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bert-large-uncased-sst-2-16-13

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Inference Endpoints
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+ ---
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+ license: apache-2.0
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+ base_model: bert-large-uncased
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: bert-large-uncased-sst-2-16-13
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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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+ # bert-large-uncased-sst-2-16-13
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+
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+ This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6280
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+ - Accuracy: 0.7812
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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: 1e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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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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+ - lr_scheduler_warmup_steps: 50
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+ - num_epochs: 150
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 1 | 0.7881 | 0.5 |
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+ | No log | 2.0 | 2 | 0.7873 | 0.5 |
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+ | No log | 3.0 | 3 | 0.7860 | 0.5 |
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+ | No log | 4.0 | 4 | 0.7840 | 0.5 |
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+ | No log | 5.0 | 5 | 0.7810 | 0.5 |
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+ | No log | 6.0 | 6 | 0.7772 | 0.5 |
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+ | No log | 7.0 | 7 | 0.7723 | 0.5 |
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+ | No log | 8.0 | 8 | 0.7668 | 0.5 |
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+ | No log | 9.0 | 9 | 0.7600 | 0.5 |
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+ | 0.782 | 10.0 | 10 | 0.7522 | 0.5 |
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+ | 0.782 | 11.0 | 11 | 0.7438 | 0.5 |
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+ | 0.782 | 12.0 | 12 | 0.7344 | 0.5 |
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+ | 0.782 | 13.0 | 13 | 0.7252 | 0.5 |
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+ | 0.782 | 14.0 | 14 | 0.7148 | 0.5 |
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+ | 0.782 | 15.0 | 15 | 0.7043 | 0.5 |
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+ | 0.782 | 16.0 | 16 | 0.6943 | 0.5 |
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+ | 0.782 | 17.0 | 17 | 0.6857 | 0.4688 |
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+ | 0.782 | 18.0 | 18 | 0.6769 | 0.5 |
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+ | 0.782 | 19.0 | 19 | 0.6674 | 0.5312 |
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+ | 0.685 | 20.0 | 20 | 0.6591 | 0.5938 |
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+ | 0.685 | 21.0 | 21 | 0.6526 | 0.625 |
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+ | 0.685 | 22.0 | 22 | 0.6435 | 0.625 |
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+ | 0.685 | 23.0 | 23 | 0.6347 | 0.5938 |
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+ | 0.685 | 24.0 | 24 | 0.6278 | 0.625 |
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+ | 0.685 | 25.0 | 25 | 0.6261 | 0.5938 |
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+ | 0.685 | 26.0 | 26 | 0.6250 | 0.625 |
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+ | 0.685 | 27.0 | 27 | 0.6247 | 0.625 |
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+ | 0.685 | 28.0 | 28 | 0.6225 | 0.625 |
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+ | 0.685 | 29.0 | 29 | 0.6159 | 0.6562 |
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+ | 0.4699 | 30.0 | 30 | 0.6056 | 0.6562 |
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+ | 0.4699 | 31.0 | 31 | 0.5906 | 0.6875 |
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+ | 0.4699 | 32.0 | 32 | 0.5795 | 0.6875 |
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+ | 0.4699 | 33.0 | 33 | 0.5844 | 0.7812 |
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+ | 0.4699 | 34.0 | 34 | 0.5925 | 0.7188 |
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+ | 0.4699 | 35.0 | 35 | 0.5942 | 0.7188 |
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+ | 0.4699 | 36.0 | 36 | 0.5956 | 0.6875 |
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+ | 0.4699 | 37.0 | 37 | 0.5921 | 0.6875 |
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+ | 0.4699 | 38.0 | 38 | 0.5860 | 0.6875 |
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+ | 0.4699 | 39.0 | 39 | 0.5844 | 0.6875 |
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+ | 0.3039 | 40.0 | 40 | 0.5793 | 0.7188 |
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+ | 0.3039 | 41.0 | 41 | 0.5738 | 0.75 |
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+ | 0.3039 | 42.0 | 42 | 0.5734 | 0.75 |
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+ | 0.3039 | 43.0 | 43 | 0.5744 | 0.75 |
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+ | 0.3039 | 44.0 | 44 | 0.5782 | 0.6875 |
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+ | 0.3039 | 45.0 | 45 | 0.5817 | 0.6875 |
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+ | 0.3039 | 46.0 | 46 | 0.5858 | 0.6875 |
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+ | 0.3039 | 47.0 | 47 | 0.5888 | 0.6875 |
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+ | 0.3039 | 48.0 | 48 | 0.5836 | 0.6875 |
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+ | 0.3039 | 49.0 | 49 | 0.5724 | 0.7188 |
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+ | 0.1969 | 50.0 | 50 | 0.5572 | 0.7188 |
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+ | 0.1969 | 51.0 | 51 | 0.5442 | 0.7812 |
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+ | 0.1969 | 52.0 | 52 | 0.5347 | 0.7812 |
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+ | 0.1969 | 53.0 | 53 | 0.5288 | 0.7812 |
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+ | 0.1969 | 54.0 | 54 | 0.5284 | 0.75 |
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+ | 0.1969 | 55.0 | 55 | 0.5307 | 0.7812 |
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+ | 0.1969 | 56.0 | 56 | 0.5386 | 0.7812 |
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+ | 0.1969 | 57.0 | 57 | 0.5475 | 0.75 |
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+ | 0.1969 | 58.0 | 58 | 0.5535 | 0.75 |
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+ | 0.1969 | 59.0 | 59 | 0.5550 | 0.7188 |
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+ | 0.1348 | 60.0 | 60 | 0.5533 | 0.7188 |
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+ | 0.1348 | 61.0 | 61 | 0.5412 | 0.7812 |
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+ | 0.1348 | 62.0 | 62 | 0.5322 | 0.7812 |
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+ | 0.1348 | 63.0 | 63 | 0.5256 | 0.8125 |
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+ | 0.1348 | 64.0 | 64 | 0.5189 | 0.8125 |
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+ | 0.1348 | 65.0 | 65 | 0.5148 | 0.8125 |
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+ | 0.1348 | 66.0 | 66 | 0.5154 | 0.7812 |
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+ | 0.1348 | 67.0 | 67 | 0.5162 | 0.75 |
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+ | 0.1348 | 68.0 | 68 | 0.5202 | 0.75 |
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+ | 0.1348 | 69.0 | 69 | 0.5255 | 0.75 |
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+ | 0.0823 | 70.0 | 70 | 0.5330 | 0.75 |
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+ | 0.0823 | 71.0 | 71 | 0.5367 | 0.75 |
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+ | 0.0823 | 72.0 | 72 | 0.5413 | 0.75 |
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+ | 0.0823 | 73.0 | 73 | 0.5434 | 0.75 |
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+ | 0.0823 | 74.0 | 74 | 0.5415 | 0.75 |
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+ | 0.0823 | 75.0 | 75 | 0.5395 | 0.75 |
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+ | 0.0823 | 76.0 | 76 | 0.5394 | 0.75 |
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+ | 0.0823 | 77.0 | 77 | 0.5380 | 0.75 |
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+ | 0.0823 | 78.0 | 78 | 0.5379 | 0.75 |
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+ | 0.0823 | 79.0 | 79 | 0.5396 | 0.75 |
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+ | 0.0519 | 80.0 | 80 | 0.5426 | 0.75 |
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+ | 0.0519 | 81.0 | 81 | 0.5426 | 0.75 |
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+ | 0.0519 | 82.0 | 82 | 0.5419 | 0.75 |
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+ | 0.0519 | 83.0 | 83 | 0.5446 | 0.75 |
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+ | 0.0519 | 84.0 | 84 | 0.5467 | 0.75 |
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+ | 0.0519 | 85.0 | 85 | 0.5487 | 0.75 |
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+ | 0.0519 | 86.0 | 86 | 0.5522 | 0.75 |
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+ | 0.0519 | 87.0 | 87 | 0.5566 | 0.75 |
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+ | 0.0519 | 88.0 | 88 | 0.5614 | 0.75 |
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+ | 0.0519 | 89.0 | 89 | 0.5672 | 0.75 |
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+ | 0.0382 | 90.0 | 90 | 0.5713 | 0.75 |
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+ | 0.0382 | 91.0 | 91 | 0.5744 | 0.75 |
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+ | 0.0382 | 92.0 | 92 | 0.5773 | 0.75 |
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+ | 0.0382 | 93.0 | 93 | 0.5799 | 0.75 |
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+ | 0.0382 | 94.0 | 94 | 0.5806 | 0.75 |
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+ | 0.0382 | 95.0 | 95 | 0.5777 | 0.75 |
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+ | 0.0382 | 96.0 | 96 | 0.5761 | 0.75 |
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+ | 0.0382 | 97.0 | 97 | 0.5746 | 0.75 |
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+ | 0.0382 | 98.0 | 98 | 0.5710 | 0.7812 |
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+ | 0.0382 | 99.0 | 99 | 0.5697 | 0.7812 |
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+ | 0.0266 | 100.0 | 100 | 0.5676 | 0.7812 |
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+ | 0.0266 | 101.0 | 101 | 0.5650 | 0.7812 |
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+ | 0.0266 | 102.0 | 102 | 0.5637 | 0.7812 |
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+ | 0.0266 | 103.0 | 103 | 0.5623 | 0.7812 |
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+ | 0.0266 | 104.0 | 104 | 0.5631 | 0.7812 |
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+ | 0.0266 | 105.0 | 105 | 0.5633 | 0.7812 |
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+ | 0.0266 | 106.0 | 106 | 0.5635 | 0.7812 |
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+ | 0.0266 | 107.0 | 107 | 0.5638 | 0.8125 |
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+ | 0.0266 | 108.0 | 108 | 0.5646 | 0.7812 |
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+ | 0.0266 | 109.0 | 109 | 0.5662 | 0.7812 |
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+ | 0.0205 | 110.0 | 110 | 0.5694 | 0.7812 |
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+ | 0.0205 | 111.0 | 111 | 0.5737 | 0.7812 |
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+ | 0.0205 | 112.0 | 112 | 0.5797 | 0.7812 |
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+ | 0.0205 | 113.0 | 113 | 0.5851 | 0.7812 |
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+ | 0.0205 | 114.0 | 114 | 0.5923 | 0.7812 |
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+ | 0.0205 | 115.0 | 115 | 0.6008 | 0.7812 |
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+ | 0.0205 | 116.0 | 116 | 0.6091 | 0.7812 |
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+ | 0.0205 | 117.0 | 117 | 0.6162 | 0.75 |
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+ | 0.0205 | 118.0 | 118 | 0.6201 | 0.75 |
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+ | 0.0205 | 119.0 | 119 | 0.6233 | 0.75 |
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+ | 0.0168 | 120.0 | 120 | 0.6255 | 0.75 |
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+ | 0.0168 | 121.0 | 121 | 0.6274 | 0.75 |
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+ | 0.0168 | 122.0 | 122 | 0.6293 | 0.75 |
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+ | 0.0168 | 123.0 | 123 | 0.6265 | 0.75 |
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+ | 0.0168 | 124.0 | 124 | 0.6245 | 0.75 |
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+ | 0.0168 | 125.0 | 125 | 0.6239 | 0.75 |
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+ | 0.0168 | 126.0 | 126 | 0.6232 | 0.75 |
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+ | 0.0168 | 127.0 | 127 | 0.6221 | 0.7812 |
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+ | 0.0168 | 128.0 | 128 | 0.6216 | 0.7812 |
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+ | 0.0168 | 129.0 | 129 | 0.6213 | 0.7812 |
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+ | 0.0139 | 130.0 | 130 | 0.6214 | 0.7812 |
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+ | 0.0139 | 131.0 | 131 | 0.6212 | 0.7812 |
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+ | 0.0139 | 132.0 | 132 | 0.6218 | 0.7812 |
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+ | 0.0139 | 133.0 | 133 | 0.6234 | 0.7812 |
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+ | 0.0139 | 134.0 | 134 | 0.6248 | 0.7812 |
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+ | 0.0139 | 135.0 | 135 | 0.6259 | 0.7812 |
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+ | 0.0139 | 136.0 | 136 | 0.6269 | 0.7812 |
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+ | 0.0139 | 137.0 | 137 | 0.6275 | 0.7812 |
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+ | 0.0139 | 138.0 | 138 | 0.6277 | 0.7812 |
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+ | 0.0139 | 139.0 | 139 | 0.6280 | 0.7812 |
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+ | 0.0126 | 140.0 | 140 | 0.6281 | 0.7812 |
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+ | 0.0126 | 141.0 | 141 | 0.6283 | 0.7812 |
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+ | 0.0126 | 142.0 | 142 | 0.6281 | 0.7812 |
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+ | 0.0126 | 143.0 | 143 | 0.6279 | 0.7812 |
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+ | 0.0126 | 144.0 | 144 | 0.6279 | 0.7812 |
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+ | 0.0126 | 145.0 | 145 | 0.6278 | 0.7812 |
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+ | 0.0126 | 146.0 | 146 | 0.6278 | 0.7812 |
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+ | 0.0126 | 147.0 | 147 | 0.6279 | 0.7812 |
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+ | 0.0126 | 148.0 | 148 | 0.6279 | 0.7812 |
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+ | 0.0126 | 149.0 | 149 | 0.6279 | 0.7812 |
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+ | 0.0121 | 150.0 | 150 | 0.6280 | 0.7812 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.32.0.dev0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.4.0
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+ - Tokenizers 0.13.3