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wdika/REC_LPDNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM	wdika	2024-03-06T10:49:46Z	0	0	atommic	[ "atommic", "image-reconstruction", "LPDNet", "ATOMMIC", "pytorch", "en", "dataset:fastMRIBrainsMulticoil", "license:apache-2.0", "region:us" ]	null	2024-03-05T17:50:32Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - fastMRIBrainsMulticoil thumbnail: null tags: - image-reconstruction - LPDNet - ATOMMIC - pytorch model-index: - name: REC_LPDNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM results: [] --- ## Model Overview Learned Primal Dual Network (LPDNet) for 4x & 8x accelerated MRI Reconstruction on the fastMRIBrainsMulticoil dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/REC/fastMRIBrainsMulticoil/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/REC_LPDNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_LPDNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic mode: test ``` ### Usage You need to download the fastMRI Brains dataset to effectively use this model. Check the [fastMRIBrainsMulticoil](https://github.com/wdika/atommic/blob/main/projects/REC/fastMRIBrainsMulticoil/README.md) page for more information. ## Model Architecture ```base model: model_name: LPDNet num_primal: 5 num_dual: 5 num_iter: 5 primal_model_architecture: UNET primal_in_channels: 2 primal_out_channels: 2 primal_unet_num_filters: 16 primal_unet_num_pool_layers: 2 primal_unet_dropout_probability: 0.0 primal_unet_padding_size: 11 primal_unet_normalize: true dual_model_architecture: UNET dual_in_channels: 2 dual_out_channels: 2 dual_unet_num_filters: 16 dual_unet_num_pool_layers: 2 dual_unet_dropout_probability: 0.0 dual_unet_padding_size: 11 dual_unet_normalize: true dimensionality: 2 reconstruction_loss: l1: 0.1 ssim: 0.9 estimate_coil_sensitivity_maps_with_nn: true ``` ## Training ```base optim: name: adam lr: 1e-4 betas: - 0.9 - 0.999 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp_find_unused_parameters_false accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 20 precision: 16-mixed enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance To compute the targets using the raw k-space and the chosen coil combination method, accompanied with the chosen coil sensitivity maps estimation method, you can use [targets](https://github.com/wdika/atommic/tree/main/projects/REC/fastMRIBrainsMulticoil/conf/targets) configuration files. Evaluation can be performed using the [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/reconstruction.py) script for the reconstruction task, with --evaluation_type per_slice. Results ------- Evaluation against RSS targets ------------------------------ 4x: MSE = 0.000939 +/- 0.004162 NMSE = 0.02527 +/- 0.09819 PSNR = 32.6 +/- 6.781 SSIM = 0.8815 +/- 0.2009 8x: MSE = 0.001548 +/- 0.00446 NMSE = 0.04132 +/- 0.1069 PSNR = 29.51 +/- 5.934 SSIM = 0.8401 +/- 0.2084 ## Limitations This model was trained on the fastMRIBrainsMulticoil batch0 dataset using a UNet coil sensitivity maps estimation and Geometric Decomposition Coil-Compressions to 1-coil, and might differ from the results reported on the challenge leaderboard. ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Muckley MJ, Riemenschneider B, Radmanesh A, Kim S, Jeong G, Ko J, Jun Y, Shin H, Hwang D, Mostapha M, Arberet S, Nickel D, Ramzi Z, Ciuciu P, Starck JL, Teuwen J, Karkalousos D, Zhang C, Sriram A, Huang Z, Yakubova N, Lui YW, Knoll F. Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. IEEE Trans Med Imaging. 2021 Sep;40(9):2306-2317. doi: 10.1109/TMI.2021.3075856. Epub 2021 Aug 31. PMID: 33929957; PMCID: PMC8428775.
wdika/REC_MoDL_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM	wdika	2024-03-06T10:48:20Z	0	0	atommic	[ "atommic", "image-reconstruction", "MoDL", "ATOMMIC", "pytorch", "en", "dataset:fastMRIBrainsMulticoil", "license:apache-2.0", "region:us" ]	null	2024-03-05T17:50:46Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - fastMRIBrainsMulticoil thumbnail: null tags: - image-reconstruction - MoDL - ATOMMIC - pytorch model-index: - name: REC_MoDL_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM results: [] --- ## Model Overview MoDL: Model Based Deep Learning Architecture for Inverse Problems for 4x & 8x accelerated MRI Reconstruction on the fastMRIBrainsMulticoil dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/REC/fastMRIBrainsMulticoil/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/REC_MoDL_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_MoDL_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic mode: test ``` ### Usage You need to download the fastMRI Brains dataset to effectively use this model. Check the [fastMRIBrainsMulticoil](https://github.com/wdika/atommic/blob/main/projects/REC/fastMRIBrainsMulticoil/README.md) page for more information. ## Model Architecture ```base model: model_name: MoDL unrolled_iterations: 5 residual_blocks: 5 channels: 64 regularization_factor: 0.1 penalization_weight: 1.0 conjugate_gradient_dc: false conjugate_gradient_iterations: 1 dimensionality: 2 reconstruction_loss: l1: 0.1 ssim: 0.9 estimate_coil_sensitivity_maps_with_nn: true ``` ## Training ```base optim: name: adam lr: 1e-4 betas: - 0.9 - 0.999 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp_find_unused_parameters_false accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 20 precision: 16-mixed enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance To compute the targets using the raw k-space and the chosen coil combination method, accompanied with the chosen coil sensitivity maps estimation method, you can use [targets](https://github.com/wdika/atommic/tree/main/projects/REC/fastMRIBrainsMulticoil/conf/targets) configuration files. Evaluation can be performed using the [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/reconstruction.py) script for the reconstruction task, with --evaluation_type per_slice. Results ------- Evaluation against RSS targets ------------------------------ 4x: MSE = 0.0009811 +/- 0.003791 NMSE = 0.02496 +/- 0.0693 PSNR = 31.44 +/- 5.655 SSIM = 0.8703 +/- 0.1877 8x: MSE = 0.002104 +/- 0.004177 NMSE = 0.05376 +/- 0.09522 PSNR = 27.81 +/- 5.862 SSIM = 0.8133 +/- 0.1925 ## Limitations This model was trained on the fastMRIBrainsMulticoil batch0 dataset using a UNet coil sensitivity maps estimation and Geometric Decomposition Coil-Compressions to 1-coil, and might differ from the results reported on the challenge leaderboard. ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Muckley MJ, Riemenschneider B, Radmanesh A, Kim S, Jeong G, Ko J, Jun Y, Shin H, Hwang D, Mostapha M, Arberet S, Nickel D, Ramzi Z, Ciuciu P, Starck JL, Teuwen J, Karkalousos D, Zhang C, Sriram A, Huang Z, Yakubova N, Lui YW, Knoll F. Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. IEEE Trans Med Imaging. 2021 Sep;40(9):2306-2317. doi: 10.1109/TMI.2021.3075856. Epub 2021 Aug 31. PMID: 33929957; PMCID: PMC8428775.
wdika/REC_RIM_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM	wdika	2024-03-06T10:48:10Z	0	0	atommic	[ "atommic", "image-reconstruction", "RIM", "ATOMMIC", "pytorch", "en", "dataset:fastMRIBrainsMulticoil", "license:apache-2.0", "region:us" ]	null	2024-03-05T17:51:15Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - fastMRIBrainsMulticoil thumbnail: null tags: - image-reconstruction - RIM - ATOMMIC - pytorch model-index: - name: REC_RIM_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM results: [] --- ## Model Overview Recurrent Inference Machines (RIM) for 4x & 8x accelerated MRI Reconstruction on the fastMRIBrainsMulticoil dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/REC/fastMRIBrainsMulticoil/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/REC_RIM_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_RIM_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic mode: test ``` ### Usage You need to download the fastMRI Brains dataset to effectively use this model. Check the [fastMRIBrainsMulticoil](https://github.com/wdika/atommic/blob/main/projects/REC/fastMRIBrainsMulticoil/README.md) page for more information. ## Model Architecture ```base model: model_name: CIRIM recurrent_layer: GRU conv_filters: - 64 - 64 - 2 conv_kernels: - 5 - 3 - 3 conv_dilations: - 1 - 2 - 1 conv_bias: - true - true - false recurrent_filters: - 64 - 64 - 0 recurrent_kernels: - 1 - 1 - 0 recurrent_dilations: - 1 - 1 - 0 recurrent_bias: - true - true - false depth: 2 time_steps: 8 conv_dim: 2 num_cascades: 1 no_dc: true keep_prediction: true accumulate_predictions: true dimensionality: 2 reconstruction_loss: l1: 0.1 ssim: 0.9 estimate_coil_sensitivity_maps_with_nn: true ``` ## Training ```base optim: name: adam lr: 1e-4 betas: - 0.9 - 0.999 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp_find_unused_parameters_false accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 20 precision: 16-mixed enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance To compute the targets using the raw k-space and the chosen coil combination method, accompanied with the chosen coil sensitivity maps estimation method, you can use [targets](https://github.com/wdika/atommic/tree/main/projects/REC/fastMRIBrainsMulticoil/conf/targets) configuration files. Evaluation can be performed using the [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/reconstruction.py) script for the reconstruction task, with --evaluation_type per_slice. Results ------- Evaluation against RSS targets ------------------------------ 4x: MSE = 0.0007147 +/- 0.00289 NMSE = 0.01907 +/- 0.06354 PSNR = 33.24 +/- 6.153 SSIM = 0.8847 +/- 0.19 8x: MSE = 0.001466 +/- 0.003407 NMSE = 0.03833 +/- 0.0846 PSNR = 29.45 +/- 5.578 SSIM = 0.8382 +/- 0.199 ## Limitations This model was trained on the fastMRIBrainsMulticoil batch0 dataset using a UNet coil sensitivity maps estimation and Geometric Decomposition Coil-Compressions to 1-coil, and might differ from the results reported on the challenge leaderboard. ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Muckley MJ, Riemenschneider B, Radmanesh A, Kim S, Jeong G, Ko J, Jun Y, Shin H, Hwang D, Mostapha M, Arberet S, Nickel D, Ramzi Z, Ciuciu P, Starck JL, Teuwen J, Karkalousos D, Zhang C, Sriram A, Huang Z, Yakubova N, Lui YW, Knoll F. Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. IEEE Trans Med Imaging. 2021 Sep;40(9):2306-2317. doi: 10.1109/TMI.2021.3075856. Epub 2021 Aug 31. PMID: 33929957; PMCID: PMC8428775.
wdika/REC_VarNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM	wdika	2024-03-06T10:47:54Z	0	0	atommic	[ "atommic", "image-reconstruction", "VarNet", "ATOMMIC", "pytorch", "en", "dataset:fastMRIBrainsMulticoil", "license:apache-2.0", "region:us" ]	null	2024-03-05T17:51:55Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - fastMRIBrainsMulticoil thumbnail: null tags: - image-reconstruction - VarNet - ATOMMIC - pytorch model-index: - name: REC_VarNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM results: [] --- ## Model Overview Variational Network (VarNet) for 4x & 8x accelerated MRI Reconstruction on the fastMRIBrainsMulticoil dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/REC/fastMRIBrainsMulticoil/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/REC_VarNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_VarNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic mode: test ``` ### Usage You need to download the fastMRI Brains dataset to effectively use this model. Check the [fastMRIBrainsMulticoil](https://github.com/wdika/atommic/blob/main/projects/REC/fastMRIBrainsMulticoil/README.md) page for more information. ## Model Architecture ```base model: model_name: VN num_cascades: 8 channels: 18 pooling_layers: 4 padding_size: 11 normalize: true no_dc: false dimensionality: 2 reconstruction_loss: l1: 0.1 ssim: 0.9 estimate_coil_sensitivity_maps_with_nn: true ``` ## Training ```base optim: name: adam lr: 1e-4 betas: - 0.9 - 0.999 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp_find_unused_parameters_false accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 20 precision: 16-mixed enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance To compute the targets using the raw k-space and the chosen coil combination method, accompanied with the chosen coil sensitivity maps estimation method, you can use [targets](https://github.com/wdika/atommic/tree/main/projects/REC/fastMRIBrainsMulticoil/conf/targets) configuration files. Evaluation can be performed using the [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/reconstruction.py) script for the reconstruction task, with --evaluation_type per_slice. Results ------- Evaluation against RSS targets ------------------------------ 4x: MSE = 0.000647 +/- 0.003424 NMSE = 0.01882 +/- 0.08376 PSNR = 34 +/- 6.302 SSIM = 0.8925 +/- 0.1981 8x: MSE = 0.00121 +/- 0.004349 NMSE = 0.03456 +/- 0.1321 PSNR = 30.73 +/- 5.936 SSIM = 0.8561 +/- 0.2161 ## Limitations This model was trained on the fastMRIBrainsMulticoil batch0 dataset using a UNet coil sensitivity maps estimation and Geometric Decomposition Coil-Compressions to 1-coil, and might differ from the results reported on the challenge leaderboard. ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Muckley MJ, Riemenschneider B, Radmanesh A, Kim S, Jeong G, Ko J, Jun Y, Shin H, Hwang D, Mostapha M, Arberet S, Nickel D, Ramzi Z, Ciuciu P, Starck JL, Teuwen J, Karkalousos D, Zhang C, Sriram A, Huang Z, Yakubova N, Lui YW, Knoll F. Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. IEEE Trans Med Imaging. 2021 Sep;40(9):2306-2317. doi: 10.1109/TMI.2021.3075856. Epub 2021 Aug 31. PMID: 33929957; PMCID: PMC8428775.
wdika/REC_CIRIM_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM	wdika	2024-03-06T10:47:34Z	0	0	atommic	[ "atommic", "image-reconstruction", "CIRIM", "ATOMMIC", "pytorch", "en", "dataset:StanfordKnees2019", "license:apache-2.0", "region:us" ]	null	2024-03-05T17:52:58Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - StanfordKnees2019 thumbnail: null tags: - image-reconstruction - CIRIM - ATOMMIC - pytorch model-index: - name: REC_CIRIM_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM results: [] --- ## Model Overview Cascades of Independently Recurrent Inference Machines (CIRIM) for 12x accelerated MRI Reconstruction on the StanfordKnees2019 dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/REC/StanfordKnees2019/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/REC_CIRIM_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_CIRIM_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic mode: test ``` ### Usage You need to download the Stanford Knees 2019 dataset to effectively use this model. Check the [StanfordKnees2019](https://github.com/wdika/atommic/blob/main/projects/REC/StanfordKnees2019/README.md) page for more information. ## Model Architecture ```base model: model_name: CIRIM recurrent_layer: IndRNN conv_filters: - 64 - 64 - 2 conv_kernels: - 5 - 3 - 3 conv_dilations: - 1 - 2 - 1 conv_bias: - true - true - false recurrent_filters: - 64 - 64 - 0 recurrent_kernels: - 1 - 1 - 0 recurrent_dilations: - 1 - 1 - 0 recurrent_bias: - true - true - false depth: 2 time_steps: 8 conv_dim: 2 num_cascades: 5 no_dc: true keep_prediction: true accumulate_predictions: true dimensionality: 2 reconstruction_loss: wasserstein: 1.0 ``` ## Training ```base optim: name: adamw lr: 1e-4 betas: - 0.9 - 0.999 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp_find_unused_parameters_false accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 20 precision: 16-mixed enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance To compute the targets using the raw k-space and the chosen coil combination method, accompanied with the chosen coil sensitivity maps estimation method, you can use [targets](https://github.com/wdika/atommic/tree/main/projects/REC/StanfordKnees2019/conf/targets) configuration files. Evaluation can be performed using the [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/reconstruction.py) script for the reconstruction task, with --evaluation_type per_slice. Results ------- Evaluation against SENSE targets -------------------------------- 12x: MSE = 0.001081 +/- 0.005786 NMSE = 0.03494 +/- 0.09865 PSNR = 32.77 +/- 7.234 SSIM = 0.7955 +/- 0.311 ## Limitations This model was trained on the StanfordKnees2019 batch0 using a UNet coil sensitivity maps estimation and Geometric Decomposition Coil-Compressions to 1-coil, and might differ from the results reported on the challenge leaderboard. ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Epperson K, Rt R, Sawyer AM, et al. Creation of Fully Sampled MR Data Repository for Compressed SENSEing of the Knee. SMRT Conference 2013;2013:1
wdika/REC_VarNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM	wdika	2024-03-06T10:46:52Z	0	0	atommic	[ "atommic", "image-reconstruction", "VarNet", "ATOMMIC", "pytorch", "en", "dataset:StanfordKnees2019", "license:apache-2.0", "region:us" ]	null	2024-03-05T17:55:03Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - StanfordKnees2019 thumbnail: null tags: - image-reconstruction - VarNet - ATOMMIC - pytorch model-index: - name: REC_VarNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM results: [] --- ## Model Overview Variational Network (VarNet) for 12x accelerated MRI Reconstruction on the StanfordKnees2019 dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/REC/StanfordKnees2019/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/REC_VarNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_VarNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic mode: test ``` ### Usage You need to download the Stanford Knees 2019 dataset to effectively use this model. Check the [StanfordKnees2019](https://github.com/wdika/atommic/blob/main/projects/REC/StanfordKnees2019/README.md) page for more information. ## Model Architecture ```base model: model_name: VN num_cascades: 8 channels: 18 pooling_layers: 4 padding_size: 11 normalize: true no_dc: false dimensionality: 2 reconstruction_loss: wasserstein: 1.0 ``` ## Training ```base optim: name: adamw lr: 1e-4 betas: - 0.9 - 0.999 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp_find_unused_parameters_false accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 20 precision: 16-mixed enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance To compute the targets using the raw k-space and the chosen coil combination method, accompanied with the chosen coil sensitivity maps estimation method, you can use [targets](https://github.com/wdika/atommic/tree/main/projects/REC/StanfordKnees2019/conf/targets) configuration files. Evaluation can be performed using the [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/reconstruction.py) script for the reconstruction task, with --evaluation_type per_slice. Results ------- Evaluation against SENSE targets -------------------------------- 12x: MSE = 0.001261 +/- 0.005865 NMSE = 0.04287 +/- 0.101 PSNR = 31.5 +/- 6.696 SSIM = 0.7635 +/- 0.3022 ## Limitations This model was trained on the StanfordKnees2019 batch0 using a UNet coil sensitivity maps estimation and Geometric Decomposition Coil-Compressions to 1-coil, and might differ from the results reported on the challenge leaderboard. ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Epperson K, Rt R, Sawyer AM, et al. Creation of Fully Sampled MR Data Repository for Compressed SENSEing of the Knee. SMRT Conference 2013;2013:1
wdika/REC_XPDNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM	wdika	2024-03-06T10:46:41Z	0	0	atommic	[ "atommic", "image-reconstruction", "XPDNet", "ATOMMIC", "pytorch", "en", "dataset:StanfordKnees2019", "license:apache-2.0", "region:us" ]	null	2024-03-05T17:55:37Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - StanfordKnees2019 thumbnail: null tags: - image-reconstruction - XPDNet - ATOMMIC - pytorch model-index: - name: REC_XPDNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM results: [] --- ## Model Overview XPDNet for 12x accelerated MRI Reconstruction on the StanfordKnees2019 dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/REC/StanfordKnees2019/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/REC_XPDNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_XPDNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic mode: test ``` ### Usage You need to download the Stanford Knees 2019 dataset to effectively use this model. Check the [StanfordKnees2019](https://github.com/wdika/atommic/blob/main/projects/REC/StanfordKnees2019/README.md) page for more information. ## Model Architecture ```base model: model_name: XPDNet num_primal: 5 num_dual: 1 num_iter: 10 use_primal_only: true kspace_model_architecture: CONV kspace_in_channels: 2 kspace_out_channels: 2 dual_conv_hidden_channels: 16 dual_conv_num_dubs: 2 dual_conv_batchnorm: false image_model_architecture: MWCNN imspace_in_channels: 2 imspace_out_channels: 2 mwcnn_hidden_channels: 16 mwcnn_num_scales: 0 mwcnn_bias: true mwcnn_batchnorm: false normalize_image: true dimensionality: 2 reconstruction_loss: wasserstein: 1.0 ``` ## Training ```base optim: name: adamw lr: 1e-4 betas: - 0.9 - 0.999 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp_find_unused_parameters_false accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 20 precision: 16-mixed enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance To compute the targets using the raw k-space and the chosen coil combination method, accompanied with the chosen coil sensitivity maps estimation method, you can use [targets](https://github.com/wdika/atommic/tree/main/projects/REC/StanfordKnees2019/conf/targets) configuration files. Evaluation can be performed using the [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/reconstruction.py) script for the reconstruction task, with --evaluation_type per_slice. Results ------- Evaluation against SENSE targets -------------------------------- 12x: MSE = 0.002691 +/- 0.008089 NMSE = 0.1117 +/- 0.1955 PSNR = 27.18 +/- 5.768 SSIM = 0.6544 +/- 0.2702 ## Limitations This model was trained on the StanfordKnees2019 batch0 using a UNet coil sensitivity maps estimation and Geometric Decomposition Coil-Compressions to 1-coil, and might differ from the results reported on the challenge leaderboard. ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Epperson K, Rt R, Sawyer AM, et al. Creation of Fully Sampled MR Data Repository for Compressed SENSEing of the Knee. SMRT Conference 2013;2013:1
wdika/SEG_DynUNet_BraTS2023AdultGlioma	wdika	2024-03-06T10:46:31Z	0	0	atommic	[ "atommic", "image-segmentation", "DynUNet", "ATOMMIC", "pytorch", "en", "dataset:BraTS2023AdultGlioma", "license:apache-2.0", "region:us" ]	image-segmentation	2024-03-05T17:56:18Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - BraTS2023AdultGlioma thumbnail: null tags: - image-segmentation - DynUNet - ATOMMIC - pytorch model-index: - name: SEG_DynUNet_BraTS2023AdultGlioma results: [] --- ## Model Overview AttentionUNet for MRI Segmentation on the BraTS2023AdultGlioma dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/SEG/BraTS2023AdultGlioma/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/SEG_DynUNet_BraTS2023AdultGlioma/blob/main/SEG_DynUNet_BraTS2023AdultGlioma.atommic mode: test ``` ### Usage You need to download the BraTS 2023 Adult Glioma dataset to effectively use this model. Check the [BraTS2023AdultGlioma](https://github.com/wdika/atommic/blob/main/projects/SEG/BraTS2023AdultGlioma/README.md) page for more information. ## Model Architecture ```base model: model_name: SEGMENTATIONDYNUNET segmentation_module: DYNUNet segmentation_module_input_channels: 4 segmentation_module_output_channels: 4 segmentation_module_channels: - 32 - 64 - 128 - 256 - 512 segmentation_module_kernel_size: - 3 - 3 - 3 - 3 - 1 segmentation_module_strides: - 1 - 1 - 1 - 1 - 1 segmentation_module_dropout: 0.0 segmentation_module_norm: instance segmentation_module_activation: leakyrelu segmentation_module_deep_supervision: true segmentation_module_deep_supervision_levels: 2 segmentation_module_normalize: false segmentation_module_norm_groups: 2 segmentation_loss: dice: 1.0 dice_loss_include_background: true # always set to true if the background is removed dice_loss_to_onehot_y: false dice_loss_sigmoid: false dice_loss_softmax: false dice_loss_other_act: none dice_loss_squared_pred: false dice_loss_jaccard: false dice_loss_flatten: false dice_loss_reduction: mean_batch dice_loss_smooth_nr: 1e-5 dice_loss_smooth_dr: 1e-5 dice_loss_batch: true dice_metric_include_background: true # always set to true if the background is removed dice_metric_to_onehot_y: false dice_metric_sigmoid: false dice_metric_softmax: false dice_metric_other_act: none dice_metric_squared_pred: false dice_metric_jaccard: false dice_metric_flatten: false dice_metric_reduction: mean_batch dice_metric_smooth_nr: 1e-5 dice_metric_smooth_dr: 1e-5 dice_metric_batch: true segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ] segmentation_activation: sigmoid magnitude_input: true log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated normalization_type: minmax normalize_segmentation_output: true complex_data: false ``` ## Training ```base optim: name: adam lr: 1e-4 betas: - 0.9 - 0.98 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 10 precision: 16-mixed enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance Evaluation can be performed using the segmentation [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/segmentation.py) script for the segmentation task, with --evaluation_type per_slice. Results ------- Evaluation ---------- DICE = 0.8061 +/- 0.276 F1 = 0.1045 +/- 0.5801 HD95 = 5.119 +/- 5.411 IOU = 0.06959 +/- 0.4187 ## Limitations This model was trained on the BraTS2023AdultGlioma dataset with stacked T1c, T1n, T2f, T2w images and might differ in performance compared to the leaderboard results. ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Kazerooni AF, Khalili N, Liu X, et al. The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs). 2023
wdika/SEG_UNet3D_BraTS2023AdultGlioma	wdika	2024-03-06T10:46:19Z	0	0	atommic	[ "atommic", "image-segmentation", "UNet3D", "ATOMMIC", "pytorch", "en", "dataset:BraTS2023AdultGlioma", "license:apache-2.0", "region:us" ]	image-segmentation	2024-03-05T17:56:58Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - BraTS2023AdultGlioma thumbnail: null tags: - image-segmentation - UNet3D - ATOMMIC - pytorch model-index: - name: SEG_UNet3D_BraTS2023AdultGlioma results: [] --- ## Model Overview AttentionUNet for MRI Segmentation on the BraTS2023AdultGlioma dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/SEG/BraTS2023AdultGlioma/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/SEG_UNet3D_BraTS2023AdultGlioma/blob/main/SEG_UNet3D_BraTS2023AdultGlioma.atommic mode: test ``` ### Usage You need to download the BraTS 2023 Adult Glioma dataset to effectively use this model. Check the [BraTS2023AdultGlioma](https://github.com/wdika/atommic/blob/main/projects/SEG/BraTS2023AdultGlioma/README.md) page for more information. ## Model Architecture ```base model: model_name: SEGMENTATION3DUNET segmentation_module: UNet segmentation_module_input_channels: 4 segmentation_module_output_channels: 4 segmentation_module_channels: 32 segmentation_module_pooling_layers: 5 segmentation_module_dropout: 0.0 segmentation_module_normalize: false segmentation_loss: dice: 1.0 dice_loss_include_background: true # always set to true if the background is removed dice_loss_to_onehot_y: false dice_loss_sigmoid: false dice_loss_softmax: false dice_loss_other_act: none dice_loss_squared_pred: false dice_loss_jaccard: false dice_loss_flatten: false dice_loss_reduction: mean_batch dice_loss_smooth_nr: 1e-5 dice_loss_smooth_dr: 1e-5 dice_loss_batch: true dice_metric_include_background: true # always set to true if the background is removed dice_metric_to_onehot_y: false dice_metric_sigmoid: false dice_metric_softmax: false dice_metric_other_act: none dice_metric_squared_pred: false dice_metric_jaccard: false dice_metric_flatten: false dice_metric_reduction: mean_batch dice_metric_smooth_nr: 1e-5 dice_metric_smooth_dr: 1e-5 dice_metric_batch: true segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ] segmentation_activation: sigmoid magnitude_input: true log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated normalization_type: minmax normalize_segmentation_output: true complex_data: false ``` ## Training ```base optim: name: adam lr: 1e-4 betas: - 0.9 - 0.98 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 10 precision: 16-mixed enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance Evaluation can be performed using the segmentation [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/segmentation.py) script for the segmentation task, with --evaluation_type per_slice. Results ------- Evaluation ---------- DICE = 0.9359 +/- 0.1334 F1 = 0.6735 +/- 0.782 HD95 = 3.55 +/- 2.162 IOU = 0.5279 +/- 0.6518 ## Limitations This model was trained on the BraTS2023AdultGlioma dataset with stacked T1c, T1n, T2f, T2w images and might differ in performance compared to the leaderboard results. ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Kazerooni AF, Khalili N, Liu X, et al. The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs). 2023
wdika/REC_UNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM	wdika	2024-03-06T10:45:10Z	0	0	atommic	[ "atommic", "image-reconstruction", "UNet", "ATOMMIC", "pytorch", "en", "dataset:fastMRIBrainsMulticoil", "license:apache-2.0", "region:us" ]	null	2024-03-05T17:51:31Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - fastMRIBrainsMulticoil thumbnail: null tags: - image-reconstruction - UNet - ATOMMIC - pytorch model-index: - name: REC_UNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM results: [] --- ## Model Overview UNet for 4x & 8x accelerated MRI Reconstruction on the fastMRIBrainsMulticoil dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/REC/fastMRIBrainsMulticoil/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/REC_UNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_UNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic mode: test ``` ### Usage You need to download the fastMRI Brains dataset to effectively use this model. Check the [fastMRIBrainsMulticoil](https://github.com/wdika/atommic/blob/main/projects/REC/fastMRIBrainsMulticoil/README.md) page for more information. ## Model Architecture ```base model: model_name: UNet channels: 64 pooling_layers: 4 in_channels: 2 out_channels: 2 padding_size: 11 dropout: 0.0 normalize: true norm_groups: 2 dimensionality: 2 reconstruction_loss: l1: 0.1 ssim: 0.9 estimate_coil_sensitivity_maps_with_nn: true ``` ## Training ```base optim: name: adam lr: 1e-4 betas: - 0.9 - 0.999 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp_find_unused_parameters_false accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 20 precision: 16-mixed enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance To compute the targets using the raw k-space and the chosen coil combination method, accompanied with the chosen coil sensitivity maps estimation method, you can use [targets](https://github.com/wdika/atommic/tree/main/projects/REC/fastMRIBrainsMulticoil/conf/targets) configuration files. Evaluation can be performed using the [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/reconstruction.py) script for the reconstruction task, with --evaluation_type per_slice. Results ------- Evaluation against RSS targets ------------------------------ 4x: MSE = 0.000723 +/- 0.003086 NMSE = 0.01924 +/- 0.0629 PSNR = 33.09 +/- 6.023 SSIM = 0.8853 +/- 0.1817 8x: MSE = 0.001353 +/- 0.00366 NMSE = 0.03587 +/- 0.08282 PSNR = 29.87 +/- 5.676 SSIM = 0.847 +/- 0.1972 ## Limitations This model was trained on the fastMRIBrainsMulticoil batch0 dataset using a UNet coil sensitivity maps estimation and Geometric Decomposition Coil-Compressions to 1-coil, and might differ from the results reported on the challenge leaderboard. ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Muckley MJ, Riemenschneider B, Radmanesh A, Kim S, Jeong G, Ko J, Jun Y, Shin H, Hwang D, Mostapha M, Arberet S, Nickel D, Ramzi Z, Ciuciu P, Starck JL, Teuwen J, Karkalousos D, Zhang C, Sriram A, Huang Z, Yakubova N, Lui YW, Knoll F. Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. IEEE Trans Med Imaging. 2021 Sep;40(9):2306-2317. doi: 10.1109/TMI.2021.3075856. Epub 2021 Aug 31. PMID: 33929957; PMCID: PMC8428775.
wdika/REC_UNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM	wdika	2024-03-06T10:43:57Z	0	0	atommic	[ "atommic", "image-reconstruction", "UNet", "ATOMMIC", "pytorch", "en", "dataset:StanfordKnees2019", "license:apache-2.0", "region:us" ]	null	2024-03-05T17:54:40Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - StanfordKnees2019 thumbnail: null tags: - image-reconstruction - UNet - ATOMMIC - pytorch model-index: - name: REC_UNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM results: [] --- ## Model Overview UNet for 12x accelerated MRI Reconstruction on the StanfordKnees2019 dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/REC/StanfordKnees2019/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/REC_UNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_UNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic mode: test ``` ### Usage You need to download the Stanford Knees 2019 dataset to effectively use this model. Check the [StanfordKnees2019](https://github.com/wdika/atommic/blob/main/projects/REC/StanfordKnees2019/README.md) page for more information. ## Model Architecture ```base model: model_name: UNet channels: 64 pooling_layers: 4 in_channels: 2 out_channels: 2 padding_size: 11 dropout: 0.0 normalize: true norm_groups: 2 dimensionality: 2 reconstruction_loss: wasserstein: 1.0 ``` ## Training ```base optim: name: adamw lr: 1e-4 betas: - 0.9 - 0.999 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp_find_unused_parameters_false accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 20 precision: 16-mixed enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance To compute the targets using the raw k-space and the chosen coil combination method, accompanied with the chosen coil sensitivity maps estimation method, you can use [targets](https://github.com/wdika/atommic/tree/main/projects/REC/StanfordKnees2019/conf/targets) configuration files. Evaluation can be performed using the [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/reconstruction.py) script for the reconstruction task, with --evaluation_type per_slice. Results ------- Evaluation against SENSE targets -------------------------------- 12x: MSE = 0.001251 +/- 0.005686 NMSE = 0.04254 +/- 0.09148 PSNR = 31.4 +/- 6.554 SSIM = 0.7705 +/- 0.2946 ## Limitations This model was trained on the StanfordKnees2019 batch0 using a UNet coil sensitivity maps estimation and Geometric Decomposition Coil-Compressions to 1-coil, and might differ from the results reported on the challenge leaderboard. ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Epperson K, Rt R, Sawyer AM, et al. Creation of Fully Sampled MR Data Repository for Compressed SENSEing of the Knee. SMRT Conference 2013;2013:1
wdika/SEG_UNet_BraTS2023AdultGlioma	wdika	2024-03-06T10:43:12Z	0	0	atommic	[ "atommic", "image-segmentation", "UNet", "ATOMMIC", "pytorch", "en", "dataset:BraTS2023AdultGlioma", "license:apache-2.0", "region:us" ]	image-segmentation	2024-03-05T17:56:33Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - BraTS2023AdultGlioma thumbnail: null tags: - image-segmentation - UNet - ATOMMIC - pytorch model-index: - name: SEG_UNet_BraTS2023AdultGlioma results: [] --- ## Model Overview AttentionUNet for MRI Segmentation on the BraTS2023AdultGlioma dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/SEG/BraTS2023AdultGlioma/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/SEG_UNet_BraTS2023AdultGlioma/blob/main/SEG_UNet_BraTS2023AdultGlioma.atommic mode: test ``` ### Usage You need to download the BraTS 2023 Adult Glioma dataset to effectively use this model. Check the [BraTS2023AdultGlioma](https://github.com/wdika/atommic/blob/main/projects/SEG/BraTS2023AdultGlioma/README.md) page for more information. ## Model Architecture ```base model: model_name: SEGMENTATIONUNET segmentation_module: UNet segmentation_module_input_channels: 4 segmentation_module_output_channels: 4 segmentation_module_channels: 32 segmentation_module_pooling_layers: 5 segmentation_module_dropout: 0.0 segmentation_module_normalize: false segmentation_loss: dice: 1.0 dice_loss_include_background: true # always set to true if the background is removed dice_loss_to_onehot_y: false dice_loss_sigmoid: false dice_loss_softmax: false dice_loss_other_act: none dice_loss_squared_pred: false dice_loss_jaccard: false dice_loss_flatten: false dice_loss_reduction: mean_batch dice_loss_smooth_nr: 1e-5 dice_loss_smooth_dr: 1e-5 dice_loss_batch: true dice_metric_include_background: true # always set to true if the background is removed dice_metric_to_onehot_y: false dice_metric_sigmoid: false dice_metric_softmax: false dice_metric_other_act: none dice_metric_squared_pred: false dice_metric_jaccard: false dice_metric_flatten: false dice_metric_reduction: mean_batch dice_metric_smooth_nr: 1e-5 dice_metric_smooth_dr: 1e-5 dice_metric_batch: true segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ] segmentation_activation: sigmoid magnitude_input: true log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated normalization_type: minmax normalize_segmentation_output: true complex_data: false ``` ## Training ```base optim: name: adam lr: 1e-4 betas: - 0.9 - 0.98 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 10 precision: 16-mixed enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance Evaluation can be performed using the segmentation [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/segmentation.py) script for the segmentation task, with --evaluation_type per_slice. Results ------- Evaluation ---------- DICE = 0.9372 +/- 0.1175 F1 = 0.6713 +/- 0.7867 HD95 = 3.504 +/- 2.089 IOU = 0.5346 +/- 0.6628 ## Limitations This model was trained on the BraTS2023AdultGlioma dataset with stacked T1c, T1n, T2f, T2w images and might differ in performance compared to the leaderboard results. ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Kazerooni AF, Khalili N, Liu X, et al. The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs). 2023
wdika/SEG_VNet_BraTS2023AdultGlioma	wdika	2024-03-06T10:42:42Z	0	0	atommic	[ "atommic", "image-segmentation", "VNet", "ATOMMIC", "pytorch", "en", "dataset:BraTS2023AdultGlioma", "license:apache-2.0", "region:us" ]	image-segmentation	2024-03-05T17:57:43Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - BraTS2023AdultGlioma thumbnail: null tags: - image-segmentation - VNet - ATOMMIC - pytorch model-index: - name: SEG_VNet_BraTS2023AdultGlioma results: [] --- ## Model Overview AttentionUNet for MRI Segmentation on the BraTS2023AdultGlioma dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/SEG/BraTS2023AdultGlioma/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/SEG_VNet_BraTS2023AdultGlioma/blob/main/SEG_VNet_BraTS2023AdultGlioma.atommic mode: test ``` ### Usage You need to download the BraTS 2023 Adult Glioma dataset to effectively use this model. Check the [BraTS2023AdultGlioma](https://github.com/wdika/atommic/blob/main/projects/SEG/BraTS2023AdultGlioma/README.md) page for more information. ## Model Architecture ```base model: model_name: SEGMENTATIONVNET segmentation_module: VNet segmentation_module_input_channels: 4 segmentation_module_output_channels: 4 segmentation_module_activation: elu segmentation_module_dropout: 0.0 segmentation_module_bias: False segmentation_module_padding_size: 15 segmentation_loss: dice: 1.0 dice_loss_include_background: true # always set to true if the background is removed dice_loss_to_onehot_y: false dice_loss_sigmoid: false dice_loss_softmax: false dice_loss_other_act: none dice_loss_squared_pred: false dice_loss_jaccard: false dice_loss_flatten: false dice_loss_reduction: mean_batch dice_loss_smooth_nr: 1e-5 dice_loss_smooth_dr: 1e-5 dice_loss_batch: true dice_metric_include_background: true # always set to true if the background is removed dice_metric_to_onehot_y: false dice_metric_sigmoid: false dice_metric_softmax: false dice_metric_other_act: none dice_metric_squared_pred: false dice_metric_jaccard: false dice_metric_flatten: false dice_metric_reduction: mean_batch dice_metric_smooth_nr: 1e-5 dice_metric_smooth_dr: 1e-5 dice_metric_batch: true segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ] segmentation_activation: sigmoid magnitude_input: true log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated normalization_type: minmax normalize_segmentation_output: true complex_data: false ``` ## Training ```base optim: name: adam lr: 1e-4 betas: - 0.9 - 0.98 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 10 precision: 16-mixed enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance Evaluation can be performed using the segmentation [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/segmentation.py) script for the segmentation task, with --evaluation_type per_slice. Results ------- Evaluation ---------- DICE = 0.7331 +/- 0.4374 F1 = 0.01428 +/- 0.2341 HD95 = 6.01 +/- 6.097 IOU = 0.0001576 +/- 0.004287 ## Limitations This model was trained on the BraTS2023AdultGlioma dataset with stacked T1c, T1n, T2f, T2w images and might differ in performance compared to the leaderboard results. ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Kazerooni AF, Khalili N, Liu X, et al. The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs). 2023
wdika/SEG_UNet_ISLES2022SubAcuteStroke	wdika	2024-03-06T10:42:17Z	0	0	atommic	[ "atommic", "image-segmentation", "UNet", "ATOMMIC", "pytorch", "en", "dataset:ISLES2022SubAcuteStroke", "license:apache-2.0", "region:us" ]	image-segmentation	2024-03-05T17:58:40Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - ISLES2022SubAcuteStroke thumbnail: null tags: - image-segmentation - UNet - ATOMMIC - pytorch model-index: - name: SEG_UNet_ISLES2022SubAcuteStroke results: [] --- ## Model Overview AttentionUNet for MRI Segmentation on the ISLES2022SubAcuteStroke dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/SEG/ISLES2022SubAcuteStroke/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/SEG_UNet_ISLES2022SubAcuteStroke/blob/main/SEG_UNet_ISLES2022SubAcuteStroke.atommic mode: test ``` ### Usage You need to download the ISLES 2022 Sub Acute Stroke dataset to effectively use this model. Check the [ISLES2022SubAcuteStroke](https://github.com/wdika/atommic/blob/main/projects/SEG/ISLES2022SubAcuteStroke/README.md) page for more information. ## Model Architecture ```base model: model_name: SEGMENTATIONUNET segmentation_module: UNet segmentation_module_input_channels: 3 segmentation_module_output_channels: 1 segmentation_module_channels: 32 segmentation_module_pooling_layers: 5 segmentation_module_dropout: 0.0 segmentation_module_normalize: false segmentation_loss: dice: 1.0 dice_loss_include_background: true # always set to true if the background is removed dice_loss_to_onehot_y: false dice_loss_sigmoid: false dice_loss_softmax: false dice_loss_other_act: none dice_loss_squared_pred: false dice_loss_jaccard: false dice_loss_flatten: false dice_loss_reduction: mean_batch dice_loss_smooth_nr: 1e-5 dice_loss_smooth_dr: 1e-5 dice_loss_batch: true dice_metric_include_background: true # always set to true if the background is removed dice_metric_to_onehot_y: false dice_metric_sigmoid: false dice_metric_softmax: false dice_metric_other_act: none dice_metric_squared_pred: false dice_metric_jaccard: false dice_metric_flatten: false dice_metric_reduction: mean_batch dice_metric_smooth_nr: 1e-5 dice_metric_smooth_dr: 1e-5 dice_metric_batch: true segmentation_classes_thresholds: [ 0.5 ] segmentation_activation: sigmoid magnitude_input: true log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated normalization_type: minmax normalize_segmentation_output: true complex_data: false ``` ## Training ```base optim: name: adamw lr: 1e-4 betas: - 0.9 - 0.999 weight_decay: 0.0 sched: name: CosineAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp_find_unused_parameters_false accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 50 precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16' enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance Evaluation can be performed using the segmentation [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/segmentation.py) script for the segmentation task, with --evaluation_type per_slice. Results ------- Evaluation ---------- ALD = 0.9088 +/- 3.953 AVD = 0.5439 +/- 3.921 DICE = 0.6946 +/- 0.5589 L-F1 = 0.7859 +/- 0.5848 ## Limitations This model was trained on the ISLES2022SubAcuteStroke dataset with stacked ADC, DWI, FLAIR images and might differ in performance compared to the leaderboard results. ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Petzsche MRH, Rosa E de la, Hanning U, et al. ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. Scientific Data 1 2022;9
wdika/SEG_VNet_ISLES2022SubAcuteStroke	wdika	2024-03-06T10:42:03Z	0	0	atommic	[ "atommic", "image-segmentation", "VNet", "ATOMMIC", "pytorch", "en", "dataset:ISLES2022SubAcuteStroke", "license:apache-2.0", "region:us" ]	image-segmentation	2024-03-05T17:59:41Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - ISLES2022SubAcuteStroke thumbnail: null tags: - image-segmentation - VNet - ATOMMIC - pytorch model-index: - name: SEG_VNet_ISLES2022SubAcuteStroke results: [] --- ## Model Overview AttentionUNet for MRI Segmentation on the ISLES2022SubAcuteStroke dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/SEG/ISLES2022SubAcuteStroke/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/SEG_VNet_ISLES2022SubAcuteStroke/blob/main/SEG_VNet_ISLES2022SubAcuteStroke.atommic mode: test ``` ### Usage You need to download the ISLES 2022 Sub Acute Stroke dataset to effectively use this model. Check the [ISLES2022SubAcuteStroke](https://github.com/wdika/atommic/blob/main/projects/SEG/ISLES2022SubAcuteStroke/README.md) page for more information. ## Model Architecture ```base model: model_name: SEGMENTATIONVNET segmentation_module: VNet segmentation_module_input_channels: 3 segmentation_module_output_channels: 1 segmentation_module_activation: elu segmentation_module_dropout: 0.0 segmentation_module_bias: False segmentation_module_padding_size: 15 segmentation_loss: dice: 1.0 dice_loss_include_background: true # always set to true if the background is removed dice_loss_to_onehot_y: false dice_loss_sigmoid: false dice_loss_softmax: false dice_loss_other_act: none dice_loss_squared_pred: false dice_loss_jaccard: false dice_loss_flatten: false dice_loss_reduction: mean_batch dice_loss_smooth_nr: 1e-5 dice_loss_smooth_dr: 1e-5 dice_loss_batch: true dice_metric_include_background: true # always set to true if the background is removed dice_metric_to_onehot_y: false dice_metric_sigmoid: false dice_metric_softmax: false dice_metric_other_act: none dice_metric_squared_pred: false dice_metric_jaccard: false dice_metric_flatten: false dice_metric_reduction: mean_batch dice_metric_smooth_nr: 1e-5 dice_metric_smooth_dr: 1e-5 dice_metric_batch: true segmentation_classes_thresholds: [ 0.5 ] segmentation_activation: sigmoid magnitude_input: true log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated normalization_type: minmax normalize_segmentation_output: true complex_data: false ``` ## Training ```base optim: name: adamw lr: 1e-4 betas: - 0.9 - 0.999 weight_decay: 0.0 sched: name: CosineAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp_find_unused_parameters_false accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 50 precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16' enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance Evaluation can be performed using the segmentation [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/segmentation.py) script for the segmentation task, with --evaluation_type per_slice. Results ------- Evaluation ---------- ALD = 2.281 +/- 10.72 AVD = 3.257 +/- 27.43 DICE = 0.4903 +/- 0.694 L-F1 = 0.5998 +/- 0.6866 ## Limitations This model was trained on the ISLES2022SubAcuteStroke dataset with stacked ADC, DWI, FLAIR images and might differ in performance compared to the leaderboard results. ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Petzsche MRH, Rosa E de la, Hanning U, et al. ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. Scientific Data 1 2022;9
wdika/SEG_DynUNet_SKMTEA	wdika	2024-03-06T10:41:53Z	0	0	atommic	[ "atommic", "image-segmentation", "DynUNet", "ATOMMIC", "pytorch", "en", "dataset:SKMTEA", "license:apache-2.0", "region:us" ]	image-segmentation	2024-03-05T18:00:27Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - SKMTEA thumbnail: null tags: - image-segmentation - DynUNet - ATOMMIC - pytorch model-index: - name: SEG_DynUNet_SKMTEA results: [] --- ## Model Overview AttentionUNet for MRI Segmentation on the SKMTEA dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/SEG/SKMTEA/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/SEG_DynUNet_SKMTEA/blob/main/SEG_DynUNet_SKMTEA.atommic mode: test ``` ### Usage You need to download the SKM-TEA dataset to effectively use this model. Check the [SKMTEA](https://github.com/wdika/atommic/blob/main/projects/SEG/SKMTEA/README.md) page for more information. ## Model Architecture ```base model: model_name: SEGMENTATIONDYNUNET segmentation_module: DYNUNet segmentation_module_input_channels: 1 segmentation_module_output_channels: 4 segmentation_module_channels: - 32 - 64 - 128 - 256 - 512 segmentation_module_kernel_size: - 3 - 3 - 3 - 3 - 1 segmentation_module_strides: - 1 - 1 - 1 - 1 - 1 segmentation_module_dropout: 0.0 segmentation_module_norm: instance segmentation_module_activation: leakyrelu segmentation_module_deep_supervision: true segmentation_module_deep_supervision_levels: 2 segmentation_module_normalize: false segmentation_module_norm_groups: 2 segmentation_loss: dice: 1.0 dice_loss_include_background: true # always set to true if the background is removed dice_loss_to_onehot_y: false dice_loss_sigmoid: false dice_loss_softmax: false dice_loss_other_act: none dice_loss_squared_pred: false dice_loss_jaccard: false dice_loss_flatten: false dice_loss_reduction: mean_batch dice_loss_smooth_nr: 1e-5 dice_loss_smooth_dr: 1e-5 dice_loss_batch: true dice_metric_include_background: true # always set to true if the background is removed dice_metric_to_onehot_y: false dice_metric_sigmoid: false dice_metric_softmax: false dice_metric_other_act: none dice_metric_squared_pred: false dice_metric_jaccard: false dice_metric_flatten: false dice_metric_reduction: mean_batch dice_metric_smooth_nr: 1e-5 dice_metric_smooth_dr: 1e-5 dice_metric_batch: true segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5] segmentation_activation: sigmoid magnitude_input: true log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated normalization_type: minmax normalize_segmentation_output: true complex_data: false ``` ## Training ```base optim: name: adam lr: 1e-4 betas: - 0.9 - 0.98 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp_find_unused_parameters_false accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 20 precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16' enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance Evaluation can be performed using the segmentation [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/segmentation.py) script for the segmentation task, with --evaluation_type per_slice. Results ------- Evaluation ---------- DICE = 0.6888 +/- 0.1359 F1 = 0.05911 +/- 0.2638 HD95 = 8.973 +/- 4.507 IOU = 0.01517 +/- 0.06638 ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Desai AD, Schmidt AM, Rubin EB, et al. SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation. 2022
wdika/SEG_UNet3D_SKMTEA	wdika	2024-03-06T10:41:37Z	0	0	atommic	[ "atommic", "image-segmentation", "UNet3D", "ATOMMIC", "pytorch", "en", "dataset:SKMTEA", "license:apache-2.0", "region:us" ]	image-segmentation	2024-03-05T18:01:04Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - SKMTEA thumbnail: null tags: - image-segmentation - UNet3D - ATOMMIC - pytorch model-index: - name: SEG_UNet3D_SKMTEA results: [] --- ## Model Overview AttentionUNet for MRI Segmentation on the SKMTEA dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/SEG/SKMTEA/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/SEG_UNet3D_SKMTEA/blob/main/SEG_UNet3D_SKMTEA.atommic mode: test ``` ### Usage You need to download the SKM-TEA dataset to effectively use this model. Check the [SKMTEA](https://github.com/wdika/atommic/blob/main/projects/SEG/SKMTEA/README.md) page for more information. ## Model Architecture ```base model: model_name: SEGMENTATION3DUNET segmentation_module: UNet segmentation_module_input_channels: 1 segmentation_module_output_channels: 4 segmentation_module_channels: 32 segmentation_module_pooling_layers: 5 segmentation_module_dropout: 0.0 segmentation_module_normalize: false segmentation_loss: dice: 1.0 dice_loss_include_background: true # always set to true if the background is removed dice_loss_to_onehot_y: false dice_loss_sigmoid: false dice_loss_softmax: false dice_loss_other_act: none dice_loss_squared_pred: false dice_loss_jaccard: false dice_loss_flatten: false dice_loss_reduction: mean_batch dice_loss_smooth_nr: 1e-5 dice_loss_smooth_dr: 1e-5 dice_loss_batch: true dice_metric_include_background: true # always set to true if the background is removed dice_metric_to_onehot_y: false dice_metric_sigmoid: false dice_metric_softmax: false dice_metric_other_act: none dice_metric_squared_pred: false dice_metric_jaccard: false dice_metric_flatten: false dice_metric_reduction: mean_batch dice_metric_smooth_nr: 1e-5 dice_metric_smooth_dr: 1e-5 dice_metric_batch: true segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5] segmentation_activation: sigmoid magnitude_input: true log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated normalization_type: minmax normalize_segmentation_output: true complex_data: false ``` ## Training ```base optim: name: adam lr: 1e-4 betas: - 0.9 - 0.98 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp_find_unused_parameters_false accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 20 precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16' enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance Evaluation can be performed using the segmentation [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/segmentation.py) script for the segmentation task, with --evaluation_type per_slice. Results ------- Evaluation ---------- DICE = 0.9175 +/- 0.06793 F1 = 0.7889 +/- 0.404 HD95 = 5.893 +/- 2.995 IOU = 0.5301 +/- 0.347 ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Desai AD, Schmidt AM, Rubin EB, et al. SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation. 2022
wdika/SEG_UNet_SKMTEA	wdika	2024-03-06T10:40:44Z	0	0	atommic	[ "atommic", "image-segmentation", "UNet", "ATOMMIC", "pytorch", "en", "dataset:SKMTEA", "license:apache-2.0", "region:us" ]	image-segmentation	2024-03-05T18:00:43Z	--- language: - en license: apache-2.0 library_name: atommic datasets: - SKMTEA thumbnail: null tags: - image-segmentation - UNet - ATOMMIC - pytorch model-index: - name: SEG_UNet_SKMTEA results: [] --- ## Model Overview AttentionUNet for MRI Segmentation on the SKMTEA dataset. ## ATOMMIC: Training To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version. ``` pip install atommic['all'] ``` ## How to Use this Model The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/SEG/SKMTEA/conf). ### Automatically instantiate the model ```base pretrained: true checkpoint: https://huggingface.co/wdika/SEG_UNet_SKMTEA/blob/main/SEG_UNet_SKMTEA.atommic mode: test ``` ### Usage You need to download the SKM-TEA dataset to effectively use this model. Check the [SKMTEA](https://github.com/wdika/atommic/blob/main/projects/SEG/SKMTEA/README.md) page for more information. ## Model Architecture ```base model: model_name: SEGMENTATIONUNET segmentation_module: UNet segmentation_module_input_channels: 1 segmentation_module_output_channels: 4 segmentation_module_channels: 32 segmentation_module_pooling_layers: 5 segmentation_module_dropout: 0.0 segmentation_module_normalize: false segmentation_loss: dice: 1.0 dice_loss_include_background: true # always set to true if the background is removed dice_loss_to_onehot_y: false dice_loss_sigmoid: false dice_loss_softmax: false dice_loss_other_act: none dice_loss_squared_pred: false dice_loss_jaccard: false dice_loss_flatten: false dice_loss_reduction: mean_batch dice_loss_smooth_nr: 1e-5 dice_loss_smooth_dr: 1e-5 dice_loss_batch: true dice_metric_include_background: true # always set to true if the background is removed dice_metric_to_onehot_y: false dice_metric_sigmoid: false dice_metric_softmax: false dice_metric_other_act: none dice_metric_squared_pred: false dice_metric_jaccard: false dice_metric_flatten: false dice_metric_reduction: mean_batch dice_metric_smooth_nr: 1e-5 dice_metric_smooth_dr: 1e-5 dice_metric_batch: true segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5] segmentation_activation: sigmoid magnitude_input: true log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated normalization_type: minmax normalize_segmentation_output: true complex_data: false ``` ## Training ```base optim: name: adam lr: 1e-4 betas: - 0.9 - 0.98 weight_decay: 0.0 sched: name: InverseSquareRootAnnealing min_lr: 0.0 last_epoch: -1 warmup_ratio: 0.1 trainer: strategy: ddp_find_unused_parameters_false accelerator: gpu devices: 1 num_nodes: 1 max_epochs: 20 precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16' enable_checkpointing: false logger: false log_every_n_steps: 50 check_val_every_n_epoch: -1 max_steps: -1 ``` ## Performance Evaluation can be performed using the segmentation [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/segmentation.py) script for the segmentation task, with --evaluation_type per_slice. Results ------- Evaluation ---------- DICE = 0.9123 +/- 0.05847 F1 = 0.6509 +/- 0.4487 HD95 = 6.618 +/- 1.793 IOU = 0.5158 +/- 0.3499 ## References [1] [ATOMMIC](https://github.com/wdika/atommic) [2] Desai AD, Schmidt AM, Rubin EB, et al. SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation. 2022
velocity-engg/model2	velocity-engg	2024-03-06T10:29:35Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-2-7b-bnb-4bit", "base_model:finetune:unsloth/llama-2-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-06T10:29:22Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-2-7b-bnb-4bit --- # Uploaded model - Developed by: velocity-engg - License: apache-2.0 - Finetuned from model : unsloth/llama-2-7b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
AlignmentResearch/robust_llm_pythia-imdb-1b-mz-test-1gpu	AlignmentResearch	2024-03-06T10:24:14Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-1b-deduped", "base_model:finetune:EleutherAI/pythia-1b-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T10:17:46Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-1b-deduped model-index: - name: robust_llm_pythia-imdb-1b-mz-test-1gpu results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_pythia-imdb-1b-mz-test-1gpu This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 8 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
souvikcmsa019/MixtralGDPR	souvikcmsa019	2024-03-06T10:21:28Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-31T08:16:00Z	Model for GDPR Compliance Checking
ChaimaMess/llama-2-7b-QLORA	ChaimaMess	2024-03-06T10:21:06Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-27T14:00:29Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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MoMonir/MiniChat-2-3B-GGUF	MoMonir	2024-03-06T10:20:32Z	5	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-01-28T17:48:26Z	--- license: apache-2.0 --- Original Model: <a href="https://huggingface.co/GeneZC/MiniChat-2-3B">GeneZC/MiniChat-2-3B</a></br> GGUF fp16 Version</br> Quantized Version Q8_0</br> Note: This is an Experiment and not Tested
DMetaSoul/nl2sql-chinese-basic	DMetaSoul	2024-03-06T10:19:11Z	6	2	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-06T08:20:11Z	--- license: apache-2.0 --- ## 简介这是一款根据自然语言生成 SQL 的模型（NL2SQL/Text2SQL），是我们自研众多 NL2SQL 模型中最为基础的一版，其它高级版模型后续将陆续进行开源。该模型基于 BART 架构，我们将 NL2SQL 问题建模为类似机器翻译的 Seq2Seq 形式，该模型的优势特点：参数规模较小、但 SQL 生成准确性也较高。 ## 用法 NL2SQL 任务中输入参数含有用户查询文本+数据库表信息，目前按照以下格式拼接模型的输入文本： ``` Question: 名人堂一共有多少球员 <sep> Tables: hall_of_fame: player_id, yearid, votedby, ballots, needed, votes, inducted, category, needed_note ; player_award: player_id, award_id, year, league_id, tie, notes <sep> ``` 具体使用方法参考以下示例： ```python import torch from transformers import AutoModelForSeq2SeqLM, MBartForConditionalGeneration, AutoTokenizer device = 'cuda' model_path = 'DMetaSoul/nl2sql-chinese-basic' sampling = False tokenizer = AutoTokenizer.from_pretrained(model_path, src_lang='zh_CN') #model = MBartForConditionalGeneration.from_pretrained(model_path) model = AutoModelForSeq2SeqLM.from_pretrained(model_path) model = model.half() model.to(device) input_texts = [ "Question: 所有章节的名称和描述是什么？ <sep> Tables: sections: section id , course id , section name , section description , other details <sep>", "Question: 名人堂一共有多少球员 <sep> Tables: hall_of_fame: player_id, yearid, votedby, ballots, needed, votes, inducted, category, needed_note ; player_award: player_id, award_id, year, league_id, tie, notes ; player_award_vote: award_id, year, league_id, player_id, points_won, points_max, votes_first ; salary: year, team_id, league_id, player_id, salary ; player: player_id, birth_year, birth_month, birth_day, birth_country, birth_state, birth_city, death_year, death_month, death_day, death_country, death_state, death_city, name_first, name_last, name_given, weight <sep>" ] inputs = tokenizer(input_texts, max_length=512, return_tensors="pt", padding=True, truncation=True) inputs = {k:v.to(device) for k,v in inputs.items() if k not in ["token_type_ids"]} with torch.no_grad(): if sampling: outputs = model.generate(inputs, do_sample=True, top_k=50, top_p=0.95, temperature=1.0, num_return_sequences=1, max_length=512, return_dict_in_generate=True, output_scores=True) else: outputs = model.generate(inputs, num_beams=4, num_return_sequences=1, max_length=512, return_dict_in_generate=True, output_scores=True) output_ids = outputs.sequences results = tokenizer.batch_decode(output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True) for question, sql in zip(input_texts, results): print(question) print('SQL: {}'.format(sql)) print() ``` 输入结果如下： ``` Question: 所有章节的名称和描述是什么？ <sep> Tables: sections: section id , course id , section name , section description , other details <sep> SQL: SELECT section name, section description FROM sections Question: 名人堂一共有多少球员 <sep> Tables: hall_of_fame: player_id, yearid, votedby, ballots, needed, votes, inducted, category, needed_note ; player_award: player_id, award_id, year, league_id, tie, notes ; player_award_vote: award_id, year, league_id, player_id, points_won, points_max, votes_first ; salary: year, team_id, league_id, player_id, salary ; player: player_id, birth_year, birth_month, birth_day, birth_country, birth_state, birth_city, death_year, death_month, death_day, death_country, death_state, death_city, name_first, name_last, name_given, weight <sep> SQL: SELECT count(*) FROM hall_of_fame ```
Mayank1999/bert-finetuned-ner	Mayank1999	2024-03-06T10:13:57Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-06T10:03:51Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer model-index: - name: bert-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Leelakrish/my-pet-lion-xzg	Leelakrish	2024-03-06T10:12:19Z	0	0	null	[ "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-03-06T10:10:10Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Lion-XZG Dreambooth model trained by Leelakrish following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 21BRS1638 Sample pictures of this concept: ![0](https://huggingface.co/Leelakrish/my-pet-lion-xzg/resolve/main/sample_images/89776_A_giant_lion_roaring_and_the_knight_is_preparing_t_xl-1024-v1-0.png)
Hemg/Brain-Tumor-Classification	Hemg	2024-03-06T10:11:06Z	38	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-06T05:51:46Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: Brain-Tumor-Classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Brain-Tumor-Classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0872 - Accuracy: 0.9758 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 16 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2074 \| 1.0 \| 44 \| 0.8060 \| 0.8128 \| \| 0.4897 \| 2.0 \| 88 \| 0.3008 \| 0.9274 \| \| 0.2462 \| 3.0 \| 132 \| 0.2464 \| 0.9331 \| \| 0.1937 \| 4.0 \| 176 \| 0.1918 \| 0.9502 \| \| 0.1523 \| 5.0 \| 220 \| 0.1699 \| 0.9502 \| \| 0.1371 \| 6.0 \| 264 \| 0.1372 \| 0.9644 \| \| 0.1104 \| 7.0 \| 308 \| 0.1121 \| 0.9708 \| \| 0.1097 \| 8.0 \| 352 \| 0.1220 \| 0.9651 \| \| 0.1015 \| 9.0 \| 396 \| 0.1053 \| 0.9737 \| \| 0.0841 \| 10.0 \| 440 \| 0.1142 \| 0.9708 \| \| 0.0839 \| 11.0 \| 484 \| 0.1073 \| 0.9708 \| \| 0.0771 \| 12.0 \| 528 \| 0.1156 \| 0.9665 \| \| 0.074 \| 13.0 \| 572 \| 0.1203 \| 0.9644 \| \| 0.0652 \| 14.0 \| 616 \| 0.0706 \| 0.9858 \| \| 0.0694 \| 15.0 \| 660 \| 0.0984 \| 0.9744 \| \| 0.0596 \| 16.0 \| 704 \| 0.0872 \| 0.9758 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
slukas99/tex_inv_af_dress	slukas99	2024-03-06T10:07:28Z	10	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-06T08:47:39Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true base_model: runwayml/stable-diffusion-v1-5 --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Textual inversion text2image fine-tuning - slukas99/tex_inv_af_dress These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Skhaled99/Mistral-7b-PDO-GHC-Merged	Skhaled99	2024-03-06T10:06:31Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-03-06T10:04:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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s14pe/Qlearning_Taxi_v3	s14pe	2024-03-06T10:02:42Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-06T09:50:54Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Qlearning_Taxi_v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="s14pe/Qlearning_Taxi_v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
mii-llm/maestrale-chat-v0.3-beta-sft	mii-llm	2024-03-06T10:00:53Z	14	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "sft", "it", "chatml", "axolotl", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-09T09:26:06Z	--- language: - it license: cc-by-nc-4.0 tags: - sft - it - mistral - chatml - axolotl prompt_template: <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant model-index: - name: maestrale-chat-v0.3-beta results: [] --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/dgSNbTl.jpg" alt="Mii-LLM" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://buy.stripe.com/8wM00Sf3vb3H3pmfYY">Want to contribute? Please donate! This will let us work on better datasets and models!</a></p> </div> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Maestrale chat beta ༄ By @efederici and @mferraretto ## Model description - Language Model: Mistral-7b for the Italian language, continued pre-training for Italian on a curated large-scale high-quality corpus. - Fine-Tuning: SFT performed on convs/instructions for three epochs. v0.3 - Function calling - Reduced default system prompt to avoid wasting tokens (pre-alignment) This model uses ChatML prompt format: ``` <\|im_start\|>system Sei un assistente utile.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Usage: ```python from transformers import ( AutoTokenizer, AutoModelForCausalLM, GenerationConfig, TextStreamer ) import torch tokenizer = AutoTokenizer.from_pretrained("mii-llm/maestrale-chat-v0.3-beta") model = AutoModelForCausalLM.from_pretrained("mii-llm/maestrale-chat-v0.3-beta", load_in_8bit=True, device_map="auto") gen = GenerationConfig( do_sample=True, temperature=0.7, repetition_penalty=1.2, top_k=50, top_p=0.95, max_new_tokens=500, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.convert_tokens_to_ids("<\|im_end\|>") ) messages = [ {"role": "system", "content": "Sei un assistente utile."}, {"role": "user", "content": "{prompt}"} ] with torch.no_grad(), torch.backends.cuda.sdp_kernel( enable_flash=True, enable_math=False, enable_mem_efficient=False ): temp = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(temp, return_tensors="pt").to("cuda") streamer = TextStreamer(tokenizer, skip_prompt=True) _ = model.generate( **inputs, streamer=streamer, generation_config=gen ) ``` ## Intended uses & limitations It's a beta sft version, but it's not `aligned`. It's a first test. We are working on alignment data and evals. [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
nyunai/OpenHathi-7B-Hi-v0.1-Base-AWQ-samvaad-hi-v1-chat-format	nyunai	2024-03-06T10:00:46Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-03-06T09:34:58Z	--- library_name: transformers tags: [] --- ## Model Description This model is a compressed version of the OpenHathi-7B-Hi base model, optimized for chat format text data in the Hindi language. It has been quantized using the AWQ technique with calibration data from the samvaad-hi-v1 dataset. The compression process aims to reduce the model size while preserving its performance on chat-oriented tasks. ## Model Usage: The compressed model can be utilized for various natural language processing tasks, particularly those involving chat format text data in Hindi. It can be deployed in conversational AI systems, chatbots, or any application requiring efficient processing of chat-style interactions. ## Performance Metrics: - Model Size: 4.15 GB - Compression Technique: AWQ - Calibration Data: [samvaad-hi-v1 chat format](https://huggingface.co/datasets/shwubham/samvaad-hi-v1-chat-format) dataset - Tokenization Model Size: 968 KB - Performance: The compressed model's performance has been evaluated on various chat-oriented tasks, demonstrating efficiency in handling conversational text data while maintaining comparable performance to the original base model. Limitations: While the compressed model offers significant reductions in size, there may be slight trade-offs in performance compared to the full-sized base model. It may not perform optimally on tasks outside the scope of chat-oriented text data in Hindi.
joshus/esg_base_pos_3	joshus	2024-03-06T09:57:24Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-06T09:57:07Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # joshus/esg_base_pos_3 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('joshus/esg_base_pos_3') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=joshus/esg_base_pos_3) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 360 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 108, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
OmarHaroon01/compressed_byt5_pretrained	OmarHaroon01	2024-03-06T09:53:56Z	4	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-06T09:53:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
s14pe/q-FrozenLake-v1-4x4-noSlippery	s14pe	2024-03-06T09:47:04Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-06T09:47:02Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="s14pe/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
jelldps/malaysian-mistral-7b-32k-instructions-v4-gguf	jelldps	2024-03-06T09:41:56Z	6	3	transformers	[ "transformers", "gguf", "mistral", "text-generation", "conversational", "ms", "base_model:mesolitica/malaysian-mistral-7b-32k-instructions-v3.5", "base_model:quantized:mesolitica/malaysian-mistral-7b-32k-instructions-v3.5", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-02-27T10:32:08Z	--- base_model: mesolitica/malaysian-mistral-7b-32k-instructions-v3.5 language: - ms --- # malaysian-mistral-7b-32k-instructions-v4 - GGUF - Model creator: [Mesolitica](https://huggingface.co/mesolitica) - Original model: [malaysian-mistral-7b-32k-instructions-v4](https://huggingface.co/mesolitica/malaysian-mistral-7b-32k-instructions-v4)
vidhi0206/setfit-paraphrase-mpnet-emotion	vidhi0206	2024-03-06T09:41:22Z	4	0	setfit	[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2", "model-index", "region:us" ]	text-classification	2024-02-28T12:34:57Z	--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: i honestly thought impossible at this point i feel pretty - text: i feel convinced that im going to shy away from whatever is really good for me - text: i feel guilt that i should be more caring and im not - text: i found myself feeling nostalgic as i thought about the temporarily abandoned little bishop chronicles - text: i am feeling very indecisive and spontaneous pipeline_tag: text-classification inference: true base_model: sentence-transformers/paraphrase-mpnet-base-v2 model-index: - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.5225 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 6 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 1 \| <ul><li>'i feel so much better about that number'</li><li>'i feel like i have reached a plateau where im not buying as much as i use to and feeling more satisfied with my wardrobe and personal style'</li><li>'i feel especially thankful'</li></ul> \| \| 3 \| <ul><li>'i feel so violent just want to break some glass'</li><li>'i always feel rushed on the way to visit no comments'</li><li>'i think maybe about how strongly she feels about him and being there for him but brad looks really distracted'</li></ul> \| \| 5 \| <ul><li>'i feel like when i was a kid it was constantly impressed upon me how awesome ants are'</li><li>'i feel like it s a boy i would be pretty shocked if it was so somewhere in there my gut or my brain is saying girl'</li><li>'i feel like every day i walk around with so much stress and sadness that im literally amazed im still here that i still function that im still basically a friendly stable person'</li></ul> \| \| 0 \| <ul><li>'i would feel that a few words would be not only inadequate but a travesty'</li><li>'i attributed this depression to feeling inadequate against the unrealistic ideals of the lds church and while i still hold those ideals somewhat responsible i recognize this pattern of behavior'</li><li>'ive been resting and feeling generally unpleasant and queasy but in that frustrating background way where you dont feel right but cant place an exact cause'</li></ul> \| \| 4 \| <ul><li>'i was starting to feel scared for both of their safety and i wish those officers hadn t left no matter how much i hated them'</li><li>'i am already feeling frantic'</li><li>'i believe in you moment we all feel til then it s one more skeptical song'</li></ul> \| \| 2 \| <ul><li>'i do feel sympathetic to the parties involved now that their careers are down the drain'</li><li>'i like frappes and shit when im feeling naughty but i drink tea daily'</li><li>'i will pay a month for months and feel shame every time i grill a hot dog from that point on'</li></ul> \| ## Evaluation ### Metrics \| Label \| Accuracy \| \|:--------\|:---------\| \| all \| 0.5225 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("vidhi0206/setfit-paraphrase-mpnet-emotion") # Run inference preds = model("i am feeling very indecisive and spontaneous") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:----\| \| Word count \| 4 \| 19.3333 \| 48 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0 \| 8 \| \| 1 \| 8 \| \| 2 \| 8 \| \| 3 \| 8 \| \| 4 \| 8 \| \| 5 \| 8 \| ### Training Hyperparameters - batch_size: (8, 8) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 20 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:----:\|:-------------:\|:---------------:\| \| 0.0042 \| 1 \| 0.3009 \| - \| \| 0.2083 \| 50 \| 0.1916 \| - \| \| 0.4167 \| 100 \| 0.0393 \| - \| \| 0.625 \| 150 \| 0.0129 \| - \| \| 0.8333 \| 200 \| 0.0034 \| - \| ### Framework Versions - Python: 3.8.10 - SetFit: 1.0.3 - Sentence Transformers: 2.3.1 - Transformers: 4.37.2 - PyTorch: 2.2.0+cu121 - Datasets: 2.17.0 - Tokenizers: 0.15.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
aparna-01/my-pet-cat-sdf	aparna-01	2024-03-06T09:32:54Z	0	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-06T09:28:45Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Cat-SDF Dreambooth model trained by aparna-01 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 23/CSE/111 Sample pictures of this concept: ![0](https://huggingface.co/aparna-01/my-pet-cat-sdf/resolve/main/sample_images/IMG-20240306-WA0004.jpg)
AlignmentResearch/robust_llm_z5ph5m7h_from_EleutherAI_pythia-14m	AlignmentResearch	2024-03-06T09:24:30Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-14m", "base_model:finetune:EleutherAI/pythia-14m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T09:24:23Z	--- tags: - generated_from_trainer base_model: EleutherAI/pythia-14m model-index: - name: robust_llm_z5ph5m7h_from_EleutherAI_pythia-14m results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_z5ph5m7h_from_EleutherAI_pythia-14m This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 2 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
s14pe/ppo-LunarLander-v2	s14pe	2024-03-06T09:23:50Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-05T14:14:49Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 279.00 +/- 15.83 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
AlignmentResearch/robust_llm_m857mz1i_from_EleutherAI_pythia-14m	AlignmentResearch	2024-03-06T09:23:12Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-14m", "base_model:finetune:EleutherAI/pythia-14m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T09:23:05Z	--- tags: - generated_from_trainer base_model: EleutherAI/pythia-14m model-index: - name: robust_llm_m857mz1i_from_EleutherAI_pythia-14m results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_m857mz1i_from_EleutherAI_pythia-14m This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 2 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_w9a5ielg_from_EleutherAI_pythia-14m	AlignmentResearch	2024-03-06T09:22:05Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-14m", "base_model:finetune:EleutherAI/pythia-14m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T09:21:58Z	--- tags: - generated_from_trainer base_model: EleutherAI/pythia-14m model-index: - name: robust_llm_w9a5ielg_from_EleutherAI_pythia-14m results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_w9a5ielg_from_EleutherAI_pythia-14m This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
alfredplpl/gemma-2b-it-ja-poc-2	alfredplpl	2024-03-06T09:21:13Z	2	2	peft	[ "peft", "safetensors", "ja", "en", "license:other", "region:us" ]	null	2024-03-05T12:17:24Z	--- language: - ja - en license: other library_name: peft license_name: gemma-terms-of-use license_link: https://www.kaggle.com/models/google/gemma/license/consent --- # はじめになんか日本語が話せる商用利用可能なAIです。 [Google Colab](https://colab.research.google.com/drive/1AZ3oW1RJ8JDi4DGh3_z__aAd1lUVlswi?usp=sharing) # Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch from peft import PeftModel # トークナイザーとモデルの準備 tokenizer = AutoTokenizer.from_pretrained("alfredplpl/ja-aozora-wikipedia-gemmba-2b") model = AutoModelForCausalLM.from_pretrained("alfredplpl/ja-aozora-wikipedia-gemmba-2b") model = PeftModel.from_pretrained(model = model, model_id = "alfredplpl/gemma-2b-it-ja-poc-2") # プロンプトの準備 prompt=""" あなたは親切なアシスタントです。英語は喋らず、日本語だけ喋ってください。 <start_of_turn>user 人生で大切なことはなんですか？<end_of_turn> <start_of_turn>model """ # 推論の実行 input_ids = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **input_ids, max_new_tokens=128, do_sample=True, top_p=0.95, temperature=0.2, repetition_penalty=1.1, ) print(tokenizer.decode(outputs[0])) ``` ## Result ```bash <bos> あなたは親切なアシスタントです。英語は喋らず、日本語だけ喋ってください。 <start_of_turn>user 人生で大切なことはなんですか？<end_of_turn> <start_of_turn>model 人生で大切なのは、幸せになることです。<end_of_turn> <eos> ``` # Chat Templete ```bash <bos> {{system prompt}} <start_of_turn>user {{prompt}}<end_of_turn> <start_of_turn>model {{response}}<end_of_turn> <eos> ``` # Base model - free-ai-ltd/ja-aozora-wikipedia-gemmba-2b (private) # Dataset for Instruction tuning - llm-jp/databricks-dolly-15k-ja - llm-jp/oasst1-21k-ja - kunishou/oasst1-chat-44k-ja - kunishou/oasst2-chat-68k-ja - kunishou/cnn-dailymail-27k-ja - kunishou/databricks-dolly-69k-ja-en-translation - kunishou/databricks-dolly-15k-ja - shi3z/OpenOrcaJapanese # How to make this model - [LoRA](https://gist.github.com/alfredplpl/e20cad036c151f38645a1abc87f56a2f)
Bajiyo/Transliteration_from_malayalam_to_english	Bajiyo	2024-03-06T09:17:20Z	3	0	tf-keras	[ "tf-keras", "license:other", "region:us" ]	null	2024-03-06T09:15:23Z	--- license: other license_name: other license_link: LICENSE ---
DhairyaSarin/promotional-text-analyser-v2	DhairyaSarin	2024-03-06T09:11:17Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "region:us" ]	null	2024-03-06T09:10:46Z	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.1 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.9.0
alibaba-pai/pai-bloom-1b1-text2prompt-sd	alibaba-pai	2024-03-06T09:07:42Z	124	35	transformers	[ "transformers", "pytorch", "safetensors", "bloom", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-08T08:55:33Z	--- license: apache-2.0 widget: - text: "Instruction: Give a simple description of the image to generate a drawing prompt.\nInput: 1 girl\nOutput:" tags: - pytorch - transformers - text-generation --- # BeautifulPrompt ## 简介 Brief Introduction 我们开源了一个自动Prompt生成模型，您可以直接输入一个极其简单的Prompt，就可以得到经过语言模型优化过的Prompt，帮助您更简单地生成高颜值图像。 We release an automatic Prompt generation model, you can directly enter an extremely simple Prompt and get a Prompt optimized by the language model to help you generate more beautiful images simply. * Github: [EasyNLP](https://github.com/alibaba/EasyNLP) ## 使用 Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained('alibaba-pai/pai-bloom-1b1-text2prompt-sd') model = AutoModelForCausalLM.from_pretrained('alibaba-pai/pai-bloom-1b1-text2prompt-sd').eval().cuda() raw_prompt = '1 girl' input = f'Instruction: Give a simple description of the image to generate a drawing prompt.\nInput: {raw_prompt}\nOutput:' input_ids = tokenizer.encode(input, return_tensors='pt').cuda() outputs = model.generate( input_ids, max_length=384, do_sample=True, temperature=1.0, top_k=50, top_p=0.95, repetition_penalty=1.2, num_return_sequences=5) prompts = tokenizer.batch_decode(outputs[:, input_ids.size(1):], skip_special_tokens=True) prompts = [p.strip() for p in prompts] print(prompts) ``` ## 作品展示 Gallery <style> table th:first-of-type { width: 50%; } table th:nth-of-type(2) { width: 50%; } </style> \| Original \| BeautifulPrompt \| \| ---------------------------------------- \| ---------------------------------- \| \| prompt: taylor swift, country, golden, fearless,wavehair \| prompt: portrait of taylor swift as a beautiful woman, long hair, country, golden ratio, intricate, symmetrical, cinematic lighting, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration \| \| ![](example1.png) \| ![](example2.png) \| \| Original \| BeautifulPrompt \| \| ---------------------------------------- \| ---------------------------------- \| \| prompt: A majestic sailing ship \| prompt: a massive sailing ship, epic, cinematic, artstation, greg rutkowski, james gurney, sparth \| \| ![](example3.png) \| ![](example4.png) \| ## 使用须知 Notice for Use 使用上述模型需遵守[AIGC模型开源特别条款](https://terms.alicdn.com/legal-agreement/terms/common_platform_service/20230505180457947/20230505180457947.html)。 If you want to use this model, please read this [document](https://terms.alicdn.com/legal-agreement/terms/common_platform_service/20230505180457947/20230505180457947.html) carefully and abide by the terms. ## Paper Citation If you find the model useful, please consider cite the paper: ``` @inproceedings{emnlp2023a, author = {Tingfeng Cao and Chengyu Wang and Bingyan Liu and Ziheng Wu and Jinhui Zhu and Jun Huang}, title = {BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis}, booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track}, pages = {1--11}, year = {2023} } ```
zxhezexin/openlrm-mix-small-1.1	zxhezexin	2024-03-06T08:56:32Z	31	1	transformers	[ "transformers", "pytorch", "safetensors", "image-to-3d", "dataset:allenai/objaverse", "arxiv:2311.04400", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	image-to-3d	2024-03-04T07:05:06Z	--- license: cc-by-nc-4.0 datasets: - allenai/objaverse pipeline_tag: image-to-3d --- # Model Card for OpenLRM V1.1 ## Overview - This model card is for the [OpenLRM](https://github.com/3DTopia/OpenLRM) project, which is an open-source implementation of the paper [LRM](https://arxiv.org/abs/2311.04400). - Information contained in this model card corresponds to [Version 1.1](https://github.com/3DTopia/OpenLRM/releases). ## Model Details - Training data \| Model \| Training Data \| \| :---: \| :---: \| \| [openlrm-obj-small-1.1](https://huggingface.co/zxhezexin/openlrm-obj-small-1.1) \| Objaverse \| \| [openlrm-obj-base-1.1](https://huggingface.co/zxhezexin/openlrm-obj-base-1.1) \| Objaverse \| \| [openlrm-obj-large-1.1](https://huggingface.co/zxhezexin/openlrm-obj-large-1.1) \| Objaverse \| \| [openlrm-mix-small-1.1](https://huggingface.co/zxhezexin/openlrm-mix-small-1.1) \| Objaverse + MVImgNet \| \| [openlrm-mix-base-1.1](https://huggingface.co/zxhezexin/openlrm-mix-base-1.1) \| Objaverse + MVImgNet \| \| [openlrm-mix-large-1.1](https://huggingface.co/zxhezexin/openlrm-mix-large-1.1) \| Objaverse + MVImgNet \| - Model architecture (version==1.1) \| Type \| Layers \| Feat. Dim \| Attn. Heads \| Triplane Dim. \| Input Res. \| Image Encoder \| Size \| \| :---: \| :----: \| :-------: \| :---------: \| :-----------: \| :--------: \| :---------------: \| :---: \| \| small \| 12 \| 512 \| 8 \| 32 \| 224 \| dinov2_vits14_reg \| 446M \| \| base \| 12 \| 768 \| 12 \| 48 \| 336 \| dinov2_vitb14_reg \| 1.04G \| \| large \| 16 \| 1024 \| 16 \| 80 \| 448 \| dinov2_vitb14_reg \| 1.81G \| - Training settings \| Type \| Rend. Res. \| Rend. Patch \| Ray Samples \| \| :---: \| :--------: \| :---------: \| :---------: \| \| small \| 192 \| 64 \| 96 \| \| base \| 288 \| 96 \| 96 \| \| large \| 384 \| 128 \| 128 \| ## Notable Differences from the Original Paper - We do not use the deferred back-propagation technique in the original paper. - We used random background colors during training. - The image encoder is based on the [DINOv2](https://github.com/facebookresearch/dinov2) model with register tokens. - The triplane decoder contains 4 layers in our implementation. ## License - The model weights are released under the [Creative Commons Attribution-NonCommercial 4.0 International License](LICENSE_WEIGHT). - They are provided for research purposes only, and CANNOT be used commercially. ## Disclaimer This model is an open-source implementation and is NOT the official release of the original research paper. While it aims to reproduce the original results as faithfully as possible, there may be variations due to model implementation, training data, and other factors. ### Ethical Considerations - This model should be used responsibly and ethically, and should not be used for malicious purposes. - Users should be aware of potential biases in the training data. - The model should not be used under the circumstances that could lead to harm or unfair treatment of individuals or groups. ### Usage Considerations - The model is provided "as is" without warranty of any kind. - Users are responsible for ensuring that their use complies with all relevant laws and regulations. - The developers and contributors of this model are not liable for any damages or losses arising from the use of this model. --- This model card is subject to updates and modifications. Users are advised to check for the latest version regularly.
teknow/gemmaWithQuotes	teknow	2024-03-06T08:56:22Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T08:38:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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aryachakraborty/DeepSeek-1.3B-IT-NL-SQL-V2	aryachakraborty	2024-03-06T08:49:27Z	13	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T08:47:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ANWAR101/lora-bart-base-youtube-cnn	ANWAR101	2024-03-06T08:48:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-06T08:47:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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VinitRuparelia/mountain	VinitRuparelia	2024-03-06T08:47:03Z	0	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-06T08:40:23Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### Mountain Dreambooth model trained by VinitRuparelia following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: RGIT_669 Sample pictures of this concept: ![0](https://huggingface.co/VinitRuparelia/mountain/resolve/main/sample_images/WhatsApp_Image_2024-03-06_at_1.50.14_PM.jpeg) ![1](https://huggingface.co/VinitRuparelia/mountain/resolve/main/sample_images/WhatsApp_Image_2024-03-06_at_1.50.15_PM.jpeg)
Kudod/bloom-560m_model_colab	Kudod	2024-03-06T08:43:47Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "bloom", "text-generation", "generated_from_trainer", "base_model:bigscience/bloom-560m", "base_model:finetune:bigscience/bloom-560m", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T08:31:53Z	--- license: bigscience-bloom-rail-1.0 base_model: bigscience/bloom-560m tags: - generated_from_trainer model-index: - name: bloom-560m_model_colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bloom-560m_model_colab This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0976 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 102 \| 1.4784 \| \| No log \| 2.0 \| 204 \| 1.5105 \| \| No log \| 3.0 \| 306 \| 0.7721 \| \| No log \| 4.0 \| 408 \| 0.4614 \| \| 1.1878 \| 5.0 \| 510 \| 0.2513 \| \| 1.1878 \| 6.0 \| 612 \| 0.0976 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
minhah/videomae-base-finetuned-ucf101-subset-finetuned-elder-UFC-prtuned	minhah	2024-03-06T08:43:17Z	4	0	transformers	[ "transformers", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:minhah/videomae-base-finetuned-ucf101-subset", "base_model:finetune:minhah/videomae-base-finetuned-ucf101-subset", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2024-03-06T07:10:58Z	--- license: cc-by-nc-4.0 base_model: minhah/videomae-base-finetuned-ucf101-subset tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101-subset-finetuned-elder-UFC-prtuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-ucf101-subset-finetuned-elder-UFC-prtuned This model is a fine-tuned version of [minhah/videomae-base-finetuned-ucf101-subset](https://huggingface.co/minhah/videomae-base-finetuned-ucf101-subset) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6593 - Accuracy: 0.3481 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 576 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.729 \| 0.13 \| 73 \| 1.6346 \| 0.3408 \| \| 1.683 \| 1.13 \| 146 \| 1.6505 \| 0.3029 \| \| 1.6889 \| 2.13 \| 219 \| 1.6359 \| 0.3408 \| \| 1.6853 \| 3.13 \| 292 \| 1.6739 \| 0.2398 \| \| 1.5793 \| 4.13 \| 365 \| 1.6679 \| 0.2588 \| \| 1.5783 \| 5.13 \| 438 \| 1.6091 \| 0.3324 \| \| 1.5745 \| 6.13 \| 511 \| 1.6306 \| 0.3072 \| \| 1.5704 \| 7.11 \| 576 \| 1.6573 \| 0.2707 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Litzy619/V0305P2	Litzy619	2024-03-06T08:39:00Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:yahma/llama-7b-hf", "base_model:finetune:yahma/llama-7b-hf", "license:other", "region:us" ]	null	2024-03-06T02:27:50Z	--- license: other base_model: yahma/llama-7b-hf tags: - generated_from_trainer model-index: - name: V0305P2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # V0305P2 This model is a fine-tuned version of [yahma/llama-7b-hf](https://huggingface.co/yahma/llama-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0602 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.3061 \| 0.09 \| 10 \| 0.1617 \| \| 0.1712 \| 0.17 \| 20 \| 0.1558 \| \| 0.1564 \| 0.26 \| 30 \| 0.1535 \| \| 0.1526 \| 0.34 \| 40 \| 0.1479 \| \| 0.1503 \| 0.43 \| 50 \| 0.1506 \| \| 0.1563 \| 0.51 \| 60 \| 0.1505 \| \| 0.1517 \| 0.6 \| 70 \| 0.1507 \| \| 0.1533 \| 0.68 \| 80 \| 0.1489 \| \| 0.1491 \| 0.77 \| 90 \| 0.1488 \| \| 0.1523 \| 0.85 \| 100 \| 0.1471 \| \| 0.1522 \| 0.94 \| 110 \| 0.1433 \| \| 0.1381 \| 1.02 \| 120 \| 0.1229 \| \| 0.1303 \| 1.11 \| 130 \| 0.1206 \| \| 0.1155 \| 1.19 \| 140 \| 0.1018 \| \| 0.1095 \| 1.28 \| 150 \| 0.0933 \| \| 0.103 \| 1.37 \| 160 \| 0.0906 \| \| 0.1007 \| 1.45 \| 170 \| 0.0904 \| \| 0.0895 \| 1.54 \| 180 \| 0.0887 \| \| 0.0914 \| 1.62 \| 190 \| 0.0840 \| \| 0.0943 \| 1.71 \| 200 \| 0.0808 \| \| 0.0938 \| 1.79 \| 210 \| 0.0757 \| \| 0.0884 \| 1.88 \| 220 \| 0.0666 \| \| 0.0862 \| 1.96 \| 230 \| 0.0733 \| \| 0.0709 \| 2.05 \| 240 \| 0.0748 \| \| 0.0601 \| 2.13 \| 250 \| 0.0730 \| \| 0.0593 \| 2.22 \| 260 \| 0.0632 \| \| 0.059 \| 2.3 \| 270 \| 0.0757 \| \| 0.06 \| 2.39 \| 280 \| 0.0620 \| \| 0.0647 \| 2.47 \| 290 \| 0.0605 \| \| 0.0619 \| 2.56 \| 300 \| 0.0624 \| \| 0.0651 \| 2.65 \| 310 \| 0.0605 \| \| 0.0578 \| 2.73 \| 320 \| 0.0597 \| \| 0.0585 \| 2.82 \| 330 \| 0.0598 \| \| 0.0575 \| 2.9 \| 340 \| 0.0601 \| \| 0.0566 \| 2.99 \| 350 \| 0.0602 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
AnonymousSub/FPDM_bertlarge_model	AnonymousSub	2024-03-06T08:32:26Z	4	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-03-06T08:30:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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SenswiseData/berturk_cased_profanity	SenswiseData	2024-03-06T08:22:01Z	4	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dbmdz/bert-base-turkish-cased", "base_model:finetune:dbmdz/bert-base-turkish-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T08:21:29Z	--- license: mit base_model: dbmdz/bert-base-turkish-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_model This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1353 - Accuracy: 0.9635 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 338 \| 0.1606 \| 0.9502 \| \| 0.3717 \| 2.0 \| 676 \| 0.1353 \| 0.9635 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
VRSneha/kamal_camembert_dummy	VRSneha	2024-03-06T08:13:06Z	4	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-03-06T08:12:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
obudzecie/distilbert-base-uncased-finetuned-cola	obudzecie	2024-03-06T07:58:21Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-27T13:04:09Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4341 - Matthews Correlation: 0.4600 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9.881638457643646e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 37 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.45 \| 1.0 \| 1069 \| 0.9061 \| 0.2926 \| \| 0.3901 \| 2.0 \| 2138 \| 0.7333 \| 0.3877 \| \| 0.2976 \| 3.0 \| 3207 \| 0.8140 \| 0.3997 \| \| 0.2158 \| 4.0 \| 4276 \| 1.1014 \| 0.4422 \| \| 0.0857 \| 5.0 \| 5345 \| 1.4341 \| 0.4600 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Dangurangu/my-awesome-setfit-model	Dangurangu	2024-03-06T07:54:55Z	6	0	setfit	[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "dataset:SetFit/SentEval-CR", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2", "model-index", "region:us" ]	text-classification	2024-03-06T07:54:02Z	--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer datasets: - SetFit/SentEval-CR metrics: - accuracy widget: - text: you can take pic of your friends and the picture will pop up when they call . - text: the speakerphone , the radio , all features work perfectly . - text: 'a ) the picture quality ( color and sharpness of focusing ) are so great , it completely eliminated my doubt about digital imaging -- - how could one eat rice one grain at a time : - ) )' - text: so far the dvd works so i hope it does n 't break down like the reviews i 've read . - text: i have a couple hundred contacts and the menu loads within a few seconds , no big deal . pipeline_tag: text-classification inference: true base_model: sentence-transformers/paraphrase-mpnet-base-v2 model-index: - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: SetFit/SentEval-CR type: SetFit/SentEval-CR split: test metrics: - type: accuracy value: 0.8804780876494024 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [SetFit/SentEval-CR](https://huggingface.co/datasets/SetFit/SentEval-CR) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 2 classes - Training Dataset: [SetFit/SentEval-CR](https://huggingface.co/datasets/SetFit/SentEval-CR) <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 1 \| <ul><li>'* slick-looking design and improved interface'</li><li>'as for bluetooth , no problems at all .'</li><li>'2 ) storage capacity'</li></ul> \| \| 0 \| <ul><li>"the day finally arrived when i was sure i 'd leave sprint ."</li><li>"neither message was answered ( they ask for 24 hours before replying - i 've been waiting 27 days . )"</li><li>'only problem is that is a bit heavy .'</li></ul> \| ## Evaluation ### Metrics \| Label \| Accuracy \| \|:--------\|:---------\| \| all \| 0.8805 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("dangurangu/my-awesome-setfit-model") # Run inference preds = model("the speakerphone , the radio , all features work perfectly .") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:----\| \| Word count \| 4 \| 18.0625 \| 44 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0 \| 7 \| \| 1 \| 9 \| ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 20 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:-----:\|:----:\|:-------------:\|:---------------:\| \| 0.025 \| 1 \| 0.2205 \| - \| ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 2.5.1 - Transformers: 4.38.1 - PyTorch: 2.1.0+cu121 - Datasets: 2.18.0 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
ise-uiuc/Magicoder-DS-6.7B	ise-uiuc	2024-03-06T07:40:45Z	203	38	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:ise-uiuc/Magicoder-OSS-Instruct-75K", "arxiv:2312.02120", "arxiv:2305.06161", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-03T19:29:41Z	--- license: other library_name: transformers datasets: - ise-uiuc/Magicoder-OSS-Instruct-75K license_name: deepseek pipeline_tag: text-generation --- # 🎩 Magicoder: Source Code Is All You Need > Refer to our GitHub repo [ise-uiuc/magicoder](https://github.com/ise-uiuc/magicoder/) for an up-to-date introduction to the Magicoder family! * 🎩Magicoder is a model family empowered by 🪄OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets for generating low-bias and high-quality instruction data for code. * 🪄OSS-Instruct mitigates the inherent bias of the LLM-synthesized instruction data by empowering them with a wealth of open-source references to produce more diverse, realistic, and controllable data. ![Overview of OSS-Instruct](assets/overview.svg) ![Overview of Result](assets/result.png) ## Model Details ### Model Description * Developed by: [Yuxiang Wei](https://yuxiang.cs.illinois.edu), [Zhe Wang](https://github.com/zhewang2001), [Jiawei Liu](https://jiawei-site.github.io), [Yifeng Ding](https://yifeng-ding.com), [Lingming Zhang](https://lingming.cs.illinois.edu) * License: [DeepSeek](https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL) * Finetuned from model: [deepseek-coder-6.7b-base](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base) ### Model Sources * Repository: <https://github.com/ise-uiuc/magicoder> * Paper: <https://arxiv.org/abs/2312.02120> * Demo (powered by [Gradio](https://www.gradio.app)): <https://github.com/ise-uiuc/magicoder/tree/main/demo> ### Training Data * [Magicoder-OSS-Instruct-75K](https://huggingface.co/datasets/ise-uiuc/Magicoder_oss_instruct_75k): generated through OSS-Instruct using `gpt-3.5-turbo-1106` and used to train both Magicoder and Magicoder-S series. ## Uses ### Direct Use Magicoders are designed and best suited for coding tasks. ### Out-of-Scope Use Magicoders may not work well in non-coding tasks. ## Bias, Risks, and Limitations Magicoders may sometimes make errors, producing misleading contents, or struggle to manage tasks that are not related to coding. ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. ## How to Get Started with the Model Use the code below to get started with the model. Make sure you installed the [transformers](https://huggingface.co/docs/transformers/index) library. ```python from transformers import pipeline import torch MAGICODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions. @@ Instruction {instruction} @@ Response """ instruction = <Your code instruction here> prompt = MAGICODER_PROMPT.format(instruction=instruction) generator = pipeline( model="ise-uiuc/Magicoder-DS-6.7B", task="text-generation", torch_dtype=torch.bfloat16, device_map="auto", ) result = generator(prompt, max_length=1024, num_return_sequences=1, temperature=0.0) print(result[0]["generated_text"]) ``` ## Technical Details Refer to our GitHub repo: [ise-uiuc/magicoder](https://github.com/ise-uiuc/magicoder/). ## 📝 Citation ```bibtex @misc{magicoder, title={Magicoder: Source Code Is All You Need}, author={Yuxiang Wei and Zhe Wang and Jiawei Liu and Yifeng Ding and Lingming Zhang}, year={2023}, eprint={2312.02120}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## 🙏 Acknowledgements * [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder): Evol-Instruct * [DeepSeek-Coder](https://github.com/deepseek-ai/DeepSeek-Coder): Base model for Magicoder-DS * [CodeLlama](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/): Base model for Magicoder-CL * [StarCoder](https://arxiv.org/abs/2305.06161): Data decontamination ## Important Note Magicoder models are trained on the synthetic data generated by OpenAI models. Please pay attention to OpenAI's [terms of use](https://openai.com/policies/terms-of-use) when using the models and the datasets. Magicoders will not compete with OpenAI's commercial products.
ise-uiuc/Magicoder-S-DS-6.7B	ise-uiuc	2024-03-06T07:40:23Z	843	201	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:ise-uiuc/Magicoder-OSS-Instruct-75K", "dataset:ise-uiuc/Magicoder-Evol-Instruct-110K", "arxiv:2312.02120", "arxiv:2305.06161", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-03T19:37:23Z	--- license: other library_name: transformers datasets: - ise-uiuc/Magicoder-OSS-Instruct-75K - ise-uiuc/Magicoder-Evol-Instruct-110K license_name: deepseek pipeline_tag: text-generation --- # 🎩 Magicoder: Source Code Is All You Need > Refer to our GitHub repo [ise-uiuc/magicoder](https://github.com/ise-uiuc/magicoder/) for an up-to-date introduction to the Magicoder family! * 🎩Magicoder is a model family empowered by 🪄OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets for generating low-bias and high-quality instruction data for code. * 🪄OSS-Instruct mitigates the inherent bias of the LLM-synthesized instruction data by empowering them with a wealth of open-source references to produce more diverse, realistic, and controllable data. ![Overview of OSS-Instruct](assets/overview.svg) ![Overview of Result](assets/result.png) ## Model Details ### Model Description * Developed by: [Yuxiang Wei](https://yuxiang.cs.illinois.edu), [Zhe Wang](https://github.com/zhewang2001), [Jiawei Liu](https://jiawei-site.github.io), [Yifeng Ding](https://yifeng-ding.com), [Lingming Zhang](https://lingming.cs.illinois.edu) * License: [DeepSeek](https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL) * Finetuned from model: [deepseek-coder-6.7b-base](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base) ### Model Sources * Repository: <https://github.com/ise-uiuc/magicoder> * Paper: <https://arxiv.org/abs/2312.02120> * Demo (powered by [Gradio](https://www.gradio.app)): <https://github.com/ise-uiuc/magicoder/tree/main/demo> ### Training Data * [Magicoder-OSS-Instruct-75K](https://huggingface.co/datasets/ise-uiuc/Magicoder_oss_instruct_75k): generated through OSS-Instruct using `gpt-3.5-turbo-1106` and used to train both Magicoder and Magicoder-S series. * [Magicoder-Evol-Instruct-110K](https://huggingface.co/datasets/ise-uiuc/Magicoder_evol_instruct_110k): decontaminated and redistributed from [theblackcat102/evol-codealpaca-v1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1), used to further finetune Magicoder series and obtain Magicoder-S models. ## Uses ### Direct Use Magicoders are designed and best suited for coding tasks. ### Out-of-Scope Use Magicoders may not work well in non-coding tasks. ## Bias, Risks, and Limitations Magicoders may sometimes make errors, producing misleading contents, or struggle to manage tasks that are not related to coding. ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. ## How to Get Started with the Model Use the code below to get started with the model. Make sure you installed the [transformers](https://huggingface.co/docs/transformers/index) library. ```python from transformers import pipeline import torch MAGICODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions. @@ Instruction {instruction} @@ Response """ instruction = <Your code instruction here> prompt = MAGICODER_PROMPT.format(instruction=instruction) generator = pipeline( model="ise-uiuc/Magicoder-S-DS-6.7B", task="text-generation", torch_dtype=torch.bfloat16, device_map="auto", ) result = generator(prompt, max_length=1024, num_return_sequences=1, temperature=0.0) print(result[0]["generated_text"]) ``` ## Technical Details Refer to our GitHub repo: [ise-uiuc/magicoder](https://github.com/ise-uiuc/magicoder/). ## Citation ```bibtex @misc{magicoder, title={Magicoder: Source Code Is All You Need}, author={Yuxiang Wei and Zhe Wang and Jiawei Liu and Yifeng Ding and Lingming Zhang}, year={2023}, eprint={2312.02120}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Acknowledgements * [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder): Evol-Instruct * [DeepSeek-Coder](https://github.com/deepseek-ai/DeepSeek-Coder): Base model for Magicoder-DS * [CodeLlama](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/): Base model for Magicoder-CL * [StarCoder](https://arxiv.org/abs/2305.06161): Data decontamination ## Important Note Magicoder models are trained on the synthetic data generated by OpenAI models. Please pay attention to OpenAI's [terms of use](https://openai.com/policies/terms-of-use) when using the models and the datasets. Magicoders will not compete with OpenAI's commercial products.
ITT-AF/ITT-42dot_LLM-PLM-1.3B-v6.0	ITT-AF	2024-03-06T07:40:07Z	60	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T06:35:07Z	--- license: cc-by-nc-4.0 --- # ITT-AF/ITT-42dot_LLM-PLM-1.3B-v6.0 This model is a fine-tuned version of [42dot/42dot_LLM-PLM-1.3B](https://huggingface.co/42dot/42dot_LLM-PLM-1.3B) on an custom dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 24 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.0.0 - Tokenizers 0.15.0
Sumail/Golden_Waves06_2b	Sumail	2024-03-06T07:38:00Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "base_model:Sumail/Bubble_bee04_2b", "base_model:finetune:Sumail/Bubble_bee04_2b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T07:35:07Z	--- base_model: - Sumail/Bubble_bee04_2b - 0x0dad0/nous_nb00 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [Sumail/Bubble_bee04_2b](https://huggingface.co/Sumail/Bubble_bee04_2b) * [0x0dad0/nous_nb00](https://huggingface.co/0x0dad0/nous_nb00) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: 0x0dad0/nous_nb00 layer_range: [0, 18] - model: Sumail/Bubble_bee04_2b layer_range: [0, 18] merge_method: slerp base_model: 0x0dad0/nous_nb00 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.75 dtype: bfloat16 ```
venkatarajendra/rm-falcon-7b	venkatarajendra	2024-03-06T07:34:06Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:tiiuae/falcon-7b", "base_model:adapter:tiiuae/falcon-7b", "region:us" ]	null	2024-03-06T07:33:47Z	--- library_name: peft base_model: tiiuae/falcon-7b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.9.1.dev0
anum231/food_classifier	anum231	2024-03-06T07:26:58Z	46	0	transformers	[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "base_model:anum231/cancer_classifier_100", "base_model:finetune:anum231/cancer_classifier_100", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-01-27T05:41:37Z	--- license: apache-2.0 base_model: anum231/cancer_classifier_100 tags: - generated_from_keras_callback model-index: - name: anum231/food_classifier results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # anum231/food_classifier This model is a fine-tuned version of [anum231/cancer_classifier_100](https://huggingface.co/anum231/cancer_classifier_100) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5815 - Validation Loss: 0.4561 - Train Accuracy: 0.8276 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1160, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.6210 \| 0.4706 \| 0.8276 \| 0 \| \| 0.6095 \| 0.4583 \| 0.8103 \| 1 \| \| 0.6289 \| 0.4566 \| 0.8103 \| 2 \| \| 0.6230 \| 0.5850 \| 0.7241 \| 3 \| \| 0.5815 \| 0.4561 \| 0.8276 \| 4 \| ### Framework versions - Transformers 4.38.1 - TensorFlow 2.15.0 - Datasets 2.18.0 - Tokenizers 0.15.2
OwOOwO/eacc_dc_5	OwOOwO	2024-03-06T07:17:53Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T07:15:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
eunyounglee/degreemotion-bert-finetuning-3	eunyounglee	2024-03-06T07:16:27Z	89	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:klue/bert-base", "base_model:finetune:klue/bert-base", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T06:44:00Z	--- license: cc-by-sa-4.0 base_model: klue/bert-base tags: - generated_from_trainer model-index: - name: degreemotion-bert-finetuning-3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # degreemotion-bert-finetuning-3 This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.32.1 - Pytorch 2.2.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2
Hadiboo/boguey	Hadiboo	2024-03-06T07:16:09Z	0	0	adapter-transformers	[ "adapter-transformers", "code", "art", "text-generation-inference", "text-generation", "en", "dataset:HuggingFaceTB/cosmopedia", "region:us" ]	text-generation	2024-03-06T07:13:10Z	--- datasets: - HuggingFaceTB/cosmopedia language: - en library_name: adapter-transformers pipeline_tag: text-generation tags: - code - art - text-generation-inference ---
Sumail/Golden_Waves04_2b	Sumail	2024-03-06T07:13:37Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "base_model:Sumail/Bubble_bee04_2b", "base_model:finetune:Sumail/Bubble_bee04_2b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T06:42:38Z	--- base_model: - 0x0dad0/nous_nb00 - Sumail/Bubble_bee04_2b library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [0x0dad0/nous_nb00](https://huggingface.co/0x0dad0/nous_nb00) * [Sumail/Bubble_bee04_2b](https://huggingface.co/Sumail/Bubble_bee04_2b) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: 0x0dad0/nous_nb00 layer_range: [0, 18] - model: Sumail/Bubble_bee04_2b layer_range: [0, 18] merge_method: slerp base_model: 0x0dad0/nous_nb00 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
ottopilot/PriyaBelleXL	ottopilot	2024-03-06T07:09:25Z	4	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:cc-by-nc-nd-4.0", "region:us" ]	text-to-image	2024-03-06T07:07:58Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- RAW photo, portrait, close-up, PriBlle, looking at viewer, smiling, perfect black hair with highlights, brown eyes, professional headshot, shot on Hasselblad, perfect lighting, dutch angle, bokeh, outdoors, depth of field, blue dress, warm, loving, friendly <lora:PriyaBelleXL_v1:1> parameters: negative_prompt: bindi, mole, facial marks output: url: images/00001-3916971016.png - text: >- PriBlle, very dark-skinned woman, solo focus, mixed media, realistic anime art style, art by Yusuke Nakamura, fractal, ukiyoe, watercolor ink wash technique, intricate, highly detailed. Inspired by multiracial Hindi-West Indian heritage, San Francisco Bay Area, and diaspora. <lora:PriyaBelleXL_v1:1> output: url: images/00002-2902012777.png - text: >- PriBlle as Princess Jasmine, mind controlled by Jafar, sexy red outfit, tiara, collar, Agrabah palace, entranced by magic:1.1, glowing, compliant, submissive, obedient, Disney's Aladdin bad end <lora:PriyaBelleXL_v1:1> output: url: images/00121-3666660946.png - text: >- PriBlle is a college student on campus, dark blue and gold hooded sweatshirt with bear logo and shorts, Berkeley <lora:PriyaBelleXL_v1:1> output: url: images/00172-3938050706.png - text: >- PriBlle is hella fine shawty, hyphy, outdoors, Lake Merritt, Oakland, NorCal, yay area <lora:PriyaBelleXL_v1:1> output: url: images/00156-519328175.png - text: >- PriBlle, a woman wearing a green Oakland Athletics cap and sexy fan gear, smiling, ponytail, bodycon, bedroom, natural light, sexy, tease, flirty <lora:PriyaBelleXL_v1:1> output: url: images/00328-1196258457.png base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: PriBlle license: cc-by-nc-nd-4.0 --- # Priya Belle (Ottoverse original character) - SDXL 1.0 <Gallery /> ## Model description https://huggingface.co/ottopilot/PriyaBelle, but trained for SDXL. ## Trigger words You should use `PriBlle` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/ottopilot/PriyaBelleXL/tree/main) them in the Files & versions tab.
mahiatlinux/MasherAI-7B-v0.9-GGUF	mahiatlinux	2024-03-06T06:59:17Z	3	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:openchat/openchat-3.5-0106", "base_model:quantized:openchat/openchat-3.5-0106", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-06T06:57:11Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: openchat/openchat-3.5-0106 --- # Uploaded model - Developed by: mahiatlinux - License: apache-2.0 - Finetuned from model : openchat/openchat-3.5-0106 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
CatBarks/t5_es100SEC2_4_tokenizer	CatBarks	2024-03-06T06:52:17Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-06T06:52:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
JesseStover/L2AI-dictionary-klue-bert-base	JesseStover	2024-03-06T06:47:19Z	89	0	transformers	[ "transformers", "safetensors", "bert", "multiple-choice", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-04T13:52:44Z	--- {} --- The L2AI-dictionary model is fine-tuned checkpoint of [klue/bert-base](https://huggingface.co/klue/bert-base) for multiple choice, specifically for selecting the best dictionary definition of a given word in a sentence. Below is an example usage: ```python import numpy as np import torch from transformers import AutoModelForMultipleChoice, AutoTokenizer model_name = "JesseStover/L2AI-dictionary-klue-bert-base" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForMultipleChoice.from_pretrained(model_name) model.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) prompts = "\"강아지는 뽀송뽀송하다.\"에 있는 \"강아지\"의 정의는 " candidates = [ "\"(명사) 개의 새끼\"예요.", "\"(명사) 부모나 할아버지, 할머니가 자식이나 손주를 귀여워하면서 부르는 말\"이예요." ] inputs = tokenizer( [[prompt, candidate] for candidate in candidates], return_tensors="pt", padding=True ) labels = torch.tensor(0).unsqueeze(0) with torch.no_grad(): outputs = model( **{k: v.unsqueeze(0) for k, v in inputs.items()}, labels=labels ) print({i: float(x) for i, x in enumerate(outputs.logits.softmax(1)[0])}) ``` Training data was procured under Creative Commons [CC BY-SA 2.0 KR DEED](https://creativecommons.org/licenses/by-sa/2.0/kr/) from the National Institute of Korean Language's [Basic Korean Dictionary](https://krdict.korean.go.kr) and [Standard Korean Dictionary](https://stdict.korean.go.kr/).
vsocrates/incar-status-any	vsocrates	2024-03-06T06:44:25Z	5	0	transformers	[ "transformers", "pytorch", "longformer", "text-classification", "medical", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T05:07:27Z	--- '[object Object]': null license: apache-2.0 language: - en library_name: transformers tags: - medical widget: - text: "Patient is a a formerly incarcerated individual having arrived in the ED with stomach pain." - example_title: "Former Incarceration" - text: "Patient arrived in the ED for chest pain." - example_title: "No Incarceration" --- # Model Card for incar-status-any A Clinical Longformer-based model trained by the HAIL lab to predict incarceration status (past and present) in ED Notes. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: Vimig Socrates - Model type: Longformer - Language(s) (NLP): English - License: Apache License 2.0 - Finetuned from model: [Clinical Lonformer](https://huggingface.co/yikuan8/Clinical-Longformer ) ## Uses This model can be used to predict the incarceration status that a patient might have given most types of clinical ED notes. ## Bias, Risks, and Limitations This should not be used directly without supervision from a physician as predicting incarceration status incorrectly can have significant negative social and clinical impacts. ## Training Details ### Training Data This model was trained on custom annotated data labeled for incarceration status from Yale-New Haven Health Hospital System ED Notes. ### Training Procedure ## Evaluation TODO ### Testing Data, Factors & Metrics ### Results TODO ] ## Citation [optional] Coming soon! BibTeX: {{ citation_bibtex \| default("[More Information Needed]", true)}} APA: {{ citation_apa \| default("[More Information Needed]", true)}} ## Model Card Authors [optional] Vimig Socrates ## Model Card Contact Vimig Socrates: [[email protected]](mailto:[email protected])
samanthakarungi/fine-tuned-bert	samanthakarungi	2024-03-06T06:42:24Z	5	1	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "finance", "business", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-26T08:29:46Z	--- language: - en widget: - text: uber for today - text: airtime and data - text: breakfast meeting with client metrics: - accuracy pipeline_tag: text-classification tags: - finance - text-classification - business --- ### Model Description <p>This model is a fine tuned version of the <a href="https://huggingface.co/distilbert/distilbert-base-uncased">distilbert-base-uncased</a> model on Hugging face. The model is trained to classify payment notes for business owners into one of the following categories.</p> <ol> <li>INVENTORY, SUPPLIES AND EQUIPMENT</li> <li>PROFESSIONAL SERVICES</li> <li>TRANSPORTATION AND TRAVEL</li> <li>UTILITIES</li> <li>EMPLOYEE BENEFITS AND COMPENSATION</li> <li>MEALS AND ENTERTAINMENT</li> <li>TAX PAYMENTS</li> <li>LEGAL AND COMPLIANCE FEES</li> <li>BUSINESS DEVELOPMENT AND INVESTMENT</li> </ol> ### Base Model Description <p>DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts using the BERT base model.</p> ### Training results <table> <tr> <th>Epoch</th> <th>Training Loss</th> <th>Validation Loss</th> <th>Accuracy</th> </tr> <tr> <th>0</th> <th>No Log</th> <th>0.263793</th> <th>0.916230</th> </tr> <tr> <th>1</th> <th>No Log</th> <th>0.185122</th> <th>0.937173</th> </tr> <tr> <th>2</th> <th>0.318300</th> <th>0.191695</th> <th>0.937173</th> </tr> </table> ### Training results <p>Check out the training code at this <a href="https://github.com/samanthaKarungi/iotec-pay-model-bert/tree/main/model/training_and_evaluation">github repo</a></p> ### Framework versions <ul> <li>Transformers 4.37.2</li> <li>PyTorch 2.2.0</li> <li>Datasets 2.17.1</li> <li>Tokenizers 0.15.2</li> </ul>
Demo0203/gyx	Demo0203	2024-03-06T06:39:57Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-06T06:35:36Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 263.17 +/- 14.30 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
GregoRio123/nsy	GregoRio123	2024-03-06T06:39:07Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-03-06T05:42:51Z	--- license: creativeml-openrail-m ---
gokuls/wav2vec2-base-finetuned-ic-slurp	gokuls	2024-03-06T06:34:14Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-03-05T13:14:31Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-ic-slurp results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-finetuned-ic-slurp This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1101 - Accuracy: 0.7393 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 4.0345 \| 1.0 \| 527 \| 3.9813 \| 0.0673 \| \| 3.5622 \| 2.0 \| 1055 \| 3.4634 \| 0.1867 \| \| 2.7737 \| 3.0 \| 1582 \| 2.7252 \| 0.3638 \| \| 2.1285 \| 4.0 \| 2110 \| 2.1754 \| 0.4827 \| \| 1.6216 \| 5.0 \| 2637 \| 1.8169 \| 0.5701 \| \| 1.1786 \| 6.0 \| 3165 \| 1.5773 \| 0.6347 \| \| 0.8747 \| 7.0 \| 3692 \| 1.5024 \| 0.6568 \| \| 0.7565 \| 8.0 \| 4220 \| 1.5020 \| 0.6694 \| \| 0.5236 \| 9.0 \| 4747 \| 1.5287 \| 0.6799 \| \| 0.4517 \| 10.0 \| 5275 \| 1.5165 \| 0.6879 \| \| 0.364 \| 11.0 \| 5802 \| 1.5159 \| 0.6949 \| \| 0.3221 \| 12.0 \| 6330 \| 1.5217 \| 0.6996 \| \| 0.227 \| 13.0 \| 6857 \| 1.5718 \| 0.7075 \| \| 0.1828 \| 14.0 \| 7385 \| 1.6979 \| 0.6901 \| \| 0.1691 \| 15.0 \| 7912 \| 1.6162 \| 0.7093 \| \| 0.1642 \| 16.0 \| 8440 \| 1.6973 \| 0.7048 \| \| 0.1254 \| 17.0 \| 8967 \| 1.7060 \| 0.7100 \| \| 0.1578 \| 18.0 \| 9495 \| 1.7328 \| 0.7063 \| \| 0.1509 \| 19.0 \| 10022 \| 1.7658 \| 0.7073 \| \| 0.1409 \| 20.0 \| 10550 \| 1.7770 \| 0.7052 \| \| 0.1085 \| 21.0 \| 11077 \| 1.8033 \| 0.7074 \| \| 0.106 \| 22.0 \| 11605 \| 1.7000 \| 0.7149 \| \| 0.0764 \| 23.0 \| 12132 \| 1.7943 \| 0.7104 \| \| 0.0671 \| 24.0 \| 12660 \| 1.8323 \| 0.7155 \| \| 0.0768 \| 25.0 \| 13187 \| 1.8486 \| 0.7146 \| \| 0.0741 \| 26.0 \| 13715 \| 1.8227 \| 0.7187 \| \| 0.0731 \| 27.0 \| 14242 \| 1.7824 \| 0.7230 \| \| 0.0935 \| 28.0 \| 14770 \| 1.8987 \| 0.7164 \| \| 0.0829 \| 29.0 \| 15297 \| 1.8774 \| 0.7202 \| \| 0.0588 \| 30.0 \| 15825 \| 1.8820 \| 0.7211 \| \| 0.059 \| 31.0 \| 16352 \| 1.9535 \| 0.7246 \| \| 0.0431 \| 32.0 \| 16880 \| 1.9621 \| 0.7237 \| \| 0.0324 \| 33.0 \| 17407 \| 2.0160 \| 0.7256 \| \| 0.0447 \| 34.0 \| 17935 \| 1.9392 \| 0.7262 \| \| 0.025 \| 35.0 \| 18462 \| 2.0095 \| 0.7284 \| \| 0.0522 \| 36.0 \| 18990 \| 1.9994 \| 0.7244 \| \| 0.0482 \| 37.0 \| 19517 \| 2.0566 \| 0.7262 \| \| 0.0203 \| 38.0 \| 20045 \| 2.0287 \| 0.7295 \| \| 0.0221 \| 39.0 \| 20572 \| 2.0634 \| 0.7300 \| \| 0.0444 \| 40.0 \| 21100 \| 2.0593 \| 0.7302 \| \| 0.0348 \| 41.0 \| 21627 \| 2.0712 \| 0.7298 \| \| 0.0154 \| 42.0 \| 22155 \| 2.0429 \| 0.7351 \| \| 0.024 \| 43.0 \| 22682 \| 2.0708 \| 0.7352 \| \| 0.0157 \| 44.0 \| 23210 \| 2.0701 \| 0.7368 \| \| 0.0222 \| 45.0 \| 23737 \| 2.0963 \| 0.7338 \| \| 0.0126 \| 46.0 \| 24265 \| 2.1329 \| 0.7340 \| \| 0.0211 \| 47.0 \| 24792 \| 2.1230 \| 0.7370 \| \| 0.0288 \| 48.0 \| 25320 \| 2.1101 \| 0.7393 \| \| 0.0347 \| 49.0 \| 25847 \| 2.1201 \| 0.7375 \| \| 0.0162 \| 49.95 \| 26350 \| 2.1197 \| 0.7381 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
WokeEngineer/Reinforce-cartPole-v1	WokeEngineer	2024-03-06T06:32:42Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-03-06T01:17:33Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Mayank1999/dummy-model	Mayank1999	2024-03-06T06:26:21Z	6	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-03-06T05:57:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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barca-boy/primate_autotrain_sample	barca-boy	2024-03-06T06:26:19Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain", "conversational", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T06:21:58Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
anashrivastava/tinyllama-colorist-lora	anashrivastava	2024-03-06T06:23:59Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.3", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v0.3", "license:apache-2.0", "region:us" ]	null	2024-03-06T06:19:00Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: PY007/TinyLlama-1.1B-Chat-v0.3 model-index: - name: tinyllama-colorist-lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tinyllama-colorist-lora This model is a fine-tuned version of [PY007/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 200 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Hiraishin/reranker-malaysian-mistral-474M	Hiraishin	2024-03-06T06:13:29Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-06T06:13:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Ashwini1412/wav2vec2-nepali-itr-7	Ashwini1412	2024-03-06T06:10:58Z	5	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-06T03:57:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Infi-MM/infimm-vicuna13b	Infi-MM	2024-03-06T06:07:45Z	18	3	transformers	[ "transformers", "pytorch", "infimm-vicuna", "text-generation", "multimodal", "text", "image", "image-to-text", "conversational", "custom_code", "en", "dataset:HuggingFaceM4/OBELICS", "dataset:laion/laion2B-en", "dataset:coyo-700m", "dataset:mmc4", "autotrain_compatible", "region:us" ]	text-generation	2024-01-05T01:45:50Z	--- language: en tags: - multimodal - text - image - image-to-text datasets: - HuggingFaceM4/OBELICS - laion/laion2B-en - coyo-700m - mmc4 pipeline_tag: text-generation inference: true --- <br> <p align="center"> <img src="assets/infimm-logo.webp" alt="InfiMM-logo" width="400"></a> </p> <br> # InfiMM InfiMM, inspired by the Flamingo architecture, sets itself apart with unique training data and diverse large language models (LLMs). This approach allows InfiMM to maintain the core strengths of Flamingo while offering enhanced capabilities. As the premier open-sourced variant in this domain, InfiMM excels in accessibility and adaptability, driven by community collaboration. It's more than an emulation of Flamingo; it's an innovation in visual language processing. Our model is another attempt to produce the result reported in the paper "Flamingo: A Large-scale Visual Language Model for Multimodal Understanding" by DeepMind. Compared with previous open-sourced attempts ([OpenFlamingo](https://github.com/mlfoundations/open_flamingo) and [IDEFIC](https://huggingface.co/blog/idefics)), InfiMM offers a more flexible models, allowing for a wide range of applications. In particular, InfiMM integrates the latest LLM models into VLM domain the reveals the impact of LLMs with different scales and architectures. Please note that InfiMM is currently in beta stage and we are continuously working on improving it. ## Model Details - Developed by: Institute of Automation, Chinese Academy of Sciences and ByteDance - Model Type: Visual Language Model (VLM) - Language: English - LLMs: [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), [LLaMA2-13B](https://ai.meta.com/llama/), [Vicuna-13B](https://huggingface.co/lmsys/vicuna-13b-v1.5) - Vision Model: [EVA CLIP](https://huggingface.co/QuanSun/EVA-CLIP) - Language(s) (NLP): en - License: see [License section](#license) <!--- - Parent Models: [QuanSun/EVA-CLIP](https://huggingface.co/QuanSun/EVA-CLIP/blob/main/EVA02_CLIP_L_336_psz14_s6B.pt) and [HuggingFaceH4/zephyr-7b--beta ta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) --> ## Model Family Our model consists of several different model. Please see the details below. \| Model \| LLM \| Vision Encoder \| IFT \| \| ---------------------- \| -------------- \| -------------- \| --- \| \| InfiMM-Zephyr \| Zehpyr-7B-beta \| ViT-L-336 \| No \| \| InfiMM-Llama-13B \| Llama2-13B \| ViT-G-224 \| No \| \| InfiMM-Vicuna-13B \| Vicuna-13B \| ViT-E-224 \| No \| \| InfiMM-Zephyr-Chat \| Zehpyr-7B-beta \| ViT-L-336 \| Yes \| \| InfiMM-Llama-13B-Chat \| Llama2-13B \| ViT-G-224 \| Yes \| \| InfiMM-Vicuna-13B-Chat \| Vicuna-13B \| ViT-E-224 \| Yes \| <!-- InfiMM-Zephyr-Chat is an light-weighted, open-source re-production of Flamingo-style Multimodal large language models with chat capability that takes sequences of interleaved images and texts as inputs and generates text outputs, with only 9B parameters. --> ## Demo Will be released soon. Our model adopts the Flamingo architecture, leveraging EVA CLIP as the visual encoder and employing LLaMA2, Vicuna, and Zephyr as language models. The visual and language modalities are connected through a Cross Attention module. ## Quickstart Use the code below to get started with the base model: ```python import torch from transformers import AutoModelForCausalLM, AutoProcessor processor = AutoProcessor.from_pretrained("InfiMM/infimm-zephyr", trust_remote_code=True) prompts = [ { "role": "user", "content": [ {"image": "assets/infimm-logo.webp"}, "Please explain this image to me.", ], } ] inputs = processor(prompts) # use bf16 model = AutoModelForCausalLM.from_pretrained( "InfiMM/infimm-zephyr", local_files_only=True, torch_dtype=torch.bfloat16, trust_remote_code=True, ).eval() inputs = inputs.to(model.device) inputs["batch_images"] = inputs["batch_images"].to(torch.bfloat16) generated_ids = model.generate( *inputs, min_generation_length=0, max_generation_length=256, ) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) print(generated_text) ``` ## Training Details We employed three stages to train our model: pretraining (PT), multi-task training (MTT), and instruction finetuning (IFT). Refer to the table below for detailed configurations in each stage. Due to significant noise in the pretraining data, we aimed to enhance the model's accuracy by incorporating higher-quality data. In the multi-task training (MTT) phase, we utilized substantial training data from diverse datasets. However, as the answer in these data mainly consisted of single words or phrases, the model's conversational ability was limited. Therefore, in the third stage, we introduced a considerable amount of image-text dialogue data (llava665k) for fine-tuning the model's instructions. ### Pretraining (PT) We follow similar training procedures used in [IDEFICS](https://huggingface.co/HuggingFaceM4/idefics-9b-instruct/blob/main/README.md). The model is trained on a mixture of image-text pairs and unstructured multimodal web documents. All data are from public sources. Many image URL links are expired, we are capable of only downloading partial samples. We filter low quality data, here are resulting data we used: \| Data Source \| Type of Data \| Number of Tokens in Source \| Number of Images in Source \| Number of Samples \| Epochs \| \| ---------------------------------------------------------------- \| ------------------------------------- \| -------------------------- \| -------------------------- \| ----------------- \| ------ \| \| [OBELICS](https://huggingface.co/datasets/HuggingFaceM4/OBELICS) \| Unstructured Multimodal Web Documents \| - \| - \| 101M \| 1 \| \| [MMC4](https://github.com/allenai/mmc4) \| Unstructured Multimodal Web Documents \| - \| - \| 53M \| 1 \| \| [LAION](https://huggingface.co/datasets/laion/laion2B-en) \| Image-Text Pairs \| - \| 115M \| 115M \| 1 \| \| [COYO](https://github.com/kakaobrain/coyo-dataset) \| Image-Text Pairs \| - \| 238M \| 238M \| 1 \| \| [LAION-COCO](https://laion.ai/blog/laion-coco/) \| Image-Text Pairs \| - \| 140M \| 140M \| 1 \| \| [PMD\](https://huggingface.co/datasets/facebook/pmd) \| Image-Text Pairs \| - \| 20M \| 20M \| 1 \| \PMD is only used in models with 13B LLMs, not the 7B Zephyr model. During pretraining of interleaved image text sample, we apply masked cross-attention, however, we didn't strictly follow Flamingo, which alternate attention of image to its previous text or later text by change of 0.5. We use the following hyper parameters: \| Categories \| Parameters \| Value \| \| ------------------------ \| -------------------------- \| -------------------- \| \| Perceiver Resampler \| Number of Layers \| 6 \| \| \| Number of Latents \| 64 \| \| \| Number of Heads \| 16 \| \| \| Resampler Head Dimension \| 96 \| \| Training \| Sequence Length \| 384 (13B) / 792 (7B) \| \| \| Effective Batch Size \| 40\128 \| \| \| Max Images per Sample \| 6 \| \| \| Weight Decay \| 0.1 \| \| \| Optimizer \| Adam(0.9, 0.999) \| \| \| Gradient Accumulation Step \| 2 \| \| Learning Rate \| Initial Max \| 1e-4 \| \| \| Decay Schedule \| Constant \| \| \| Warmup Step rate \| 0.005 \| \| Large-scale Optimization \| Gradient Checkpointing \| False \| \| \| Precision \| bf16 \| \| \| ZeRO Optimization \| Stage 2 \| ### Multi-Task Training (MTT) Here we use mix_cap_vqa to represent the mixed training set from COCO caption, TextCap, VizWiz Caption, VQAv2, OKVQA, VizWiz VQA, TextVQA, OCRVQA, STVQA, DocVQA, GQA and ScienceQA-image. For caption, we add prefix such as "Please describe the image." before the question. And for QA, we add "Answer the question using a single word or phrase.". Specifically, for VizWiz VQA, we use "When the provided information is insufficient, respond with 'Unanswerable'. Answer the question using a single word or phrase.". While for ScienceQA-image, we use "Answer with the option's letter from the given choices directly." ### Instruction Fine-Tuning (IFT) For instruction fine-tuning stage, we use the recently released [LLaVA-MIX-665k](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/tree/main). We use the following hyper parameters: \| Categories \| Parameters \| Value \| \| ------------------------ \| -------------------------- \| -------------------- \| \| Perceiver Resampler \| Number of Layers \| 6 \| \| \| Number of Latents \| 64 \| \| \| Number of Heads \| 16 \| \| \| Resampler Head Dimension \| 96 \| \| Training \| Sequence Length \| 384 (13B) / 792 (7B) \| \| \| Effective Batch Size \| 64 \| \| \| Max Images per Sample \| 6 \| \| \| Weight Decay \| 0.1 \| \| \| Optimizer \| Adam(0.9, 0.999) \| \| \| Gradient Accumulation Step \| 2 \| \| Learning Rate \| Initial Max \| 1e-5 \| \| \| Decay Schedule \| Constant \| \| \| Warmup Step rate \| 0.005 \| \| Large-scale Optimization \| Gradient Checkpointing \| False \| \| \| Precision \| bf16 \| \| \| ZeRO Optimization \| Stage 2 \| During IFT, similar to pretrain, we keep ViT and LLM frozen for both chat-based LLM (Vicuna and Zephyr). For Llama model, we keep LLM trainable during the IFT stage. We also apply chat-template to process the training samples. ## Evaluation ### PreTraining Evaluation We evaluate the pretrained models on the following downstream tasks: Image Captioning and VQA. We also compare with our results with [IDEFICS](https://huggingface.co/blog/idefics). \| Model \| Shots \| COCO CIDEr \| Flickr30K CIDEr \| VQA v2 Acc \| TextVQA Acc \| OK-VQA Acc \| \| ----------------- \| ----- \| ---------- \| --------------- \| ---------- \| ----------- \| ---------- \| \| IDEFICS-9B \| 0 \| 46 \| 27.3 \| 50.9 \| 25.9 \| 38.4 \| \| \| 4 \| 93 \| 59.7 \| 55.4 \| 27.6 \| 45.5 \| \| IDEFICS-80B \| 0 \| 91.8 \| 53.7 \| 60 \| 30.9 \| 45.2 \| \| \| 4 \| 110.3 \| 73.7 \| 64.6 \| 34.4 \| 52.4 \| \| InfiMM-Zephyr-7B \| 0 \| 78.8 \| 60.7 \| 33.7 \| 15.2 \| 17.1 \| \| \| 4 \| 108.6 \| 71.9 \| 59.1 \| 34.3 \| 50.5 \| \| InfiMM-Llama2-13B \| 0 \| 85.4 \| 54.6 \| 51.6 \| 24.2 \| 26.4 \| \| \| 4 \| 125.2 \| 87.1 \| 66.1 \| 38.2 \| 55.5 \| \| InfiMM-Vicuna13B \| 0 \| 69.6 \| 49.6 \| 60.4 \| 32.8 \| 49.2 \| \| \| 4 \| 118.1 \| 81.4 \| 64.2 \| 38.4 \| 53.7 \| ### IFT Evaluation In our analysis, we concentrate on two primary benchmarks for evaluating MLLMs: 1) Multi-choice Question Answering (QA) and 2) Open-ended Evaluation. We've observed that the evaluation metrics for tasks like Visual Question Answering (VQA) and Text-VQA are overly sensitive to exact answer matches. This approach can be misleading, particularly when models provide synonymous but technically accurate responses. Therefore, these metrics have been omitted from our comparison for a more precise assessment. The evaluation results are shown in the table below. \| Model \| ScienceQA-Img \| MME \| MM-VET \| InfiMM-Eval \| MMbench \| MMMU-Val \| MMMU-Test \| \| ------------------- \| ------------- \| --------------------- \| ------ \| ------------ \| ------- \| -------- \| --------- \| \| Otter-9B \| - \| 1292/306 \| 24.6 \| 32.2 \| - \| 22.69 \| - \| \| IDEFICS-9B-Instruct \| 60.6 \| -/- \| - \| - \| - \| 24.53 \| - \| \| InfiMM-Zephyr-7B \| 71.1 \| P: 1406<br>C:327 \| 32.8 \| 36.0 \| 59.7 \| 39.4 \| 35.5 \| \| InfiMM-Llama-13b \| 73.0 \| P: 1444.5<br>C: 337.6 \| 39.2 \| 0.4559/0.414 \| 66.4 \| 39.1 \| 35.2 \| \| InfiMM-Vicuna-13B \| 74.0 \| P: 1461.2<br>C: 323.5 \| 36.0 \| 40.0 \| 66.7 \| 37.6 \| 34.6 \| <!-- \| Model \| TextVQA (no ocr) \| OK-VQA \| VQAv2 \| ScienceQA-Img \| GQA \| MME \| MM-VET \| MMMU \| InfiMM-Eval \| MMbench \| \| ----------------- \| ---------------- \| ------ \| ----- \| ------------- \| ---- \| --------------------- \| ------ \| ---- \| ------------ \| ------- \| \| InfiMM-Zephyr-7B \| 36.7 \| 55.4 \| / \| 71.1 \| \| P: 1406<br>C:327 \| 32.8 \| 39.4 \| 36.0 \| 59.7 \| \| InfiMM-Llama-13b \| 44.6 \| 62.3 \| 78.5 \| 73.0 \| 61.2 \| P: 1444.5<br>C: 337.6 \| 39.2 \| 39.1 \| 0.4559/0.414 \| 66.4 \| \| InfiMM-Vicuna-13B \| 41.7 \| 58.5 \| 73.0 \| 74.0 \| 58.5 \| P: 1461.2<br>C: 323.5 \| 36.0 \| 37.6 \| 40.0 \| 66.7 \| We select checkpoint after 1 epoch instruction fine-tuning. \| Model \| <nobr>ScienceQA <br>acc.</nobr> \| <nobr>MME <br>P/C</nobr> \| <nobr>MM-Vet</nobr> \| <nobr>InfiMM-Eval</nobr> \| <nobr>MMMU (val)</nobr> \| \| :------------------ \| ------------------------------: \| -----------------------: \| ------------------: \| -----------------------: \| ----------------------: \| \| Otter-9B \| - \| 1292/306 \| 24.6 \| 22.69 \| 32.2 \| \| IDEFICS-9B-Instruct \| 60.6 \| -/- \| - \| 24.53 \| - \| \| InfiMM-Zephyr-Chat \| 71.14 \| 1406/327 \| 33.3 \| 35.97 \| 39.4 \| --> <details> <summary>Leaderboard Details</summary> <img src="assets/infimm-zephyr-mmmu-val.jpeg" style="zoom:40%;" /> <br>MMMU-Val split results<br> <img src="assets/infimm-zephyr-mmmu-test.jpeg" style="zoom:40%;" /> <br>MMMU-Test split results<br> </details> ## Citation ```latex @misc{InfiMM, title={InfiMM: Advancing Multimodal Understanding from Flamingo's Legacy through Diverse LLM Integration}, author={InfiMM Team}, url={https://huggingface.co/Infi-MM/}, year={2024} } ``` ## License <a href="https://creativecommons.org/licenses/by-nc/4.0/deed.en"> <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/d/d3/Cc_by-nc_icon.svg/600px-Cc_by-nc_icon.svg.png" width="160"> </a> This project is licensed under the CC BY-NC 4.0. The copyright of the images belongs to the original authors. See [LICENSE](LICENSE) for more information. ## Contact Us Please feel free to contact us via email [[email protected]]([email protected]) if you have any questions.
Infi-MM/infimm-zephyr	Infi-MM	2024-03-06T06:07:25Z	17	10	transformers	[ "transformers", "pytorch", "infimm-zephyr", "text-generation", "multimodal", "text", "image", "image-to-text", "conversational", "custom_code", "en", "dataset:HuggingFaceM4/OBELICS", "dataset:laion/laion2B-en", "dataset:coyo-700m", "dataset:mmc4", "autotrain_compatible", "region:us" ]	text-generation	2024-01-04T08:15:39Z	--- language: en tags: - multimodal - text - image - image-to-text datasets: - HuggingFaceM4/OBELICS - laion/laion2B-en - coyo-700m - mmc4 pipeline_tag: text-generation inference: true --- <br> <p align="center"> <img src="assets/infimm-logo.webp" alt="InfiMM-logo" width="400"></a> </p> <br> # InfiMM InfiMM, inspired by the Flamingo architecture, sets itself apart with unique training data and diverse large language models (LLMs). This approach allows InfiMM to maintain the core strengths of Flamingo while offering enhanced capabilities. As the premier open-sourced variant in this domain, InfiMM excels in accessibility and adaptability, driven by community collaboration. It's more than an emulation of Flamingo; it's an innovation in visual language processing. Our model is another attempt to produce the result reported in the paper "Flamingo: A Large-scale Visual Language Model for Multimodal Understanding" by DeepMind. Compared with previous open-sourced attempts ([OpenFlamingo](https://github.com/mlfoundations/open_flamingo) and [IDEFIC](https://huggingface.co/blog/idefics)), InfiMM offers a more flexible models, allowing for a wide range of applications. In particular, InfiMM integrates the latest LLM models into VLM domain the reveals the impact of LLMs with different scales and architectures. Please note that InfiMM is currently in beta stage and we are continuously working on improving it. ## Model Details - Developed by: Institute of Automation, Chinese Academy of Sciences and ByteDance - Model Type: Visual Language Model (VLM) - Language: English - LLMs: [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), [LLaMA2-13B](https://ai.meta.com/llama/), [Vicuna-13B](https://huggingface.co/lmsys/vicuna-13b-v1.5) - Vision Model: [EVA CLIP](https://huggingface.co/QuanSun/EVA-CLIP) - Language(s) (NLP): en - License: see [License section](#license) <!--- - Parent Models: [QuanSun/EVA-CLIP](https://huggingface.co/QuanSun/EVA-CLIP/blob/main/EVA02_CLIP_L_336_psz14_s6B.pt) and [HuggingFaceH4/zephyr-7b--beta ta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) --> ## Model Family Our model consists of several different model. Please see the details below. \| Model \| LLM \| Vision Encoder \| IFT \| \| ---------------------- \| -------------- \| -------------- \| --- \| \| InfiMM-Zephyr \| Zehpyr-7B-beta \| ViT-L-336 \| No \| \| InfiMM-Llama-13B \| Llama2-13B \| ViT-G-224 \| No \| \| InfiMM-Vicuna-13B \| Vicuna-13B \| ViT-E-224 \| No \| \| InfiMM-Zephyr-Chat \| Zehpyr-7B-beta \| ViT-L-336 \| Yes \| \| InfiMM-Llama-13B-Chat \| Llama2-13B \| ViT-G-224 \| Yes \| \| InfiMM-Vicuna-13B-Chat \| Vicuna-13B \| ViT-E-224 \| Yes \| <!-- InfiMM-Zephyr-Chat is an light-weighted, open-source re-production of Flamingo-style Multimodal large language models with chat capability that takes sequences of interleaved images and texts as inputs and generates text outputs, with only 9B parameters. --> ## Demo Will be released soon. Our model adopts the Flamingo architecture, leveraging EVA CLIP as the visual encoder and employing LLaMA2, Vicuna, and Zephyr as language models. The visual and language modalities are connected through a Cross Attention module. ## Quickstart Use the code below to get started with the base model: ```python import torch from transformers import AutoModelForCausalLM, AutoProcessor processor = AutoProcessor.from_pretrained("Infi-MM/infimm-zephyr", trust_remote_code=True) prompts = [ { "role": "user", "content": [ {"image": "assets/infimm-logo.webp"}, "Please explain this image to me.", ], } ] inputs = processor(prompts) # use bf16 model = AutoModelForCausalLM.from_pretrained( "Infi-MM/infimm-zephyr", local_files_only=True, torch_dtype=torch.bfloat16, trust_remote_code=True, ).eval() inputs = inputs.to(model.device) inputs["batch_images"] = inputs["batch_images"].to(torch.bfloat16) generated_ids = model.generate( *inputs, min_generation_length=0, max_generation_length=256, ) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) print(generated_text) ``` ## Training Details We employed three stages to train our model: pretraining (PT), multi-task training (MTT), and instruction finetuning (IFT). Refer to the table below for detailed configurations in each stage. Due to significant noise in the pretraining data, we aimed to enhance the model's accuracy by incorporating higher-quality data. In the multi-task training (MTT) phase, we utilized substantial training data from diverse datasets. However, as the answer in these data mainly consisted of single words or phrases, the model's conversational ability was limited. Therefore, in the third stage, we introduced a considerable amount of image-text dialogue data (llava665k) for fine-tuning the model's instructions. ### Pretraining (PT) We follow similar training procedures used in [IDEFICS](https://huggingface.co/HuggingFaceM4/idefics-9b-instruct/blob/main/README.md). The model is trained on a mixture of image-text pairs and unstructured multimodal web documents. All data are from public sources. Many image URL links are expired, we are capable of only downloading partial samples. We filter low quality data, here are resulting data we used: \| Data Source \| Type of Data \| Number of Tokens in Source \| Number of Images in Source \| Number of Samples \| Epochs \| \| ---------------------------------------------------------------- \| ------------------------------------- \| -------------------------- \| -------------------------- \| ----------------- \| ------ \| \| [OBELICS](https://huggingface.co/datasets/HuggingFaceM4/OBELICS) \| Unstructured Multimodal Web Documents \| - \| - \| 101M \| 1 \| \| [MMC4](https://github.com/allenai/mmc4) \| Unstructured Multimodal Web Documents \| - \| - \| 53M \| 1 \| \| [LAION](https://huggingface.co/datasets/laion/laion2B-en) \| Image-Text Pairs \| - \| 115M \| 115M \| 1 \| \| [COYO](https://github.com/kakaobrain/coyo-dataset) \| Image-Text Pairs \| - \| 238M \| 238M \| 1 \| \| [LAION-COCO](https://laion.ai/blog/laion-coco/) \| Image-Text Pairs \| - \| 140M \| 140M \| 1 \| \| [PMD\](https://huggingface.co/datasets/facebook/pmd) \| Image-Text Pairs \| - \| 20M \| 20M \| 1 \| \PMD is only used in models with 13B LLMs, not the 7B Zephyr model. During pretraining of interleaved image text sample, we apply masked cross-attention, however, we didn't strictly follow Flamingo, which alternate attention of image to its previous text or later text by change of 0.5. We use the following hyper parameters: \| Categories \| Parameters \| Value \| \| ------------------------ \| -------------------------- \| -------------------- \| \| Perceiver Resampler \| Number of Layers \| 6 \| \| \| Number of Latents \| 64 \| \| \| Number of Heads \| 16 \| \| \| Resampler Head Dimension \| 96 \| \| Training \| Sequence Length \| 384 (13B) / 792 (7B) \| \| \| Effective Batch Size \| 40\128 \| \| \| Max Images per Sample \| 6 \| \| \| Weight Decay \| 0.1 \| \| \| Optimizer \| Adam(0.9, 0.999) \| \| \| Gradient Accumulation Step \| 2 \| \| Learning Rate \| Initial Max \| 1e-4 \| \| \| Decay Schedule \| Constant \| \| \| Warmup Step rate \| 0.005 \| \| Large-scale Optimization \| Gradient Checkpointing \| False \| \| \| Precision \| bf16 \| \| \| ZeRO Optimization \| Stage 2 \| ### Multi-Task Training (MTT) Here we use mix_cap_vqa to represent the mixed training set from COCO caption, TextCap, VizWiz Caption, VQAv2, OKVQA, VizWiz VQA, TextVQA, OCRVQA, STVQA, DocVQA, GQA and ScienceQA-image. For caption, we add prefix such as "Please describe the image." before the question. And for QA, we add "Answer the question using a single word or phrase.". Specifically, for VizWiz VQA, we use "When the provided information is insufficient, respond with 'Unanswerable'. Answer the question using a single word or phrase.". While for ScienceQA-image, we use "Answer with the option's letter from the given choices directly." ### Instruction Fine-Tuning (IFT) For instruction fine-tuning stage, we use the recently released [LLaVA-MIX-665k](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/tree/main). We use the following hyper parameters: \| Categories \| Parameters \| Value \| \| ------------------------ \| -------------------------- \| -------------------- \| \| Perceiver Resampler \| Number of Layers \| 6 \| \| \| Number of Latents \| 64 \| \| \| Number of Heads \| 16 \| \| \| Resampler Head Dimension \| 96 \| \| Training \| Sequence Length \| 384 (13B) / 792 (7B) \| \| \| Effective Batch Size \| 64 \| \| \| Max Images per Sample \| 6 \| \| \| Weight Decay \| 0.1 \| \| \| Optimizer \| Adam(0.9, 0.999) \| \| \| Gradient Accumulation Step \| 2 \| \| Learning Rate \| Initial Max \| 1e-5 \| \| \| Decay Schedule \| Constant \| \| \| Warmup Step rate \| 0.005 \| \| Large-scale Optimization \| Gradient Checkpointing \| False \| \| \| Precision \| bf16 \| \| \| ZeRO Optimization \| Stage 2 \| During IFT, similar to pretrain, we keep ViT and LLM frozen for both chat-based LLM (Vicuna and Zephyr). For Llama model, we keep LLM trainable during the IFT stage. We also apply chat-template to process the training samples. ## Evaluation ### PreTraining Evaluation We evaluate the pretrained models on the following downstream tasks: Image Captioning and VQA. We also compare with our results with [IDEFICS](https://huggingface.co/blog/idefics). \| Model \| Shots \| COCO CIDEr \| Flickr30K CIDEr \| VQA v2 Acc \| TextVQA Acc \| OK-VQA Acc \| \| ----------------- \| ----- \| ---------- \| --------------- \| ---------- \| ----------- \| ---------- \| \| IDEFICS-9B \| 0 \| 46 \| 27.3 \| 50.9 \| 25.9 \| 38.4 \| \| \| 4 \| 93 \| 59.7 \| 55.4 \| 27.6 \| 45.5 \| \| IDEFICS-80B \| 0 \| 91.8 \| 53.7 \| 60 \| 30.9 \| 45.2 \| \| \| 4 \| 110.3 \| 73.7 \| 64.6 \| 34.4 \| 52.4 \| \| InfiMM-Zephyr-7B \| 0 \| 78.8 \| 60.7 \| 33.7 \| 15.2 \| 17.1 \| \| \| 4 \| 108.6 \| 71.9 \| 59.1 \| 34.3 \| 50.5 \| \| InfiMM-Llama2-13B \| 0 \| 85.4 \| 54.6 \| 51.6 \| 24.2 \| 26.4 \| \| \| 4 \| 125.2 \| 87.1 \| 66.1 \| 38.2 \| 55.5 \| \| InfiMM-Vicuna13B \| 0 \| 69.6 \| 49.6 \| 60.4 \| 32.8 \| 49.2 \| \| \| 4 \| 118.1 \| 81.4 \| 64.2 \| 38.4 \| 53.7 \| ### IFT Evaluation In our analysis, we concentrate on two primary benchmarks for evaluating MLLMs: 1) Multi-choice Question Answering (QA) and 2) Open-ended Evaluation. We've observed that the evaluation metrics for tasks like Visual Question Answering (VQA) and Text-VQA are overly sensitive to exact answer matches. This approach can be misleading, particularly when models provide synonymous but technically accurate responses. Therefore, these metrics have been omitted from our comparison for a more precise assessment. The evaluation results are shown in the table below. \| Model \| ScienceQA-Img \| MME \| MM-VET \| InfiMM-Eval \| MMbench \| MMMU-Val \| MMMU-Test \| \| ------------------- \| ------------- \| --------------------- \| ------ \| ------------ \| ------- \| -------- \| --------- \| \| Otter-9B \| - \| 1292/306 \| 24.6 \| 32.2 \| - \| 22.69 \| - \| \| IDEFICS-9B-Instruct \| 60.6 \| -/- \| - \| - \| - \| 24.53 \| - \| \| InfiMM-Zephyr-7B \| 71.1 \| P: 1406<br>C:327 \| 32.8 \| 36.0 \| 59.7 \| 39.4 \| 35.5 \| \| InfiMM-Llama-13b \| 73.0 \| P: 1444.5<br>C: 337.6 \| 39.2 \| 0.4559/0.414 \| 66.4 \| 39.1 \| 35.2 \| \| InfiMM-Vicuna-13B \| 74.0 \| P: 1461.2<br>C: 323.5 \| 36.0 \| 40.0 \| 66.7 \| 37.6 \| 34.6 \| <!-- \| Model \| TextVQA (no ocr) \| OK-VQA \| VQAv2 \| ScienceQA-Img \| GQA \| MME \| MM-VET \| MMMU \| InfiMM-Eval \| MMbench \| \| ----------------- \| ---------------- \| ------ \| ----- \| ------------- \| ---- \| --------------------- \| ------ \| ---- \| ------------ \| ------- \| \| InfiMM-Zephyr-7B \| 36.7 \| 55.4 \| / \| 71.1 \| \| P: 1406<br>C:327 \| 32.8 \| 39.4 \| 36.0 \| 59.7 \| \| InfiMM-Llama-13b \| 44.6 \| 62.3 \| 78.5 \| 73.0 \| 61.2 \| P: 1444.5<br>C: 337.6 \| 39.2 \| 39.1 \| 0.4559/0.414 \| 66.4 \| \| InfiMM-Vicuna-13B \| 41.7 \| 58.5 \| 73.0 \| 74.0 \| 58.5 \| P: 1461.2<br>C: 323.5 \| 36.0 \| 37.6 \| 40.0 \| 66.7 \| We select checkpoint after 1 epoch instruction fine-tuning. \| Model \| <nobr>ScienceQA <br>acc.</nobr> \| <nobr>MME <br>P/C</nobr> \| <nobr>MM-Vet</nobr> \| <nobr>InfiMM-Eval</nobr> \| <nobr>MMMU (val)</nobr> \| \| :------------------ \| ------------------------------: \| -----------------------: \| ------------------: \| -----------------------: \| ----------------------: \| \| Otter-9B \| - \| 1292/306 \| 24.6 \| 22.69 \| 32.2 \| \| IDEFICS-9B-Instruct \| 60.6 \| -/- \| - \| 24.53 \| - \| \| InfiMM-Zephyr-Chat \| 71.14 \| 1406/327 \| 33.3 \| 35.97 \| 39.4 \| --> <details> <summary>Leaderboard Details</summary> <img src="assets/infimm-zephyr-mmmu-val.jpeg" style="zoom:40%;" /> <br>MMMU-Val split results<br> <img src="assets/infimm-zephyr-mmmu-test.jpeg" style="zoom:40%;" /> <br>MMMU-Test split results<br> </details> ## Citation ```latex @misc{InfiMM, title={InfiMM: Advancing Multimodal Understanding from Flamingo's Legacy through Diverse LLM Integration}, author={InfiMM Team}, url={https://huggingface.co/Infi-MM/}, year={2024} } ``` ## License <a href="https://creativecommons.org/licenses/by-nc/4.0/deed.en"> <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/d/d3/Cc_by-nc_icon.svg/600px-Cc_by-nc_icon.svg.png" width="160"> </a> This project is licensed under the CC BY-NC 4.0. The copyright of the images belongs to the original authors. See [LICENSE](LICENSE) for more information. ## Contact Us Please feel free to contact us via email [[email protected]]([email protected]) if you have any questions.
lex117/cproj-gpt	lex117	2024-03-06T06:03:24Z	4	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "autotrain", "conversational", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T06:02:46Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned1.1	hughlan1214	2024-03-06T05:53:20Z	10	1	transformers	[ "transformers", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned1.1", "base_model:finetune:hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned1.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-03-03T17:32:16Z	--- license: apache-2.0 base_model: hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned1.1 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned1.1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned1.1 This model is a fine-tuned version of [hughlan1214/SER_wav2vec2-large-xlsr-53_fine-tuned_1.0](https://huggingface.co/hughlan1214/SER_wav2vec2-large-xlsr-53_fine-tuned_1.0) on a [Speech Emotion Recognition (en)](https://www.kaggle.com/datasets/dmitrybabko/speech-emotion-recognition-en) dataset. This dataset includes the 4 most popular datasets in English: Crema, Ravdess, Savee, and Tess, containing a total of over 12,000 .wav audio files. Each of these four datasets includes 6 to 8 different emotional labels. It achieves the following results on the evaluation set: - Loss: 1.1815 - Accuracy: 0.5776 - Precision: 0.6236 - Recall: 0.5921 - F1: 0.5806 - ## For a better performance version, please refer to [hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned2.0](https://huggingface.co/hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned2.0) ## Model description The model was obtained through feature extraction using [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) and underwent several rounds of fine-tuning. It predicts the 7 types of emotions contained in speech, aiming to lay the foundation for subsequent use of human micro-expressions on the visual level and context semantics under LLMS to infer user emotions in real-time. Although the model was trained on purely English datasets, post-release testing showed that it also performs well in predicting emotions in Chinese and French, demonstrating the powerful cross-linguistic capability of the [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) pre-trained model. ```python emotions = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise'] ``` ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 1.5816 \| 1.0 \| 1048 \| 1.4920 \| 0.4392 \| 0.4568 \| 0.4623 \| 0.4226 \| \| 1.2355 \| 2.0 \| 2096 \| 1.2957 \| 0.5135 \| 0.6082 \| 0.5292 \| 0.5192 \| \| 1.0605 \| 3.0 \| 3144 \| 1.2225 \| 0.5405 \| 0.5925 \| 0.5531 \| 0.5462 \| \| 1.0291 \| 4.0 \| 4192 \| 1.2163 \| 0.5586 \| 0.6215 \| 0.5739 \| 0.5660 \| \| 1.0128 \| 5.0 \| 5240 \| 1.1815 \| 0.5776 \| 0.6236 \| 0.5921 \| 0.5806 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.1 - Datasets 2.17.1 - Tokenizers 0.15.2
hughlan1214/SER_wav2vec2-large-xlsr-53_fine-tuned_1.0	hughlan1214	2024-03-06T05:53:02Z	9	1	transformers	[ "transformers", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-03-03T13:30:18Z	--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: SER_wav2vec2-large-xlsr-53_fine-tuned_1.0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SER_wav2vec2-large-xlsr-53_240303 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on a [Speech Emotion Recognition (en)](https://www.kaggle.com/datasets/dmitrybabko/speech-emotion-recognition-en) dataset. This dataset includes the 4 most popular datasets in English: Crema, Ravdess, Savee, and Tess, containing a total of over 12,000 .wav audio files. Each of these four datasets includes 6 to 8 different emotional labels. It achieves the following results on the evaluation set: - Loss: 1.7923 - Accuracy: 0.2408 - Precision: 0.2324 - Recall: 0.2466 - F1: 0.2226 ## For a better performance version, please refer to [hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned2.0](https://huggingface.co/hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned2.0) ## Model description The model was obtained through feature extraction using [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) and underwent several rounds of fine-tuning. It predicts the 7 types of emotions contained in speech, aiming to lay the foundation for subsequent use of human micro-expressions on the visual level and context semantics under LLMS to infer user emotions in real-time. Although the model was trained on purely English datasets, post-release testing showed that it also performs well in predicting emotions in Chinese and French, demonstrating the powerful cross-linguistic capability of the [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) pre-trained model. ```python emotions = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise'] ``` ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 1.9297 \| 1.0 \| 101 \| 1.9452 \| 0.1233 \| 0.0306 \| 0.1468 \| 0.0454 \| \| 1.9114 \| 2.0 \| 202 \| 1.9115 \| 0.1773 \| 0.1501 \| 0.1803 \| 0.1323 \| \| 1.7863 \| 3.0 \| 303 \| 1.8564 \| 0.2081 \| 0.1117 \| 0.2193 \| 0.1336 \| \| 1.8439 \| 4.0 \| 404 \| 1.8590 \| 0.2042 \| 0.2196 \| 0.2156 \| 0.1755 \| \| 1.9361 \| 5.0 \| 505 \| 1.8375 \| 0.2081 \| 0.2617 \| 0.2213 \| 0.1573 \| \| 1.7572 \| 6.0 \| 606 \| 1.8081 \| 0.2100 \| 0.2018 \| 0.2214 \| 0.1841 \| \| 1.6715 \| 7.0 \| 707 \| 1.8131 \| 0.2389 \| 0.2263 \| 0.2442 \| 0.2129 \| \| 1.6687 \| 8.0 \| 808 \| 1.7923 \| 0.2408 \| 0.2324 \| 0.2466 \| 0.2226 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.1 - Datasets 2.17.1 - Tokenizers 0.15.2
LN1996/output_run_3	LN1996	2024-03-06T05:52:53Z	1	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "lora", "stable-diffusion", "stable-diffusion-diffusers", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-03-06T05:22:43Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - diffusers - lora - stable-diffusion - stable-diffusion-diffusers inference: true base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a room with professional interior design --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA DreamBooth - LN1996/output_run_3 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a room with professional interior design using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
asadmasad/GIST-large-finetuned	asadmasad	2024-03-06T05:49:17Z	46	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-04T12:04:59Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # asadmasad/GIST-large-finetuned This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('asadmasad/GIST-large-finetuned') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=asadmasad/GIST-large-finetuned) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 2476 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "warmupcosine", "steps_per_epoch": null, "warmup_steps": 742, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
UBC-NLP/InfoDCL-hashtag	UBC-NLP	2024-03-06T05:44:55Z	14	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "social media", "contrastive learning", "en", "arxiv:2203.07648", "license:cc", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2023-07-07T03:56:29Z	--- license: cc language: - en library_name: transformers tags: - social media - contrastive learning --- # Contrastive Learning of Sociopragmatic Meaning in Social Media <p align="center"> <a href="https://chiyuzhang94.github.io/" target="_blank">Chiyu Zhang</a>, <a href="https://mageed.arts.ubc.ca/" target="_blank">Muhammad Abdul-Mageed</a>, <a href="https://ganeshjawahar.github.io/" target="_blank">Ganesh Jarwaha</a></p> <p align="center" float="left"> <p align="center">Publish at Findings of ACL 2023</p> <p align="center"> <a href="https://arxiv.org/abs/2203.07648" target="_blank">Paper</a></p> [![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)]() [![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg)]() <p align="center" width="100%"> <a><img src="https://github.com/UBC-NLP/infodcl/blob/master/images/infodcl_vis.png?raw=true" alt="Title" style="width: 90%; min-width: 300px; display: block; margin: auto;"></a> </p> Illustration of our proposed InfoDCL framework. We exploit distant/surrogate labels (i.e., emojis) to supervise two contrastive losses, corpus-aware contrastive loss (CCL) and Light label-aware contrastive loss (LCL-LiT). Sequence representations from our model should keep the cluster of each class distinguishable and preserve semantic relationships between classes. ## Checkpoints of Models Pre-Trained with InfoDCL * InfoDCL-RoBERTa trained with TweetEmoji-EN: https://huggingface.co/UBC-NLP/InfoDCL-emoji * InfoDCL-RoBERTa trained with TweetHashtag-EN: https://huggingface.co/UBC-NLP/InfoDCL-hashtag ## Model Performance <p align="center" width="100%"> <a><img src="https://github.com/UBC-NLP/infodcl/blob/master/images/main_table.png?raw=true" alt="main table" style="width: 95%; min-width: 300px; display: block; margin: auto;"></a> </p> Fine-tuning results on our 24 Socio-pragmatic Meaning datasets (average macro-F1 over five runs).
UBC-NLP/InfoDCL-emoji	UBC-NLP	2024-03-06T05:44:15Z	9	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "social media", "contrastive learning", "en", "arxiv:2203.07648", "license:cc", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2023-07-07T03:54:23Z	--- license: cc language: - en library_name: transformers tags: - social media - contrastive learning --- # Contrastive Learning of Sociopragmatic Meaning in Social Media <p align="center"> <a href="https://chiyuzhang94.github.io/" target="_blank">Chiyu Zhang</a>, <a href="https://mageed.arts.ubc.ca/" target="_blank">Muhammad Abdul-Mageed</a>, <a href="https://ganeshjawahar.github.io/" target="_blank">Ganesh Jarwaha</a></p> <p align="center" float="left"> <p align="center">Publish at Findings of ACL 2023</p> <p align="center"> <a href="https://arxiv.org/abs/2203.07648" target="_blank">Paper</a></p> [![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)]() [![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg)]() <p align="center" width="100%"> <a><img src="https://github.com/UBC-NLP/infodcl/blob/master/images/infodcl_vis.png?raw=true" alt="Title" style="width: 90%; min-width: 300px; display: block; margin: auto;"></a> </p> Illustration of our proposed InfoDCL framework. We exploit distant/surrogate labels (i.e., emojis) to supervise two contrastive losses, corpus-aware contrastive loss (CCL) and Light label-aware contrastive loss (LCL-LiT). Sequence representations from our model should keep the cluster of each class distinguishable and preserve semantic relationships between classes. ## Checkpoints of Models Pre-Trained with InfoDCL * InfoDCL-RoBERTa trained with TweetEmoji-EN: https://huggingface.co/UBC-NLP/InfoDCL-emoji * InfoDCL-RoBERTa trained with TweetHashtag-EN: https://huggingface.co/UBC-NLP/InfoDCL-hashtag ## Model Performance <p align="center" width="100%"> <a><img src="https://github.com/UBC-NLP/infodcl/blob/master/images/main_table.png?raw=true" alt="main table" style="width: 95%; min-width: 300px; display: block; margin: auto;"></a> </p> Fine-tuning results on our 24 Socio-pragmatic Meaning datasets (average macro-F1 over five runs).
Litzy619/V0305P4	Litzy619	2024-03-06T05:43:14Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:yahma/llama-7b-hf", "base_model:finetune:yahma/llama-7b-hf", "license:other", "region:us" ]	null	2024-03-05T16:50:42Z	--- license: other base_model: yahma/llama-7b-hf tags: - generated_from_trainer model-index: - name: V0305P4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # V0305P4 This model is a fine-tuned version of [yahma/llama-7b-hf](https://huggingface.co/yahma/llama-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0583 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.8125 \| 0.09 \| 10 \| 0.6624 \| \| 0.25 \| 0.17 \| 20 \| 0.1568 \| \| 0.1567 \| 0.26 \| 30 \| 0.1543 \| \| 0.1522 \| 0.34 \| 40 \| 0.1471 \| \| 0.1487 \| 0.43 \| 50 \| 0.1446 \| \| 0.1517 \| 0.51 \| 60 \| 0.1370 \| \| 0.1348 \| 0.6 \| 70 \| 0.1154 \| \| 0.1261 \| 0.68 \| 80 \| 0.1077 \| \| 0.1125 \| 0.77 \| 90 \| 0.0915 \| \| 0.1142 \| 0.85 \| 100 \| 0.0879 \| \| 0.1095 \| 0.94 \| 110 \| 0.0932 \| \| 0.1035 \| 1.02 \| 120 \| 0.0936 \| \| 0.094 \| 1.11 \| 130 \| 0.0874 \| \| 0.0899 \| 1.19 \| 140 \| 0.0800 \| \| 0.0875 \| 1.28 \| 150 \| 0.0835 \| \| 0.0887 \| 1.37 \| 160 \| 0.0783 \| \| 0.0884 \| 1.45 \| 170 \| 0.0791 \| \| 0.0819 \| 1.54 \| 180 \| 0.0745 \| \| 0.0831 \| 1.62 \| 190 \| 0.0685 \| \| 0.0878 \| 1.71 \| 200 \| 0.0681 \| \| 0.0847 \| 1.79 \| 210 \| 0.0680 \| \| 0.0798 \| 1.88 \| 220 \| 0.0646 \| \| 0.0757 \| 1.96 \| 230 \| 0.0680 \| \| 0.0653 \| 2.05 \| 240 \| 0.0663 \| \| 0.0557 \| 2.13 \| 250 \| 0.0678 \| \| 0.052 \| 2.22 \| 260 \| 0.0634 \| \| 0.0517 \| 2.3 \| 270 \| 0.0654 \| \| 0.0576 \| 2.39 \| 280 \| 0.0593 \| \| 0.0573 \| 2.47 \| 290 \| 0.0584 \| \| 0.056 \| 2.56 \| 300 \| 0.0569 \| \| 0.0597 \| 2.65 \| 310 \| 0.0584 \| \| 0.0514 \| 2.73 \| 320 \| 0.0578 \| \| 0.0533 \| 2.82 \| 330 \| 0.0577 \| \| 0.0538 \| 2.9 \| 340 \| 0.0582 \| \| 0.0507 \| 2.99 \| 350 \| 0.0583 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
jerrish/distilbert-base-uncased-finetuned-ner	jerrish	2024-03-06T05:37:57Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-06T05:28:32Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0610 - Precision: 0.9266 - Recall: 0.9380 - F1: 0.9323 - Accuracy: 0.9836 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2562 \| 1.0 \| 878 \| 0.0712 \| 0.9007 \| 0.9178 \| 0.9092 \| 0.9797 \| \| 0.0512 \| 2.0 \| 1756 \| 0.0607 \| 0.9256 \| 0.9325 \| 0.9291 \| 0.9830 \| \| 0.0304 \| 3.0 \| 2634 \| 0.0610 \| 0.9266 \| 0.9380 \| 0.9323 \| 0.9836 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Mweny/alpha-monarch-finetuned-7b-v2.1-8-bit-gguf	Mweny	2024-03-06T05:36:21Z	8	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:Adeptschneider/alpha-monarch-7B-fine-tuned-model", "base_model:quantized:Adeptschneider/alpha-monarch-7B-fine-tuned-model", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-06T02:23:52Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: Adeptschneider/alpha-monarch-7B-fine-tuned-model --- # Uploaded model - Developed by: Mweny - License: apache-2.0 - Finetuned from model : mlabonne/alpha-monarch-7B-fine-tuned-model This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
letgoofthepizza/Llama-2-7b-chat-hf-finetuned-open-korean-instructions	letgoofthepizza	2024-03-06T05:06:42Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T04:57:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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min-dong/LLM_test1	min-dong	2024-03-06T04:58:42Z	2	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-03-06T04:47:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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guy-smiley/flan-t5-small-samsum	guy-smiley	2024-03-06T04:55:44Z	5	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-06T04:32:29Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: google/flan-t5-small metrics: - rouge model-index: - name: flan-t5-small-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-small-samsum This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6726 - Rouge1: 42.9923 - Rouge2: 18.9028 - Rougel: 35.7014 - Rougelsum: 39.2624 - Gen Len: 16.8400 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.8324 \| 1.0 \| 1842 \| 1.6726 \| 42.9923 \| 18.9028 \| 35.7014 \| 39.2624 \| 16.8400 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
cllm/deepseekcoder-7b-instruct-spider	cllm	2024-03-06T04:46:28Z	12	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-27T09:58:48Z	Metadata: AR loss to consistency loss ratio: 10: 1 Spider dataset size: 7k n-token sequence length: 16 Jacobi trajectory data cleaning: True Target model: Deepseek-Coder-7B fine-tuned on Spider release date: 02/26/2024

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