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---
base_model: ManojAlexender/roberta-base_MLM
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Trail_run_final_roberta
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. -->
# Trail_run_final_roberta
This model is a fine-tuned version of [ManojAlexender/roberta-base_MLM](https://huggingface.co/ManojAlexender/roberta-base_MLM) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2322
- Accuracy: 0.9163
- F1: 0.9159
- Precision: 0.9181
- Recall: 0.9163
## 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: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4057 | 0.01 | 100 | 0.3391 | 0.8808 | 0.8795 | 0.8865 | 0.8808 |
| 0.3447 | 0.01 | 200 | 0.3667 | 0.8470 | 0.8417 | 0.8728 | 0.8470 |
| 0.3202 | 0.02 | 300 | 0.3871 | 0.8689 | 0.8656 | 0.8869 | 0.8689 |
| 0.2354 | 0.03 | 400 | 0.4020 | 0.8597 | 0.8557 | 0.8805 | 0.8597 |
| 0.3268 | 0.04 | 500 | 0.3679 | 0.8318 | 0.8252 | 0.8610 | 0.8318 |
| 0.2771 | 0.04 | 600 | 0.2474 | 0.8924 | 0.8912 | 0.8982 | 0.8924 |
| 0.2288 | 0.05 | 700 | 0.2297 | 0.9103 | 0.9103 | 0.9103 | 0.9103 |
| 0.2307 | 0.06 | 800 | 0.2633 | 0.8944 | 0.8939 | 0.8957 | 0.8944 |
| 0.3375 | 0.06 | 900 | 0.2458 | 0.8988 | 0.8979 | 0.9024 | 0.8988 |
| 0.26 | 0.07 | 1000 | 0.2428 | 0.9071 | 0.9065 | 0.9099 | 0.9071 |
| 0.274 | 0.08 | 1100 | 0.2395 | 0.9035 | 0.9036 | 0.9036 | 0.9035 |
| 0.2513 | 0.09 | 1200 | 0.4167 | 0.8569 | 0.8532 | 0.8751 | 0.8569 |
| 0.2281 | 0.09 | 1300 | 0.3968 | 0.8633 | 0.8598 | 0.8815 | 0.8633 |
| 0.249 | 0.1 | 1400 | 0.2548 | 0.8804 | 0.8783 | 0.8920 | 0.8804 |
| 0.1986 | 0.11 | 1500 | 0.2590 | 0.9020 | 0.9020 | 0.9021 | 0.9020 |
| 0.26 | 0.11 | 1600 | 0.3084 | 0.8804 | 0.8784 | 0.8913 | 0.8804 |
| 0.2272 | 0.12 | 1700 | 0.2827 | 0.8884 | 0.8870 | 0.8956 | 0.8884 |
| 0.2312 | 0.13 | 1800 | 0.2373 | 0.9067 | 0.9068 | 0.9068 | 0.9067 |
| 0.2563 | 0.14 | 1900 | 0.2628 | 0.9008 | 0.9008 | 0.9011 | 0.9008 |
| 0.1876 | 0.14 | 2000 | 0.2744 | 0.8852 | 0.8840 | 0.8906 | 0.8852 |
| 0.284 | 0.15 | 2100 | 0.2751 | 0.8928 | 0.8914 | 0.9002 | 0.8928 |
| 0.203 | 0.16 | 2200 | 0.2406 | 0.9031 | 0.9034 | 0.9054 | 0.9031 |
| 0.2278 | 0.16 | 2300 | 0.2378 | 0.9115 | 0.9112 | 0.9123 | 0.9115 |
| 0.2204 | 0.17 | 2400 | 0.4288 | 0.8677 | 0.8646 | 0.8837 | 0.8677 |
| 0.2323 | 0.18 | 2500 | 0.2331 | 0.9115 | 0.9113 | 0.9118 | 0.9115 |
| 0.2508 | 0.19 | 2600 | 0.2932 | 0.8956 | 0.8955 | 0.8955 | 0.8956 |
| 0.2838 | 0.19 | 2700 | 0.2454 | 0.9035 | 0.9036 | 0.9037 | 0.9035 |
| 0.221 | 0.2 | 2800 | 0.3153 | 0.8800 | 0.8783 | 0.8881 | 0.8800 |
| 0.2167 | 0.21 | 2900 | 0.3200 | 0.8745 | 0.8724 | 0.8838 | 0.8745 |
| 0.2336 | 0.21 | 3000 | 0.2842 | 0.8880 | 0.8866 | 0.8947 | 0.8880 |
| 0.2653 | 0.22 | 3100 | 0.2353 | 0.9059 | 0.9059 | 0.9059 | 0.9059 |
| 0.2953 | 0.23 | 3200 | 0.2374 | 0.9051 | 0.9044 | 0.9087 | 0.9051 |
| 0.174 | 0.24 | 3300 | 0.2810 | 0.8964 | 0.8954 | 0.9006 | 0.8964 |
| 0.2184 | 0.24 | 3400 | 0.2127 | 0.9127 | 0.9125 | 0.9131 | 0.9127 |
| 0.2519 | 0.25 | 3500 | 0.2286 | 0.9083 | 0.9085 | 0.9126 | 0.9083 |
| 0.2326 | 0.26 | 3600 | 0.2904 | 0.8948 | 0.8944 | 0.8956 | 0.8948 |
| 0.1862 | 0.26 | 3700 | 0.2203 | 0.9259 | 0.9258 | 0.9259 | 0.9259 |
| 0.2098 | 0.27 | 3800 | 0.2350 | 0.9075 | 0.9074 | 0.9077 | 0.9075 |
| 0.2152 | 0.28 | 3900 | 0.2319 | 0.9063 | 0.9063 | 0.9063 | 0.9063 |
| 0.3154 | 0.29 | 4000 | 0.2184 | 0.9071 | 0.9070 | 0.9072 | 0.9071 |
| 0.1679 | 0.29 | 4100 | 0.4091 | 0.8764 | 0.8740 | 0.8892 | 0.8764 |
| 0.1535 | 0.3 | 4200 | 0.2574 | 0.9091 | 0.9090 | 0.9092 | 0.9091 |
| 0.1487 | 0.31 | 4300 | 0.2510 | 0.9063 | 0.9060 | 0.9072 | 0.9063 |
| 0.2337 | 0.31 | 4400 | 0.2163 | 0.9131 | 0.9128 | 0.9138 | 0.9131 |
| 0.3144 | 0.32 | 4500 | 0.2627 | 0.9051 | 0.9047 | 0.9062 | 0.9051 |
| 0.2487 | 0.33 | 4600 | 0.2557 | 0.8992 | 0.8985 | 0.9014 | 0.8992 |
| 0.2194 | 0.34 | 4700 | 0.2363 | 0.9159 | 0.9157 | 0.9163 | 0.9159 |
| 0.2602 | 0.34 | 4800 | 0.2374 | 0.9051 | 0.9053 | 0.9058 | 0.9051 |
| 0.2353 | 0.35 | 4900 | 0.2482 | 0.9059 | 0.9057 | 0.9062 | 0.9059 |
| 0.2107 | 0.36 | 5000 | 0.2903 | 0.9008 | 0.8998 | 0.9052 | 0.9008 |
| 0.2364 | 0.36 | 5100 | 0.2901 | 0.8760 | 0.8746 | 0.8815 | 0.8760 |
| 0.2009 | 0.37 | 5200 | 0.2491 | 0.9091 | 0.9086 | 0.9116 | 0.9091 |
| 0.2469 | 0.38 | 5300 | 0.3049 | 0.8992 | 0.8988 | 0.9000 | 0.8992 |
| 0.162 | 0.39 | 5400 | 0.2847 | 0.9059 | 0.9055 | 0.9071 | 0.9059 |
| 0.24 | 0.39 | 5500 | 0.2146 | 0.9135 | 0.9132 | 0.9143 | 0.9135 |
| 0.2667 | 0.4 | 5600 | 0.2379 | 0.9075 | 0.9072 | 0.9085 | 0.9075 |
| 0.2165 | 0.41 | 5700 | 0.2662 | 0.8844 | 0.8829 | 0.8915 | 0.8844 |
| 0.2007 | 0.41 | 5800 | 0.2539 | 0.9047 | 0.9039 | 0.9087 | 0.9047 |
| 0.221 | 0.42 | 5900 | 0.2272 | 0.9047 | 0.9046 | 0.9047 | 0.9047 |
| 0.2028 | 0.43 | 6000 | 0.3618 | 0.8669 | 0.8638 | 0.8826 | 0.8669 |
| 0.3003 | 0.44 | 6100 | 0.2454 | 0.9071 | 0.9071 | 0.9071 | 0.9071 |
| 0.2025 | 0.44 | 6200 | 0.2103 | 0.9175 | 0.9175 | 0.9175 | 0.9175 |
| 0.253 | 0.45 | 6300 | 0.2470 | 0.8992 | 0.8981 | 0.9044 | 0.8992 |
| 0.1955 | 0.46 | 6400 | 0.2887 | 0.9000 | 0.8992 | 0.9031 | 0.9000 |
| 0.1621 | 0.46 | 6500 | 0.2245 | 0.9151 | 0.9149 | 0.9155 | 0.9151 |
| 0.2532 | 0.47 | 6600 | 0.2493 | 0.8912 | 0.8907 | 0.8924 | 0.8912 |
| 0.1898 | 0.48 | 6700 | 0.2313 | 0.9083 | 0.9082 | 0.9083 | 0.9083 |
| 0.1858 | 0.49 | 6800 | 0.2514 | 0.9031 | 0.9026 | 0.9049 | 0.9031 |
| 0.1977 | 0.49 | 6900 | 0.2155 | 0.9167 | 0.9166 | 0.9167 | 0.9167 |
| 0.2247 | 0.5 | 7000 | 0.2280 | 0.9059 | 0.9056 | 0.9070 | 0.9059 |
| 0.1931 | 0.51 | 7100 | 0.2431 | 0.9047 | 0.9042 | 0.9066 | 0.9047 |
| 0.1746 | 0.51 | 7200 | 0.2400 | 0.9155 | 0.9152 | 0.9164 | 0.9155 |
| 0.2579 | 0.52 | 7300 | 0.2707 | 0.9107 | 0.9102 | 0.9125 | 0.9107 |
| 0.2139 | 0.53 | 7400 | 0.2625 | 0.8920 | 0.8910 | 0.8965 | 0.8920 |
| 0.2703 | 0.54 | 7500 | 0.2500 | 0.8980 | 0.8972 | 0.9013 | 0.8980 |
| 0.1412 | 0.54 | 7600 | 0.2210 | 0.9159 | 0.9158 | 0.9160 | 0.9159 |
| 0.2382 | 0.55 | 7700 | 0.2712 | 0.9028 | 0.9020 | 0.9064 | 0.9028 |
| 0.2498 | 0.56 | 7800 | 0.2200 | 0.9195 | 0.9193 | 0.9197 | 0.9195 |
| 0.2002 | 0.56 | 7900 | 0.3254 | 0.8832 | 0.8813 | 0.8935 | 0.8832 |
| 0.2359 | 0.57 | 8000 | 0.3023 | 0.8928 | 0.8918 | 0.8973 | 0.8928 |
| 0.2193 | 0.58 | 8100 | 0.2837 | 0.8892 | 0.8875 | 0.8988 | 0.8892 |
| 0.2436 | 0.59 | 8200 | 0.2221 | 0.9143 | 0.9142 | 0.9143 | 0.9143 |
| 0.1704 | 0.59 | 8300 | 0.2402 | 0.9123 | 0.9119 | 0.9136 | 0.9123 |
| 0.1979 | 0.6 | 8400 | 0.2722 | 0.8912 | 0.8896 | 0.9003 | 0.8912 |
| 0.2476 | 0.61 | 8500 | 0.2165 | 0.9211 | 0.9209 | 0.9216 | 0.9211 |
| 0.1996 | 0.61 | 8600 | 0.2374 | 0.9151 | 0.9148 | 0.9163 | 0.9151 |
| 0.2278 | 0.62 | 8700 | 0.2357 | 0.9079 | 0.9080 | 0.9083 | 0.9079 |
| 0.1625 | 0.63 | 8800 | 0.2205 | 0.9231 | 0.9228 | 0.9237 | 0.9231 |
| 0.2197 | 0.64 | 8900 | 0.3041 | 0.9020 | 0.9011 | 0.9063 | 0.9020 |
| 0.1868 | 0.64 | 9000 | 0.2280 | 0.9207 | 0.9205 | 0.9212 | 0.9207 |
| 0.2979 | 0.65 | 9100 | 0.2931 | 0.8948 | 0.8935 | 0.9011 | 0.8948 |
| 0.1973 | 0.66 | 9200 | 0.2322 | 0.9163 | 0.9159 | 0.9181 | 0.9163 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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