license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['stable-diffusion', 'text-to-image'] | false | Example Pictures from Rebecca_3.5k <table> <tr> <td><img src=https://i.imgur.com/h9milQd.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/3Uxe6Bi.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/FHczkJj.png width=100% height=100%/></td> </tr> </table> | 55436e315c285924d5d8e211ab48c5ad |
creativeml-openrail-m | [] | false | **Prompts:** The model is dreamboothed on tagged suisei no majo images; some prompts that work are 1. suletta mercury 2. miorine rembran 3. gundam aerial --- **Training details:** Trained with [kanewallmann Dreambooth repository](https://github.com/kanewallmann/Dreambooth-Stable-Diffusion) using tags as captions ... | d0c3d85ef2ee5bac51dffa85ef44972b |
mit | ['generated_from_trainer'] | false | bart-large-cnn-samsum-ElectrifAi_v6 This model is a fine-tuned version of [philschmid/bart-large-cnn-samsum](https://huggingface.co/philschmid/bart-large-cnn-samsum) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4591 - Rouge1: 70.5822 - Rouge2: 55.7529 - Rougel: 63.7452 - R... | ab58bc855863e4fade83b6e4f6acfbc4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 20 | 0.7010 | 63.9182 | 44.7625 | 53.1206 | 63.0249 | 10... | 4b3ea461a202534a517a3f4ac69546e5 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-EL8-DL4 (Deep-Narrow version) T5-Efficient-SMALL-EL8-DL4 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* check... | fe0d61ce253e09bbfac30d94b64193a8 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-el8-dl4** - is of model type **Small** with the following variations: - **el** is **8** - **dl** is **4** It has **58.42** million parameters and thus requires *ca.* **233.69 MB** of memory in full precision (*fp32*) or **116.84 MB** of memory... | 1035fb58e5cf2bd8daae90259d89d914 |
mit | ['generated_from_trainer'] | false | hyunwoongko-kobart-eb-finetuned-papers-meetings This model is a fine-tuned version of [hyunwoongko/kobart](https://huggingface.co/hyunwoongko/kobart) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3136 - Rouge1: 18.3166 - Rouge2: 8.0509 - Rougel: 18.3332 - Rougelsum: 18.3146 -... | 149933beb82c201c6b8e5690559d8281 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 0.2118 | 1.0 | 7739 | 0.2951 | 18.0837 | 7.9585 | 18.0787 | 18.0784 | 19... | da4c7026afea624ec82b20be6fd93d15 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384 A ConvNeXt image classification model. CLIP image tower weights pretrained in [OpenCLIP](https://github.com/mlfoundations/open_clip) on LAION and fine-tuned on ImageNet-1k in `timm` by Ross Wightman. Please see related OpenCLIP model cards for more de... | 05a741375635a16e1c44f760290a6117 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 200.1 - GMACs: 101.1 - Activations (M): 126.7 - Image size: 384 x 384 - **Papers:** - LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402... | 84de9ed4c17aa6ebb63dfc36c6c9bc6d |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384', p... | 5e72c51ea0463bf48a8bc07ac75ac159 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_large_mlp.clip_laion2b_augreg_ft_in1k_... | 14555db102d34ba84f1ef8326b73ac8b |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384', ... | c1256afe16c1a1fa48562c964338da88 |
apache-2.0 | ['image-classification', 'timm'] | false | By Top-1 All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP. |model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size| |----------------------------------------------|------|------|--------|-----------|------|------|---------------|--... | fc7dab2c6dd893b7f063efa956690409 |
apache-2.0 | ['image-classification', 'timm'] | false | By Throughput (samples / sec) All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP. |model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size| |----------------------------------------------|------|------|--------|-----------|------|----... | 24051d84314462bfd306b594ad6ba23c |
mit | [] | false | Eddie on Stable Diffusion This is the `Eddie` 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 ... | eebc1a68a76003bd3c8ec32e163aaddd |
apache-2.0 | ['generated_from_keras_callback'] | false | leabum/distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.5824 - Train End Logits Accuracy: 0.0347 - Train Start Logits A... | e6b961217bc8d176343965f2768a3369 |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 2eeb4bd030ce5a8be5ef265823ff2c9d |
creativeml-openrail-m | [] | false | **Model Description** The model was created by merging Well-known models. (Waifu Diffusion, Novel AI, Anything 3.0, etc) There is no separate trigger word, and keywords commonly applied in Waifu Diffusion and Novel AI can be used. ๋ชจ๋ธ์ ์ ์๋ ค์ง ๊ณต๊ฐ ๋ชจ๋ธ์ ๋ณํฉํ์ฌ ๋ง๋ค์์ต๋๋ค. (Waifu Diffusion, Novel AI, Anything 3.0 ๋ฑ) ๋ณ๋์ ํธ๋ฆฌ๊ฑฐ ๋จ์ด๋ ... | 056a3a2be5e29cb0b523d47026f8be8a |
creativeml-openrail-m | [] | false | **Vox-mix Samples**  >(masterpiece, best quality, ultra-detailed, illustration, painting), >best illumination, dynamic angle, finely detail, >(full body shot of a High Quality Victorian Era cute girl), (oil painting), >(Francois Boucher), alphonse mucha, (Claude Monet), Franz Xaver Winterh... | 44a9b69a4db2f7d0566a46fa74a272bb |
creativeml-openrail-m | [] | false | **Vox-mix2 Samples**  >((masterpiece, best quality, ultra-detailed, illustration, painting), (poster illustration), trending on artstation, (4girls:1.6), >(a High Quality Victorian Era sexy girl), (nsfw:1.2), (intricate see-through dress:1.2), >((1girl)), long hair, (PERFECT FACE... | 6da6c8ee49217415ec3774972ab7ff89 |
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: 0.2251 - Accuracy: 0.923 - F1: 0.9230 | e4b09526d7d111d8b8bf34df25875bdd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8643 | 1.0 | 250 | 0.3395 | 0.901 | 0.8969 | | 0.2615 | 2.0 | 500 | 0.2251 | 0.923 | 0.9230 | | 377cc1866666920cf46369578c73bd45 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-misogyny-sexism-fr-indomain-trans 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: 0.9813 - Accuracy: 0.8708 - F1: 0.0 - Precision: 0.0 - Recall: 0.0 - Mae: 0.1292 | ca7045111b1e49fb6dc2c907796652e4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:---------:|:------:|:------:| | 0.3606 | 1.0 | 2297 | 0.8082 | 0.8710 | 0.0 | 0.0 | 0.0 | 0.1290 | | 0.316... | d3aef9ef2f2495e92f980ed8d2f9eba0 |
mit | [] | false | obama_self_2 on Stable Diffusion This is the `<Obama>` 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 tr... | 8ba69381026fcad0320fdc3153865cc0 |
apache-2.0 | ['translation'] | false | opus-mt-sv-en * source languages: sv * target languages: en * OPUS readme: [sv-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-02-26.zip](https://... | 954511eea51ce49882d7224ceea181f0 |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9043 | dd7f4d5329a7bbb42459cc2bbe7d5e27 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 25 | 3.9350 | | No log | 2.0 | 50 | 3.9107 | | No log | 3.0 | 75 | 3.9043 | | 4813839bf4eefbb617a68b3c1d4aeacd |
mit | [] | false | youtooz candy on Stable Diffusion This is the `<youtooz-candy>` 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 ca... | a77ca9d79e85ded60c493830875fe8ec |
apache-2.0 | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - gradient_accumulation_steps: 4 - optimizer: AdamW with betas=(0.9, 0.999), weight_decay=0.01 and epsilon=1e-08 - lr_scheduler: constant - lr_warmup_steps: 500 - ema_in... | df272cf1a72f539b1a94c7631f12b2ac |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_6_ternary 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: 1.6625 - F1: 0.7588 | 8a6dc33aa3443c40fb14eac861ea9158 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 292 | 0.5117 | 0.7306 | | 0.5701 | 2.0 | 584 | 0.5273 | 0.7296 | | 0.5701 | 3.0 | 876 | 0.6037 | 0.7415 | |... | 380757bb48c353dd465db0fca7298b87 |
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 - training_steps: 50 - mixed_precision... | dd50778c8280013d5f8a53d35ea42bd8 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__subj__train-8-8 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: 0.3160 - Accuracy: 0.8735 | 99ee26781cfbd769cd8d86b157ece1f3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7187 | 1.0 | 3 | 0.6776 | 1.0 | | 0.684 | 2.0 | 6 | 0.6608 | 1.0 | | 0.6532 | 3.0 | 9 | 0.6364 | 1.... | 29a21f5c6b78b4a2bbdde611ca29c152 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-meta-7-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4797 - Accuracy: 0.28 | 11766b1414c2a26389919263d669961c |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_age_teens-2_sixties-8_s786 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 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 3ecb53a290d042681bf2d54db4f918ad |
mit | ['generated_from_trainer'] | false | camembert-base-finetuned-paraphrase This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the pawsx dataset. It achieves the following results on the evaluation set: - Loss: 0.2708 - Accuracy: 0.9085 - F1: 0.9089 | 8166c519c20e41039b11700ec3be499b |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | b66cc38c0c9542137d92224d0e5232e7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3918 | 1.0 | 772 | 0.3211 | 0.869 | 0.8696 | | 0.2103 | 2.0 | 1544 | 0.2448 | 0.9075 | 0.9077 | | 0.1622 |... | 9ddaba3aeeb37cd18007c9a491d6ad54 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Jak's Creepy Critter Pack v2.0-768px! Higher resolution 768px images used for training with fine tuning to now allow better control of output images. Compared to v1.0 which creates messy blob monsters (which is still fun), this version allows finer control to unleash your creativity! Enjoy! Tips: use "food_crit... | 67d2fcc3d29572f6931c6c23a68f6e18 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-finetuned-imdb 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: - Loss: 1.3367 - Accuracy: 0.625 | 858dcd318c6ee5e8e515d8d9a759306f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.687 | 1.0 | 20 | 1.4339 | 0.625 | | 1.4117 | 2.0 | 40 | 1.3367 | 0.625 | | 91ff10a66242f4cae73b698186ba9d33 |
apache-2.0 | ['generated_from_keras_callback'] | false | example_workflow_model This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4118 - Train Sparse Categorical Accuracy: 0.8765 - Validation Loss: 0.5309 - Va... | c694764985ee9a9bcea0f6be8e190bcc |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| | 0.7242 | 0.7814 | 0.5739 ... | a07f90f43bb41f597b906aa3d985be6a |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased_cls_subj This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1860 - Accuracy: 0.9675 | 9f537c18bd616f0f3acf344d682de7a6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2427 | 1.0 | 500 | 0.1733 | 0.9585 | | 0.1349 | 2.0 | 1000 | 0.1377 | 0.958 | | 0.0487 | 3.0 | 1500 | 0.1701 | 0.... | 7fbef32b4c185f6eb1b11e67eea343cb |
apache-2.0 | ['text-classfication', 'int8', 'Intelยฎ Neural Compressor', 'PostTrainingDynamic'] | false | Post-training dynamic quantization This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intelยฎ Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [bart-large... | ff3041288c3a140ddaa6904e5445da5d |
apache-2.0 | ['text-classfication', 'int8', 'Intelยฎ Neural Compressor', 'PostTrainingDynamic'] | false | Load with optimum: ```python from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification int8_model = IncQuantizedModelForSequenceClassification.from_pretrained( 'Intel/bart-large-mrpc-int8-dynamic', ) ``` | e91601e030d119a0b8fc09ee4f2c505b |
mit | ['spacy', 'token-classification'] | false | | Feature | Description | | --- |-----------------------------------------| | **Name** | `it_tei2go` | | **Version** | `0.0.0` | | **spaCy** | `>=3.2.4,<3.3.0` | | **Default Pipeline** | `ner` ... | 63486c449d92f747644a061d9a52def3 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-test-amazon This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9515 - Rouge1: 30.3066 - Rouge2: 3.3019 - Rougel: 30.1887 - Rougelsum: 30.0314 | ff5d806ff67e996d7dd9cc6aa1629a20 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 10.0147 | 1.0 | 1004 | 2.9904 | 7.3703 | 0.2358 | 7.3703 | 7.4292 | | 3.4892 | 2.0 |... | a441d7f82a7711c03210540b997816e1 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_50v1_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni50v1_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.5851 - Precision: 0.1466 - Recall: 0.0256 - F1: 0.0437 - Accuracy... | f766c3ce35a8232733e088f2bc62b625 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 24 | 0.6704 | 0.0 | 0.0 | 0.0 | 0.7775 | | No log | 2.0 |... | 8760eeaf22901af9896ccad8e8678225 |
other | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | segformer-b0-finetuned-segments-sidewalk-2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 2.6306 - Mean Iou: 0.1027 - Mean Accuracy: 0.1574 - Overall Accuracy: 0.655... | b197c3d73cd8dc1e0d199a4f460ace58 |
other | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-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 - num_epochs: 1 | d6a426364496904f69a46637e886f461 |
other | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou ... | d496bfce511c51310ed688c5bc5d6c94 |
apache-2.0 | ['generated_from_trainer'] | false | Vin11-P3 This model is a fine-tuned version of [HuyenNguyen/Vin9-P3](https://huggingface.co/HuyenNguyen/Vin9-P3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2151 - Wer: 11.6220 | de440983dddde2aa51a9786dbd967074 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1595 | 0.15 | 300 | 0.2195 | 11.2807 | | 0.1691 | 0.31 | 600 | 0.2151 | 11.6220 | | 7332116a0415d4929ddf31925517dae2 |
mit | ['generated_from_trainer'] | false | aces-roberta-base-reduced This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3101 - Precision: 0.9036 - Recall: 0.9038 - F1: 0.9029 - Accuracy: 0.9038 - F1 Who: 0.8727 - F1 What: 0.8295 - F... | 827eea1b55416d52e2cea3c3927ab7e3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | F1 Who | F1 What | F1 Where | F1 How | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|:-------:|:--------:|:------:| | 0.6299 | 1.0 | 48 | 0.3723... | a748aeca3b626d41c545c7947b361e83 |
mit | ['text-classification'] | false | Multi2ConvAI-Logistics: finetuned Bert for Croatian
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Logistics (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: Croatian (hr)
- mode... | beb94420bbc6ac548a027db1085a5525 |
mit | ['text-classification'] | false | Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-logistics-hr-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-logistics-hr-bert")
````
... | b17e6bb9518ac95b2b2dbe6662188851 |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `lmqg/mbart-large-cc25-itquad-qag` This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question & answer pair generation task on the [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) (dataset_name: default) via [`lmqg`](ht... | a1f9e61c20d704e0456c5f4866286560 |
cc-by-4.0 | ['questions and answers generation'] | false | Overview - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) - **Language:** it - **Training data:** [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https:/... | 9bb1cc0aec7cceab1b0f7b090244baea |
cc-by-4.0 | ['questions and answers generation'] | false | model prediction question_answer_pairs = model.generate_qa("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-itquad-qag") output = pipe("Dop... | 97e254f6ce238c4a425aefa1fe80e199 |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-itquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_itquad.default.json) | | Score | Type | Dataset ... | 6ef0d8fb7537c81b168282844f7cdbfb |
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_itquad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: facebook/mbart-large-cc25 - max_length: 512 - max_length_output: 256 - ... | d76d6557c98d57f48d7072a984ba5b71 |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Wav2vec2-large-uralic-voxpopuli-v2 for Finnish ASR This acoustic model is a fine-tuned version of [facebook/wav2vec2-large-uralic-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-large-uralic-voxpopuli-v2) for Finnish ASR. The model has been fine-tuned with 276.7 hours of Finnish transcribed speech data. Wav2Ve... | 3a1ef7a2930f3a9383ff41f1c3d7c158 |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Model description [Wav2vec2-large-uralic-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-large-uralic-voxpopuli-v2) is Facebook AI's pretrained model for uralic language family (Finnish, Estonian, Hungarian) speech. It is pretrained on 42.5k hours of unlabeled Finnish, Estonian and Hungarian speech from [VoxPo... | 7ed498843f3ed60181c305e9e0752fad |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | How to use Check the [run-finnish-asr-models.ipynb](https://huggingface.co/Finnish-NLP/wav2vec2-large-uralic-voxpopuli-v2-finnish/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model. | d88d3176f0497ec7b3263201791a63bb |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-04 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_schedul... | fd2e0a19ab0d78dc1a3fed28a480aa8d |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.9421 | 0.17 | 500 | 0.8633 | 0.8870 | | 0.572 | 0.33 | 1000 | 0.1650 | 0.1829 | | 0.5149 | 0.5 | 1500 | 0.1416 | 0.171... | 76290987857d87219eb589a4d9ecac31 |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Common Voice 7.0 testing To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id Finnish-NLP/wav2vec2-large-uralic-voxpopuli-v2-finnish --dataset mozilla-foundation/common_voice_7_0 --config fi --split test ``` This model (the second row of the table) achieves the fol... | 701197139e247db22f9b7cb97b762f57 |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Common Voice 9.0 testing To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id Finnish-NLP/wav2vec2-large-uralic-voxpopuli-v2-finnish --dataset mozilla-foundation/common_voice_9_0 --config fi --split test ``` This model (the second row of the table) achieves the fol... | 96dd17d73ad521b2c9eb7376e3b0d630 |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | FLEURS ASR testing To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id Finnish-NLP/wav2vec2-large-uralic-voxpopuli-v2-finnish --dataset google/fleurs --config fi_fi --split test ``` This model (the second row of the table) achieves the following WER (Word Error Ra... | 3a0e757245df4ea44ed79a59058d57ae |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 49a284e69308d81c142b89795de255b4ce290c54 pip install -e . cd egs2/talromur/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/GunnarThor_talromur_d_fastspeech2 ``` | 8c2821066af1642bb751bd8910a12162 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | TTS config <details><summary>expand</summary> ``` config: conf/tuning/train_fastspeech2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/d/tts_train_fastspeech2_raw_phn_none ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_... | 4fa67bdfed972fcefebb1081e6d6f009 |
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 amazon_polarity dataset. It achieves the following results on the evaluation set: - Loss: 0.8170 - Accuracy: 0.9225 - F1: 0.9241 | 44cbe4c5ba4e233f9861f81714346716 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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 - num_epochs: 20 | d0e7733bbf196c2a98097cce5bd1c800 |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-en-ro-finetuned-en-to-ro This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2915 - Bleu: 27.9273 - Gen Len: 34.0935 | 50ccbad7d523d4d7b39319db38ff9c66 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.7448 | 1.0 | 38145 | 1.2915 | 27.9273 | 34.0935 | | 021a37579be37aa47b83dda1060882ef |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-sentiment-new This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5872 - Accuracy: 0.7243 - Precision: 0.7192 - Recall: 0.7243 - F1: 0.71... | 809706f251d91d2474f4308ffdf59b58 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.6038 | 0.6787 | 0.7049 | 0.6787 | 0.6235 | | 0.5926 | 2.0 |... | be9156a8f0097bafc65e71a11cc8b0ae |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | About this bad ass beast of a checkpoint: I merged a few checkpoints and got something buttery and amazing. Does great with things other then people too. It can do anything really. It doesn't need crazy prompts either. Keep it simple. No need for all the artist names and trending on whatever. | 4919af7f70ecccccd311227f038b98dd |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-4000-samples_en This model is a fine-tuned version of [zboxi7/finetuning-sentiment-model-3000-samples_fr](https://huggingface.co/zboxi7/finetuning-sentiment-model-3000-samples_fr) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3887 | 57a1dfcb8bdb87e81918cb0bc82796fa |
apache-2.0 | ['translation'] | false | mul-eng * source group: Multiple languages * target group: English * OPUS readme: [mul-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/mul-eng/README.md) * model: transformer * source language(s): abk acm ady afb afh_Latn afr akl_Latn aln amh ang_Latn apc ara arg arq ary arz asm ast avk... | 4000841094938cdda1a9c0225c47d964 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2014-hineng.hin.eng | 8.5 | 0.341 | | newsdev2015-enfi-fineng.fin.eng | 16.8 | 0.441 | | newsdev2016-enro-roneng.ron.eng | 31.3 | 0.580 | | newsdev2016-entr-tureng.tur.eng | 16.4 | 0.422 | | newsdev2017-enl... | b0f16068c90a93c8569c5dcf2907f67a |
apache-2.0 | ['translation'] | false | System Info: - hf_name: mul-eng - source_languages: mul - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/mul-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ca', 'es', 'os', 'eo', 'ro', 'fy', 'cy', 'is', 'lb',... | 26c7d57662d07c480349b541c9c2205e |
mit | ['generated_from_trainer'] | false | language-modeling This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4229 | 58a1d3ea85e91aa5e7db119b8a47d66a |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-en-to-ro-epoch.04375 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.4137 - Bleu: 7.3292 - Gen Len: 18.2541 | af908d268e979bb27114c76cb54d5fb6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.04375 - mixed_precision_training: Native AMP | d76b0ff66e1fe18c4395dd841dcbe1b5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | 0.6211 | 0.04 | 1669 | 1.4137 | 7.3292 | 18.2541 | | da955dfa2b3d65d24f1a17e1463cc76d |
apache-2.0 | ['generated_from_keras_callback'] | false | KubiakJakub01/finetuned-distilbert-base-augumented This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4522 - Validation Loss: 0.4260 - Train Accuracy: 0.8129 - E... | 1f372c7f281e84aabe646ac8853467b8 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', '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': 470, 'en... | 8a33087798e297517adb3300ab034a12 |
mit | [] | false | Freefonix-Style on Stable Diffusion This is the `<Freefonix>` 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 ... | a8448d31f44162d039de6dc7d3ee8b2e |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'bas', 'robust-speech-event', 'hf-asr-leaderboard'] | false | wav2vec2-xls-r-300m-bas-CV8-v2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.6121 - Wer: 0.5697 | 2317d6ff405e522d19d30e783ac074b8 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'bas', 'robust-speech-event', 'hf-asr-leaderboard'] | 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_sch... | cd9fc1a5969de0c42e1b2311d481043e |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'bas', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.5211 | 16.13 | 500 | 1.2661 | 0.9153 | | 0.7026 | 32.25 | 1000 | 0.6245 | 0.6516 | | 0.3752 | 48.38 | 1500 | 0.6039 | 0.6148 | |... | a2dff43e98f248761f5f466220538145 |
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