transformers pytorch roberta text-classification generated_from_trainer dataset:tweet_eval license:mit model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- tweet_eval
license: mit
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: TweetEval_roBERTa_5E
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tweet_eval
      type: tweet_eval
      config: sentiment
      split: train
      args: sentiment
    metrics:
    - type: accuracy
      value: 0.9466666666666667
      name: Accuracy
---

<!-- 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. -->

# TweetEval_roBERTa_5E

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2770
- Accuracy: 0.9467

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5967        | 0.04  | 50   | 0.4851          | 0.7333   |
| 0.4085        | 0.08  | 100  | 0.2177          | 0.9333   |
| 0.3449        | 0.12  | 150  | 0.2164          | 0.9333   |
| 0.2739        | 0.16  | 200  | 0.2285          | 0.9267   |
| 0.2588        | 0.2   | 250  | 0.2748          | 0.92     |
| 0.3406        | 0.24  | 300  | 0.1956          | 0.9467   |
| 0.2726        | 0.28  | 350  | 0.2285          | 0.92     |
| 0.2645        | 0.32  | 400  | 0.2192          | 0.9267   |
| 0.2549        | 0.37  | 450  | 0.2115          | 0.9333   |
| 0.2387        | 0.41  | 500  | 0.2230          | 0.9333   |
| 0.2415        | 0.45  | 550  | 0.2156          | 0.94     |
| 0.2829        | 0.49  | 600  | 0.2575          | 0.9267   |
| 0.2865        | 0.53  | 650  | 0.1572          | 0.9467   |
| 0.2107        | 0.57  | 700  | 0.1437          | 0.9467   |
| 0.2609        | 0.61  | 750  | 0.1595          | 0.94     |
| 0.2234        | 0.65  | 800  | 0.2611          | 0.9333   |
| 0.266         | 0.69  | 850  | 0.1544          | 0.9467   |
| 0.2407        | 0.73  | 900  | 0.2145          | 0.9333   |
| 0.2529        | 0.77  | 950  | 0.1861          | 0.9333   |
| 0.2083        | 0.81  | 1000 | 0.1448          | 0.9533   |
| 0.2942        | 0.85  | 1050 | 0.1703          | 0.9333   |
| 0.1916        | 0.89  | 1100 | 0.1831          | 0.94     |
| 0.2425        | 0.93  | 1150 | 0.2349          | 0.9333   |
| 0.2521        | 0.97  | 1200 | 0.1268          | 0.94     |
| 0.1742        | 1.01  | 1250 | 0.1782          | 0.9333   |
| 0.172         | 1.06  | 1300 | 0.2636          | 0.9333   |
| 0.1487        | 1.1   | 1350 | 0.1987          | 0.9467   |
| 0.1805        | 1.14  | 1400 | 0.3030          | 0.9333   |
| 0.1295        | 1.18  | 1450 | 0.2229          | 0.94     |
| 0.2114        | 1.22  | 1500 | 0.1441          | 0.9467   |
| 0.1714        | 1.26  | 1550 | 0.2157          | 0.9467   |
| 0.1886        | 1.3   | 1600 | 0.2353          | 0.9267   |
| 0.1666        | 1.34  | 1650 | 0.2572          | 0.94     |
| 0.2254        | 1.38  | 1700 | 0.1569          | 0.9467   |
| 0.1531        | 1.42  | 1750 | 0.2351          | 0.9333   |
| 0.2174        | 1.46  | 1800 | 0.2137          | 0.9267   |
| 0.2015        | 1.5   | 1850 | 0.2234          | 0.94     |
| 0.1785        | 1.54  | 1900 | 0.1944          | 0.9333   |
| 0.1954        | 1.58  | 1950 | 0.2013          | 0.9467   |
| 0.1481        | 1.62  | 2000 | 0.2196          | 0.94     |
| 0.1426        | 1.66  | 2050 | 0.2005          | 0.9467   |
| 0.1951        | 1.7   | 2100 | 0.2281          | 0.9467   |
| 0.1943        | 1.75  | 2150 | 0.1934          | 0.94     |
| 0.2027        | 1.79  | 2200 | 0.1845          | 0.96     |
| 0.2119        | 1.83  | 2250 | 0.1338          | 0.9533   |
| 0.208         | 1.87  | 2300 | 0.1605          | 0.94     |
| 0.1972        | 1.91  | 2350 | 0.1460          | 0.9533   |
| 0.1876        | 1.95  | 2400 | 0.1488          | 0.9467   |
| 0.1923        | 1.99  | 2450 | 0.2055          | 0.9533   |
| 0.1391        | 2.03  | 2500 | 0.2245          | 0.9533   |
| 0.1416        | 2.07  | 2550 | 0.2194          | 0.9533   |
| 0.1521        | 2.11  | 2600 | 0.2234          | 0.9533   |
| 0.0943        | 2.15  | 2650 | 0.2114          | 0.9533   |
| 0.1452        | 2.19  | 2700 | 0.1772          | 0.9467   |
| 0.1148        | 2.23  | 2750 | 0.2541          | 0.9333   |
| 0.1706        | 2.27  | 2800 | 0.2151          | 0.9533   |
| 0.12          | 2.31  | 2850 | 0.2521          | 0.9467   |
| 0.181         | 2.35  | 2900 | 0.2518          | 0.9467   |
| 0.1308        | 2.39  | 2950 | 0.2610          | 0.9533   |
| 0.1482        | 2.44  | 3000 | 0.1789          | 0.9533   |
| 0.1019        | 2.48  | 3050 | 0.2377          | 0.9467   |
| 0.1474        | 2.52  | 3100 | 0.2468          | 0.94     |
| 0.0843        | 2.56  | 3150 | 0.3056          | 0.94     |
| 0.1521        | 2.6   | 3200 | 0.2067          | 0.96     |
| 0.1333        | 2.64  | 3250 | 0.1921          | 0.94     |
| 0.1318        | 2.68  | 3300 | 0.1699          | 0.96     |
| 0.1503        | 2.72  | 3350 | 0.2186          | 0.94     |
| 0.1242        | 2.76  | 3400 | 0.2322          | 0.94     |
| 0.1179        | 2.8   | 3450 | 0.2313          | 0.9467   |
| 0.1247        | 2.84  | 3500 | 0.2298          | 0.9467   |
| 0.1289        | 2.88  | 3550 | 0.2502          | 0.94     |
| 0.1597        | 2.92  | 3600 | 0.1875          | 0.9467   |
| 0.1645        | 2.96  | 3650 | 0.2469          | 0.94     |
| 0.1366        | 3.0   | 3700 | 0.2469          | 0.94     |
| 0.1418        | 3.04  | 3750 | 0.2457          | 0.9467   |
| 0.1146        | 3.08  | 3800 | 0.2188          | 0.9467   |
| 0.091         | 3.12  | 3850 | 0.2476          | 0.94     |
| 0.0972        | 3.17  | 3900 | 0.2791          | 0.94     |
| 0.0976        | 3.21  | 3950 | 0.2933          | 0.9333   |
| 0.0872        | 3.25  | 4000 | 0.2877          | 0.9467   |
| 0.0857        | 3.29  | 4050 | 0.2664          | 0.9467   |
| 0.1368        | 3.33  | 4100 | 0.2533          | 0.9467   |
| 0.0713        | 3.37  | 4150 | 0.2855          | 0.9467   |
| 0.1101        | 3.41  | 4200 | 0.2716          | 0.9533   |
| 0.0871        | 3.45  | 4250 | 0.2654          | 0.9467   |
| 0.1152        | 3.49  | 4300 | 0.2449          | 0.9467   |
| 0.0441        | 3.53  | 4350 | 0.2904          | 0.9467   |
| 0.1503        | 3.57  | 4400 | 0.2784          | 0.9467   |
| 0.0763        | 3.61  | 4450 | 0.2804          | 0.9467   |
| 0.083         | 3.65  | 4500 | 0.3278          | 0.94     |
| 0.1111        | 3.69  | 4550 | 0.2899          | 0.9333   |
| 0.0791        | 3.73  | 4600 | 0.3137          | 0.9333   |
| 0.0837        | 3.77  | 4650 | 0.2799          | 0.9467   |
| 0.1048        | 3.81  | 4700 | 0.2496          | 0.9533   |
| 0.1031        | 3.86  | 4750 | 0.2689          | 0.9533   |
| 0.0837        | 3.9   | 4800 | 0.2753          | 0.9533   |
| 0.0929        | 3.94  | 4850 | 0.2357          | 0.9467   |
| 0.0856        | 3.98  | 4900 | 0.2615          | 0.9467   |
| 0.0619        | 4.02  | 4950 | 0.2983          | 0.9467   |
| 0.0974        | 4.06  | 5000 | 0.2706          | 0.9533   |
| 0.0548        | 4.1   | 5050 | 0.2978          | 0.9467   |
| 0.0425        | 4.14  | 5100 | 0.3217          | 0.9333   |
| 0.0808        | 4.18  | 5150 | 0.3054          | 0.94     |
| 0.0466        | 4.22  | 5200 | 0.3142          | 0.94     |
| 0.0593        | 4.26  | 5250 | 0.3193          | 0.9267   |
| 0.0551        | 4.3   | 5300 | 0.3017          | 0.9333   |
| 0.0493        | 4.34  | 5350 | 0.2954          | 0.94     |
| 0.0897        | 4.38  | 5400 | 0.2912          | 0.9467   |
| 0.0529        | 4.42  | 5450 | 0.2956          | 0.94     |
| 0.0924        | 4.46  | 5500 | 0.2858          | 0.94     |
| 0.1018        | 4.5   | 5550 | 0.2826          | 0.94     |
| 0.1137        | 4.55  | 5600 | 0.2711          | 0.94     |
| 0.0667        | 4.59  | 5650 | 0.2776          | 0.94     |
| 0.0521        | 4.63  | 5700 | 0.2955          | 0.94     |
| 0.0334        | 4.67  | 5750 | 0.2972          | 0.94     |
| 0.0298        | 4.71  | 5800 | 0.3133          | 0.94     |
| 0.1261        | 4.75  | 5850 | 0.2891          | 0.9467   |
| 0.0514        | 4.79  | 5900 | 0.2804          | 0.9467   |
| 0.0416        | 4.83  | 5950 | 0.2809          | 0.94     |
| 0.0745        | 4.87  | 6000 | 0.2774          | 0.9467   |
| 0.1134        | 4.91  | 6050 | 0.2715          | 0.9467   |
| 0.0446        | 4.95  | 6100 | 0.2748          | 0.9467   |
| 0.0581        | 4.99  | 6150 | 0.2770          | 0.9467   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.3.2
- Tokenizers 0.13.2
