transformers pytorch albert text-classification generated_from_trainer dataset:tweet_eval license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- tweet_eval
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: TweetEval_ALBERT_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: 0.9266666666666666
      name: Accuracy
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# TweetEval_ALBERT_5E

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1990
- Accuracy: 0.9267

## 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: 3e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4636        | 0.04  | 50   | 0.3662          | 0.8667   |
| 0.442         | 0.08  | 100  | 0.3471          | 0.84     |
| 0.3574        | 0.12  | 150  | 0.3446          | 0.86     |
| 0.392         | 0.16  | 200  | 0.6776          | 0.6267   |
| 0.4801        | 0.2   | 250  | 0.4307          | 0.7667   |
| 0.487         | 0.24  | 300  | 0.5127          | 0.8      |
| 0.4414        | 0.28  | 350  | 0.3912          | 0.8133   |
| 0.4495        | 0.32  | 400  | 0.4056          | 0.8333   |
| 0.4637        | 0.37  | 450  | 0.3635          | 0.8533   |
| 0.4231        | 0.41  | 500  | 0.4235          | 0.84     |
| 0.4049        | 0.45  | 550  | 0.4094          | 0.8067   |
| 0.4481        | 0.49  | 600  | 0.3977          | 0.7733   |
| 0.4024        | 0.53  | 650  | 0.3361          | 0.8733   |
| 0.3901        | 0.57  | 700  | 0.3014          | 0.8667   |
| 0.3872        | 0.61  | 750  | 0.3363          | 0.8533   |
| 0.377         | 0.65  | 800  | 0.3754          | 0.8      |
| 0.459         | 0.69  | 850  | 0.3861          | 0.8      |
| 0.437         | 0.73  | 900  | 0.3834          | 0.8333   |
| 0.3823        | 0.77  | 950  | 0.3541          | 0.8733   |
| 0.3561        | 0.81  | 1000 | 0.3177          | 0.84     |
| 0.4536        | 0.85  | 1050 | 0.4291          | 0.78     |
| 0.4457        | 0.89  | 1100 | 0.3193          | 0.86     |
| 0.3478        | 0.93  | 1150 | 0.3159          | 0.8533   |
| 0.4613        | 0.97  | 1200 | 0.3605          | 0.84     |
| 0.4081        | 1.01  | 1250 | 0.4291          | 0.7867   |
| 0.3849        | 1.06  | 1300 | 0.3114          | 0.8733   |
| 0.4071        | 1.1   | 1350 | 0.2939          | 0.8667   |
| 0.3484        | 1.14  | 1400 | 0.3212          | 0.84     |
| 0.3869        | 1.18  | 1450 | 0.2717          | 0.8933   |
| 0.3877        | 1.22  | 1500 | 0.3459          | 0.84     |
| 0.4245        | 1.26  | 1550 | 0.3404          | 0.8733   |
| 0.4148        | 1.3   | 1600 | 0.2863          | 0.8667   |
| 0.3542        | 1.34  | 1650 | 0.3377          | 0.86     |
| 0.4093        | 1.38  | 1700 | 0.2972          | 0.8867   |
| 0.3579        | 1.42  | 1750 | 0.3926          | 0.86     |
| 0.3892        | 1.46  | 1800 | 0.2870          | 0.8667   |
| 0.3569        | 1.5   | 1850 | 0.4027          | 0.8467   |
| 0.3493        | 1.54  | 1900 | 0.3069          | 0.8467   |
| 0.36          | 1.58  | 1950 | 0.3197          | 0.8733   |
| 0.3532        | 1.62  | 2000 | 0.3711          | 0.8667   |
| 0.3311        | 1.66  | 2050 | 0.2897          | 0.8867   |
| 0.346         | 1.7   | 2100 | 0.2938          | 0.88     |
| 0.3389        | 1.75  | 2150 | 0.2734          | 0.8933   |
| 0.3289        | 1.79  | 2200 | 0.2606          | 0.8867   |
| 0.3558        | 1.83  | 2250 | 0.3070          | 0.88     |
| 0.3277        | 1.87  | 2300 | 0.2757          | 0.8867   |
| 0.3166        | 1.91  | 2350 | 0.2759          | 0.8733   |
| 0.3223        | 1.95  | 2400 | 0.2053          | 0.9133   |
| 0.317         | 1.99  | 2450 | 0.2307          | 0.8867   |
| 0.3408        | 2.03  | 2500 | 0.2557          | 0.9067   |
| 0.3212        | 2.07  | 2550 | 0.2508          | 0.8867   |
| 0.2806        | 2.11  | 2600 | 0.2472          | 0.88     |
| 0.3567        | 2.15  | 2650 | 0.2790          | 0.8933   |
| 0.2887        | 2.19  | 2700 | 0.3197          | 0.88     |
| 0.3222        | 2.23  | 2750 | 0.2943          | 0.8667   |
| 0.2773        | 2.27  | 2800 | 0.2297          | 0.88     |
| 0.2728        | 2.31  | 2850 | 0.2813          | 0.8733   |
| 0.3115        | 2.35  | 2900 | 0.3470          | 0.8867   |
| 0.3001        | 2.39  | 2950 | 0.2702          | 0.8933   |
| 0.3464        | 2.44  | 3000 | 0.2855          | 0.9      |
| 0.3041        | 2.48  | 3050 | 0.2366          | 0.8867   |
| 0.2717        | 2.52  | 3100 | 0.3220          | 0.88     |
| 0.2903        | 2.56  | 3150 | 0.2230          | 0.9      |
| 0.2959        | 2.6   | 3200 | 0.2439          | 0.9067   |
| 0.2753        | 2.64  | 3250 | 0.2918          | 0.8733   |
| 0.2515        | 2.68  | 3300 | 0.2493          | 0.88     |
| 0.295         | 2.72  | 3350 | 0.2673          | 0.8867   |
| 0.2572        | 2.76  | 3400 | 0.2842          | 0.8733   |
| 0.2988        | 2.8   | 3450 | 0.2306          | 0.9067   |
| 0.2923        | 2.84  | 3500 | 0.2329          | 0.8933   |
| 0.2856        | 2.88  | 3550 | 0.2374          | 0.88     |
| 0.2867        | 2.92  | 3600 | 0.2294          | 0.8733   |
| 0.306         | 2.96  | 3650 | 0.2169          | 0.92     |
| 0.2312        | 3.0   | 3700 | 0.2456          | 0.88     |
| 0.2438        | 3.04  | 3750 | 0.2134          | 0.8867   |
| 0.2103        | 3.08  | 3800 | 0.2242          | 0.92     |
| 0.2469        | 3.12  | 3850 | 0.2407          | 0.92     |
| 0.2346        | 3.17  | 3900 | 0.1866          | 0.92     |
| 0.2275        | 3.21  | 3950 | 0.2318          | 0.92     |
| 0.2542        | 3.25  | 4000 | 0.2256          | 0.9      |
| 0.2544        | 3.29  | 4050 | 0.2246          | 0.9133   |
| 0.2468        | 3.33  | 4100 | 0.2436          | 0.8733   |
| 0.2105        | 3.37  | 4150 | 0.2098          | 0.9067   |
| 0.2818        | 3.41  | 4200 | 0.2304          | 0.88     |
| 0.2041        | 3.45  | 4250 | 0.2430          | 0.8933   |
| 0.28          | 3.49  | 4300 | 0.1990          | 0.9067   |
| 0.1997        | 3.53  | 4350 | 0.2515          | 0.8933   |
| 0.2409        | 3.57  | 4400 | 0.2315          | 0.9      |
| 0.1969        | 3.61  | 4450 | 0.2160          | 0.8933   |
| 0.2246        | 3.65  | 4500 | 0.1979          | 0.92     |
| 0.2185        | 3.69  | 4550 | 0.2238          | 0.9      |
| 0.259         | 3.73  | 4600 | 0.2011          | 0.9067   |
| 0.2407        | 3.77  | 4650 | 0.1911          | 0.92     |
| 0.2198        | 3.81  | 4700 | 0.2083          | 0.92     |
| 0.235         | 3.86  | 4750 | 0.1724          | 0.9267   |
| 0.26          | 3.9   | 4800 | 0.1640          | 0.9333   |
| 0.2334        | 3.94  | 4850 | 0.1778          | 0.9267   |
| 0.2121        | 3.98  | 4900 | 0.2062          | 0.8933   |
| 0.173         | 4.02  | 4950 | 0.1987          | 0.92     |
| 0.1942        | 4.06  | 5000 | 0.2509          | 0.8933   |
| 0.1703        | 4.1   | 5050 | 0.2179          | 0.9      |
| 0.1735        | 4.14  | 5100 | 0.2429          | 0.8867   |
| 0.2098        | 4.18  | 5150 | 0.1938          | 0.9267   |
| 0.2126        | 4.22  | 5200 | 0.1971          | 0.92     |
| 0.164         | 4.26  | 5250 | 0.2539          | 0.9067   |
| 0.2271        | 4.3   | 5300 | 0.1765          | 0.94     |
| 0.2245        | 4.34  | 5350 | 0.1894          | 0.94     |
| 0.182         | 4.38  | 5400 | 0.1790          | 0.9467   |
| 0.1835        | 4.42  | 5450 | 0.2014          | 0.9333   |
| 0.2185        | 4.46  | 5500 | 0.1881          | 0.9467   |
| 0.2113        | 4.5   | 5550 | 0.1742          | 0.9333   |
| 0.1997        | 4.55  | 5600 | 0.1762          | 0.94     |
| 0.1959        | 4.59  | 5650 | 0.1657          | 0.9467   |
| 0.2035        | 4.63  | 5700 | 0.1973          | 0.92     |
| 0.228         | 4.67  | 5750 | 0.1769          | 0.9467   |
| 0.1632        | 4.71  | 5800 | 0.1968          | 0.9267   |
| 0.1468        | 4.75  | 5850 | 0.1822          | 0.9467   |
| 0.1936        | 4.79  | 5900 | 0.1832          | 0.94     |
| 0.1743        | 4.83  | 5950 | 0.1987          | 0.9267   |
| 0.1654        | 4.87  | 6000 | 0.1943          | 0.9267   |
| 0.1859        | 4.91  | 6050 | 0.1990          | 0.92     |
| 0.2039        | 4.95  | 6100 | 0.1982          | 0.9267   |
| 0.2325        | 4.99  | 6150 | 0.1990          | 0.9267   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2
