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 1. Trained for 10000 steps probably at the default learning ratet lr=1e-6 2. Dataset: around 500 tagged images of suise no majo + thousands of customized reg images --- **Problems:** As the model is trained only on tagged images, it is more flexible but it is only harder to prompt. Some detailed description may be needed to get the character right, especially when trying to prompt suletta and miorine in the same image. --- **Example Generations:**           | 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 - Rougelsum: 69.9659 - Gen Len: 113.6 | 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 | 102.5 | | No log | 2.0 | 40 | 0.5084 | 68.113 | 52.0277 | 60.5913 | 67.282 | 114.8 | | No log | 3.0 | 60 | 0.4591 | 70.5822 | 55.7529 | 63.7452 | 69.9659 | 113.6 | | 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* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the modelโs depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. | 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 in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | | 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 - Gen Len: 19.9143 | 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 | 19.896 | | 0.1598 | 2.0 | 15478 | 0.2812 | 18.529 | 7.9891 | 18.5421 | 18.5271 | 19.8977 | | 0.1289 | 3.0 | 23217 | 0.2807 | 18.0638 | 7.8086 | 18.0787 | 18.0583 | 19.9129 | | 0.0873 | 4.0 | 30956 | 0.2923 | 18.3483 | 8.0233 | 18.3716 | 18.3696 | 19.914 | | 0.0844 | 5.0 | 38695 | 0.3136 | 18.3166 | 8.0509 | 18.3332 | 18.3146 | 19.9143 | | 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 details on pretrain: * https://huggingface.co/laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg * https://huggingface.co/laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg * https://huggingface.co/laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K | 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 - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020 - **Original:** https://github.com/mlfoundations/open_clip - **Pretrain Dataset:** LAION-2B - **Dataset:** ImageNet-1k | 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', pretrained=True) model = model.eval() | 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_384', pretrained=True, features_only=True, ) model = model.eval() | 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', pretrained=True, num_classes=0, | 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| |----------------------------------------------|------|------|--------|-----------|------|------|---------------|----------| |[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_large_mlp.clip_laion2b_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384)|87.870|98.452|384 |200.13 |101.11 |126.74 |197.92 |256 | |[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 | | 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| |----------------------------------------------|------|------|--------|-----------|------|------|---------------|----------| |[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_mlp.clip_laion2b_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384)|87.870 |98.452 |384 |200.13 |101.11 |126.74 |197.92 |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 | | 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 own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:      | 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 Accuracy: 0.0694 - Validation Loss: 5.8343 - Validation End Logits Accuracy: 0.0 - Validation Start Logits Accuracy: 0.0 - Epoch: 1 | 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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 5.8427 | 0.0069 | 0.0069 | 5.8688 | 0.0 | 0.0 | 0 | | 5.5824 | 0.0347 | 0.0694 | 5.8343 | 0.0 | 0.0 | 1 | | 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 ์์ผ๋ฉฐ, Waifu Diffusion๊ณผ Novel AI ์์ ์ผ๋ฐ์ ์ผ๋ก ์ ์ฉ๋๋ ํค์๋๋ฅผ ์ฌ์ฉ ํ ์ ์์ต๋๋ค. | 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 Winterhalter, [NORMAN ROCKWELL], >(PERFECT FACE:1.2), (SEXY FACE:1.2), (DETAILED PUPILS:1.2), (SMIRK), (HIGH DETAIL:1.2), SHARP, glitter many particles, artgerm, ((intricate details)), ((highres)), (finely detailed), >absurdres, soft lighting, glow, (1girl), (solo), beautiful detailed glow, (large breasts), cleavage, sideswept hair, hair bowtie, (intricate halter backless dress), gloves, (highheels:1.14), [ornate mansion's foyer, bannisters in background], >Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, extra fingers, mutation, bad anatomy, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, (nipples on navel:1.34), (nipples on stomach:1.34), (3 or more nipples:1.34), (censored:1.22), (censor bar:1.22), (ugly:1.48), (duplicate:1.34), (morbid:1.22), (mutilated:1.22), (tranny:1.34), (trans:1.34), (trannsexual:1.34), (hermaphrodite:1.1), extra fingers, mutated hands, (poorly drawn hands:1.22), (poorly drawn face:1.22), (mutation:1.34), (deformed:1.34), (ugly:1.22), blurry, (bad anatomy:1.22), >Seed: 3483746954, Steps: 50, CFG scale: 8  >(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 Winterhalter, [NORMAN ROCKWELL], >(PERFECT FACE:1.2), (SEXY FACE:1.2), (DETAILED PUPILS:1.2), (SMIRK), (HIGH DETAIL:1.2), SHARP, glitter many particles, artgerm, ((intricate details)), ((highres)), (finely detailed), >absurdres, soft lighting, glow, (1girl), (solo), beautiful detailed glow, (large breasts), cleavage, sideswept hair, hair bowtie, (intricate halter backless dress), gloves, (highheels:1.14), [ornate mansion's foyer, bannisters in background], >Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, extra fingers, mutation, bad anatomy, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, (nipples on navel:1.34), (nipples on stomach:1.34), (3 or more nipples:1.34), (censored:1.22), (censor bar:1.22), (ugly:1.48), (duplicate:1.34), (morbid:1.22), (mutilated:1.22), (tranny:1.34), (trans:1.34), (trannsexual:1.34), (hermaphrodite:1.1), extra fingers, mutated hands, (poorly drawn hands:1.22), (poorly drawn face:1.22), (mutation:1.34), (deformed:1.34), (ugly:1.22), blurry, (bad anatomy:1.22), >Seed: 4009463661, Steps: 50, Sampler: DDIM, CFG scale: 8  >(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 Winterhalter, [NORMAN ROCKWELL], >(PERFECT FACE:1.2), (SEXY FACE:1.2), (DETAILED PUPILS:1.2), (SMIRK), (HIGH DETAIL:1.2), SHARP, glitter many particles, artgerm, ((intricate details)), ((highres)), (finely detailed), (wearing a sexy see-through backless dress:1.28), >absurdres, soft lighting, glow, (1girl), (solo), beautiful detailed glow, (large breasts), cleavage, sideswept hair, hair bowtie, gloves, (highheels:1.12), [ornate mansion's foyer, bannisters in background], (NSFW:1.2) >Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, extra fingers, mutation, bad anatomy, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, (nipples on navel:1.34), (nipples on stomach:1.34), (3 or more nipples:1.34), (censored:1.22), (censor bar:1.22), (ugly:1.48), (duplicate:1.34), (morbid:1.22), (mutilated:1.22), (tranny:1.34), (trans:1.34), (trannsexual:1.34), (hermaphrodite:1.1), extra fingers, mutated hands, (poorly drawn hands:1.22), (poorly drawn face:1.22), (mutation:1.34), (deformed:1.34), (ugly:1.22), blurry, (bad anatomy:1.22), >Seed: 3002977200, Steps: 50, Sampler: DDIM, CFG scale: 10 | 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:1.2), (SEXY FACE:1.2), (DETAILED PUPILS:1.2), (SMIRK), sideswept hair, (full body:1.2), >((1girl)), pixie cut, (sexy eyes), detailed face, detailed eyes, slight smile, (full body:1.2), >((1girl)), shot hair, (PERFECT FACE:1.2), (DETAILED PUPILS:1.2), (SMIRK), (full body:1.2), >((1girl)), wave hair, (bride), (beautiful face), (sexy eyes), (DETAILED PUPILS:1.2), (SMIRK), ideswept hair, (full body:1.2), >((1girl)), bob cut, (beautiful face), (sexy eyes), detailed face, detailed eyes, (full body:1.2), >SHARP, glitter many particles, ((intricate details)), ((highres)), (finely detailed), >oil painting by ((Francois Boucher), (alphonse mucha:0.8), (Claude Monet), Franz Xaver Winterhalter, (NORMAN ROCKWELL:0.8)), > >Negative prompt:logo, title, text, caption, solo:1.5, identical outfits, split panels, white background, plain background, black background, simple background, gradient background, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, extra fingers, mutation, bad anatomy, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, (nipples on navel:1.34), (nipples on stomach:1.34), (3 or more nipples:1.34), (censored:1.22), (censor bar:1.22), (ugly:1.48), (duplicate:1.34), (morbid:1.22), (mutilated:1.22), (tranny:1.34), (trans:1.34), (trannsexual:1.34), (hermaphrodite:1.1), extra fingers, mutated hands, (poorly drawn hands:1.22), (poorly drawn face:1.22), (mutation:1.34), (deformed:1.34), (ugly:1.22), blurry, (bad anatomy:1.22), (bad proportions:1.34), (extra limbs:1.22), cloned face, (disfigured:1.34), (more than 2 nipples:1.34), extra limbs, (bad anatomy:1.1), gross proportions, (malformed limbs:1.1), (missing arms:1.22), (missing legs:1.22), (extra arms:1.34), (extra legs:1.34), mutated hands, (fused fingers:1.1), (too many fingers:1.1), (long neck:1.34), (out of frame:1.1), (more than one person in focus:1.1), (bad anatomy:1.1), (more than two arm per body:1.48), (more than two leg per body:1.48), (more than five fingers on one hand:1.48), bad detailed background, unclear architectural outline, non-linear background, over one person in focus, (over four finger:1.05), (fingers excluding thumb:1.98), fused anatomy, (bad anatomybody:1.1), (bad anatomyhand:1.1), (bad anatomyfinger:1.1), (four fingers excluding thumbfingers:1.98), (bad anatomyarms:1.1), (over two armsbody:1.1), (bad anatomyleg:1.1), (over two legsbody:1.1), (bad anatomyarm:1.1), (bad detailfinger:1.05), (bad anatomyfingers:1.1), (multifulfingers:1.1), (bad anatomyfinger:1.1), (bad anatomyfingers:1.1), (fusedfingers:1.1), (over four fingerfingers excluding thumb:1.98), (multifulhands:1.1), (multifularms:1.1), (multifullegs:1.1), ((frame)) > >Steps: 50, Sampler: DDIM, CFG scale: 12, Seed: 1023642063, Size: 768x512, Model hash: ab05b088cd, Model: 20_Vox-mix2anu, Denoising strength: 0.53, ENSD: -1, Hires upscale: 2, Hires upscaler: R-ESRGAN 4x+ Anime6B **License** This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. ์ด ๋ชจ๋ธ์ ๊ถํ ๋ฐ ์ฌ์ฉ์ ์ถ๊ฐ๋ก ์ง์ ํ๋ CreativeML OpenRAIL-M ๋ผ์ด์ ์ค๋ฅผ ํตํด ๋ชจ๋ ์ฌ๋์ด ์ก์ธ์คํ ์ ์๊ณ ์ฌ์ฉํ ์ ์์ต๋๋ค. The CreativeML OpenRAIL License specifies: CreativeML OpenRAIL ๋ผ์ด์ ์ค๋ ๋ค์์ ์ง์ ํฉ๋๋ค. 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content / ๋ชจ๋ธ์ ์ฌ์ฉํ์ฌ ๋ถ๋ฒ์ ์ด๊ฑฐ๋ ์ ํดํ ์ถ๋ ฅ๋ฌผ ๋๋ ์ฝํ
์ธ ๋ฅผ ์๋์ ์ผ๋ก ์์ฑํ๊ฑฐ๋ ๊ณต์ ํ ์ ์์ต๋๋ค. 2. 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 / ์์ฑ์๋ ๊ทํ๊ฐ ์์ฑํ ๊ฒฐ๊ณผ๋ฌผ์ ๋ํด ์ด๋ ํ ๊ถ๋ฆฌ๋ ์ฃผ์ฅํ์ง ์์ผ๋ฉฐ, ๊ทํ๋ ์ด๋ฅผ ์์ ๋กญ๊ฒ ์ฌ์ฉํ ์ ์์ผ๋ฉฐ ๋ผ์ด์ผ์ค์ ์ค์ ๋ ์กฐํญ์ ์๋ฐฐ๋์ง ์๋ ์ฌ์ฉ์ ๋ํด ์ฑ
์์ ์ง๋๋ค. 3. You may re-distribute the weights and use the model 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)ย / ๊ฐ์ค์น๋ฅผ ์ฌ๋ฐฐํฌํ๊ณ ๋ชจ๋ธ์ ์์
์ ๋ฐ/๋๋ ์๋น์ค๋ก ์ฌ์ฉํ ์ ์์ต๋๋ค. ๊ทธ๋ ๊ฒ ํ๋ ๊ฒฝ์ฐ ๋ผ์ด์ ์ค์ ์๋ ๊ฒ๊ณผ ๋์ผํ ์ฌ์ฉ ์ ํ์ ํฌํจํ๊ณ ๋ชจ๋ ์ฌ์ฉ์์๊ฒ CreativeML OpenRAIL-M ์ฌ๋ณธ์ ๊ณต์ ํด์ผ ํฉ๋๋ค(๋ผ์ด์ ์ค๋ฅผ ์์ ํ ์ฃผ์ ๊น๊ฒ ์ฝ์ผ์ญ์์ค). **[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)** | 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 | | 0.3169 | 2.0 | 4594 | 0.8868 | 0.8702 | 0.0 | 0.0 | 0.0 | 0.1298 | | 0.2708 | 3.0 | 6891 | 0.9082 | 0.8710 | 0.0 | 0.0 | 0.0 | 0.1290 | | 0.2337 | 4.0 | 9188 | 0.9813 | 0.8708 | 0.0 | 0.0 | 0.0 | 0.1292 | | 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 train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:     | 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://object.pouta.csc.fi/OPUS-MT-models/sv-en/opus-2020-02-26.zip) * test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-en/opus-2020-02-26.test.txt) * test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-en/opus-2020-02-26.eval.txt) | 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 can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:       | 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_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 | 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 | | 0.2468 | 4.0 | 1168 | 0.7132 | 0.7318 | | 0.2468 | 5.0 | 1460 | 0.8980 | 0.7504 | | 0.12 | 6.0 | 1752 | 1.0343 | 0.7369 | | 0.0486 | 7.0 | 2044 | 1.1860 | 0.7333 | | 0.0486 | 8.0 | 2336 | 1.3348 | 0.7437 | | 0.019 | 9.0 | 2628 | 1.3040 | 0.7561 | | 0.019 | 10.0 | 2920 | 1.4649 | 0.7293 | | 0.0152 | 11.0 | 3212 | 1.4870 | 0.7431 | | 0.0078 | 12.0 | 3504 | 1.5668 | 0.7455 | | 0.0078 | 13.0 | 3796 | 1.5280 | 0.7378 | | 0.0091 | 14.0 | 4088 | 1.5672 | 0.7410 | | 0.0091 | 15.0 | 4380 | 1.5948 | 0.7491 | | 0.0052 | 16.0 | 4672 | 1.6625 | 0.7588 | | 0.0052 | 17.0 | 4964 | 1.6544 | 0.7411 | | 0.0048 | 18.0 | 5256 | 1.7124 | 0.7425 | | 0.0024 | 19.0 | 5548 | 1.7211 | 0.7477 | | 0.0024 | 20.0 | 5840 | 1.8216 | 0.7373 | | 0.001 | 21.0 | 6132 | 1.8325 | 0.7361 | | 0.001 | 22.0 | 6424 | 1.8089 | 0.7498 | | 0.0015 | 23.0 | 6716 | 1.8026 | 0.7506 | | 0.0005 | 24.0 | 7008 | 1.8026 | 0.7464 | | 0.0005 | 25.0 | 7300 | 1.8043 | 0.7464 | | 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_training: Native AMP | 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 | 1.0 | | 0.5996 | 4.0 | 12 | 0.6119 | 1.0 | | 0.5242 | 5.0 | 15 | 0.5806 | 1.0 | | 0.4612 | 6.0 | 18 | 0.5320 | 1.0 | | 0.4192 | 7.0 | 21 | 0.4714 | 1.0 | | 0.3274 | 8.0 | 24 | 0.4071 | 1.0 | | 0.2871 | 9.0 | 27 | 0.3378 | 1.0 | | 0.2082 | 10.0 | 30 | 0.2822 | 1.0 | | 0.1692 | 11.0 | 33 | 0.2271 | 1.0 | | 0.1242 | 12.0 | 36 | 0.1793 | 1.0 | | 0.0977 | 13.0 | 39 | 0.1417 | 1.0 | | 0.0776 | 14.0 | 42 | 0.1117 | 1.0 | | 0.0631 | 15.0 | 45 | 0.0894 | 1.0 | | 0.0453 | 16.0 | 48 | 0.0733 | 1.0 | | 0.0399 | 17.0 | 51 | 0.0617 | 1.0 | | 0.0333 | 18.0 | 54 | 0.0528 | 1.0 | | 0.0266 | 19.0 | 57 | 0.0454 | 1.0 | | 0.0234 | 20.0 | 60 | 0.0393 | 1.0 | | 0.0223 | 21.0 | 63 | 0.0345 | 1.0 | | 0.0195 | 22.0 | 66 | 0.0309 | 1.0 | | 0.0161 | 23.0 | 69 | 0.0281 | 1.0 | | 0.0167 | 24.0 | 72 | 0.0260 | 1.0 | | 0.0163 | 25.0 | 75 | 0.0242 | 1.0 | | 0.0134 | 26.0 | 78 | 0.0227 | 1.0 | | 0.0128 | 27.0 | 81 | 0.0214 | 1.0 | | 0.0101 | 28.0 | 84 | 0.0204 | 1.0 | | 0.0109 | 29.0 | 87 | 0.0194 | 1.0 | | 0.0112 | 30.0 | 90 | 0.0186 | 1.0 | | 0.0108 | 31.0 | 93 | 0.0179 | 1.0 | | 0.011 | 32.0 | 96 | 0.0174 | 1.0 | | 0.0099 | 33.0 | 99 | 0.0169 | 1.0 | | 0.0083 | 34.0 | 102 | 0.0164 | 1.0 | | 0.0096 | 35.0 | 105 | 0.0160 | 1.0 | | 0.01 | 36.0 | 108 | 0.0156 | 1.0 | | 0.0084 | 37.0 | 111 | 0.0152 | 1.0 | | 0.0089 | 38.0 | 114 | 0.0149 | 1.0 | | 0.0073 | 39.0 | 117 | 0.0146 | 1.0 | | 0.0082 | 40.0 | 120 | 0.0143 | 1.0 | | 0.008 | 41.0 | 123 | 0.0141 | 1.0 | | 0.0093 | 42.0 | 126 | 0.0139 | 1.0 | | 0.0078 | 43.0 | 129 | 0.0138 | 1.0 | | 0.0086 | 44.0 | 132 | 0.0136 | 1.0 | | 0.009 | 45.0 | 135 | 0.0135 | 1.0 | | 0.0072 | 46.0 | 138 | 0.0134 | 1.0 | | 0.0075 | 47.0 | 141 | 0.0133 | 1.0 | | 0.0082 | 48.0 | 144 | 0.0133 | 1.0 | | 0.0068 | 49.0 | 147 | 0.0132 | 1.0 | | 0.0074 | 50.0 | 150 | 0.0132 | 1.0 | | 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 that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 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 | 3.0 | 2316 | 0.2577 | 0.9055 | 0.9059 | | 0.1344 | 4.0 | 3088 | 0.2708 | 0.9085 | 0.9089 | | 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" to start your prompt add "3d, ceramic, octane render" to add a shiny 3D appearance go wild Sample pictures of this concept using the 768px model:        | 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 - Validation Sparse Categorical Accuracy: 0.8448 - Epoch: 1 | 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 | 0.8254 | 0 | | 0.4118 | 0.8765 | 0.5309 | 0.8448 | 1 | | 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 | 0.9635 | | 0.0184 | 4.0 | 2000 | 0.1906 | 0.9675 | | 0.0144 | 5.0 | 2500 | 0.1860 | 0.9675 | | 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-mrpc](https://huggingface.co/Intel/bart-large-mrpc). | 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` | | **Components** | `ner` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | n/a | | **License** | MIT | | **Author** | [n/a]() | | 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 | 2008 | 2.4061 | 23.4178 | 2.4764 | 23.2901 | 23.3097 | | 2.724 | 3.0 | 3012 | 2.1630 | 26.6706 | 2.8302 | 26.6509 | 26.5723 | | 2.4395 | 4.0 | 4016 | 2.0815 | 26.7296 | 2.9481 | 26.6313 | 26.533 | | 2.2881 | 5.0 | 5020 | 2.0048 | 30.1887 | 3.3019 | 30.0708 | 29.9135 | | 2.1946 | 6.0 | 6024 | 1.9712 | 29.4811 | 2.9481 | 29.4025 | 29.3042 | | 2.1458 | 7.0 | 7028 | 1.9545 | 29.8153 | 3.3019 | 29.717 | 29.5204 | | 2.1069 | 8.0 | 8032 | 1.9515 | 30.3066 | 3.3019 | 30.1887 | 30.0314 | | 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: 0.7941 | 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 | 48 | 0.5824 | 0.1479 | 0.0154 | 0.0279 | 0.7895 | | No log | 3.0 | 72 | 0.5851 | 0.1466 | 0.0256 | 0.0437 | 0.7941 | | 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: 0.6552 - Per Category Iou: [0.0, 0.40932069741697885, 0.6666047315185674, 0.0015527279135260222, 0.000557997451181134, 0.004734463745284192, 0.0, 0.00024311836753505628, 0.0, 0.0, 0.5448608416905849, 0.005644290758731727, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4689142754019952, 0.0, 0.00039031599380590526, 0.010175747938072128, 0.0, 0.0, 0.0, 0.0008842445754996234, 0.0, 0.0, 0.6689560919488968, 0.10178439680971307, 0.7089823411348399, 0.0, 0.0, 0.0, 0.0] - Per Category Accuracy: [nan, 0.6798160901382586, 0.8601972223213155, 0.001563543652833044, 0.0005586801134972854, 0.004789605465686377, nan, 0.00024743825184288725, 0.0, 0.0, 0.8407289173400536, 0.012641370267169317, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7574833533176979, 0.0, 0.00039110009377117975, 0.013959849889225483, 0.0, nan, 0.0, 0.0009309900323061499, 0.0, 0.0, 0.9337304207449932, 0.12865528611713883, 0.8019892660736478, 0.0, 0.0, 0.0, 0.0] | 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 | Per Category Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 2.8872 | 0.5 | 20 | 3.1018 | 0.0995 | 0.1523 | 0.6415 | [0.0, 0.3982872425364927, 0.6582689116809847, 0.0, 0.00044314555867048773, 0.019651883205738383, 0.0, 0.0006528617866575068, 0.0, 0.0, 0.4861235900758522, 0.003961411405960721, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4437814560942763, 0.0, 1.1600860783870164e-06, 0.019965880301918204, 0.0, 0.0, 0.0, 0.0074026601990928, 0.0, 0.0, 0.666238976894996, 0.13012673492067245, 0.6486315429686865, 0.0, 2.0656177918545805e-05, 0.0001944735843164534, 0.0] | [nan, 0.6263716501798601, 0.8841421548179447, 0.0, 0.00044410334445801165, 0.020659891877382746, nan, 0.0006731258604635891, 0.0, 0.0, 0.8403154629142631, 0.017886412063596133, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6324385775164868, 0.0, 1.160534402881839e-06, 0.06036834410935781, 0.0, nan, 0.0, 0.010232933175604348, 0.0, 0.0, 0.9320173945724101, 0.15828224740687694, 0.6884182010535304, 0.0, 2.3169780427714147e-05, 0.00019505205451704924, 0.0] | | 2.6167 | 1.0 | 40 | 2.6306 | 0.1027 | 0.1574 | 0.6552 | [0.0, 0.40932069741697885, 0.6666047315185674, 0.0015527279135260222, 0.000557997451181134, 0.004734463745284192, 0.0, 0.00024311836753505628, 0.0, 0.0, 0.5448608416905849, 0.005644290758731727, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4689142754019952, 0.0, 0.00039031599380590526, 0.010175747938072128, 0.0, 0.0, 0.0, 0.0008842445754996234, 0.0, 0.0, 0.6689560919488968, 0.10178439680971307, 0.7089823411348399, 0.0, 0.0, 0.0, 0.0] | [nan, 0.6798160901382586, 0.8601972223213155, 0.001563543652833044, 0.0005586801134972854, 0.004789605465686377, nan, 0.00024743825184288725, 0.0, 0.0, 0.8407289173400536, 0.012641370267169317, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7574833533176979, 0.0, 0.00039110009377117975, 0.013959849889225483, 0.0, nan, 0.0, 0.0009309900323061499, 0.0, 0.0, 0.9337304207449932, 0.12865528611713883, 0.8019892660736478, 0.0, 0.0, 0.0, 0.0] | | 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 - F1 Where: 0.8468 - F1 How: 0.9414 | 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 | 0.8943 | 0.8946 | 0.8939 | 0.8946 | 0.8846 | 0.8179 | 0.8559 | 0.9208 | | 0.3067 | 2.0 | 96 | 0.3481 | 0.8911 | 0.8803 | 0.8820 | 0.8803 | 0.8649 | 0.8102 | 0.7766 | 0.9365 | | 0.2054 | 3.0 | 144 | 0.3018 | 0.9129 | 0.9121 | 0.9117 | 0.9121 | 0.8649 | 0.8571 | 0.8720 | 0.9430 | | 0.2196 | 4.0 | 192 | 0.3061 | 0.9108 | 0.9105 | 0.9098 | 0.9105 | 0.8649 | 0.8385 | 0.8610 | 0.9508 | | 0.1505 | 5.0 | 240 | 0.3101 | 0.9036 | 0.9038 | 0.9029 | 0.9038 | 0.8727 | 0.8295 | 0.8468 | 0.9414 | | 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)
- model type: finetuned Bert
| 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`](https://github.com/asahi417/lm-question-generation). | 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://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) | 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("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.") ``` | 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 | |:--------------------------------|--------:|:--------|:-------------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 72.96 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) | | QAAlignedF1Score (MoverScore) | 51.25 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) | | QAAlignedPrecision (BERTScore) | 74.2 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) | | QAAlignedPrecision (MoverScore) | 52.44 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) | | QAAlignedRecall (BERTScore) | 71.83 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) | | QAAlignedRecall (MoverScore) | 50.21 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) | | 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 - epoch: 14 - batch: 8 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 16 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-itquad-qag/raw/main/trainer_config.json). | 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. Wav2Vec2 was introduced in [this paper](https://arxiv.org/abs/2006.11477) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec | 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 [VoxPopuli V2 dataset](https://github.com/facebookresearch/voxpopuli/) with the wav2vec 2.0 objective. This model is fine-tuned version of the pretrained model for Finnish ASR. | 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_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP The pretrained `facebook/wav2vec2-large-uralic-voxpopuli-v2` model was initialized with following hyperparameters: - attention_dropout: 0.094 - hidden_dropout: 0.047 - feat_proj_dropout: 0.04 - mask_time_prob: 0.082 - layerdrop: 0.041 - activation_dropout: 0.055 - ctc_loss_reduction: "mean" | 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 | 0.1711 | | 0.4884 | 0.66 | 2000 | 0.1265 | 0.1605 | | 0.4729 | 0.83 | 2500 | 0.1205 | 0.1485 | | 0.4723 | 1.0 | 3000 | 0.1108 | 0.1403 | | 0.443 | 1.16 | 3500 | 0.1175 | 0.1439 | | 0.4378 | 1.33 | 4000 | 0.1083 | 0.1482 | | 0.4313 | 1.49 | 4500 | 0.1110 | 0.1398 | | 0.4182 | 1.66 | 5000 | 0.1024 | 0.1418 | | 0.3884 | 1.83 | 5500 | 0.1032 | 0.1395 | | 0.4034 | 1.99 | 6000 | 0.0985 | 0.1318 | | 0.3735 | 2.16 | 6500 | 0.1008 | 0.1355 | | 0.4174 | 2.32 | 7000 | 0.0970 | 0.1361 | | 0.3581 | 2.49 | 7500 | 0.0968 | 0.1297 | | 0.3783 | 2.66 | 8000 | 0.0881 | 0.1284 | | 0.3827 | 2.82 | 8500 | 0.0921 | 0.1352 | | 0.3651 | 2.99 | 9000 | 0.0861 | 0.1298 | | 0.3684 | 3.15 | 9500 | 0.0844 | 0.1270 | | 0.3784 | 3.32 | 10000 | 0.0870 | 0.1248 | | 0.356 | 3.48 | 10500 | 0.0828 | 0.1214 | | 0.3524 | 3.65 | 11000 | 0.0878 | 0.1218 | | 0.3879 | 3.82 | 11500 | 0.0874 | 0.1216 | | 0.3521 | 3.98 | 12000 | 0.0860 | 0.1210 | | 0.3527 | 4.15 | 12500 | 0.0818 | 0.1184 | | 0.3529 | 4.31 | 13000 | 0.0787 | 0.1185 | | 0.3114 | 4.48 | 13500 | 0.0852 | 0.1202 | | 0.3495 | 4.65 | 14000 | 0.0807 | 0.1187 | | 0.34 | 4.81 | 14500 | 0.0796 | 0.1162 | | 0.3646 | 4.98 | 15000 | 0.0782 | 0.1149 | | 0.3004 | 5.14 | 15500 | 0.0799 | 0.1142 | | 0.3167 | 5.31 | 16000 | 0.0847 | 0.1123 | | 0.3249 | 5.48 | 16500 | 0.0837 | 0.1171 | | 0.3202 | 5.64 | 17000 | 0.0749 | 0.1109 | | 0.3104 | 5.81 | 17500 | 0.0798 | 0.1093 | | 0.3039 | 5.97 | 18000 | 0.0810 | 0.1132 | | 0.3157 | 6.14 | 18500 | 0.0847 | 0.1156 | | 0.3133 | 6.31 | 19000 | 0.0833 | 0.1140 | | 0.3203 | 6.47 | 19500 | 0.0838 | 0.1113 | | 0.3178 | 6.64 | 20000 | 0.0907 | 0.1141 | | 0.3182 | 6.8 | 20500 | 0.0938 | 0.1143 | | 0.3 | 6.97 | 21000 | 0.0854 | 0.1133 | | 0.3151 | 7.14 | 21500 | 0.0859 | 0.1109 | | 0.2963 | 7.3 | 22000 | 0.0832 | 0.1122 | | 0.3099 | 7.47 | 22500 | 0.0865 | 0.1103 | | 0.322 | 7.63 | 23000 | 0.0833 | 0.1105 | | 0.3064 | 7.8 | 23500 | 0.0865 | 0.1078 | | 0.2964 | 7.97 | 24000 | 0.0859 | 0.1096 | | 0.2869 | 8.13 | 24500 | 0.0872 | 0.1100 | | 0.315 | 8.3 | 25000 | 0.0869 | 0.1099 | | 0.3003 | 8.46 | 25500 | 0.0878 | 0.1105 | | 0.2947 | 8.63 | 26000 | 0.0884 | 0.1084 | | 0.297 | 8.8 | 26500 | 0.0891 | 0.1102 | | 0.3049 | 8.96 | 27000 | 0.0863 | 0.1081 | | 0.2957 | 9.13 | 27500 | 0.0846 | 0.1083 | | 0.2908 | 9.29 | 28000 | 0.0848 | 0.1059 | | 0.2955 | 9.46 | 28500 | 0.0846 | 0.1085 | | 0.2991 | 9.62 | 29000 | 0.0839 | 0.1081 | | 0.3112 | 9.79 | 29500 | 0.0832 | 0.1071 | | 0.29 | 9.96 | 30000 | 0.0828 | 0.1075 | | 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 following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models and their parameter counts: | | Model parameters | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) | |-------------------------------------------------------|------------------|---------------|------------------|---------------|------------------| |Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned | 95 million |5.85 |13.52 |1.35 |2.44 | |Finnish-NLP/wav2vec2-large-uralic-voxpopuli-v2-finnish | 300 million |4.13 |**9.66** |0.90 |1.66 | |Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm | 300 million |8.16 |17.92 |1.97 |3.36 | |Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm | 1000 million |5.65 |13.11 |1.20 |2.23 | |Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 | 1000 million |**4.09** |9.73 |**0.88** |**1.65** | | 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 following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models and their parameter counts: | | Model parameters | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) | |-------------------------------------------------------|------------------|---------------|------------------|---------------|------------------| |Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned | 95 million |5.93 |14.08 |1.40 |2.59 | |Finnish-NLP/wav2vec2-large-uralic-voxpopuli-v2-finnish | 300 million |4.13 |9.83 |0.92 |1.71 | |Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm | 300 million |7.42 |16.45 |1.79 |3.07 | |Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm | 1000 million |5.35 |13.00 |1.14 |2.20 | |Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 | 1000 million |**3.72** |**8.96** |**0.80** |**1.52** | | 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 Rate) and CER (Character Error Rate) results compared to our other models and their parameter counts: | | Model parameters | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) | |-------------------------------------------------------|------------------|---------------|------------------|---------------|------------------| |Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned | 95 million |13.99 |17.16 |6.07 |6.61 | |Finnish-NLP/wav2vec2-large-uralic-voxpopuli-v2-finnish | 300 million |12.44 |**14.63** |5.77 |6.22 | |Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm | 300 million |17.72 |23.30 |6.78 |7.67 | |Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm | 1000 million |20.34 |16.67 |6.97 |6.35 | |Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 | 1000 million |**12.11** |14.89 |**5.65** |**6.06** | | 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_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min - - train - loss - min keep_nbest_models: 5 nbest_averaging_interval: 0 grad_clip: 1.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 8 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: 800 batch_size: 20 valid_batch_size: null batch_bins: 2500000 valid_batch_bins: null train_shape_file: - exp/d/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/text_shape.phn - exp/d/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/speech_shape valid_shape_file: - exp/d/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/valid/text_shape.phn - exp/d/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/valid/speech_shape batch_type: numel valid_batch_type: null fold_length: - 150 - 204800 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_d_phn/text - text - text - - exp/d/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/train_d_phn/durations - durations - text_int - - dump/raw/train_d_phn/wav.scp - speech - sound valid_data_path_and_name_and_type: - - dump/raw/dev_d_phn/text - text - text - - exp/d/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/dev_d_phn/durations - durations - text_int - - dump/raw/dev_d_phn/wav.scp - speech - sound allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 1.0 scheduler: noamlr scheduler_conf: model_size: 384 warmup_steps: 4000 token_list: - <blank> - <unk> - ',' - . - r - t - n - a0 - s - I0 - D - l - Y0 - m - v - h - E1 - k - a:1 - E:1 - j - f - T - G - a1 - p - c - au:1 - i:1 - O:1 - E0 - I:1 - r_0 - I1 - t_h - k_h - Y1 - i0 - ei1 - u:1 - ou:1 - ei:1 - O1 - N - l_0 - '91' - ou0 - ai0 - n_0 - au1 - O0 - ou1 - ai:1 - ei0 - '9:1' - ai1 - i1 - c_h - '90' - au0 - x - C - p_h - u0 - 9i:1 - Y:1 - 9i1 - J - u1 - 9i0 - N_0 - m_0 - J_0 - Oi1 - Yi0 - Yi1 - Oi0 - '9:0' - au:0 - E:0 - <sos/eos> odim: null model_conf: {} use_preprocessor: true token_type: phn bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null feats_extract: fbank feats_extract_conf: n_fft: 1024 hop_length: 256 win_length: null fs: 22050 fmin: 80 fmax: 7600 n_mels: 80 normalize: global_mvn normalize_conf: stats_file: exp/d/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/feats_stats.npz tts: fastspeech2 tts_conf: adim: 384 aheads: 2 elayers: 4 eunits: 1536 dlayers: 4 dunits: 1536 positionwise_layer_type: conv1d positionwise_conv_kernel_size: 3 duration_predictor_layers: 2 duration_predictor_chans: 256 duration_predictor_kernel_size: 3 postnet_layers: 5 postnet_filts: 5 postnet_chans: 256 use_masking: true use_scaled_pos_enc: true encoder_normalize_before: true decoder_normalize_before: true reduction_factor: 1 init_type: xavier_uniform init_enc_alpha: 1.0 init_dec_alpha: 1.0 transformer_enc_dropout_rate: 0.2 transformer_enc_positional_dropout_rate: 0.2 transformer_enc_attn_dropout_rate: 0.2 transformer_dec_dropout_rate: 0.2 transformer_dec_positional_dropout_rate: 0.2 transformer_dec_attn_dropout_rate: 0.2 pitch_predictor_layers: 5 pitch_predictor_chans: 256 pitch_predictor_kernel_size: 5 pitch_predictor_dropout: 0.5 pitch_embed_kernel_size: 1 pitch_embed_dropout: 0.0 stop_gradient_from_pitch_predictor: true energy_predictor_layers: 2 energy_predictor_chans: 256 energy_predictor_kernel_size: 3 energy_predictor_dropout: 0.5 energy_embed_kernel_size: 1 energy_embed_dropout: 0.0 stop_gradient_from_energy_predictor: false pitch_extract: dio pitch_extract_conf: fs: 22050 n_fft: 1024 hop_length: 256 f0max: 400 f0min: 80 reduction_factor: 1 pitch_normalize: global_mvn pitch_normalize_conf: stats_file: exp/d/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/pitch_stats.npz energy_extract: energy energy_extract_conf: fs: 22050 n_fft: 1024 hop_length: 256 win_length: null reduction_factor: 1 energy_normalize: global_mvn energy_normalize_conf: stats_file: exp/d/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/energy_stats.npz required: - output_dir - token_list version: 0.10.7a1 distributed: false ``` </details> | 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: 0.7175 | 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 | 592 | 0.5532 | 0.7148 | 0.7118 | 0.7148 | 0.6994 | | 0.5926 | 3.0 | 888 | 0.5480 | 0.7243 | 0.7199 | 0.7243 | 0.7144 | | 0.4946 | 4.0 | 1184 | 0.5535 | 0.7300 | 0.7255 | 0.7300 | 0.7220 | | 0.4946 | 5.0 | 1480 | 0.5858 | 0.7186 | 0.7140 | 0.7186 | 0.7146 | | 0.4267 | 6.0 | 1776 | 0.5872 | 0.7243 | 0.7192 | 0.7243 | 0.7175 | | 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_Latn awa aze_Latn bak bam_Latn bel bel_Latn ben bho bod bos_Latn bre brx brx_Latn bul bul_Latn cat ceb ces cha che chr chv cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant cor cos crh crh_Latn csb_Latn cym dan deu dsb dtp dws_Latn egl ell enm_Latn epo est eus ewe ext fao fij fin fkv_Latn fra frm_Latn frr fry fuc fuv gan gcf_Latn gil gla gle glg glv gom gos got_Goth grc_Grek grn gsw guj hat hau_Latn haw heb hif_Latn hil hin hnj_Latn hoc hoc_Latn hrv hsb hun hye iba ibo ido ido_Latn ike_Latn ile_Latn ilo ina_Latn ind isl ita izh jav jav_Java jbo jbo_Cyrl jbo_Latn jdt_Cyrl jpn kab kal kan kat kaz_Cyrl kaz_Latn kek_Latn kha khm khm_Latn kin kir_Cyrl kjh kpv krl ksh kum kur_Arab kur_Latn lad lad_Latn lao lat_Latn lav ldn_Latn lfn_Cyrl lfn_Latn lij lin lit liv_Latn lkt lld_Latn lmo ltg ltz lug lzh lzh_Hans mad mah mai mal mar max_Latn mdf mfe mhr mic min mkd mlg mlt mnw moh mon mri mwl mww mya myv nan nau nav nds niu nld nno nob nob_Hebr nog non_Latn nov_Latn npi nya oci ori orv_Cyrl oss ota_Arab ota_Latn pag pan_Guru pap pau pdc pes pes_Latn pes_Thaa pms pnb pol por ppl_Latn prg_Latn pus quc qya qya_Latn rap rif_Latn roh rom ron rue run rus sag sah san_Deva scn sco sgs shs_Latn shy_Latn sin sjn_Latn slv sma sme smo sna snd_Arab som spa sqi srp_Cyrl srp_Latn stq sun swe swg swh tah tam tat tat_Arab tat_Latn tel tet tgk_Cyrl tha tir tlh_Latn tly_Latn tmw_Latn toi_Latn ton tpw_Latn tso tuk tuk_Latn tur tvl tyv tzl tzl_Latn udm uig_Arab uig_Cyrl ukr umb urd uzb_Cyrl uzb_Latn vec vie vie_Hani vol_Latn vro war wln wol wuu xal xho yid yor yue yue_Hans yue_Hant zho zho_Hans zho_Hant zlm_Latn zsm_Latn zul zza * target language(s): eng * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/mul-eng/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/mul-eng/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/mul-eng/opus2m-2020-08-01.eval.txt) | 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-enlv-laveng.lav.eng | 21.3 | 0.502 | | newsdev2017-enzh-zhoeng.zho.eng | 12.7 | 0.409 | | newsdev2018-enet-esteng.est.eng | 19.8 | 0.467 | | newsdev2019-engu-gujeng.guj.eng | 13.3 | 0.385 | | newsdev2019-enlt-liteng.lit.eng | 19.9 | 0.482 | | newsdiscussdev2015-enfr-fraeng.fra.eng | 26.7 | 0.520 | | newsdiscusstest2015-enfr-fraeng.fra.eng | 29.8 | 0.541 | | newssyscomb2009-ceseng.ces.eng | 21.1 | 0.487 | | newssyscomb2009-deueng.deu.eng | 22.6 | 0.499 | | newssyscomb2009-fraeng.fra.eng | 25.8 | 0.530 | | newssyscomb2009-huneng.hun.eng | 15.1 | 0.430 | | newssyscomb2009-itaeng.ita.eng | 29.4 | 0.555 | | newssyscomb2009-spaeng.spa.eng | 26.1 | 0.534 | | news-test2008-deueng.deu.eng | 21.6 | 0.491 | | news-test2008-fraeng.fra.eng | 22.3 | 0.502 | | news-test2008-spaeng.spa.eng | 23.6 | 0.514 | | newstest2009-ceseng.ces.eng | 19.8 | 0.480 | | newstest2009-deueng.deu.eng | 20.9 | 0.487 | | newstest2009-fraeng.fra.eng | 25.0 | 0.523 | | newstest2009-huneng.hun.eng | 14.7 | 0.425 | | newstest2009-itaeng.ita.eng | 27.6 | 0.542 | | newstest2009-spaeng.spa.eng | 25.7 | 0.530 | | newstest2010-ceseng.ces.eng | 20.6 | 0.491 | | newstest2010-deueng.deu.eng | 23.4 | 0.517 | | newstest2010-fraeng.fra.eng | 26.1 | 0.537 | | newstest2010-spaeng.spa.eng | 29.1 | 0.561 | | newstest2011-ceseng.ces.eng | 21.0 | 0.489 | | newstest2011-deueng.deu.eng | 21.3 | 0.494 | | newstest2011-fraeng.fra.eng | 26.8 | 0.546 | | newstest2011-spaeng.spa.eng | 28.2 | 0.549 | | newstest2012-ceseng.ces.eng | 20.5 | 0.485 | | newstest2012-deueng.deu.eng | 22.3 | 0.503 | | newstest2012-fraeng.fra.eng | 27.5 | 0.545 | | newstest2012-ruseng.rus.eng | 26.6 | 0.532 | | newstest2012-spaeng.spa.eng | 30.3 | 0.567 | | newstest2013-ceseng.ces.eng | 22.5 | 0.498 | | newstest2013-deueng.deu.eng | 25.0 | 0.518 | | newstest2013-fraeng.fra.eng | 27.4 | 0.537 | | newstest2013-ruseng.rus.eng | 21.6 | 0.484 | | newstest2013-spaeng.spa.eng | 28.4 | 0.555 | | newstest2014-csen-ceseng.ces.eng | 24.0 | 0.517 | | newstest2014-deen-deueng.deu.eng | 24.1 | 0.511 | | newstest2014-fren-fraeng.fra.eng | 29.1 | 0.563 | | newstest2014-hien-hineng.hin.eng | 14.0 | 0.414 | | newstest2014-ruen-ruseng.rus.eng | 24.0 | 0.521 | | newstest2015-encs-ceseng.ces.eng | 21.9 | 0.481 | | newstest2015-ende-deueng.deu.eng | 25.5 | 0.519 | | newstest2015-enfi-fineng.fin.eng | 17.4 | 0.441 | | newstest2015-enru-ruseng.rus.eng | 22.4 | 0.494 | | newstest2016-encs-ceseng.ces.eng | 23.0 | 0.500 | | newstest2016-ende-deueng.deu.eng | 30.1 | 0.560 | | newstest2016-enfi-fineng.fin.eng | 18.5 | 0.461 | | newstest2016-enro-roneng.ron.eng | 29.6 | 0.562 | | newstest2016-enru-ruseng.rus.eng | 22.0 | 0.495 | | newstest2016-entr-tureng.tur.eng | 14.8 | 0.415 | | newstest2017-encs-ceseng.ces.eng | 20.2 | 0.475 | | newstest2017-ende-deueng.deu.eng | 26.0 | 0.523 | | newstest2017-enfi-fineng.fin.eng | 19.6 | 0.465 | | newstest2017-enlv-laveng.lav.eng | 16.2 | 0.454 | | newstest2017-enru-ruseng.rus.eng | 24.2 | 0.510 | | newstest2017-entr-tureng.tur.eng | 15.0 | 0.412 | | newstest2017-enzh-zhoeng.zho.eng | 13.7 | 0.412 | | newstest2018-encs-ceseng.ces.eng | 21.2 | 0.486 | | newstest2018-ende-deueng.deu.eng | 31.5 | 0.564 | | newstest2018-enet-esteng.est.eng | 19.7 | 0.473 | | newstest2018-enfi-fineng.fin.eng | 15.1 | 0.418 | | newstest2018-enru-ruseng.rus.eng | 21.3 | 0.490 | | newstest2018-entr-tureng.tur.eng | 15.4 | 0.421 | | newstest2018-enzh-zhoeng.zho.eng | 12.9 | 0.408 | | newstest2019-deen-deueng.deu.eng | 27.0 | 0.529 | | newstest2019-fien-fineng.fin.eng | 17.2 | 0.438 | | newstest2019-guen-gujeng.guj.eng | 9.0 | 0.342 | | newstest2019-lten-liteng.lit.eng | 22.6 | 0.512 | | newstest2019-ruen-ruseng.rus.eng | 24.1 | 0.503 | | newstest2019-zhen-zhoeng.zho.eng | 13.9 | 0.427 | | newstestB2016-enfi-fineng.fin.eng | 15.2 | 0.428 | | newstestB2017-enfi-fineng.fin.eng | 16.8 | 0.442 | | newstestB2017-fien-fineng.fin.eng | 16.8 | 0.442 | | Tatoeba-test.abk-eng.abk.eng | 2.4 | 0.190 | | Tatoeba-test.ady-eng.ady.eng | 1.1 | 0.111 | | Tatoeba-test.afh-eng.afh.eng | 1.7 | 0.108 | | Tatoeba-test.afr-eng.afr.eng | 53.0 | 0.672 | | Tatoeba-test.akl-eng.akl.eng | 5.9 | 0.239 | | Tatoeba-test.amh-eng.amh.eng | 25.6 | 0.464 | | Tatoeba-test.ang-eng.ang.eng | 11.7 | 0.289 | | Tatoeba-test.ara-eng.ara.eng | 26.4 | 0.443 | | Tatoeba-test.arg-eng.arg.eng | 35.9 | 0.473 | | Tatoeba-test.asm-eng.asm.eng | 19.8 | 0.365 | | Tatoeba-test.ast-eng.ast.eng | 31.8 | 0.467 | | Tatoeba-test.avk-eng.avk.eng | 0.4 | 0.119 | | Tatoeba-test.awa-eng.awa.eng | 9.7 | 0.271 | | Tatoeba-test.aze-eng.aze.eng | 37.0 | 0.542 | | Tatoeba-test.bak-eng.bak.eng | 13.9 | 0.395 | | Tatoeba-test.bam-eng.bam.eng | 2.2 | 0.094 | | Tatoeba-test.bel-eng.bel.eng | 36.8 | 0.549 | | Tatoeba-test.ben-eng.ben.eng | 39.7 | 0.546 | | Tatoeba-test.bho-eng.bho.eng | 33.6 | 0.540 | | Tatoeba-test.bod-eng.bod.eng | 1.1 | 0.147 | | Tatoeba-test.bre-eng.bre.eng | 14.2 | 0.303 | | Tatoeba-test.brx-eng.brx.eng | 1.7 | 0.130 | | Tatoeba-test.bul-eng.bul.eng | 46.0 | 0.621 | | Tatoeba-test.cat-eng.cat.eng | 46.6 | 0.636 | | Tatoeba-test.ceb-eng.ceb.eng | 17.4 | 0.347 | | Tatoeba-test.ces-eng.ces.eng | 41.3 | 0.586 | | Tatoeba-test.cha-eng.cha.eng | 7.9 | 0.232 | | Tatoeba-test.che-eng.che.eng | 0.7 | 0.104 | | Tatoeba-test.chm-eng.chm.eng | 7.3 | 0.261 | | Tatoeba-test.chr-eng.chr.eng | 8.8 | 0.244 | | Tatoeba-test.chv-eng.chv.eng | 11.0 | 0.319 | | Tatoeba-test.cor-eng.cor.eng | 5.4 | 0.204 | | Tatoeba-test.cos-eng.cos.eng | 58.2 | 0.643 | | Tatoeba-test.crh-eng.crh.eng | 26.3 | 0.399 | | Tatoeba-test.csb-eng.csb.eng | 18.8 | 0.389 | | Tatoeba-test.cym-eng.cym.eng | 23.4 | 0.407 | | Tatoeba-test.dan-eng.dan.eng | 50.5 | 0.659 | | Tatoeba-test.deu-eng.deu.eng | 39.6 | 0.579 | | Tatoeba-test.dsb-eng.dsb.eng | 24.3 | 0.449 | | Tatoeba-test.dtp-eng.dtp.eng | 1.0 | 0.149 | | Tatoeba-test.dws-eng.dws.eng | 1.6 | 0.061 | | Tatoeba-test.egl-eng.egl.eng | 7.6 | 0.236 | | Tatoeba-test.ell-eng.ell.eng | 55.4 | 0.682 | | Tatoeba-test.enm-eng.enm.eng | 28.0 | 0.489 | | Tatoeba-test.epo-eng.epo.eng | 41.8 | 0.591 | | Tatoeba-test.est-eng.est.eng | 41.5 | 0.581 | | Tatoeba-test.eus-eng.eus.eng | 37.8 | 0.557 | | Tatoeba-test.ewe-eng.ewe.eng | 10.7 | 0.262 | | Tatoeba-test.ext-eng.ext.eng | 25.5 | 0.405 | | Tatoeba-test.fao-eng.fao.eng | 28.7 | 0.469 | | Tatoeba-test.fas-eng.fas.eng | 7.5 | 0.281 | | Tatoeba-test.fij-eng.fij.eng | 24.2 | 0.320 | | Tatoeba-test.fin-eng.fin.eng | 35.8 | 0.534 | | Tatoeba-test.fkv-eng.fkv.eng | 15.5 | 0.434 | | Tatoeba-test.fra-eng.fra.eng | 45.1 | 0.618 | | Tatoeba-test.frm-eng.frm.eng | 29.6 | 0.427 | | Tatoeba-test.frr-eng.frr.eng | 5.5 | 0.138 | | Tatoeba-test.fry-eng.fry.eng | 25.3 | 0.455 | | Tatoeba-test.ful-eng.ful.eng | 1.1 | 0.127 | | Tatoeba-test.gcf-eng.gcf.eng | 16.0 | 0.315 | | Tatoeba-test.gil-eng.gil.eng | 46.7 | 0.587 | | Tatoeba-test.gla-eng.gla.eng | 20.2 | 0.358 | | Tatoeba-test.gle-eng.gle.eng | 43.9 | 0.592 | | Tatoeba-test.glg-eng.glg.eng | 45.1 | 0.623 | | Tatoeba-test.glv-eng.glv.eng | 3.3 | 0.119 | | Tatoeba-test.gos-eng.gos.eng | 20.1 | 0.364 | | Tatoeba-test.got-eng.got.eng | 0.1 | 0.041 | | Tatoeba-test.grc-eng.grc.eng | 2.1 | 0.137 | | Tatoeba-test.grn-eng.grn.eng | 1.7 | 0.152 | | Tatoeba-test.gsw-eng.gsw.eng | 18.2 | 0.334 | | Tatoeba-test.guj-eng.guj.eng | 21.7 | 0.373 | | Tatoeba-test.hat-eng.hat.eng | 34.5 | 0.502 | | Tatoeba-test.hau-eng.hau.eng | 10.5 | 0.295 | | Tatoeba-test.haw-eng.haw.eng | 2.8 | 0.160 | | Tatoeba-test.hbs-eng.hbs.eng | 46.7 | 0.623 | | Tatoeba-test.heb-eng.heb.eng | 33.0 | 0.492 | | Tatoeba-test.hif-eng.hif.eng | 17.0 | 0.391 | | Tatoeba-test.hil-eng.hil.eng | 16.0 | 0.339 | | Tatoeba-test.hin-eng.hin.eng | 36.4 | 0.533 | | Tatoeba-test.hmn-eng.hmn.eng | 0.4 | 0.131 | | Tatoeba-test.hoc-eng.hoc.eng | 0.7 | 0.132 | | Tatoeba-test.hsb-eng.hsb.eng | 41.9 | 0.551 | | Tatoeba-test.hun-eng.hun.eng | 33.2 | 0.510 | | Tatoeba-test.hye-eng.hye.eng | 32.2 | 0.487 | | Tatoeba-test.iba-eng.iba.eng | 9.4 | 0.278 | | Tatoeba-test.ibo-eng.ibo.eng | 5.8 | 0.200 | | Tatoeba-test.ido-eng.ido.eng | 31.7 | 0.503 | | Tatoeba-test.iku-eng.iku.eng | 9.1 | 0.164 | | Tatoeba-test.ile-eng.ile.eng | 42.2 | 0.595 | | Tatoeba-test.ilo-eng.ilo.eng | 29.7 | 0.485 | | Tatoeba-test.ina-eng.ina.eng | 42.1 | 0.607 | | Tatoeba-test.isl-eng.isl.eng | 35.7 | 0.527 | | Tatoeba-test.ita-eng.ita.eng | 54.8 | 0.686 | | Tatoeba-test.izh-eng.izh.eng | 28.3 | 0.526 | | Tatoeba-test.jav-eng.jav.eng | 10.0 | 0.282 | | Tatoeba-test.jbo-eng.jbo.eng | 0.3 | 0.115 | | Tatoeba-test.jdt-eng.jdt.eng | 5.3 | 0.140 | | Tatoeba-test.jpn-eng.jpn.eng | 18.8 | 0.387 | | Tatoeba-test.kab-eng.kab.eng | 3.9 | 0.205 | | Tatoeba-test.kal-eng.kal.eng | 16.9 | 0.329 | | Tatoeba-test.kan-eng.kan.eng | 16.2 | 0.374 | | Tatoeba-test.kat-eng.kat.eng | 31.1 | 0.493 | | Tatoeba-test.kaz-eng.kaz.eng | 24.5 | 0.437 | | Tatoeba-test.kek-eng.kek.eng | 7.4 | 0.192 | | Tatoeba-test.kha-eng.kha.eng | 1.0 | 0.154 | | Tatoeba-test.khm-eng.khm.eng | 12.2 | 0.290 | | Tatoeba-test.kin-eng.kin.eng | 22.5 | 0.355 | | Tatoeba-test.kir-eng.kir.eng | 27.2 | 0.470 | | Tatoeba-test.kjh-eng.kjh.eng | 2.1 | 0.129 | | Tatoeba-test.kok-eng.kok.eng | 4.5 | 0.259 | | Tatoeba-test.kom-eng.kom.eng | 1.4 | 0.099 | | Tatoeba-test.krl-eng.krl.eng | 26.1 | 0.387 | | Tatoeba-test.ksh-eng.ksh.eng | 5.5 | 0.256 | | Tatoeba-test.kum-eng.kum.eng | 9.3 | 0.288 | | Tatoeba-test.kur-eng.kur.eng | 9.6 | 0.208 | | Tatoeba-test.lad-eng.lad.eng | 30.1 | 0.475 | | Tatoeba-test.lah-eng.lah.eng | 11.6 | 0.284 | | Tatoeba-test.lao-eng.lao.eng | 4.5 | 0.214 | | Tatoeba-test.lat-eng.lat.eng | 21.5 | 0.402 | | Tatoeba-test.lav-eng.lav.eng | 40.2 | 0.577 | | Tatoeba-test.ldn-eng.ldn.eng | 0.8 | 0.115 | | Tatoeba-test.lfn-eng.lfn.eng | 23.0 | 0.433 | | Tatoeba-test.lij-eng.lij.eng | 9.3 | 0.287 | | Tatoeba-test.lin-eng.lin.eng | 2.4 | 0.196 | | Tatoeba-test.lit-eng.lit.eng | 44.0 | 0.597 | | Tatoeba-test.liv-eng.liv.eng | 1.6 | 0.115 | | Tatoeba-test.lkt-eng.lkt.eng | 2.0 | 0.113 | | Tatoeba-test.lld-eng.lld.eng | 18.3 | 0.312 | | Tatoeba-test.lmo-eng.lmo.eng | 25.4 | 0.395 | | Tatoeba-test.ltz-eng.ltz.eng | 35.9 | 0.509 | | Tatoeba-test.lug-eng.lug.eng | 5.1 | 0.357 | | Tatoeba-test.mad-eng.mad.eng | 2.8 | 0.123 | | Tatoeba-test.mah-eng.mah.eng | 5.7 | 0.175 | | Tatoeba-test.mai-eng.mai.eng | 56.3 | 0.703 | | Tatoeba-test.mal-eng.mal.eng | 37.5 | 0.534 | | Tatoeba-test.mar-eng.mar.eng | 22.8 | 0.470 | | Tatoeba-test.mdf-eng.mdf.eng | 2.0 | 0.110 | | Tatoeba-test.mfe-eng.mfe.eng | 59.2 | 0.764 | | Tatoeba-test.mic-eng.mic.eng | 9.0 | 0.199 | | Tatoeba-test.mkd-eng.mkd.eng | 44.3 | 0.593 | | Tatoeba-test.mlg-eng.mlg.eng | 31.9 | 0.424 | | Tatoeba-test.mlt-eng.mlt.eng | 38.6 | 0.540 | | Tatoeba-test.mnw-eng.mnw.eng | 2.5 | 0.101 | | Tatoeba-test.moh-eng.moh.eng | 0.3 | 0.110 | | Tatoeba-test.mon-eng.mon.eng | 13.5 | 0.334 | | Tatoeba-test.mri-eng.mri.eng | 8.5 | 0.260 | | Tatoeba-test.msa-eng.msa.eng | 33.9 | 0.520 | | Tatoeba-test.multi.eng | 34.7 | 0.518 | | Tatoeba-test.mwl-eng.mwl.eng | 37.4 | 0.630 | | Tatoeba-test.mya-eng.mya.eng | 15.5 | 0.335 | | Tatoeba-test.myv-eng.myv.eng | 0.8 | 0.118 | | Tatoeba-test.nau-eng.nau.eng | 9.0 | 0.186 | | Tatoeba-test.nav-eng.nav.eng | 1.3 | 0.144 | | Tatoeba-test.nds-eng.nds.eng | 30.7 | 0.495 | | Tatoeba-test.nep-eng.nep.eng | 3.5 | 0.168 | | Tatoeba-test.niu-eng.niu.eng | 42.7 | 0.492 | | Tatoeba-test.nld-eng.nld.eng | 47.9 | 0.640 | | Tatoeba-test.nog-eng.nog.eng | 12.7 | 0.284 | | Tatoeba-test.non-eng.non.eng | 43.8 | 0.586 | | Tatoeba-test.nor-eng.nor.eng | 45.5 | 0.619 | | Tatoeba-test.nov-eng.nov.eng | 26.9 | 0.472 | | Tatoeba-test.nya-eng.nya.eng | 33.2 | 0.456 | | Tatoeba-test.oci-eng.oci.eng | 17.9 | 0.370 | | Tatoeba-test.ori-eng.ori.eng | 14.6 | 0.305 | | Tatoeba-test.orv-eng.orv.eng | 11.0 | 0.283 | | Tatoeba-test.oss-eng.oss.eng | 4.1 | 0.211 | | Tatoeba-test.ota-eng.ota.eng | 4.1 | 0.216 | | Tatoeba-test.pag-eng.pag.eng | 24.3 | 0.468 | | Tatoeba-test.pan-eng.pan.eng | 16.4 | 0.358 | | Tatoeba-test.pap-eng.pap.eng | 53.2 | 0.628 | | Tatoeba-test.pau-eng.pau.eng | 3.7 | 0.173 | | Tatoeba-test.pdc-eng.pdc.eng | 45.3 | 0.569 | | Tatoeba-test.pms-eng.pms.eng | 14.0 | 0.345 | | Tatoeba-test.pol-eng.pol.eng | 41.7 | 0.588 | | Tatoeba-test.por-eng.por.eng | 51.4 | 0.669 | | Tatoeba-test.ppl-eng.ppl.eng | 0.4 | 0.134 | | Tatoeba-test.prg-eng.prg.eng | 4.1 | 0.198 | | Tatoeba-test.pus-eng.pus.eng | 6.7 | 0.233 | | Tatoeba-test.quc-eng.quc.eng | 3.5 | 0.091 | | Tatoeba-test.qya-eng.qya.eng | 0.2 | 0.090 | | Tatoeba-test.rap-eng.rap.eng | 17.5 | 0.230 | | Tatoeba-test.rif-eng.rif.eng | 4.2 | 0.164 | | Tatoeba-test.roh-eng.roh.eng | 24.6 | 0.464 | | Tatoeba-test.rom-eng.rom.eng | 3.4 | 0.212 | | Tatoeba-test.ron-eng.ron.eng | 45.2 | 0.620 | | Tatoeba-test.rue-eng.rue.eng | 21.4 | 0.390 | | Tatoeba-test.run-eng.run.eng | 24.5 | 0.392 | | Tatoeba-test.rus-eng.rus.eng | 42.7 | 0.591 | | Tatoeba-test.sag-eng.sag.eng | 3.4 | 0.187 | | Tatoeba-test.sah-eng.sah.eng | 5.0 | 0.177 | | Tatoeba-test.san-eng.san.eng | 2.0 | 0.172 | | Tatoeba-test.scn-eng.scn.eng | 35.8 | 0.410 | | Tatoeba-test.sco-eng.sco.eng | 34.6 | 0.520 | | Tatoeba-test.sgs-eng.sgs.eng | 21.8 | 0.299 | | Tatoeba-test.shs-eng.shs.eng | 1.8 | 0.122 | | Tatoeba-test.shy-eng.shy.eng | 1.4 | 0.104 | | Tatoeba-test.sin-eng.sin.eng | 20.6 | 0.429 | | Tatoeba-test.sjn-eng.sjn.eng | 1.2 | 0.095 | | Tatoeba-test.slv-eng.slv.eng | 37.0 | 0.545 | | Tatoeba-test.sma-eng.sma.eng | 4.4 | 0.147 | | Tatoeba-test.sme-eng.sme.eng | 8.9 | 0.229 | | Tatoeba-test.smo-eng.smo.eng | 37.7 | 0.483 | | Tatoeba-test.sna-eng.sna.eng | 18.0 | 0.359 | | Tatoeba-test.snd-eng.snd.eng | 28.1 | 0.444 | | Tatoeba-test.som-eng.som.eng | 23.6 | 0.472 | | Tatoeba-test.spa-eng.spa.eng | 47.9 | 0.645 | | Tatoeba-test.sqi-eng.sqi.eng | 46.9 | 0.634 | | Tatoeba-test.stq-eng.stq.eng | 8.1 | 0.379 | | Tatoeba-test.sun-eng.sun.eng | 23.8 | 0.369 | | Tatoeba-test.swa-eng.swa.eng | 6.5 | 0.193 | | Tatoeba-test.swe-eng.swe.eng | 51.4 | 0.655 | | Tatoeba-test.swg-eng.swg.eng | 18.5 | 0.342 | | Tatoeba-test.tah-eng.tah.eng | 25.6 | 0.249 | | Tatoeba-test.tam-eng.tam.eng | 29.1 | 0.437 | | Tatoeba-test.tat-eng.tat.eng | 12.9 | 0.327 | | Tatoeba-test.tel-eng.tel.eng | 21.2 | 0.386 | | Tatoeba-test.tet-eng.tet.eng | 9.2 | 0.215 | | Tatoeba-test.tgk-eng.tgk.eng | 12.7 | 0.374 | | Tatoeba-test.tha-eng.tha.eng | 36.3 | 0.531 | | Tatoeba-test.tir-eng.tir.eng | 9.1 | 0.267 | | Tatoeba-test.tlh-eng.tlh.eng | 0.2 | 0.084 | | Tatoeba-test.tly-eng.tly.eng | 2.1 | 0.128 | | Tatoeba-test.toi-eng.toi.eng | 5.3 | 0.150 | | Tatoeba-test.ton-eng.ton.eng | 39.5 | 0.473 | | Tatoeba-test.tpw-eng.tpw.eng | 1.5 | 0.160 | | Tatoeba-test.tso-eng.tso.eng | 44.7 | 0.526 | | Tatoeba-test.tuk-eng.tuk.eng | 18.6 | 0.401 | | Tatoeba-test.tur-eng.tur.eng | 40.5 | 0.573 | | Tatoeba-test.tvl-eng.tvl.eng | 55.0 | 0.593 | | Tatoeba-test.tyv-eng.tyv.eng | 19.1 | 0.477 | | Tatoeba-test.tzl-eng.tzl.eng | 17.7 | 0.333 | | Tatoeba-test.udm-eng.udm.eng | 3.4 | 0.217 | | Tatoeba-test.uig-eng.uig.eng | 11.4 | 0.289 | | Tatoeba-test.ukr-eng.ukr.eng | 43.1 | 0.595 | | Tatoeba-test.umb-eng.umb.eng | 9.2 | 0.260 | | Tatoeba-test.urd-eng.urd.eng | 23.2 | 0.426 | | Tatoeba-test.uzb-eng.uzb.eng | 19.0 | 0.342 | | Tatoeba-test.vec-eng.vec.eng | 41.1 | 0.409 | | Tatoeba-test.vie-eng.vie.eng | 30.6 | 0.481 | | Tatoeba-test.vol-eng.vol.eng | 1.8 | 0.143 | | Tatoeba-test.war-eng.war.eng | 15.9 | 0.352 | | Tatoeba-test.wln-eng.wln.eng | 12.6 | 0.291 | | Tatoeba-test.wol-eng.wol.eng | 4.4 | 0.138 | | Tatoeba-test.xal-eng.xal.eng | 0.9 | 0.153 | | Tatoeba-test.xho-eng.xho.eng | 35.4 | 0.513 | | Tatoeba-test.yid-eng.yid.eng | 19.4 | 0.387 | | Tatoeba-test.yor-eng.yor.eng | 19.3 | 0.327 | | Tatoeba-test.zho-eng.zho.eng | 25.8 | 0.448 | | Tatoeba-test.zul-eng.zul.eng | 40.9 | 0.567 | | Tatoeba-test.zza-eng.zza.eng | 1.6 | 0.125 | | 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', 'su', 'an', 'sq', 'fr', 'ht', 'rm', 'cv', 'ig', 'am', 'eu', 'tr', 'ps', 'af', 'ny', 'ch', 'uk', 'sl', 'lt', 'tk', 'sg', 'ar', 'lg', 'bg', 'be', 'ka', 'gd', 'ja', 'si', 'br', 'mh', 'km', 'th', 'ty', 'rw', 'te', 'mk', 'or', 'wo', 'kl', 'mr', 'ru', 'yo', 'hu', 'fo', 'zh', 'ti', 'co', 'ee', 'oc', 'sn', 'mt', 'ts', 'pl', 'gl', 'nb', 'bn', 'tt', 'bo', 'lo', 'id', 'gn', 'nv', 'hy', 'kn', 'to', 'io', 'so', 'vi', 'da', 'fj', 'gv', 'sm', 'nl', 'mi', 'pt', 'hi', 'se', 'as', 'ta', 'et', 'kw', 'ga', 'sv', 'ln', 'na', 'mn', 'gu', 'wa', 'lv', 'jv', 'el', 'my', 'ba', 'it', 'hr', 'ur', 'ce', 'nn', 'fi', 'mg', 'rn', 'xh', 'ab', 'de', 'cs', 'he', 'zu', 'yi', 'ml', 'mul', 'en'] - src_constituents: {'sjn_Latn', 'cat', 'nan', 'spa', 'ile_Latn', 'pap', 'mwl', 'uzb_Latn', 'mww', 'hil', 'lij', 'avk_Latn', 'lad_Latn', 'lat_Latn', 'bos_Latn', 'oss', 'epo', 'ron', 'fry', 'cym', 'toi_Latn', 'awa', 'swg', 'zsm_Latn', 'zho_Hant', 'gcf_Latn', 'uzb_Cyrl', 'isl', 'lfn_Latn', 'shs_Latn', 'nov_Latn', 'bho', 'ltz', 'lzh', 'kur_Latn', 'sun', 'arg', 'pes_Thaa', 'sqi', 'uig_Arab', 'csb_Latn', 'fra', 'hat', 'liv_Latn', 'non_Latn', 'sco', 'cmn_Hans', 'pnb', 'roh', 'chv', 'ibo', 'bul_Latn', 'amh', 'lfn_Cyrl', 'eus', 'fkv_Latn', 'tur', 'pus', 'afr', 'brx_Latn', 'nya', 'acm', 'ota_Latn', 'cha', 'ukr', 'xal', 'slv', 'lit', 'zho_Hans', 'tmw_Latn', 'kjh', 'ota_Arab', 'war', 'tuk', 'sag', 'myv', 'hsb', 'lzh_Hans', 'ara', 'tly_Latn', 'lug', 'brx', 'bul', 'bel', 'vol_Latn', 'kat', 'gan', 'got_Goth', 'vro', 'ext', 'afh_Latn', 'gla', 'jpn', 'udm', 'mai', 'ary', 'sin', 'tvl', 'hif_Latn', 'cjy_Hant', 'bre', 'ceb', 'mah', 'nob_Hebr', 'crh_Latn', 'prg_Latn', 'khm', 'ang_Latn', 'tha', 'tah', 'tzl', 'aln', 'kin', 'tel', 'ady', 'mkd', 'ori', 'wol', 'aze_Latn', 'jbo', 'niu', 'kal', 'mar', 'vie_Hani', 'arz', 'yue', 'kha', 'san_Deva', 'jbo_Latn', 'gos', 'hau_Latn', 'rus', 'quc', 'cmn', 'yor', 'hun', 'uig_Cyrl', 'fao', 'mnw', 'zho', 'orv_Cyrl', 'iba', 'bel_Latn', 'tir', 'afb', 'crh', 'mic', 'cos', 'swh', 'sah', 'krl', 'ewe', 'apc', 'zza', 'chr', 'grc_Grek', 'tpw_Latn', 'oci', 'mfe', 'sna', 'kir_Cyrl', 'tat_Latn', 'gom', 'ido_Latn', 'sgs', 'pau', 'tgk_Cyrl', 'nog', 'mlt', 'pdc', 'tso', 'srp_Cyrl', 'pol', 'ast', 'glg', 'pms', 'fuc', 'nob', 'qya', 'ben', 'tat', 'kab', 'min', 'srp_Latn', 'wuu', 'dtp', 'jbo_Cyrl', 'tet', 'bod', 'yue_Hans', 'zlm_Latn', 'lao', 'ind', 'grn', 'nav', 'kaz_Cyrl', 'rom', 'hye', 'kan', 'ton', 'ido', 'mhr', 'scn', 'som', 'rif_Latn', 'vie', 'enm_Latn', 'lmo', 'npi', 'pes', 'dan', 'fij', 'ina_Latn', 'cjy_Hans', 'jdt_Cyrl', 'gsw', 'glv', 'khm_Latn', 'smo', 'umb', 'sma', 'gil', 'nld', 'snd_Arab', 'arq', 'mri', 'kur_Arab', 'por', 'hin', 'shy_Latn', 'sme', 'rap', 'tyv', 'dsb', 'moh', 'asm', 'lad', 'yue_Hant', 'kpv', 'tam', 'est', 'frm_Latn', 'hoc_Latn', 'bam_Latn', 'kek_Latn', 'ksh', 'tlh_Latn', 'ltg', 'pan_Guru', 'hnj_Latn', 'cor', 'gle', 'swe', 'lin', 'qya_Latn', 'kum', 'mad', 'cmn_Hant', 'fuv', 'nau', 'mon', 'akl_Latn', 'guj', 'kaz_Latn', 'wln', 'tuk_Latn', 'jav_Java', 'lav', 'jav', 'ell', 'frr', 'mya', 'bak', 'rue', 'ita', 'hrv', 'izh', 'ilo', 'dws_Latn', 'urd', 'stq', 'tat_Arab', 'haw', 'che', 'pag', 'nno', 'fin', 'mlg', 'ppl_Latn', 'run', 'xho', 'abk', 'deu', 'hoc', 'lkt', 'lld_Latn', 'tzl_Latn', 'mdf', 'ike_Latn', 'ces', 'ldn_Latn', 'egl', 'heb', 'vec', 'zul', 'max_Latn', 'pes_Latn', 'yid', 'mal', 'nds'} - tgt_constituents: {'eng'} - src_multilingual: True - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/mul-eng/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/mul-eng/opus2m-2020-08-01.test.txt - src_alpha3: mul - tgt_alpha3: eng - short_pair: mul-en - chrF2_score: 0.518 - bleu: 34.7 - brevity_penalty: 1.0 - ref_len: 72346.0 - src_name: Multiple languages - tgt_name: English - train_date: 2020-08-01 - src_alpha2: mul - tgt_alpha2: en - prefer_old: False - long_pair: mul-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 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 - Epoch: 0 | 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, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 100, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | 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 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`:     | 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_scheduler_warmup_steps: 300 - num_epochs: 90 - mixed_precision_training: Native AMP | 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 | | 0.2752 | 64.51 | 2000 | 0.6080 | 0.5808 | | 0.2155 | 80.63 | 2500 | 0.6121 | 0.5697 | | a2dff43e98f248761f5f466220538145 |
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