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segformer-b0-finetuned-segments-sidewalk-2

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  1. README.md +151 -0
  2. config.json +144 -0
  3. model.safetensors +3 -0
  4. runs/Mar04_14-58-27_67c2eaf55f1b/events.out.tfevents.1709565209.67c2eaf55f1b.20288.0 +3 -0
  5. runs/Mar04_15-29-33_67c2eaf55f1b/events.out.tfevents.1709566184.67c2eaf55f1b.20288.1 +3 -0
  6. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: other
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+ base_model: nvidia/mit-b0
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+ tags:
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+ - vision
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+ - image-segmentation
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+ - generated_from_trainer
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+ model-index:
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+ - name: segformer-b0-finetuned-segments-sidewalk-2
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # segformer-b0-finetuned-segments-sidewalk-2
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+
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+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6854
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+ - Mean Iou: 0.2132
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+ - Mean Accuracy: 0.2587
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+ - Overall Accuracy: 0.8151
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Flat-road: 0.8383
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+ - Accuracy Flat-sidewalk: 0.9497
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+ - Accuracy Flat-crosswalk: 0.0
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+ - Accuracy Flat-cyclinglane: 0.8212
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+ - Accuracy Flat-parkingdriveway: 0.3818
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+ - Accuracy Flat-railtrack: nan
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+ - Accuracy Flat-curb: 0.2786
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+ - Accuracy Human-person: 0.0
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+ - Accuracy Human-rider: 0.0
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+ - Accuracy Vehicle-car: 0.9368
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+ - Accuracy Vehicle-truck: 0.0
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+ - Accuracy Vehicle-bus: 0.0
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+ - Accuracy Vehicle-tramtrain: nan
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+ - Accuracy Vehicle-motorcycle: 0.0
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+ - Accuracy Vehicle-bicycle: 0.0
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+ - Accuracy Vehicle-caravan: 0.0
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+ - Accuracy Vehicle-cartrailer: 0.0
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+ - Accuracy Construction-building: 0.9300
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+ - Accuracy Construction-door: 0.0
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+ - Accuracy Construction-wall: 0.0951
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+ - Accuracy Construction-fenceguardrail: 0.0012
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+ - Accuracy Construction-bridge: 0.0
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+ - Accuracy Construction-tunnel: nan
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+ - Accuracy Construction-stairs: 0.0
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+ - Accuracy Object-pole: 0.0181
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+ - Accuracy Object-trafficsign: 0.0
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+ - Accuracy Object-trafficlight: 0.0
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+ - Accuracy Nature-vegetation: 0.9377
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+ - Accuracy Nature-terrain: 0.8734
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+ - Accuracy Sky: 0.9576
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+ - Accuracy Void-ground: 0.0
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+ - Accuracy Void-dynamic: 0.0
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+ - Accuracy Void-static: 0.0002
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+ - Accuracy Void-unclear: 0.0
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+ - Iou Unlabeled: nan
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+ - Iou Flat-road: 0.6565
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+ - Iou Flat-sidewalk: 0.8602
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+ - Iou Flat-crosswalk: 0.0
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+ - Iou Flat-cyclinglane: 0.7150
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+ - Iou Flat-parkingdriveway: 0.2892
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+ - Iou Flat-railtrack: nan
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+ - Iou Flat-curb: 0.2447
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+ - Iou Human-person: 0.0
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+ - Iou Human-rider: 0.0
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+ - Iou Vehicle-car: 0.7028
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+ - Iou Vehicle-truck: 0.0
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+ - Iou Vehicle-bus: 0.0
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+ - Iou Vehicle-tramtrain: nan
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+ - Iou Vehicle-motorcycle: 0.0
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+ - Iou Vehicle-bicycle: 0.0
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+ - Iou Vehicle-caravan: 0.0
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+ - Iou Vehicle-cartrailer: 0.0
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+ - Iou Construction-building: 0.6164
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+ - Iou Construction-door: 0.0
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+ - Iou Construction-wall: 0.0896
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+ - Iou Construction-fenceguardrail: 0.0012
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+ - Iou Construction-bridge: 0.0
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+ - Iou Construction-tunnel: nan
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+ - Iou Construction-stairs: 0.0
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+ - Iou Object-pole: 0.0180
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+ - Iou Object-trafficsign: 0.0
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+ - Iou Object-trafficlight: 0.0
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+ - Iou Nature-vegetation: 0.8065
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+ - Iou Nature-terrain: 0.7196
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+ - Iou Sky: 0.8903
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+ - Iou Void-ground: 0.0
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+ - Iou Void-dynamic: 0.0
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+ - Iou Void-static: 0.0002
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+ - Iou Void-unclear: 0.0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 6e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 2
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 1
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear |
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+ | 1.133 | 0.05 | 20 | 0.8142 | 0.1927 | 0.2365 | 0.7919 | nan | 0.8488 | 0.9396 | 0.0 | 0.6154 | 0.3232 | nan | 0.0870 | 0.0 | 0.0 | 0.9079 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9091 | 0.0 | 0.0057 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9459 | 0.8075 | 0.9417 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.5996 | 0.8350 | 0.0 | 0.5839 | 0.2497 | nan | 0.0824 | 0.0 | 0.0 | 0.6972 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5834 | 0.0 | 0.0057 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7902 | 0.6980 | 0.8488 | 0.0 | 0.0 | 0.0000 | 0.0 |
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+ | 0.6183 | 0.1 | 40 | 0.7929 | 0.1935 | 0.2387 | 0.7946 | nan | 0.8424 | 0.9426 | 0.0 | 0.6490 | 0.2786 | nan | 0.0932 | 0.0 | 0.0 | 0.9013 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9355 | 0.0 | 0.0078 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9106 | 0.9015 | 0.9372 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.6021 | 0.8449 | 0.0 | 0.5861 | 0.2298 | nan | 0.0889 | 0.0 | 0.0 | 0.6913 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5851 | 0.0 | 0.0078 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7952 | 0.6890 | 0.8787 | 0.0 | 0.0 | 0.0000 | 0.0 |
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+ | 0.7143 | 0.15 | 60 | 0.7832 | 0.1963 | 0.2407 | 0.7970 | nan | 0.8115 | 0.9508 | 0.0 | 0.6225 | 0.3488 | nan | 0.1208 | 0.0 | 0.0 | 0.9286 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9124 | 0.0 | 0.0163 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9415 | 0.8645 | 0.9439 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.6145 | 0.8356 | 0.0 | 0.5800 | 0.2642 | nan | 0.1131 | 0.0 | 0.0 | 0.6861 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6004 | 0.0 | 0.0161 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.7914 | 0.7056 | 0.8799 | 0.0 | 0.0 | 0.0000 | 0.0 |
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+ | 0.7266 | 0.2 | 80 | 0.7789 | 0.1933 | 0.2380 | 0.7922 | nan | 0.8418 | 0.9346 | 0.0 | 0.6266 | 0.3044 | nan | 0.0522 | 0.0 | 0.0 | 0.9256 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9212 | 0.0 | 0.0297 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9329 | 0.8752 | 0.9345 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5945 | 0.8266 | 0.0 | 0.5830 | 0.2447 | nan | 0.0506 | 0.0 | 0.0 | 0.6819 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6030 | 0.0 | 0.0293 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.7998 | 0.6952 | 0.8834 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.8732 | 0.25 | 100 | 0.7598 | 0.2025 | 0.2515 | 0.7986 | nan | 0.8657 | 0.9218 | 0.0 | 0.7984 | 0.3960 | nan | 0.1382 | 0.0 | 0.0 | 0.9323 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9264 | 0.0 | 0.0718 | 0.0 | 0.0 | nan | 0.0 | 0.0010 | 0.0 | 0.0 | 0.8819 | 0.9151 | 0.9484 | 0.0 | 0.0 | 0.0001 | 0.0 | nan | 0.6212 | 0.8387 | 0.0 | 0.7013 | 0.2830 | nan | 0.1285 | 0.0 | 0.0 | 0.6971 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6065 | 0.0 | 0.0696 | 0.0 | 0.0 | nan | 0.0 | 0.0010 | 0.0 | 0.0 | 0.7925 | 0.6575 | 0.8817 | 0.0 | 0.0 | 0.0001 | 0.0 |
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+ | 1.0414 | 0.3 | 120 | 0.7519 | 0.2004 | 0.2426 | 0.8027 | nan | 0.7989 | 0.9643 | 0.0 | 0.7894 | 0.2927 | nan | 0.0920 | 0.0 | 0.0 | 0.9300 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9095 | 0.0 | 0.0169 | 0.0 | 0.0 | nan | 0.0 | 0.0012 | 0.0 | 0.0 | 0.9403 | 0.8354 | 0.9507 | 0.0 | 0.0 | 0.0003 | 0.0 | nan | 0.6503 | 0.8249 | 0.0 | 0.7135 | 0.2513 | nan | 0.0851 | 0.0 | 0.0 | 0.7073 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5968 | 0.0 | 0.0167 | 0.0 | 0.0 | nan | 0.0 | 0.0012 | 0.0 | 0.0 | 0.7986 | 0.6976 | 0.8675 | 0.0 | 0.0 | 0.0003 | 0.0 |
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+ | 0.7812 | 0.35 | 140 | 0.7660 | 0.2004 | 0.2433 | 0.8008 | nan | 0.7714 | 0.9656 | 0.0 | 0.8225 | 0.2306 | nan | 0.1649 | 0.0 | 0.0 | 0.9393 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9225 | 0.0 | 0.0362 | 0.0 | 0.0 | nan | 0.0 | 0.0003 | 0.0 | 0.0 | 0.9328 | 0.8450 | 0.9101 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.6427 | 0.8220 | 0.0 | 0.7043 | 0.2006 | nan | 0.1508 | 0.0 | 0.0 | 0.6825 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5969 | 0.0 | 0.0354 | 0.0 | 0.0 | nan | 0.0 | 0.0003 | 0.0 | 0.0 | 0.8031 | 0.7024 | 0.8709 | 0.0 | 0.0 | 0.0000 | 0.0 |
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+ | 0.6117 | 0.4 | 160 | 0.7395 | 0.2078 | 0.2505 | 0.8074 | nan | 0.8021 | 0.9599 | 0.0 | 0.7951 | 0.3092 | nan | 0.2320 | 0.0 | 0.0 | 0.9291 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9184 | 0.0 | 0.0807 | 0.0001 | 0.0 | nan | 0.0 | 0.0031 | 0.0 | 0.0 | 0.9379 | 0.8574 | 0.9416 | 0.0 | 0.0 | 0.0001 | 0.0 | nan | 0.6450 | 0.8340 | 0.0 | 0.7138 | 0.2435 | nan | 0.2008 | 0.0 | 0.0 | 0.7102 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6146 | 0.0 | 0.0776 | 0.0001 | 0.0 | nan | 0.0 | 0.0031 | 0.0 | 0.0 | 0.8047 | 0.7070 | 0.8874 | 0.0 | 0.0 | 0.0001 | 0.0 |
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+ | 1.1176 | 0.45 | 180 | 0.7283 | 0.2088 | 0.2543 | 0.8066 | nan | 0.7949 | 0.9620 | 0.0 | 0.7781 | 0.3479 | nan | 0.2238 | 0.0 | 0.0 | 0.9365 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8798 | 0.0 | 0.1794 | 0.0000 | 0.0 | nan | 0.0 | 0.0070 | 0.0 | 0.0 | 0.9212 | 0.8978 | 0.9546 | 0.0 | 0.0 | 0.0004 | 0.0 | nan | 0.6398 | 0.8361 | 0.0 | 0.7046 | 0.2608 | nan | 0.1909 | 0.0 | 0.0 | 0.6770 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6329 | 0.0 | 0.1669 | 0.0000 | 0.0 | nan | 0.0 | 0.0070 | 0.0 | 0.0 | 0.7940 | 0.6798 | 0.8840 | 0.0 | 0.0 | 0.0004 | 0.0 |
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+ | 1.0874 | 0.5 | 200 | 0.7138 | 0.2074 | 0.2497 | 0.8093 | nan | 0.8548 | 0.9534 | 0.0 | 0.7502 | 0.3509 | nan | 0.2045 | 0.0 | 0.0 | 0.9139 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9440 | 0.0 | 0.0572 | 0.0 | 0.0 | nan | 0.0 | 0.0022 | 0.0 | 0.0 | 0.9277 | 0.8297 | 0.9518 | 0.0 | 0.0 | 0.0003 | 0.0 | nan | 0.6508 | 0.8521 | 0.0 | 0.6877 | 0.2737 | nan | 0.1824 | 0.0 | 0.0 | 0.7291 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5955 | 0.0 | 0.0551 | 0.0 | 0.0 | nan | 0.0 | 0.0022 | 0.0 | 0.0 | 0.8090 | 0.7067 | 0.8854 | 0.0 | 0.0 | 0.0003 | 0.0 |
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+ | 1.1744 | 0.55 | 220 | 0.7095 | 0.2072 | 0.2491 | 0.8070 | nan | 0.8193 | 0.9577 | 0.0 | 0.7556 | 0.3430 | nan | 0.2271 | 0.0 | 0.0 | 0.9157 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9490 | 0.0 | 0.0440 | 0.0000 | 0.0 | nan | 0.0 | 0.0031 | 0.0 | 0.0 | 0.9225 | 0.8357 | 0.9500 | 0.0 | 0.0 | 0.0004 | 0.0 | nan | 0.6459 | 0.8463 | 0.0 | 0.7019 | 0.2733 | nan | 0.1944 | 0.0 | 0.0 | 0.7281 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5841 | 0.0 | 0.0412 | 0.0000 | 0.0 | nan | 0.0 | 0.0031 | 0.0 | 0.0 | 0.8140 | 0.7022 | 0.8892 | 0.0 | 0.0 | 0.0004 | 0.0 |
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+ | 0.8371 | 0.6 | 240 | 0.7224 | 0.2081 | 0.2506 | 0.8073 | nan | 0.8102 | 0.9614 | 0.0 | 0.7220 | 0.3368 | nan | 0.2390 | 0.0 | 0.0 | 0.9278 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9066 | 0.0 | 0.1151 | 0.0001 | 0.0 | nan | 0.0 | 0.0039 | 0.0 | 0.0 | 0.9439 | 0.8466 | 0.9542 | 0.0 | 0.0 | 0.0005 | 0.0 | nan | 0.6394 | 0.8393 | 0.0 | 0.6850 | 0.2629 | nan | 0.2099 | 0.0 | 0.0 | 0.7123 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6215 | 0.0 | 0.1074 | 0.0001 | 0.0 | nan | 0.0 | 0.0039 | 0.0 | 0.0 | 0.7932 | 0.6883 | 0.8870 | 0.0 | 0.0 | 0.0005 | 0.0 |
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+ | 1.0493 | 0.65 | 260 | 0.7100 | 0.2093 | 0.2505 | 0.8086 | nan | 0.8021 | 0.9639 | 0.0 | 0.7634 | 0.3138 | nan | 0.2254 | 0.0 | 0.0 | 0.9232 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9235 | 0.0 | 0.0942 | 0.0011 | 0.0 | nan | 0.0 | 0.0041 | 0.0 | 0.0 | 0.9356 | 0.8677 | 0.9456 | 0.0 | 0.0 | 0.0004 | 0.0 | nan | 0.6457 | 0.8343 | 0.0 | 0.7131 | 0.2547 | nan | 0.1979 | 0.0 | 0.0 | 0.7253 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6121 | 0.0 | 0.0881 | 0.0011 | 0.0 | nan | 0.0 | 0.0041 | 0.0 | 0.0 | 0.8033 | 0.7173 | 0.8899 | 0.0 | 0.0 | 0.0004 | 0.0 |
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+ | 0.4048 | 0.7 | 280 | 0.7147 | 0.2112 | 0.2566 | 0.8087 | nan | 0.7952 | 0.9466 | 0.0 | 0.7771 | 0.4525 | nan | 0.3231 | 0.0 | 0.0 | 0.9329 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9329 | 0.0 | 0.0638 | 0.0 | 0.0 | nan | 0.0 | 0.0058 | 0.0 | 0.0 | 0.9506 | 0.8326 | 0.9420 | 0.0 | 0.0 | 0.0002 | 0.0 | nan | 0.6491 | 0.8535 | 0.0 | 0.7154 | 0.2954 | nan | 0.2641 | 0.0 | 0.0 | 0.7080 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5986 | 0.0 | 0.0604 | 0.0 | 0.0 | nan | 0.0 | 0.0058 | 0.0 | 0.0 | 0.7988 | 0.7070 | 0.8915 | 0.0 | 0.0 | 0.0002 | 0.0 |
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+ | 0.5975 | 0.75 | 300 | 0.7049 | 0.2116 | 0.2572 | 0.8123 | nan | 0.8359 | 0.9476 | 0.0 | 0.7908 | 0.4403 | nan | 0.2551 | 0.0 | 0.0 | 0.9417 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9331 | 0.0 | 0.0776 | 0.0008 | 0.0 | nan | 0.0 | 0.0070 | 0.0 | 0.0 | 0.9270 | 0.8649 | 0.9522 | 0.0 | 0.0 | 0.0002 | 0.0 | nan | 0.6454 | 0.8593 | 0.0 | 0.7080 | 0.3010 | nan | 0.2257 | 0.0 | 0.0 | 0.6990 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6080 | 0.0 | 0.0732 | 0.0008 | 0.0 | nan | 0.0 | 0.0070 | 0.0 | 0.0 | 0.8192 | 0.7197 | 0.8919 | 0.0 | 0.0 | 0.0002 | 0.0 |
139
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142
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143
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144
+
145
+
146
+ ### Framework versions
147
+
148
+ - Transformers 4.38.2
149
+ - Pytorch 2.1.0+cu121
150
+ - Datasets 2.18.0
151
+ - Tokenizers 0.15.2
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