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# roberta-base-emotion-prediction-phr

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3301
- Accuracy: 0.2814
- Micro Precision: 0.7422
- Micro Recall: 0.6510
- Micro F1: 0.6945
- Micro Roc Auc: 0.7940

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro Precision | Micro Recall | Micro F1 | Micro Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-------------:|
| 0.4952        | 0.12  | 100  | 0.4515          | 0.1574   | 0.5861          | 0.3505       | 0.4386   | 0.6404        |
| 0.4152        | 0.23  | 200  | 0.3839          | 0.2041   | 0.7102          | 0.4593       | 0.5578   | 0.7033        |
| 0.3878        | 0.35  | 300  | 0.3625          | 0.2341   | 0.7384          | 0.5198       | 0.6101   | 0.7340        |
| 0.3764        | 0.47  | 400  | 0.3506          | 0.2412   | 0.7666          | 0.5092       | 0.6119   | 0.7328        |
| 0.372         | 0.58  | 500  | 0.3450          | 0.2375   | 0.7686          | 0.5251       | 0.6239   | 0.7403        |
| 0.3588        | 0.7   | 600  | 0.3464          | 0.2249   | 0.7804          | 0.4964       | 0.6068   | 0.7286        |
| 0.3383        | 0.82  | 700  | 0.3471          | 0.2470   | 0.7503          | 0.5578       | 0.6398   | 0.7528        |
| 0.3489        | 0.94  | 800  | 0.3284          | 0.2620   | 0.7702          | 0.5682       | 0.6539   | 0.7603        |
| 0.3287        | 1.05  | 900  | 0.3214          | 0.2820   | 0.7707          | 0.5936       | 0.6706   | 0.7720        |
| 0.3158        | 1.17  | 1000 | 0.3352          | 0.2657   | 0.7580          | 0.5814       | 0.6580   | 0.7646        |
| 0.3247        | 1.29  | 1100 | 0.3219          | 0.2811   | 0.7696          | 0.6031       | 0.6763   | 0.7762        |
| 0.3159        | 1.4   | 1200 | 0.3237          | 0.2688   | 0.7479          | 0.6138       | 0.6743   | 0.7778        |
| 0.3207        | 1.52  | 1300 | 0.3217          | 0.2461   | 0.7676          | 0.5767       | 0.6586   | 0.7638        |
| 0.3087        | 1.64  | 1400 | 0.3253          | 0.2424   | 0.7484          | 0.5883       | 0.6587   | 0.7663        |
| 0.3057        | 1.75  | 1500 | 0.3174          | 0.2728   | 0.7587          | 0.6116       | 0.6773   | 0.7785        |
| 0.3099        | 1.87  | 1600 | 0.3150          | 0.2774   | 0.7683          | 0.6001       | 0.6738   | 0.7746        |
| 0.3006        | 1.99  | 1700 | 0.3176          | 0.2633   | 0.7636          | 0.5881       | 0.6645   | 0.7685        |
| 0.285         | 2.11  | 1800 | 0.3177          | 0.2722   | 0.7363          | 0.6484       | 0.6896   | 0.7915        |
| 0.2886        | 2.22  | 1900 | 0.3156          | 0.2768   | 0.7734          | 0.5935       | 0.6716   | 0.7723        |
| 0.2785        | 2.34  | 2000 | 0.3101          | 0.2808   | 0.7692          | 0.6151       | 0.6836   | 0.7816        |
| 0.2801        | 2.46  | 2100 | 0.3121          | 0.2728   | 0.7739          | 0.5956       | 0.6732   | 0.7734        |
| 0.2876        | 2.57  | 2200 | 0.3166          | 0.2777   | 0.7577          | 0.6157       | 0.6794   | 0.7802        |
| 0.2769        | 2.69  | 2300 | 0.3143          | 0.2881   | 0.7691          | 0.6124       | 0.6819   | 0.7803        |
| 0.2755        | 2.81  | 2400 | 0.3133          | 0.2792   | 0.7577          | 0.6263       | 0.6857   | 0.7850        |
| 0.2815        | 2.92  | 2500 | 0.3197          | 0.2716   | 0.7406          | 0.6466       | 0.6904   | 0.7914        |
| 0.2671        | 3.04  | 2600 | 0.3133          | 0.2857   | 0.7549          | 0.6438       | 0.6949   | 0.7925        |
| 0.2431        | 3.16  | 2700 | 0.3225          | 0.2722   | 0.7515          | 0.6320       | 0.6866   | 0.7866        |
| 0.2512        | 3.27  | 2800 | 0.3221          | 0.2743   | 0.7616          | 0.6106       | 0.6778   | 0.7784        |
| 0.2574        | 3.39  | 2900 | 0.3191          | 0.2737   | 0.7561          | 0.6214       | 0.6822   | 0.7825        |
| 0.2527        | 3.51  | 3000 | 0.3207          | 0.2666   | 0.7443          | 0.6315       | 0.6833   | 0.7852        |
| 0.2615        | 3.63  | 3100 | 0.3170          | 0.2670   | 0.7443          | 0.6471       | 0.6923   | 0.7923        |
| 0.2583        | 3.74  | 3200 | 0.3122          | 0.2685   | 0.7729          | 0.6068       | 0.6799   | 0.7783        |
| 0.2543        | 3.86  | 3300 | 0.3175          | 0.2709   | 0.7492          | 0.6432       | 0.6921   | 0.7913        |
| 0.2546        | 3.98  | 3400 | 0.3164          | 0.2752   | 0.7661          | 0.6186       | 0.6845   | 0.7828        |
| 0.2274        | 4.09  | 3500 | 0.3172          | 0.2759   | 0.7437          | 0.6426       | 0.6895   | 0.7902        |
| 0.2328        | 4.21  | 3600 | 0.3214          | 0.2737   | 0.7548          | 0.6297       | 0.6866   | 0.7861        |
| 0.2354        | 4.33  | 3700 | 0.3192          | 0.2792   | 0.7546          | 0.6310       | 0.6872   | 0.7866        |
| 0.2238        | 4.44  | 3800 | 0.3199          | 0.2709   | 0.7453          | 0.6444       | 0.6912   | 0.7912        |
| 0.2376        | 4.56  | 3900 | 0.3176          | 0.2734   | 0.7599          | 0.6247       | 0.6857   | 0.7846        |
| 0.2344        | 4.68  | 4000 | 0.3189          | 0.2639   | 0.7437          | 0.6390       | 0.6874   | 0.7885        |
| 0.2222        | 4.8   | 4100 | 0.3222          | 0.2636   | 0.7436          | 0.6409       | 0.6884   | 0.7894        |
| 0.232         | 4.91  | 4200 | 0.3227          | 0.2725   | 0.7472          | 0.6426       | 0.6910   | 0.7907        |
| 0.2367        | 5.03  | 4300 | 0.3243          | 0.2670   | 0.7463          | 0.6339       | 0.6855   | 0.7866        |
| 0.2154        | 5.15  | 4400 | 0.3257          | 0.2593   | 0.7366          | 0.6513       | 0.6913   | 0.7929        |
| 0.2089        | 5.26  | 4500 | 0.3261          | 0.2700   | 0.7416          | 0.6453       | 0.6901   | 0.7910        |
| 0.2081        | 5.38  | 4600 | 0.3269          | 0.2731   | 0.7602          | 0.6133       | 0.6789   | 0.7794        |
| 0.2116        | 5.5   | 4700 | 0.3308          | 0.2593   | 0.7229          | 0.6687       | 0.6947   | 0.7983        |
| 0.2128        | 5.61  | 4800 | 0.3263          | 0.2660   | 0.7422          | 0.6432       | 0.6891   | 0.7902        |
| 0.2059        | 5.73  | 4900 | 0.3295          | 0.2728   | 0.7356          | 0.6550       | 0.6929   | 0.7944        |
| 0.2103        | 5.85  | 5000 | 0.3301          | 0.2814   | 0.7442          | 0.6510       | 0.6945   | 0.7940        |
| 0.2151        | 5.96  | 5100 | 0.3300          | 0.2541   | 0.7221          | 0.6598       | 0.6896   | 0.7942        |
| 0.1954        | 6.08  | 5200 | 0.3325          | 0.2765   | 0.7476          | 0.6381       | 0.6885   | 0.7887        |
| 0.2028        | 6.2   | 5300 | 0.3316          | 0.2559   | 0.7364          | 0.6400       | 0.6848   | 0.7878        |
| 0.1911        | 6.32  | 5400 | 0.3332          | 0.2553   | 0.7370          | 0.6386       | 0.6843   | 0.7873        |
| 0.2015        | 6.43  | 5500 | 0.3349          | 0.2645   | 0.7308          | 0.6538       | 0.6902   | 0.7931        |
| 0.1901        | 6.55  | 5600 | 0.3389          | 0.2587   | 0.7197          | 0.6682       | 0.6930   | 0.7975        |
| 0.197         | 6.67  | 5700 | 0.3349          | 0.2728   | 0.7400          | 0.6424       | 0.6878   | 0.7895        |
| 0.1907        | 6.78  | 5800 | 0.3354          | 0.2627   | 0.7454          | 0.6349       | 0.6857   | 0.7870        |
| 0.1853        | 6.9   | 5900 | 0.3420          | 0.2657   | 0.7356          | 0.6513       | 0.6909   | 0.7927        |
| 0.1841        | 7.02  | 6000 | 0.3399          | 0.2584   | 0.7308          | 0.6554       | 0.6910   | 0.7937        |
| 0.1739        | 7.13  | 6100 | 0.3409          | 0.2620   | 0.7364          | 0.6446       | 0.6874   | 0.7898        |
| 0.1768        | 7.25  | 6200 | 0.3417          | 0.2593   | 0.7314          | 0.6474       | 0.6868   | 0.7902        |
| 0.1762        | 7.37  | 6300 | 0.3384          | 0.2654   | 0.7398          | 0.6373       | 0.6847   | 0.7871        |
| 0.177         | 7.49  | 6400 | 0.3448          | 0.2541   | 0.7237          | 0.6547       | 0.6875   | 0.7922        |
| 0.1787        | 7.6   | 6500 | 0.3422          | 0.2513   | 0.7317          | 0.6425       | 0.6842   | 0.7881        |
| 0.1793        | 7.72  | 6600 | 0.3452          | 0.2611   | 0.7231          | 0.6582       | 0.6891   | 0.7936        |
| 0.1772        | 7.84  | 6700 | 0.3470          | 0.2587   | 0.7193          | 0.6618       | 0.6894   | 0.7946        |
| 0.1799        | 7.95  | 6800 | 0.3459          | 0.2547   | 0.7238          | 0.6494       | 0.6846   | 0.7898        |
| 0.1726        | 8.07  | 6900 | 0.3477          | 0.2507   | 0.7259          | 0.6419       | 0.6813   | 0.7869        |
| 0.1672        | 8.19  | 7000 | 0.3489          | 0.2492   | 0.7215          | 0.6499       | 0.6838   | 0.7897        |
| 0.1664        | 8.3   | 7100 | 0.3474          | 0.2498   | 0.7197          | 0.6491       | 0.6826   | 0.7890        |
| 0.1712        | 8.42  | 7200 | 0.3477          | 0.2516   | 0.7309          | 0.6404       | 0.6827   | 0.7870        |
| 0.166         | 8.54  | 7300 | 0.3487          | 0.2553   | 0.7209          | 0.6547       | 0.6862   | 0.7917        |
| 0.1706        | 8.65  | 7400 | 0.3487          | 0.2538   | 0.7239          | 0.6518       | 0.6860   | 0.7909        |
| 0.1674        | 8.77  | 7500 | 0.3506          | 0.2538   | 0.7216          | 0.6541       | 0.6862   | 0.7916        |
| 0.1655        | 8.89  | 7600 | 0.3476          | 0.2553   | 0.7283          | 0.6465       | 0.6849   | 0.7893        |
| 0.1609        | 9.01  | 7700 | 0.3498          | 0.2495   | 0.7273          | 0.6443       | 0.6833   | 0.7882        |
| 0.1647        | 9.12  | 7800 | 0.3507          | 0.2522   | 0.7255          | 0.6423       | 0.6814   | 0.7870        |
| 0.1531        | 9.24  | 7900 | 0.3503          | 0.2522   | 0.7292          | 0.6426       | 0.6832   | 0.7878        |
| 0.1577        | 9.36  | 8000 | 0.3524          | 0.2528   | 0.7212          | 0.6569       | 0.6875   | 0.7927        |
| 0.1592        | 9.47  | 8100 | 0.3517          | 0.2519   | 0.7186          | 0.6536       | 0.6845   | 0.7908        |
| 0.1615        | 9.59  | 8200 | 0.3514          | 0.2510   | 0.7183          | 0.6529       | 0.6841   | 0.7905        |
| 0.1529        | 9.71  | 8300 | 0.3515          | 0.2516   | 0.7221          | 0.6489       | 0.6835   | 0.7893        |
| 0.1607        | 9.82  | 8400 | 0.3520          | 0.2528   | 0.7212          | 0.6499       | 0.6837   | 0.7896        |
| 0.1506        | 9.94  | 8500 | 0.3524          | 0.2522   | 0.7220          | 0.6522       | 0.6853   | 0.7908        |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
{"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["vibhorag101/sem_eval_2018_task_1_english_cleaned_labels", "sem_eval_2018_task_1"], "metrics": ["accuracy", "f1"], "base_model": "roberta-base", "pipeline_tag": "text-classification", "model-index": [{"name": "roberta-base-emotion-prediction-phr", "results": []}]}
transformers pytorch roberta text-classification generated_from_trainer en dataset:vibhorag101/sem_eval_2018_task_1_english_cleaned_labels dataset:sem_eval_2018_task_1 base_model:roberta-base license:mit autotrain_compatible endpoints_compatible region:us