license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Kawase Hasui Diffusion is trained on pantings by [KAWASE Hasui（川瀬巴水）](https://en.wikipedia.org/wiki/Hasui_Kawase). The model has been trained on Stable Diffusion v2-1 with DreamBooth method with a learning rate of 1.0e-6 for 2,600 steps with the batch size of 8 (8 train or reg images) on 169 training images and 664 regularization images. This model is based on SD2.1 768/v, so if you use this model in the poplular Web UI, please rename 'v2-inference-v.yaml' to 'kawase-hasui-epoch-000003.yaml' (or ~_fp16.yaml) and place it to the same folder to .safetensors. The training prompt is "picture by lvl". | 9f6c3db594d855fad924de1f649e0af1 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Examples  ``` picture by lvl, japan tourism poster seed : 968191097, sampler: k_euler_a, steps : 160, CFG scale : 5.5 ```  ``` picture by lvl, cyberpunk akihabara seed : 1418478714, sampler: k_euler_a, steps : 160, CFG scale : 5.5 ```  ``` picture by lvl, ruined castle, fantasy, dawn seed : 897433524, sampler: k_euler_a, steps : 160, CFG scale : 5.5 ```  ``` picture by lvl, fantasy, party of adventurers, ready to fight, in front of ruined temple seed : 1814292911, sampler: k_euler_a, steps : 160, CFG scale : 5.5 ``` | 1020afa311ff505d10010b768024b94a |
mit | [] | false | thunderdome-cover on Stable Diffusion This is the `<thunderdome-cover>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:                                      | 6d8ddf4ee70ac35f56af2bb3fe2dc474 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_2e-05_16_02_2022-14_25_47 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1722 - Precision: 0.3378 - Recall: 0.3615 - F1: 0.3492 - Accuracy: 0.9448 | 9799ef831f200a399b6c4b927713a447 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 096eda612ce226bcd44c71999495e36b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 38 | 0.3781 | 0.1512 | 0.2671 | 0.1931 | 0.8216 | | No log | 2.0 | 76 | 0.3020 | 0.1748 | 0.2938 | 0.2192 | 0.8551 | | No log | 3.0 | 114 | 0.2723 | 0.1938 | 0.3339 | 0.2452 | 0.8663 | | No log | 4.0 | 152 | 0.2574 | 0.2119 | 0.3506 | 0.2642 | 0.8727 | | No log | 5.0 | 190 | 0.2521 | 0.2121 | 0.3623 | 0.2676 | 0.8756 | | d5ba26ad16a8957896440096c8e581e8 |
cc0-1.0 | ['pointnet', 'segmentation', '3d', 'image'] | false | Point cloud segmentation with PointNet This repo contains [an Implementation of a PointNet-based model for segmenting point clouds.](https://keras.io/examples/vision/pointnet_segmentation/). Full credits to [Soumik Rakshit](https://github.com/soumik12345), [Sayak Paul](https://github.com/sayakpaul) | 0002ba49c13edfe78e0162c9ace9b200 |
cc0-1.0 | ['pointnet', 'segmentation', '3d', 'image'] | false | Background Information A "point cloud" is an important type of data structure for storing geometric shape data. Due to its irregular format, it's often transformed into regular 3D voxel grids or collections of images before being used in deep learning applications, a step which makes the data unnecessarily large. The PointNet family of models solves this problem by directly consuming point clouds, respecting the permutation-invariance property of the point data. The PointNet family of models provides a simple, unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. In this example, we demonstrate the implementation of the PointNet architecture for shape segmentation. **References** * [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://arxiv.org/abs/1612.00593) * [Point cloud classification with PointNet](https://keras.io/examples/vision/pointnet/) * [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025)   | 31a7f4fc639bb628e5cc860a85b8e9ba |
cc0-1.0 | ['pointnet', 'segmentation', '3d', 'image'] | false | Training Dataset This model was trained on the [ShapeNet dataset](https://shapenet.org/). The ShapeNet dataset is an ongoing effort to establish a richly-annotated, large-scale dataset of 3D shapes. ShapeNetCore is a subset of the full ShapeNet dataset with clean single 3D models and manually verified category and alignment annotations. It covers 55 common object categories, with about 51,300 unique 3D models. **Prediction example**  | ee2308729209d1a05cc2d4114eea5d5b |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 197.8 - GMACs: 34.4 - Activations (M): 43.1 - Image size: 224 x 224 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/facebookresearch/ConvNeXt - **Dataset:** ImageNet-1k | 6129d1cd316c7926abb98976d39f2c17 |
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.fb_in1k', pretrained=True) model = model.eval() | b139fd87d717fddc4fe7a6d3c73f9c0c |
apache-2.0 | ['image-classification', 'timm'] | false | get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) | 0af26c542bad611761d0b8297f820b25 |
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.fb_in1k', pretrained=True, features_only=True, ) model = model.eval() | 89fa75be69e2bac918eb02e93e625a49 |
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.fb_in1k', pretrained=True, num_classes=0, | 9e500d0cf108041e8458a1321e3d2a06 |
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| |----------------------------------------------|------|------|--------|-----------|------|------|---------------|----------| |[convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512)|88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 | |[convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384)|88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 | |[convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384)|88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 | |[convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384)|87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 | |[convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384)|87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 | |[convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384)|87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 | |[convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k)|87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 | |[convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k)|87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 | |[convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k)|87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 | |[convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384)|86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 | |[convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k)|86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 | |[convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k)|86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 | |[convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384)|86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 | |[convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k)|86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 | |[convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384)|86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 | |[convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k)|86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 | |[convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k)|85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 | |[convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384)|85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 | |[convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k)|85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 | |[convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k)|85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 | |[convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384)|85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 | |[convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384)|85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 | |[convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k)|84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 | |[convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k)|84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 | |[convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k)|84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 | |[convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k)|84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 | |[convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384)|84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 | |[convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k)|83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 | |[convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k)|83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 | |[convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384)|83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 | |[convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k)|83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 | |[convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k)|82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 | |[convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k)|82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 | |[convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k)|82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 | |[convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k)|82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 | |[convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k)|82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 | |[convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k)|82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 | |[convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k)|81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 | |[convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k)|80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 | |[convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k)|80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 | |[convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k)|80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 | |[convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k)|79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 | |[convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k)|79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 | |[convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k)|78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 | |[convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k)|77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 | |[convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k)|77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 | |[convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k)|76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 | |[convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k)|75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 | |[convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k)|75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 | | e0c56cff89854adebc8ab1b3c13f2f20 |
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| |----------------------------------------------|------|------|--------|-----------|------|------|---------------|----------| |[convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k)|75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 | |[convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k)|75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 | |[convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k)|77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 | |[convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k)|77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 | |[convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k)|79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 | |[convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k)|79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 | |[convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k)|76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 | |[convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k)|78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 | |[convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k)|82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 | |[convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k)|80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 | |[convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k)|80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 | |[convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k)|80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 | |[convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k)|82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 | |[convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k)|82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 | |[convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k)|84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 | |[convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k)|82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 | |[convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k)|81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 | |[convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k)|82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 | |[convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k)|84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 | |[convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k)|85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 | |[convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k)|83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 | |[convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k)|83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 | |[convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k)|82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 | |[convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k)|83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 | |[convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k)|85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 | |[convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384)|84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 | |[convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384)|85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 | |[convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k)|86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 | |[convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384)|83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 | |[convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k)|84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 | |[convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k)|86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 | |[convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k)|84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 | |[convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k)|86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 | |[convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384)|85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 | |[convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384)|86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 | |[convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384)|85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 | |[convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k)|87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 | |[convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k)|87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 | |[convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k)|85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 | |[convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384)|86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 | |[convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k)|87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 | |[convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384)|86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 | |[convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384)|87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 | |[convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384)|87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 | |[convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k)|86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 | |[convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384)|88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 | |[convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384)|87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 | |[convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384)|88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 | |[convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512)|88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 | | 7bea51929ef4abbb47607f44e11aabef |
apache-2.0 | ['image-classification', 'timm'] | false | Citation ```bibtex @article{liu2022convnet, author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, title = {A ConvNet for the 2020s}, journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2022}, } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} } ``` | e38552a0abdb6080b8a68fe065043472 |
creativeml-openrail-m | ['text-to-image'] | false | hulk-style-v3 Dreambooth model trained by sztanki with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: hulk (use that on your prompt)  | 40e91ee9e52869a9157ea642ef81a9f6 |
creativeml-openrail-m | [] | false | Token class word for this model is `rimu` using this will draw attention to the training data that was used and help increase the quality of the image. License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here | 3295ebbb1f4f153dac9f9ce7a548e05f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 24 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 | 8751398e997f98b4f7879149da296068 |
apache-2.0 | ['classical chinese', 'literary chinese', 'ancient chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a RoBERTa model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing, derived from [roberta-classical-chinese-large-char](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-large-char). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech) and [FEATS](https://universaldependencies.org/u/feat/). | 4976ed2025272e61ef5d106e84ba42ca |
apache-2.0 | ['classical chinese', 'literary chinese', 'ancient chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-classical-chinese-large-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-classical-chinese-large-upos") ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/roberta-classical-chinese-large-upos") ``` | 2d5488202d4c9533cf0b886f84fc5d3e |
apache-2.0 | ['classical chinese', 'literary chinese', 'ancient chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | Reference Koichi Yasuoka: [Universal Dependencies Treebank of the Four Books in Classical Chinese](http://hdl.handle.net/2433/245217), DADH2019: 10th International Conference of Digital Archives and Digital Humanities (December 2019), pp.20-28. | e6b76527fb8ca1380c7a7df0ba5eb808 |
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: 0.3120 - Accuracy: 0.87 - F1: 0.8696 | 7f7a8ef8ff33c78b81a1115d2dd24fe3 |
apache-2.0 | ['generated_from_keras_callback'] | false | nandysoham/Gregorian_calendar-theme-finetuned-overfinetuned This model is a fine-tuned version of [nandysoham/distilbert-base-uncased-finetuned-squad](https://huggingface.co/nandysoham/distilbert-base-uncased-finetuned-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1838 - Train End Logits Accuracy: 0.9500 - Train Start Logits Accuracy: 0.9688 - Validation Loss: 2.0017 - Validation End Logits Accuracy: 0.5238 - Validation Start Logits Accuracy: 0.4762 - Epoch: 8 | 2590db4c4c4237529ae88460c8077231 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 100, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | b8672afbb127ce2c235899c295038a8e |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 2.2861 | 0.3688 | 0.4062 | 1.6038 | 0.5952 | 0.5714 | 0 | | 1.2774 | 0.5938 | 0.5938 | 1.4240 | 0.5952 | 0.5714 | 1 | | 0.8752 | 0.7000 | 0.7375 | 1.4402 | 0.5952 | 0.5476 | 2 | | 0.5245 | 0.8250 | 0.8438 | 1.5027 | 0.6429 | 0.5952 | 3 | | 0.4132 | 0.8313 | 0.8938 | 1.6252 | 0.5714 | 0.5 | 4 | | 0.3140 | 0.9000 | 0.9062 | 1.7524 | 0.5476 | 0.4762 | 5 | | 0.2534 | 0.9688 | 0.9312 | 1.8646 | 0.5238 | 0.4762 | 6 | | 0.1999 | 0.9500 | 0.9563 | 1.9513 | 0.5238 | 0.4762 | 7 | | 0.1838 | 0.9500 | 0.9688 | 2.0017 | 0.5238 | 0.4762 | 8 | | 9435fb057c678f707d6860f176a4b99d |
apache-2.0 | [] | false | Adaptive Depth Transformers Implementation of the paper "How Many Layers and Why? An Analysis of the Model Depth in Transformers". In this study, we investigate the role of the multiple layers in deep transformer models. We design a variant of ALBERT that dynamically adapts the number of layers for each token of the input. | 86bdbb4392c3d3c319433ce75c98a7fc |
apache-2.0 | [] | false | Model architecture We augment a multi-layer transformer encoder with a halting mechanism, which dynamically adjusts the number of layers for each token. We directly adapted this mechanism from Graves ([2016]( | 1c189433e47130afe31d6545b91cd454 |
apache-2.0 | [] | false | Model use The architecture is not yet directly included in the Transformers library. The code used for pre-training is available in the following [github repository](https://github.com/AntoineSimoulin/adaptive-depth-transformers). So you should install the code implementation first: ```bash !pip install git+https://github.com/AntoineSimoulin/adaptive-depth-transformers$ ``` Then you can use the model directly. ```python from act import AlbertActConfig, AlbertActModel, TFAlbertActModel from transformers import AlbertTokenizer tokenizer = AlbertTokenizer.from_pretrained('asi/albert-act-base') model = AlbertActModel.from_pretrained('asi/albert-act-base') _ = model.eval() inputs = tokenizer("a lump in the middle of the monkeys stirred and then fell quiet .", return_tensors="pt") outputs = model(**inputs) outputs.updates | 6cd7e811220d9ba0fc3f8df5719860cc |
apache-2.0 | [] | false | BibTeX entry and citation info If you use our iterative transformer model for your scientific publication or your industrial applications, please cite the following [paper](https://aclanthology.org/2021.acl-srw.23/): ```bibtex @inproceedings{simoulin-crabbe-2021-many, title = "How Many Layers and Why? {A}n Analysis of the Model Depth in Transformers", author = "Simoulin, Antoine and Crabb{\'e}, Benoit", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-srw.23", doi = "10.18653/v1/2021.acl-srw.23", pages = "221--228", } ``` | 10372ee8e8ca119491faf3438c22f6e7 |
apache-2.0 | [] | false | References ><div id="graves-2016">Alex Graves. 2016. <a href="https://arxiv.org/abs/1603.08983" target="_blank">Adaptive computation time for recurrent neural networks.</a> CoRR, abs/1603.08983.</div> | af916eae46ca1f642288eea97b058aa4 |
apache-2.0 | ['generated_from_trainer'] | false | small-vanilla-target-glue-cola-linear-probe This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6097 - Matthews Correlation: 0.0 | bfab0bdc0fe00ff7cacdb9f3367d58e5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 5000 | 9b706a2bb77263e20813d6a515e825c2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6185 | 1.87 | 500 | 0.6137 | 0.0 | | 0.6093 | 3.73 | 1000 | 0.6125 | 0.0 | | 0.6073 | 5.6 | 1500 | 0.6100 | 0.0 | | 0.6052 | 7.46 | 2000 | 0.6097 | 0.0 | | 3d648a4eba75e1e3e5712261745b0e5f |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 31.4 - GMACs: 3.9 - Activations (M): 12.0 - Image size: 224 x 224 - **Original:** https://github.com/snap-research/EfficientFormer - **Papers:** - EfficientFormer: Vision Transformers at MobileNet Speed: https://arxiv.org/abs/2206.01191 - **Dataset:** ImageNet-1k | 27bfda75e395d38454aaf0a691d18868 |
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('efficientformer_l3.snap_dist_in1k', pretrained=True) model = model.eval() | 94013687a0cdcde4c5191af4bbc138e3 |
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( 'efficientformer_l3.snap_dist_in1k', pretrained=True, num_classes=0, | fe2bddca1810f7422c55e511027f1f18 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Comparison |model |top1 |top5 |param_count|img_size| |-----------------------------------|------|------|-----------|--------| |efficientformerv2_l.snap_dist_in1k |83.628|96.54 |26.32 |224 | |efficientformer_l7.snap_dist_in1k |83.368|96.534|82.23 |224 | |efficientformer_l3.snap_dist_in1k |82.572|96.24 |31.41 |224 | |efficientformerv2_s2.snap_dist_in1k|82.128|95.902|12.71 |224 | |efficientformer_l1.snap_dist_in1k |80.496|94.984|12.29 |224 | |efficientformerv2_s1.snap_dist_in1k|79.698|94.698|6.19 |224 | |efficientformerv2_s0.snap_dist_in1k|76.026|92.77 |3.6 |224 | | a265cb1d7fb27cd46e23aecea8bf4c77 |
apache-2.0 | ['image-classification', 'timm'] | false | Citation ```bibtex @article{li2022efficientformer, title={EfficientFormer: Vision Transformers at MobileNet Speed}, author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Ju and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian}, journal={arXiv preprint arXiv:2206.01191}, year={2022} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} } ``` | 3c3a4c7c73bd17d7253e544d00066e83 |
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 xlsum dataset. It achieves the following results on the evaluation set: - Loss: 1.2188 - Rouge1: 0.5217 - Rouge2: 0.0464 - Rougel: 0.527 - Rougelsum: 0.5215 - Gen Len: 6.7441 | 5179e97bf65bc9368becd474179ae725 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | a4072e505299f31790731151571601a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 1.3831 | 1.0 | 7475 | 1.2188 | 0.5217 | 0.0464 | 0.527 | 0.5215 | 6.7441 | | 4707089790cf3cdfcf13a591f27772dd |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_wavlm_s118 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | ff19fcc463e26ad9d18725757eb65d72 |
cc-by-4.0 | [] | false | Automatic Translation Alignment of Ancient Greek Texts GRC-ALIGNMENT model is an XLM-RoBERTa-based model, fine-tuned for automatic multilingual text alignment at the word level. The model is trained on 12 million monolingual ancient Greek tokens with Masked Language Model (MLM) training objective. Further, the model is fine-tuned on 45k parallel sentences, mainly in ancient Greek-English, Greek-Latin, and Greek-Georgian. | 50302e5c3cf4d8e1ee38327bf3647ef9 |
cc-by-4.0 | [] | false | Multilingual Training Dataset | Languages |Sentences | Source | |:---------------------------------------|:-----------:|:--------------------------------------------------------------------------------| | GRC-ENG | 32.500 | Perseus Digital Library (Iliad, Odyssey, Xenophon, New Testament) | | GRC-LAT | 8.200 | [Digital Fragmenta Historicorum Graecorum project](https://www.dfhg-project.org/) | | GRC-KAT <br>GRC-ENG <br>GRC-LAT<br>GRC-ITA<br>GRC-POR | 4.000 | [UGARIT Translation Alignment Editor](https://ugarit.ialigner.com/ ) | | 631524b8f2b7d51823671c754bd533c7 |
cc-by-4.0 | [] | false | Model Performance | Languages | Alignment Error Rate | |:---------:|:--------------------:| | GRC-ENG | 19.73% (IterMax) | | GRC-POR | 23.91% (IterMax) | | GRC-LAT | 10.60% (ArgMax) | The gold standard datasets are available on [Github](https://github.com/UgaritAlignment/Alignment-Gold-Standards). If you use this model, please cite our papers: <pre> @InProceedings{yousef-EtAl:2022:LREC, author = {Yousef, Tariq and Palladino, Chiara and Shamsian, Farnoosh and d’Orange Ferreira, Anise and Ferreira dos Reis, Michel}, title = {An automatic model and Gold Standard for translation alignment of Ancient Greek}, booktitle = {Proceedings of the Language Resources and Evaluation Conference}, month = {June}, year = {2022}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {5894--5905}, url = {https://aclanthology.org/2022.lrec-1.634} } @InProceedings{yousef-EtAl:2022:LT4HALA2022, author = {Yousef, Tariq and Palladino, Chiara and Wright, David J. and Berti, Monica}, title = {Automatic Translation Alignment for Ancient Greek and Latin}, booktitle = {Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages}, month = {June}, year = {2022}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {101--107}, url = {https://aclanthology.org/2022.lt4hala2022-1.14} } </pre> | 683a5d13124bad256f87ddb08ac4c086 |
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 None dataset. It achieves the following results on the evaluation set: - Loss: 0.8874 - Precision: 0.2534 - Recall: 0.3333 - F1: 0.2879 - Accuracy: 0.7603 - True predictions: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - True labels: [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 1, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | d408276f1159041e5334c96e5e11a58d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | True predictions | True labels | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | No log | 1.0 | 2 | 0.9937 | 0.2839 | 0.3072 | 0.2951 | 0.6712 | [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2] | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 1, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | | No log | 2.0 | 4 | 0.9155 | 0.2523 | 0.3273 | 0.2850 | 0.7466 | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 1, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | | No log | 3.0 | 6 | 0.8874 | 0.2534 | 0.3333 | 0.2879 | 0.7603 | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 1, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | | 32d9f97f5d0890689ba913ef06286e27 |
apache-2.0 | [] | false | ALBERT XXLarge v1 Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1909.11942) and first released in [this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make a difference between english and English. Disclaimer: The team releasing ALBERT did not write a model card for this model so this model card has been written by the Hugging Face team. | a888aab3a4495dfecf36997b0f728e22 |
apache-2.0 | [] | false | Model description ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Sentence Ordering Prediction (SOP): ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the ALBERT model as inputs. ALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers. This is the second version of the base model. Version 2 is different from version 1 due to different dropout rates, additional training data, and longer training. It has better results in nearly all downstream tasks. This model has the following configuration: - 12 repeating layers - 128 embedding dimension - 768 hidden dimension - 12 attention heads - 11M parameters | 216fe8cc5aef275388190ffe32608fff |
apache-2.0 | [] | false | Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=albert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. | 8f9034eaedc4c53b588c3c84e0c374ad |
apache-2.0 | [] | false | How to use You can use this model directly with a pipeline for masked language modeling: In tf_transformers ```python from tf_transformers.models import AlbertModel from transformers import AlbertTokenizer tokenizer = AlbertTokenizer.from_pretrained('albert-xxlarge-v1') model = AlbertModel.from_pretrained("albert-xxlarge-v1") text = "Replace me by any text you'd like." inputs_tf = {} inputs = tokenizer(text, return_tensors='tf') inputs_tf["input_ids"] = inputs["input_ids"] inputs_tf["input_type_ids"] = inputs["token_type_ids"] inputs_tf["input_mask"] = inputs["attention_mask"] outputs_tf = model(inputs_tf) ``` | 8b0e17ee6bf035c5358903834ef5fe4d |
apache-2.0 | [] | false | Training data The ALBERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). | 62625cfd4d6c7cdf3bcb54e4d209d04a |
apache-2.0 | [] | false | Preprocessing The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` | b2c8c825d7211788749310241eacc02a |
apache-2.0 | [] | false | Training The ALBERT procedure follows the BERT setup. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. | 61fcafa7dc74a696db0adda01d5f7e38 |
apache-2.0 | [] | false | Evaluation results When fine-tuned on downstream tasks, the ALBERT models achieve the following results: | | Average | SQuAD1.1 | SQuAD2.0 | MNLI | SST-2 | RACE | |----------------|----------|----------|----------|----------|----------|----------| |V2 | |ALBERT-base |82.3 |90.2/83.2 |82.1/79.3 |84.6 |92.9 |66.8 | |ALBERT-large |85.7 |91.8/85.2 |84.9/81.8 |86.5 |94.9 |75.2 | |ALBERT-xlarge |87.9 |92.9/86.4 |87.9/84.1 |87.9 |95.4 |80.7 | |ALBERT-xxlarge |90.9 |94.6/89.1 |89.8/86.9 |90.6 |96.8 |86.8 | |V1 | |ALBERT-base |80.1 |89.3/82.3 | 80.0/77.1|81.6 |90.3 | 64.0 | |ALBERT-large |82.4 |90.6/83.9 | 82.3/79.4|83.5 |91.7 | 68.5 | |ALBERT-xlarge |85.5 |92.5/86.1 | 86.1/83.1|86.4 |92.4 | 74.8 | |ALBERT-xxlarge |91.0 |94.8/89.3 | 90.2/87.4|90.8 |96.9 | 86.5 | | a76312fa5a00029260b7713c492013fd |
apache-2.0 | [] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1909-11942, author = {Zhenzhong Lan and Mingda Chen and Sebastian Goodman and Kevin Gimpel and Piyush Sharma and Radu Soricut}, title = {{ALBERT:} {A} Lite {BERT} for Self-supervised Learning of Language Representations}, journal = {CoRR}, volume = {abs/1909.11942}, year = {2019}, url = {http://arxiv.org/abs/1909.11942}, archivePrefix = {arXiv}, eprint = {1909.11942}, timestamp = {Fri, 27 Sep 2019 13:04:21 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1909-11942.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` | b2cb36ba456f0369c5a13ad9401d3af0 |
mit | ['pytorch', 'diffusers', 'dreambooth'] | false | Model Card for Dreambooth model trained on My pet Pintu's images This model is a diffusion model for unconditional image generation of my cute pet dog Pintu trained using Dreambooth concept. The token to use is sks . | e56318e3f7e5cc53fb8f911b6b772e80 |
mit | ['pytorch', 'diffusers', 'dreambooth'] | false | Usage from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained(Kugos/KgSelfie_lr_15e-6) image = pipeline('a photo of sks dog').images[0] image These are the images on which the dreambooth model is trained on  | 94dbdd1c46a7ce85943113c93fb8bd22 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP | db6ebb19a4f4af4695ab52074c837c50 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_xls-r_s957 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 07e08d37f91492d7225d4861b449c363 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 128 - seed: 2 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 64 - total_eval_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20.0 | 4873c5f6a198e14744240278d9a7aa2c |
cc-by-sa-4.0 | ['legal'] | false | Legal-CamemBERT * Legal-DistilCamemBERT is a [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base)-based model further pre-trained on [23,000+ statutory articles](https://huggingface.co/datasets/maastrichtlawtech/bsard) from the Belgian legislation. * We chose the following training set-up: 50k training steps (200 epochs) with batches of 32 sequences of length 512 with an initial learning rate of 5e-5. * Training was performed on one Tesla V100 GPU with 32 GB using the [code](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py) provided by Hugging Face. --- | 9ebdc0a769303a0449eddc75f22a1263 |
cc-by-sa-4.0 | ['legal'] | false | Load Pretrained Model ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("maastrichtlawtech/legal-distilcamembert") model = AutoModel.from_pretrained("maastrichtlawtech/legal-distilcamembert") ``` | 41b4ab14cfd829c9cf69d334e61ef8b9 |
cc-by-sa-4.0 | ['legal'] | false | About Us The [Maastricht Law & Tech Lab](https://www.maastrichtuniversity.nl/about-um/faculties/law/research/law-and-tech-lab) develops algorithms, models, and systems that allow computers to process natural language texts from the legal domain. Author: [Antoine Louis](https://antoinelouis.co) on behalf of the [Maastricht Law & Tech Lab](https://www.maastrichtuniversity.nl/about-um/faculties/law/research/law-and-tech-lab). | 89d0bed119c226ca639c2afcbd0a703a |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_One_50v8_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one50v8_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.5935 - Precision: 0.0917 - Recall: 0.0054 - F1: 0.0102 - Accuracy: 0.7849 | 11540d51af0e83bdd9d42a9bcb07d176 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 19 | 0.7198 | 0.0 | 0.0 | 0.0 | 0.7786 | | No log | 2.0 | 38 | 0.6263 | 0.0727 | 0.0010 | 0.0019 | 0.7798 | | No log | 3.0 | 57 | 0.5935 | 0.0917 | 0.0054 | 0.0102 | 0.7849 | | bcad17fa5b717c0d9872ba121e4d4d54 |
mit | ['generated_from_trainer'] | false | finetuning-profane-model-deberta This model is a fine-tuned version of [yangheng/deberta-v3-base-absa-v1.1](https://huggingface.co/yangheng/deberta-v3-base-absa-v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6243 - Accuracy: 0.8322 - F1: 0.8455 - Precision: 0.8015 - Recall: 0.8946 | c1df251fd5d5d5841e0455c4d9ae5038 |
mit | ['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: 10 | 6ec7069587b6033e466a01f3d46f86be |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | wav2vec2-common_voice-nl-demo This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - NL dataset. It achieves the following results on the evaluation set: - Loss: 0.3523 - Wer: 0.2046 | 779248a98e572d96d8d10aab00ccc11d |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15.0 - mixed_precision_training: Native AMP | 0441cb7357e652390404acf8d5caf52a |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0536 | 1.12 | 500 | 0.5349 | 0.4338 | | 0.2543 | 2.24 | 1000 | 0.3859 | 0.3029 | | 0.1472 | 3.36 | 1500 | 0.3471 | 0.2818 | | 0.1088 | 4.47 | 2000 | 0.3489 | 0.2731 | | 0.0855 | 5.59 | 2500 | 0.3582 | 0.2558 | | 0.0721 | 6.71 | 3000 | 0.3457 | 0.2471 | | 0.0653 | 7.83 | 3500 | 0.3299 | 0.2357 | | 0.0527 | 8.95 | 4000 | 0.3440 | 0.2334 | | 0.0444 | 10.07 | 4500 | 0.3417 | 0.2289 | | 0.0404 | 11.19 | 5000 | 0.3691 | 0.2204 | | 0.0345 | 12.3 | 5500 | 0.3453 | 0.2102 | | 0.0288 | 13.42 | 6000 | 0.3634 | 0.2089 | | 0.027 | 14.54 | 6500 | 0.3532 | 0.2044 | | 182f91127c4b2342ff1db0de52b3e060 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It was created by following the [huggingface tutorial](https://huggingface.co/course/chapter7/5?fw=pt). It achieves the following results on the evaluation set: - Loss: 3.0173 - Rouge1: 16.7977 - Rouge2: 8.6849 - Rougel: 16.4822 - Rougelsum: 16.4975 | ad5e4d3f61d3ae80096be8dac7375a49 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-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: 8 | 0bb29d49639e42a8ff5ce3066f506f86 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 3.4693 | 1.0 | 1209 | 3.1215 | 17.5363 | 8.3875 | 17.0229 | 16.9653 | | 3.4231 | 2.0 | 2418 | 3.0474 | 16.7927 | 8.3533 | 16.2748 | 16.2379 | | 3.271 | 3.0 | 3627 | 3.0440 | 16.7233 | 7.9129 | 16.2385 | 16.1915 | | 3.1885 | 4.0 | 4836 | 3.0264 | 16.3078 | 7.5751 | 15.844 | 15.889 | | 3.1216 | 5.0 | 6045 | 3.0277 | 17.259 | 8.7504 | 16.8293 | 16.8543 | | 3.0739 | 6.0 | 7254 | 3.0188 | 16.8374 | 8.6457 | 16.4407 | 16.4743 | | 3.0393 | 7.0 | 8463 | 3.0161 | 17.3064 | 8.7822 | 16.9423 | 16.9543 | | 3.0202 | 8.0 | 9672 | 3.0173 | 16.7977 | 8.6849 | 16.4822 | 16.4975 | | 1b75b623d5d37d773fd66fdf8738d539 |
gpl-3.0 | ['generated_from_trainer'] | false | IceBERT-finetuned-ner This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the mim_gold_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0783 - Precision: 0.8873 - Recall: 0.8627 - F1: 0.8748 - Accuracy: 0.9848 | 72ed512c34094ed909d3c203a2b95ec7 |
gpl-3.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0539 | 1.0 | 2904 | 0.0768 | 0.8732 | 0.8453 | 0.8590 | 0.9833 | | 0.0281 | 2.0 | 5808 | 0.0737 | 0.8781 | 0.8492 | 0.8634 | 0.9838 | | 0.0166 | 3.0 | 8712 | 0.0783 | 0.8873 | 0.8627 | 0.8748 | 0.9848 | | fac9aec5e2ae9eb8da3690212c431096 |
mit | ['sklearn', 'skops', 'tabular-classification', 'visual emb-gam'] | false | Model description This is a LogisticRegressionCV model trained on averages of patch embeddings from the Imagenette dataset. This forms the GAM of an [Emb-GAM](https://arxiv.org/abs/2209.11799) extended to images. Patch embeddings are meant to be extracted with the [`google/vit-base-patch16-224` ViT checkpoint](https://huggingface.co/google/vit-base-patch16-224). | 779a569ea32f9b1018686365a262f3ee |
mit | ['sklearn', 'skops', 'tabular-classification', 'visual emb-gam'] | false | Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |-------------------|-----------------------------------------------------------| | Cs | 10 | | class_weight | | | cv | StratifiedKFold(n_splits=5, random_state=1, shuffle=True) | | dual | False | | fit_intercept | True | | intercept_scaling | 1.0 | | l1_ratios | | | max_iter | 100 | | multi_class | auto | | n_jobs | | | penalty | l2 | | random_state | 1 | | refit | False | | scoring | | | solver | lbfgs | | tol | 0.0001 | | verbose | 0 | </details> | 1c75f55c07779f9ea610032c41167f7d |
mit | ['sklearn', 'skops', 'tabular-classification', 'visual emb-gam'] | false | sk-57980b4f-6828-4a54-ae50-b50e1f9f097e input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;} | 2ecc0ae7fc57124bea68bfcf540f7115 |
mit | ['sklearn', 'skops', 'tabular-classification', 'visual emb-gam'] | false | sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;} | 57450597fbe9ea320d23ca2088dbf0b1 |
mit | ['sklearn', 'skops', 'tabular-classification', 'visual emb-gam'] | false | sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;} | 45082dbf96c958efe45fd6d83b34f805 |
mit | ['sklearn', 'skops', 'tabular-classification', 'visual emb-gam'] | false | sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-text-repr-fallback {display: none;}</style><div id="sk-57980b4f-6828-4a54-ae50-b50e1f9f097e" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LogisticRegressionCV(cv=StratifiedKFold(n_splits=5, random_state=1, shuffle=True),random_state=1, refit=False)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="51ec5e46-9aaa-4487-adda-6718142c9f85" type="checkbox" checked><label for="51ec5e46-9aaa-4487-adda-6718142c9f85" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegressionCV</label><div class="sk-toggleable__content"><pre>LogisticRegressionCV(cv=StratifiedKFold(n_splits=5, random_state=1, shuffle=True),random_state=1, refit=False)</pre></div></div></div></div></div> | ced4dfa9ef473f0058d7bde359d4836b |
mit | ['sklearn', 'skops', 'tabular-classification', 'visual emb-gam'] | false | How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from PIL import Image from skops import hub_utils import torch from transformers import AutoFeatureExtractor, AutoModel import pickle import os | 6ad561164c727bed7e8c19653dbc25b9 |
mit | ['sklearn', 'skops', 'tabular-classification', 'visual emb-gam'] | false | load embedding model device = torch.device("cuda" if torch.cuda.is_available() else "cpu") feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224") model = AutoModel.from_pretrained("google/vit-base-patch16-224").eval().to(device) | 6ef358c8d3c5c64f6e878274589daede |
mit | ['sklearn', 'skops', 'tabular-classification', 'visual emb-gam'] | false | load logistic regression os.mkdir("emb-gam-vit") hub_utils.download(repo_id="Ramos-Ramos/emb-gam-vit", dst="emb-gam-vit") with open("emb-gam-vit/model.pkl", "rb") as file: logistic_regression = pickle.load(file) | bf46ac1634a59bc8e9507b0698bb9cd7 |
mit | ['sklearn', 'skops', 'tabular-classification', 'visual emb-gam'] | false | Citation Below you can find information related to citation. **BibTeX:** ``` @article{singh2022emb, title={Emb-GAM: an Interpretable and Efficient Predictor using Pre-trained Language Models}, author={Singh, Chandan and Gao, Jianfeng}, journal={arXiv preprint arXiv:2209.11799}, year={2022} } ``` | c9bf1784ed14865b70495401acb37a8e |
mit | ['msmarco', 't5', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Introduction ptt5-base-msmarco-en-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, fine-tuned on both English and Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This model was finetuned for 100k steps. Further information about the dataset or the translation method can be found on our [**mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset**](https://arxiv.org/abs/2108.13897) and [mMARCO](https://github.com/unicamp-dl/mMARCO) repository. | 4ce2aa7f4b584a9e0503d0c7a43e7c31 |
mit | ['msmarco', 't5', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'unicamp-dl/ptt5-base-msmarco-en-pt-100k-v2' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) ``` | b809c8386a7058b98e2b3059a4b94c18 |
mit | ['msmarco', 't5', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Citation If you use ptt5-base-msmarco-en-pt-100k-v2, please cite: @misc{bonifacio2021mmarco, title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset}, author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and and Roberto Lotufo and Rodrigo Nogueira}, year={2021}, eprint={2108.13897}, archivePrefix={arXiv}, primaryClass={cs.CL} } | f78d00a18ef72ac4e9274cc4213ed0f5 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_mrpc This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6197 - Accuracy: 0.6838 - F1: 0.8122 - Combined Score: 0.7480 | 1b54ebb759e3475e3d38ebb436a14b7b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6387 | 1.0 | 29 | 0.6245 | 0.6838 | 0.8122 | 0.7480 | | 0.6307 | 2.0 | 58 | 0.6234 | 0.6838 | 0.8122 | 0.7480 | | 0.6307 | 3.0 | 87 | 0.6233 | 0.6838 | 0.8122 | 0.7480 | | 0.6295 | 4.0 | 116 | 0.6231 | 0.6838 | 0.8122 | 0.7480 | | 0.6261 | 5.0 | 145 | 0.6197 | 0.6838 | 0.8122 | 0.7480 | | 0.6147 | 6.0 | 174 | 0.6344 | 0.6838 | 0.8122 | 0.7480 | | 0.6209 | 7.0 | 203 | 0.6398 | 0.6838 | 0.8122 | 0.7480 | | 0.6007 | 8.0 | 232 | 0.6338 | 0.6324 | 0.7517 | 0.6920 | | 0.5795 | 9.0 | 261 | 0.6377 | 0.625 | 0.7429 | 0.6839 | | 0.5712 | 10.0 | 290 | 0.6290 | 0.6814 | 0.8036 | 0.7425 | | dab3f1dd106ac788c8928a31fadade95 |
apache-2.0 | [] | false | <p align="center"> <br> <img src="./docs/source/en/imgs/diffusers_library.jpg" width="400"/> <br> <p> <p align="center"> <a href="https://github.com/huggingface/diffusers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/datasets.svg?color=blue"> </a> <a href="https://github.com/huggingface/diffusers/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/diffusers.svg"> </a> <a href="CODE_OF_CONDUCT.md"> <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg"> </a> </p> 🤗 Diffusers provides pretrained diffusion models across multiple modalities, such as vision and audio, and serves as a modular toolbox for inference and training of diffusion models. More precisely, 🤗 Diffusers offers: - State-of-the-art diffusion pipelines that can be run in inference with just a couple of lines of code (see [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines)). Check [this overview](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/README.md | 0ec8a616762a0eaa3a893e35ce35b55a |
apache-2.0 | [] | false | pipelines-summary) to see all supported pipelines and their corresponding official papers. - Various noise schedulers that can be used interchangeably for the preferred speed vs. quality trade-off in inference (see [src/diffusers/schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers)). - Multiple types of models, such as UNet, can be used as building blocks in an end-to-end diffusion system (see [src/diffusers/models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)). - Training examples to show how to train the most popular diffusion model tasks (see [examples](https://github.com/huggingface/diffusers/tree/main/examples), *e.g.* [unconditional-image-generation](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation)). | 57a3d5e32a953bba4078375e75ecd76d |
apache-2.0 | [] | false | For PyTorch **With `pip`** (official package) ```bash pip install --upgrade diffusers[torch] ``` **With `conda`** (maintained by the community) ```sh conda install -c conda-forge diffusers ``` | 0d897368d652de541d4b794d5b31010c |
apache-2.0 | [] | false | For Flax **With `pip`** ```bash pip install --upgrade diffusers[flax] ``` **Apple Silicon (M1/M2) support** Please, refer to [the documentation](https://huggingface.co/docs/diffusers/optimization/mps). | f2af28b501773347a9f7e739c0be46a8 |
apache-2.0 | [] | false | Contributing We ❤️ contributions from the open-source community! If you want to contribute to this library, please check out our [Contribution guide](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md). You can look out for [issues](https://github.com/huggingface/diffusers/issues) you'd like to tackle to contribute to the library. - See [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) for general opportunities to contribute - See [New model/pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) to contribute exciting new diffusion models / diffusion pipelines - See [New scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22) Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz98XR"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a>. We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or just hang out ☕. | 1caf13a86674fcbafcebcdc750d5b750 |
apache-2.0 | [] | false | Quickstart In order to get started, we recommend taking a look at two notebooks: - The [Getting started with Diffusers](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) notebook, which showcases an end-to-end example of usage for diffusion models, schedulers and pipelines. Take a look at this notebook to learn how to use the pipeline abstraction, which takes care of everything (model, scheduler, noise handling) for you, and also to understand each independent building block in the library. - The [Training a diffusers model](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) notebook summarizes diffusion models training methods. This notebook takes a step-by-step approach to training your diffusion models on an image dataset, with explanatory graphics. | 382abd4ecc225f779b23ca09cfd41055 |
apache-2.0 | [] | false | Stable Diffusion is fully compatible with `diffusers`! Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), [LAION](https://laion.ai/) and [RunwayML](https://runwayml.com/). It's trained on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 4GB VRAM. See the [model card](https://huggingface.co/CompVis/stable-diffusion) for more information. | 9b9e0db4dd060f8e1e339e212b516fbc |
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