup-1000-pad This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4168 - Rouge1: 26.2506 - Rouge2: 10.7802 - Rougel: 19.2236 - Rougelsum: 22... | 22688a45fb80f0d2f7d869fb92b71d46 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.1434 | 1.0 | 1000 | 0.4168 | 26.2506 | 10.7802 | 19.2236 | 22.6883 | 68... | 1c124e128cef7429b3914bd48ac294b6 |
mit | ['generated_from_trainer'] | false | gifted_hugle This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000... | 15a3929e841b637701cbd7ca5c507737 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tom... | fefcbe6cba28c7f5be775a98ca8e850e |
apache-2.0 | ['generated_from_trainer'] | false | TUF_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2389 - Accuracy: 0.9533 | 2a6bd6120205e9d0b96f8a7479b45d79 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5099 | 0.1 | 50 | 0.3861 | 0.8533 | | 0.2985 | 0.2 | 100 | 0.2961 | 0.8933 | | 0.2972 | 0.3 | 150 | 0.2335 | 0.... | 1657f67bc4cf26dcdf0e5345b1d5108c |
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: 0.2261 - Accuracy: 0.9205 - F1: 0.9208 | 5ff1951b509d8ee6da50715985daa8d0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8161 | 1.0 | 250 | 0.3179 | 0.903 | 0.8998 | | 0.2508 | 2.0 | 500 | 0.2261 | 0.9205 | 0.9208 | | 194e199ff485311511afc276866dbb6f |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | rembrantSito Dreambooth model trained by theguaz 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-... | ffe4dd96eab459981aa50f1de5edeb1a |
mit | ['generated_from_keras_callback'] | false | orhanxakarsu/turkishReviews-ds-mini-model This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 7.6474 - Validation Loss: 7.7414 - Epoch: 2 | 7c61a7faace1fd60694115ea57c5a42b |
mit | ['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': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps... | 1eb69d53820674168ee40c31b39ce507 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 9.4621 | 8.4755 | 0 | | 8.0411 | 7.7438 | 1 | | 7.6474 | 7.7414 | 2 | | a603c6933e4884530802706a8aa7805a |
mit | [] | false | hebrew-gpt_neo-small Hebrew text generation model based on [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo). Each was trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program. | 96fef5d217ad4c8736c7e51ef4831dcd |
mit | [] | false | Simple usage sample code ```python !pip install tokenizers==0.10.2 transformers==4.6.0 from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-small") model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-small", pad_token_id=t... | 9ba9c3fda6c25eee916fc9b9c1ff4d8e |
apache-2.0 | ['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_1300k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 4, Step 1300k 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... | e8e4835ec08457ec11e3caf282b1ac44 |
apache-2.0 | ['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_1300k'] | 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_1300k') model = TFBertModel.from_pretrained("google/multib... | 166020cf0732e81e2184650d4bb95bd3 |
apache-2.0 | ['exbert', 'multiberts'] | false | MultiBERTs Seed 24 (uncased) Seed 24 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language... | f509c2055fd6f8ea0fde0669f3907869 |
apache-2.0 | ['exbert', 'multiberts'] | 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 BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-24') model = BertModel.from_pretrained("multiberts-seed-24") text = "Replace me by any text you'd like." enco... | bdbbfb5fe36b16c7a2f90a071000139e |
apache-2.0 | ['translation'] | false | opus-mt-en-ss * source languages: en * target languages: ss * OPUS readme: [en-ss](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ss/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 2e71c20a41d2e4cab2c091d56a5703e5 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3349 - Accuracy: 0.8733 - F1: 0.8742 | 61fbfc21817146a250b98f24c7336f70 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-NER-favsbot-no-apostrophe-2022-11-07 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the favsbot dataset. It achieves the following results on the evaluation set: - Loss: 0.1169 - Precision: 0.8276 - Recall: 0.96 - F1: 0.8889 - Accuracy: 0.9444 | b2b7c0b29fdc8f4213ac19628a0deb03 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 10 | 1.6302 | 0.0 | 0.0 | 0.0 | 0.5972 | | No log | 2.0 |... | de44838ca2c415ead93682cd5defe000 |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_unispeech-ml_s417 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using... | 18750bb7a618baba4ecce0b6988e6363 |
cc0-1.0 | ['generated_from_trainer'] | false | AgitationTextV2 This model is a fine-tuned version of [bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12](https://huggingface.co/bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6268 - Accuracy: 0.73 - Precision: 0.8036 - Recall: 0.... | dec7adcc31245f8a6bdeb27d647835ba |
cc0-1.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.6535 | 1.0 | 50 | 0.6682 | 0.66 | 0.7547 | 0.6557 | 0.7018 | | 0.5874 | 2.0 |... | 7b893f9d3ab63ab5d0dd0b0c9126e421 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2663 - Bleu: 0.0 | 39d2a36e7c11d8c44151294e6c76cc49 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-viquad 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: 2.3761 | d286b18df35f8eecf01756141dfa28ac |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 259 | 2.9945 | | 3.3665 | 2.0 | 518 | 2.3761 | | 3778059e4aa2d2a0c92ef232c167724d |
mit | [] | false | About the Bert-Bank-Model It is a Turkish BERT-based model that will analyze people's bank complaints and classify them according to one of eight categories. The classification headings are as follows: - Diğer - Hesap İşlemleri - KKB Skor - Kargo - Kart İşlemleri - Kredi İşlemleri - Limit - Müşteri Temsilcisi 2464... | 70fdd6c35d6e5b6dd7a92fcfc268aa06 |
mit | [] | false | Example ```sh from transformers import AutoTokenizer, TextClassificationPipeline, TFBertForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("elifftosunn/Bert-Bank-Model") model = TFBertForSequenceClassification.from_pretrained("elifftosunn/Bert-Bank-Model", from_pt=True) pipe = TextClassificationPipe... | d06fa2c39c8161cf6f93f7b3b54666e3 |
apache-2.0 | ['Farsi'] | false | Arnavāz (ارنواز) **Model Description:** Arnavaz/gpt-arnavaz-beta is gpt2 language model that is fine-tuned using [bolbolzaban/gpt2-persian](https://huggingface.co/bolbolzaban/gpt2-persian) pretrained model. [bolbolzaban/gpt2-persian](https://huggingface.co/bolbolzaban/gpt2-persian) has been trained similar to [gpt2-m... | 259c7bfe891a839a67cbf42724d2f811 |
apache-2.0 | ['Farsi'] | false | How to utilize Using a pipeline for text generation, Arnavaz can be utilized like this: ```python from transformers import pipeline, AutoTokenizer, GPT2LMHeadModel, AutoConfig tokenizer = AutoTokenizer.from_pretrained('Arnavaz/gpt2-arnavaz-beta') model = GPT2LMHeadModel.from_pretrained('Arnavaz/gpt2-arnavaz-beta') co... | db0a832757b1b2b43f92a95288334ac0 |
apache-2.0 | [] | false | Model Description This is an encoder-decoder model based on [VisionEncoderDecoderModel](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder). [Google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) was used as the encoder, [DeepPavlov/rubert-base-cased](htt... | 5666642ae523f4102b357be2acdc4832 |
apache-2.0 | [] | false | How to use ```python from PIL import Image import torch from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel tokenizer = AutoTokenizer.from_pretrained("dumperize/movie-picture-captioning") feature_extractor = ViTFeatureExtractor.from_pretrained("dumperize/movie-picture-captioning") ... | c8887f590ca812695f2a127d562bad88 |
apache-2.0 | [] | false | Training Data We compiled a dataset from the open source of all Russian-language films for October 2022 - [kinopoisk](https://www.kinopoisk.ru/). Films with very short or very long descriptions were not included in the dataset, films with blank or very small images were excluded too. | 9262d0d2cf8e248035c6f30998430c27 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner-70percent This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5257 - Precision: 0.7997 - Recall: 0.8333 - F1: 0.8162 - Accuracy: 0.9191 | f104be22bd4f2e6cddd6f38b5378e78d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 53 | 0.5025 | 0.7844 | 0.8303 | 0.8067 | 0.9140 | | No log | 2.0 |... | 4a64436939af73bf8df4dc0e3bb131b6 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'stable-diffusion-diffusers'] | false | SDv1.5 sd15-muppet-blip model trained by Norod78 with [Huggingface Diffusers train_text_to_image script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) | 4e35dd3e2ae82cf0a8c7169a18d00d31 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'stable-diffusion-diffusers'] | false | For better results, use an explicit name of a muppet such as "Kermit, Cookie monster, etc" or simply use "muppet"  | 0708651aec764938696f5d8860436c39 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'stable-diffusion-diffusers'] | false | A few sample pictures generated with this mode (more available [here](https://huggingface.co/Norod78/sd15-muppet-blip/tree/main/sample_images)): A painting of the cookie monster, very detailed, clean, high quality, sharp image, based on H.P Lovecraft stories Negative prompt: grainy, blurry, text, watermark, inconsist... | 9d24bf4674d943b55c3dc33a6349c9e6 |
cc-by-4.0 | ['question answering'] | false | Model Card of `lmqg/t5-small-tweetqa-qa` This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question answering task on the [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | ed6dbcd1bd1bc1671c9b8049bdbe8a16 |
cc-by-4.0 | ['question answering'] | false | Overview - **Language model:** [t5-small](https://huggingface.co/t5-small) - **Language:** en - **Training data:** [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-g... | c594d1cd67851d015203cc774ef8383b |
cc-by-4.0 | ['question answering'] | false | model prediction answers = model.answer_q(list_question="What is a person called is practicing heresy?", list_context=" Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy,... | 7579cbfe9f030853b88220ab699eb2bf |
cc-by-4.0 | ['question answering'] | false | Evaluation - ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/lmqg/t5-small-tweetqa-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_tweetqa.default.json) | | Score | Type | Dataset | |... | 5877483fb7bc9c4bc21c1b2954f2f215 |
cc-by-4.0 | ['question answering'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_tweetqa - dataset_name: default - input_types: ['paragraph_question'] - output_types: ['answer'] - prefix_types: None - model: t5-small - max_length: 512 - max_length_output: 32 - epoch: 7 - batch: 6... | 6a225be3c1842484241179f344942bc1 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-Basque This is the model for Wav2Vec2-Large-XLSR-Basque, a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) model on the [Basque Common Voice dataset](https://huggingface.co/datasets/common_voice). When using this model, make sure that your spee... | 933e3ab053d25b4203fc42d895982d35 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "eu", split="test[:2%]") processor = Wav2Vec2Processor.from_pre... | 1eb2ad3dc97ff4ed2a61c9efe64fd821 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Basque test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "eu", split="test") wer =... | 11804333a490813b1d8284b41c7e6c23 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(lo... | 84e0066d4d9a1977ec4f1d96f49ca3dd |
mit | [] | false | <dr-strange> on Stable Diffusion This is the `<dr-strange>` 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... | f5fe9ba1ee4679e7942168aac7ae8a09 |
apache-2.0 | [] | false | Introduction The automatic paraphrasing model described and used in the paper "[AutoQA: From Databases to QA Semantic Parsers with Only Synthetic Training Data](https://arxiv.org/abs/2010.04806)" (EMNLP 2020). | 558395a296040cd329c5166f696a45d2 |
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