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--- |
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license: apache-2.0 |
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base_model: bert-base-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-base-uncased-sst-2-16-100 |
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results: [] |
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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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# bert-base-uncased-sst-2-16-100 |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3049 |
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- Accuracy: 0.9375 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 500 |
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- num_epochs: 150 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 1 | 0.2289 | 0.9375 | |
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| No log | 2.0 | 2 | 0.2289 | 0.9375 | |
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| No log | 3.0 | 3 | 0.2287 | 0.9375 | |
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| No log | 4.0 | 4 | 0.2285 | 0.9375 | |
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| No log | 5.0 | 5 | 0.2281 | 0.9375 | |
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| No log | 6.0 | 6 | 0.2278 | 0.9375 | |
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| No log | 7.0 | 7 | 0.2271 | 0.9375 | |
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| No log | 8.0 | 8 | 0.2265 | 0.9375 | |
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| No log | 9.0 | 9 | 0.2259 | 0.9375 | |
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| 0.3159 | 10.0 | 10 | 0.2250 | 0.9375 | |
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| 0.3159 | 11.0 | 11 | 0.2236 | 0.9375 | |
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| 0.3159 | 12.0 | 12 | 0.2224 | 0.9375 | |
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| 0.3159 | 13.0 | 13 | 0.2208 | 0.9375 | |
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| 0.3159 | 14.0 | 14 | 0.2190 | 0.9375 | |
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| 0.3159 | 15.0 | 15 | 0.2175 | 0.9375 | |
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| 0.3159 | 16.0 | 16 | 0.2166 | 0.9375 | |
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| 0.3159 | 17.0 | 17 | 0.2154 | 0.9375 | |
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| 0.3159 | 18.0 | 18 | 0.2143 | 0.9375 | |
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| 0.3159 | 19.0 | 19 | 0.2138 | 0.9375 | |
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| 0.3191 | 20.0 | 20 | 0.2133 | 0.9375 | |
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| 0.3191 | 21.0 | 21 | 0.2130 | 0.9375 | |
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| 0.3191 | 22.0 | 22 | 0.2127 | 0.9375 | |
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| 0.3191 | 23.0 | 23 | 0.2121 | 0.9375 | |
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| 0.3191 | 24.0 | 24 | 0.2115 | 0.9375 | |
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| 0.3191 | 25.0 | 25 | 0.2112 | 0.9375 | |
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| 0.3191 | 26.0 | 26 | 0.2115 | 0.9375 | |
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| 0.3191 | 27.0 | 27 | 0.2113 | 0.9375 | |
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| 0.3191 | 28.0 | 28 | 0.2118 | 0.9375 | |
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| 0.3191 | 29.0 | 29 | 0.2130 | 0.9375 | |
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| 0.2101 | 30.0 | 30 | 0.2134 | 0.9375 | |
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| 0.2101 | 31.0 | 31 | 0.2136 | 0.9375 | |
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| 0.2101 | 32.0 | 32 | 0.2138 | 0.9375 | |
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| 0.2101 | 33.0 | 33 | 0.2133 | 0.9375 | |
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| 0.2101 | 34.0 | 34 | 0.2124 | 0.9375 | |
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| 0.2101 | 35.0 | 35 | 0.2112 | 0.9375 | |
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| 0.2101 | 36.0 | 36 | 0.2107 | 0.9375 | |
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| 0.2101 | 37.0 | 37 | 0.2099 | 0.9375 | |
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| 0.2101 | 38.0 | 38 | 0.2095 | 0.9375 | |
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| 0.2101 | 39.0 | 39 | 0.2096 | 0.9375 | |
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| 0.1112 | 40.0 | 40 | 0.2096 | 0.9375 | |
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| 0.1112 | 41.0 | 41 | 0.2096 | 0.9375 | |
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| 0.1112 | 42.0 | 42 | 0.2093 | 0.9375 | |
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| 0.1112 | 43.0 | 43 | 0.2085 | 0.9375 | |
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| 0.1112 | 44.0 | 44 | 0.2084 | 0.9375 | |
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| 0.1112 | 45.0 | 45 | 0.2082 | 0.9375 | |
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| 0.1112 | 46.0 | 46 | 0.2083 | 0.9375 | |
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| 0.1112 | 47.0 | 47 | 0.2088 | 0.9375 | |
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| 0.1112 | 48.0 | 48 | 0.2097 | 0.9375 | |
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| 0.1112 | 49.0 | 49 | 0.2111 | 0.9375 | |
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| 0.0453 | 50.0 | 50 | 0.2127 | 0.9375 | |
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| 0.0453 | 51.0 | 51 | 0.2147 | 0.9375 | |
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| 0.0453 | 52.0 | 52 | 0.2170 | 0.9375 | |
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| 0.0453 | 53.0 | 53 | 0.2200 | 0.9375 | |
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| 0.0453 | 54.0 | 54 | 0.2239 | 0.9375 | |
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| 0.0453 | 55.0 | 55 | 0.2271 | 0.9375 | |
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| 0.0453 | 56.0 | 56 | 0.2299 | 0.9375 | |
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| 0.0453 | 57.0 | 57 | 0.2320 | 0.9375 | |
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| 0.0453 | 58.0 | 58 | 0.2352 | 0.9375 | |
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| 0.0453 | 59.0 | 59 | 0.2364 | 0.9375 | |
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| 0.0349 | 60.0 | 60 | 0.2360 | 0.9375 | |
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| 0.0349 | 61.0 | 61 | 0.2348 | 0.9375 | |
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| 0.0349 | 62.0 | 62 | 0.2320 | 0.9375 | |
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| 0.0349 | 63.0 | 63 | 0.2302 | 0.9375 | |
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| 0.0349 | 64.0 | 64 | 0.2283 | 0.9375 | |
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| 0.0349 | 65.0 | 65 | 0.2256 | 0.9375 | |
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| 0.0349 | 66.0 | 66 | 0.2244 | 0.9375 | |
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| 0.0349 | 67.0 | 67 | 0.2226 | 0.9375 | |
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| 0.0349 | 68.0 | 68 | 0.2214 | 0.9375 | |
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| 0.0349 | 69.0 | 69 | 0.2208 | 0.9375 | |
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| 0.0184 | 70.0 | 70 | 0.2199 | 0.9375 | |
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| 0.0184 | 71.0 | 71 | 0.2178 | 0.9375 | |
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| 0.0184 | 72.0 | 72 | 0.2161 | 0.9375 | |
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| 0.0184 | 73.0 | 73 | 0.2147 | 0.9375 | |
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| 0.0184 | 74.0 | 74 | 0.2151 | 0.9375 | |
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| 0.0184 | 75.0 | 75 | 0.2158 | 0.9375 | |
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| 0.0184 | 76.0 | 76 | 0.2171 | 0.9375 | |
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| 0.0184 | 77.0 | 77 | 0.2184 | 0.9375 | |
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| 0.0184 | 78.0 | 78 | 0.2189 | 0.9375 | |
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| 0.0184 | 79.0 | 79 | 0.2196 | 0.9375 | |
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| 0.0125 | 80.0 | 80 | 0.2204 | 0.9375 | |
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| 0.0125 | 81.0 | 81 | 0.2216 | 0.9375 | |
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| 0.0125 | 82.0 | 82 | 0.2227 | 0.9375 | |
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| 0.0125 | 83.0 | 83 | 0.2243 | 0.9375 | |
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| 0.0125 | 84.0 | 84 | 0.2260 | 0.9375 | |
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| 0.0125 | 85.0 | 85 | 0.2275 | 0.9375 | |
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| 0.0125 | 86.0 | 86 | 0.2288 | 0.9375 | |
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| 0.0125 | 87.0 | 87 | 0.2300 | 0.9375 | |
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| 0.0125 | 88.0 | 88 | 0.2313 | 0.9375 | |
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| 0.0125 | 89.0 | 89 | 0.2328 | 0.9375 | |
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| 0.0099 | 90.0 | 90 | 0.2347 | 0.9375 | |
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| 0.0099 | 91.0 | 91 | 0.2375 | 0.9375 | |
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| 0.0099 | 92.0 | 92 | 0.2402 | 0.9375 | |
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| 0.0099 | 93.0 | 93 | 0.2428 | 0.9375 | |
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| 0.0099 | 94.0 | 94 | 0.2453 | 0.9375 | |
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| 0.0099 | 95.0 | 95 | 0.2477 | 0.9375 | |
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| 0.0099 | 96.0 | 96 | 0.2501 | 0.9375 | |
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| 0.0099 | 97.0 | 97 | 0.2554 | 0.9375 | |
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| 0.0099 | 98.0 | 98 | 0.2604 | 0.9375 | |
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| 0.0099 | 99.0 | 99 | 0.2651 | 0.9375 | |
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| 0.0078 | 100.0 | 100 | 0.2694 | 0.9375 | |
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| 0.0078 | 101.0 | 101 | 0.2728 | 0.9375 | |
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| 0.0078 | 102.0 | 102 | 0.2759 | 0.9375 | |
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| 0.0078 | 103.0 | 103 | 0.2787 | 0.9375 | |
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| 0.0078 | 104.0 | 104 | 0.2812 | 0.9375 | |
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| 0.0078 | 105.0 | 105 | 0.2837 | 0.9375 | |
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| 0.0078 | 106.0 | 106 | 0.2856 | 0.9375 | |
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| 0.0078 | 107.0 | 107 | 0.2899 | 0.9375 | |
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| 0.0078 | 108.0 | 108 | 0.2939 | 0.9375 | |
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| 0.0078 | 109.0 | 109 | 0.2976 | 0.9375 | |
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| 0.0078 | 110.0 | 110 | 0.3010 | 0.9375 | |
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| 0.0078 | 111.0 | 111 | 0.3042 | 0.9375 | |
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| 0.0078 | 112.0 | 112 | 0.3049 | 0.9375 | |
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| 0.0078 | 113.0 | 113 | 0.3050 | 0.9375 | |
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| 0.0078 | 114.0 | 114 | 0.3051 | 0.9375 | |
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| 0.0078 | 115.0 | 115 | 0.3045 | 0.9375 | |
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| 0.0078 | 116.0 | 116 | 0.3041 | 0.9375 | |
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| 0.0078 | 117.0 | 117 | 0.3034 | 0.9375 | |
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| 0.0078 | 118.0 | 118 | 0.2994 | 0.9375 | |
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| 0.0078 | 119.0 | 119 | 0.2958 | 0.9375 | |
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| 0.006 | 120.0 | 120 | 0.2924 | 0.9375 | |
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| 0.006 | 121.0 | 121 | 0.2892 | 0.9375 | |
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| 0.006 | 122.0 | 122 | 0.2866 | 0.9375 | |
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| 0.006 | 123.0 | 123 | 0.2842 | 0.9375 | |
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| 0.006 | 124.0 | 124 | 0.2821 | 0.9375 | |
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| 0.006 | 125.0 | 125 | 0.2802 | 0.9375 | |
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| 0.006 | 126.0 | 126 | 0.2785 | 0.9375 | |
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| 0.006 | 127.0 | 127 | 0.2775 | 0.9375 | |
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| 0.006 | 128.0 | 128 | 0.2768 | 0.9375 | |
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| 0.006 | 129.0 | 129 | 0.2762 | 0.9375 | |
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| 0.005 | 130.0 | 130 | 0.2758 | 0.9375 | |
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| 0.005 | 131.0 | 131 | 0.2756 | 0.9375 | |
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| 0.005 | 132.0 | 132 | 0.2756 | 0.9375 | |
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| 0.005 | 133.0 | 133 | 0.2757 | 0.9375 | |
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| 0.005 | 134.0 | 134 | 0.2758 | 0.9375 | |
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| 0.005 | 135.0 | 135 | 0.2762 | 0.9375 | |
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| 0.005 | 136.0 | 136 | 0.2767 | 0.9375 | |
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| 0.005 | 137.0 | 137 | 0.2773 | 0.9375 | |
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| 0.005 | 138.0 | 138 | 0.2780 | 0.9375 | |
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| 0.005 | 139.0 | 139 | 0.2783 | 0.9375 | |
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| 0.0042 | 140.0 | 140 | 0.2789 | 0.9375 | |
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| 0.0042 | 141.0 | 141 | 0.2794 | 0.9375 | |
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| 0.0042 | 142.0 | 142 | 0.2799 | 0.9375 | |
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| 0.0042 | 143.0 | 143 | 0.2805 | 0.9062 | |
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| 0.0042 | 144.0 | 144 | 0.2812 | 0.9062 | |
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| 0.0042 | 145.0 | 145 | 0.2818 | 0.9062 | |
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| 0.0042 | 146.0 | 146 | 0.2826 | 0.9062 | |
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| 0.0042 | 147.0 | 147 | 0.2877 | 0.9375 | |
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| 0.0042 | 148.0 | 148 | 0.2934 | 0.9375 | |
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| 0.0042 | 149.0 | 149 | 0.2993 | 0.9375 | |
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| 0.004 | 150.0 | 150 | 0.3049 | 0.9375 | |
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### Framework versions |
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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 |
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