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# text-message-analyzer-finetuned

This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on the daily_dialog dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5913
- Accuracy: 0.762
- F1: 0.7650
- Precision: 0.7706
- Recall: 0.762

## 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: 15
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 0.01  | 5    | 0.8666          | 0.589    | 0.5903 | 0.6104    | 0.589  |
| No log        | 0.01  | 10   | 0.7596          | 0.661    | 0.6590 | 0.6603    | 0.661  |
| No log        | 0.02  | 15   | 1.1783          | 0.521    | 0.5244 | 0.7242    | 0.521  |
| No log        | 0.02  | 20   | 0.8909          | 0.615    | 0.6318 | 0.6910    | 0.615  |
| No log        | 0.03  | 25   | 0.7995          | 0.666    | 0.6743 | 0.6918    | 0.666  |
| No log        | 0.03  | 30   | 0.7699          | 0.65     | 0.6585 | 0.6935    | 0.65   |
| No log        | 0.04  | 35   | 0.7344          | 0.662    | 0.6691 | 0.6857    | 0.662  |
| No log        | 0.04  | 40   | 0.7326          | 0.654    | 0.6675 | 0.7036    | 0.654  |
| No log        | 0.05  | 45   | 0.9608          | 0.603    | 0.5705 | 0.7211    | 0.603  |
| No log        | 0.05  | 50   | 0.8593          | 0.628    | 0.6338 | 0.7262    | 0.628  |
| No log        | 0.06  | 55   | 0.8635          | 0.626    | 0.6066 | 0.7400    | 0.626  |
| No log        | 0.07  | 60   | 0.7101          | 0.682    | 0.6782 | 0.6911    | 0.682  |
| No log        | 0.07  | 65   | 0.7569          | 0.67     | 0.6780 | 0.7067    | 0.67   |
| No log        | 0.08  | 70   | 0.7694          | 0.653    | 0.6608 | 0.7271    | 0.653  |
| No log        | 0.08  | 75   | 0.6941          | 0.691    | 0.6925 | 0.7202    | 0.691  |
| No log        | 0.09  | 80   | 0.8646          | 0.606    | 0.6168 | 0.7450    | 0.606  |
| No log        | 0.09  | 85   | 0.6853          | 0.677    | 0.6895 | 0.7369    | 0.677  |
| No log        | 0.1   | 90   | 0.6410          | 0.727    | 0.7264 | 0.7272    | 0.727  |
| No log        | 0.1   | 95   | 0.7059          | 0.693    | 0.7020 | 0.7410    | 0.693  |
| No log        | 0.11  | 100  | 0.7398          | 0.665    | 0.6734 | 0.7441    | 0.665  |
| No log        | 0.11  | 105  | 0.7205          | 0.683    | 0.6884 | 0.7243    | 0.683  |
| No log        | 0.12  | 110  | 0.7492          | 0.661    | 0.6741 | 0.7410    | 0.661  |
| No log        | 0.12  | 115  | 0.7273          | 0.676    | 0.6932 | 0.7388    | 0.676  |
| No log        | 0.13  | 120  | 0.6670          | 0.678    | 0.6853 | 0.7079    | 0.678  |
| No log        | 0.14  | 125  | 0.7238          | 0.663    | 0.6707 | 0.7348    | 0.663  |
| No log        | 0.14  | 130  | 0.7109          | 0.68     | 0.6948 | 0.7333    | 0.68   |
| No log        | 0.15  | 135  | 0.6813          | 0.685    | 0.6832 | 0.7324    | 0.685  |
| No log        | 0.15  | 140  | 0.6859          | 0.692    | 0.7002 | 0.7304    | 0.692  |
| No log        | 0.16  | 145  | 0.7968          | 0.622    | 0.6231 | 0.7268    | 0.622  |
| No log        | 0.16  | 150  | 0.6754          | 0.695    | 0.7022 | 0.7212    | 0.695  |
| No log        | 0.17  | 155  | 0.6520          | 0.698    | 0.6981 | 0.7296    | 0.698  |
| No log        | 0.17  | 160  | 0.6198          | 0.726    | 0.7282 | 0.7334    | 0.726  |
| No log        | 0.18  | 165  | 0.6745          | 0.703    | 0.6974 | 0.7346    | 0.703  |
| No log        | 0.18  | 170  | 0.6724          | 0.707    | 0.7182 | 0.7486    | 0.707  |
| No log        | 0.19  | 175  | 0.7787          | 0.636    | 0.6392 | 0.7409    | 0.636  |
| No log        | 0.2   | 180  | 0.7098          | 0.667    | 0.6663 | 0.7338    | 0.667  |
| No log        | 0.2   | 185  | 0.6340          | 0.728    | 0.7290 | 0.7340    | 0.728  |
| No log        | 0.21  | 190  | 0.6561          | 0.698    | 0.7023 | 0.7229    | 0.698  |
| No log        | 0.21  | 195  | 0.6790          | 0.678    | 0.6804 | 0.7318    | 0.678  |
| No log        | 0.22  | 200  | 0.7213          | 0.654    | 0.6497 | 0.7337    | 0.654  |
| No log        | 0.22  | 205  | 0.7410          | 0.652    | 0.6609 | 0.7242    | 0.652  |
| No log        | 0.23  | 210  | 0.6848          | 0.703    | 0.7084 | 0.7332    | 0.703  |
| No log        | 0.23  | 215  | 0.6946          | 0.689    | 0.6796 | 0.7291    | 0.689  |
| No log        | 0.24  | 220  | 0.7092          | 0.674    | 0.6870 | 0.7311    | 0.674  |
| No log        | 0.24  | 225  | 0.6285          | 0.705    | 0.7085 | 0.7295    | 0.705  |
| No log        | 0.25  | 230  | 0.6449          | 0.696    | 0.6990 | 0.7166    | 0.696  |
| No log        | 0.25  | 235  | 0.7303          | 0.671    | 0.6694 | 0.7366    | 0.671  |
| No log        | 0.26  | 240  | 0.7583          | 0.67     | 0.6822 | 0.7399    | 0.67   |
| No log        | 0.27  | 245  | 0.7154          | 0.678    | 0.6866 | 0.7443    | 0.678  |
| No log        | 0.27  | 250  | 0.7337          | 0.686    | 0.6852 | 0.7369    | 0.686  |
| No log        | 0.28  | 255  | 0.6486          | 0.711    | 0.7136 | 0.7362    | 0.711  |
| No log        | 0.28  | 260  | 0.6231          | 0.736    | 0.7350 | 0.7410    | 0.736  |
| No log        | 0.29  | 265  | 0.6963          | 0.709    | 0.7211 | 0.7532    | 0.709  |
| No log        | 0.29  | 270  | 0.6847          | 0.693    | 0.7028 | 0.7403    | 0.693  |
| No log        | 0.3   | 275  | 0.6581          | 0.696    | 0.6969 | 0.7464    | 0.696  |
| No log        | 0.3   | 280  | 0.6182          | 0.702    | 0.7061 | 0.7187    | 0.702  |
| No log        | 0.31  | 285  | 0.6653          | 0.682    | 0.6898 | 0.7144    | 0.682  |
| No log        | 0.31  | 290  | 0.6917          | 0.699    | 0.7091 | 0.7372    | 0.699  |
| No log        | 0.32  | 295  | 0.6722          | 0.704    | 0.7067 | 0.7285    | 0.704  |
| No log        | 0.33  | 300  | 0.6582          | 0.703    | 0.7073 | 0.7238    | 0.703  |
| No log        | 0.33  | 305  | 0.6568          | 0.687    | 0.6934 | 0.7146    | 0.687  |
| No log        | 0.34  | 310  | 0.6912          | 0.665    | 0.6605 | 0.7292    | 0.665  |
| No log        | 0.34  | 315  | 0.6223          | 0.71     | 0.7119 | 0.7311    | 0.71   |
| No log        | 0.35  | 320  | 0.6409          | 0.714    | 0.7146 | 0.7244    | 0.714  |
| No log        | 0.35  | 325  | 0.7169          | 0.689    | 0.7023 | 0.7385    | 0.689  |
| No log        | 0.36  | 330  | 0.7887          | 0.649    | 0.6580 | 0.7435    | 0.649  |
| No log        | 0.36  | 335  | 0.6594          | 0.694    | 0.6987 | 0.7111    | 0.694  |
| No log        | 0.37  | 340  | 0.6559          | 0.713    | 0.7121 | 0.7137    | 0.713  |
| No log        | 0.37  | 345  | 0.6490          | 0.686    | 0.6927 | 0.7076    | 0.686  |
| No log        | 0.38  | 350  | 0.6964          | 0.67     | 0.6837 | 0.7424    | 0.67   |
| No log        | 0.39  | 355  | 0.7011          | 0.669    | 0.6873 | 0.7460    | 0.669  |
| No log        | 0.39  | 360  | 0.6987          | 0.668    | 0.6875 | 0.7409    | 0.668  |
| No log        | 0.4   | 365  | 0.6375          | 0.696    | 0.7057 | 0.7340    | 0.696  |
| No log        | 0.4   | 370  | 0.6365          | 0.695    | 0.6972 | 0.7270    | 0.695  |
| No log        | 0.41  | 375  | 0.6212          | 0.712    | 0.7190 | 0.7488    | 0.712  |
| No log        | 0.41  | 380  | 0.7102          | 0.667    | 0.6770 | 0.7532    | 0.667  |
| No log        | 0.42  | 385  | 0.7385          | 0.66     | 0.6616 | 0.7498    | 0.66   |
| No log        | 0.42  | 390  | 0.6221          | 0.723    | 0.7276 | 0.7533    | 0.723  |
| No log        | 0.43  | 395  | 0.6174          | 0.74     | 0.7469 | 0.7651    | 0.74   |
| No log        | 0.43  | 400  | 0.6092          | 0.748    | 0.7538 | 0.7644    | 0.748  |
| No log        | 0.44  | 405  | 0.5978          | 0.737    | 0.7412 | 0.7483    | 0.737  |
| No log        | 0.44  | 410  | 0.6645          | 0.697    | 0.6964 | 0.7402    | 0.697  |
| No log        | 0.45  | 415  | 0.7153          | 0.67     | 0.6654 | 0.7372    | 0.67   |
| No log        | 0.46  | 420  | 0.6236          | 0.728    | 0.7343 | 0.7560    | 0.728  |
| No log        | 0.46  | 425  | 0.7162          | 0.682    | 0.6915 | 0.7441    | 0.682  |
| No log        | 0.47  | 430  | 0.6658          | 0.712    | 0.7228 | 0.7530    | 0.712  |
| No log        | 0.47  | 435  | 0.6350          | 0.725    | 0.7326 | 0.7535    | 0.725  |
| No log        | 0.48  | 440  | 0.5977          | 0.725    | 0.7293 | 0.7378    | 0.725  |
| No log        | 0.48  | 445  | 0.5900          | 0.722    | 0.7246 | 0.7312    | 0.722  |
| No log        | 0.49  | 450  | 0.5993          | 0.716    | 0.7198 | 0.7327    | 0.716  |
| No log        | 0.49  | 455  | 0.6322          | 0.711    | 0.7189 | 0.7450    | 0.711  |
| No log        | 0.5   | 460  | 0.7598          | 0.668    | 0.6824 | 0.7507    | 0.668  |
| No log        | 0.5   | 465  | 0.7033          | 0.7      | 0.7133 | 0.7620    | 0.7    |
| No log        | 0.51  | 470  | 0.6343          | 0.726    | 0.7348 | 0.7525    | 0.726  |
| No log        | 0.52  | 475  | 0.6080          | 0.729    | 0.7352 | 0.7507    | 0.729  |
| No log        | 0.52  | 480  | 0.5939          | 0.741    | 0.7455 | 0.7539    | 0.741  |
| No log        | 0.53  | 485  | 0.6038          | 0.739    | 0.7448 | 0.7560    | 0.739  |
| No log        | 0.53  | 490  | 0.6240          | 0.734    | 0.7386 | 0.7566    | 0.734  |
| No log        | 0.54  | 495  | 0.6442          | 0.724    | 0.7323 | 0.7560    | 0.724  |
| 0.7055        | 0.54  | 500  | 0.7067          | 0.71     | 0.7237 | 0.7583    | 0.71   |
| 0.7055        | 0.55  | 505  | 0.7353          | 0.704    | 0.7133 | 0.7484    | 0.704  |
| 0.7055        | 0.55  | 510  | 0.6534          | 0.733    | 0.7377 | 0.7475    | 0.733  |
| 0.7055        | 0.56  | 515  | 0.7046          | 0.729    | 0.7315 | 0.7533    | 0.729  |
| 0.7055        | 0.56  | 520  | 0.7140          | 0.711    | 0.7130 | 0.7487    | 0.711  |
| 0.7055        | 0.57  | 525  | 0.6423          | 0.716    | 0.7193 | 0.7443    | 0.716  |
| 0.7055        | 0.57  | 530  | 0.6074          | 0.733    | 0.7377 | 0.7481    | 0.733  |
| 0.7055        | 0.58  | 535  | 0.6066          | 0.735    | 0.7405 | 0.7513    | 0.735  |
| 0.7055        | 0.59  | 540  | 0.5945          | 0.732    | 0.7374 | 0.7486    | 0.732  |
| 0.7055        | 0.59  | 545  | 0.6231          | 0.705    | 0.7112 | 0.7439    | 0.705  |
| 0.7055        | 0.6   | 550  | 0.6108          | 0.737    | 0.7460 | 0.7660    | 0.737  |
| 0.7055        | 0.6   | 555  | 0.5846          | 0.754    | 0.7572 | 0.7675    | 0.754  |
| 0.7055        | 0.61  | 560  | 0.5965          | 0.748    | 0.7496 | 0.7640    | 0.748  |
| 0.7055        | 0.61  | 565  | 0.5849          | 0.753    | 0.7577 | 0.7687    | 0.753  |
| 0.7055        | 0.62  | 570  | 0.6037          | 0.723    | 0.7269 | 0.7514    | 0.723  |
| 0.7055        | 0.62  | 575  | 0.5773          | 0.742    | 0.7455 | 0.7598    | 0.742  |
| 0.7055        | 0.63  | 580  | 0.5661          | 0.751    | 0.7545 | 0.7607    | 0.751  |
| 0.7055        | 0.63  | 585  | 0.5717          | 0.752    | 0.7555 | 0.7626    | 0.752  |
| 0.7055        | 0.64  | 590  | 0.5905          | 0.762    | 0.7674 | 0.7808    | 0.762  |
| 0.7055        | 0.65  | 595  | 0.5876          | 0.759    | 0.7649 | 0.7773    | 0.759  |
| 0.7055        | 0.65  | 600  | 0.5651          | 0.77     | 0.7717 | 0.7741    | 0.77   |
| 0.7055        | 0.66  | 605  | 0.5791          | 0.748    | 0.7465 | 0.7502    | 0.748  |
| 0.7055        | 0.66  | 610  | 0.6135          | 0.721    | 0.7210 | 0.7434    | 0.721  |
| 0.7055        | 0.67  | 615  | 0.6268          | 0.723    | 0.7242 | 0.7523    | 0.723  |
| 0.7055        | 0.67  | 620  | 0.6211          | 0.71     | 0.7106 | 0.7449    | 0.71   |
| 0.7055        | 0.68  | 625  | 0.5829          | 0.757    | 0.7607 | 0.7742    | 0.757  |
| 0.7055        | 0.68  | 630  | 0.5718          | 0.765    | 0.7681 | 0.7744    | 0.765  |
| 0.7055        | 0.69  | 635  | 0.5685          | 0.775    | 0.7769 | 0.7830    | 0.775  |
| 0.7055        | 0.69  | 640  | 0.5731          | 0.752    | 0.7545 | 0.7653    | 0.752  |
| 0.7055        | 0.7   | 645  | 0.5903          | 0.733    | 0.7356 | 0.7570    | 0.733  |
| 0.7055        | 0.7   | 650  | 0.5973          | 0.73     | 0.7327 | 0.7575    | 0.73   |
| 0.7055        | 0.71  | 655  | 0.6056          | 0.72     | 0.7213 | 0.7535    | 0.72   |
| 0.7055        | 0.72  | 660  | 0.5617          | 0.763    | 0.7648 | 0.7703    | 0.763  |
| 0.7055        | 0.72  | 665  | 0.5781          | 0.761    | 0.7576 | 0.7688    | 0.761  |
| 0.7055        | 0.73  | 670  | 0.5993          | 0.745    | 0.7409 | 0.7650    | 0.745  |
| 0.7055        | 0.73  | 675  | 0.6027          | 0.746    | 0.7504 | 0.7675    | 0.746  |
| 0.7055        | 0.74  | 680  | 0.5825          | 0.751    | 0.7534 | 0.7600    | 0.751  |
| 0.7055        | 0.74  | 685  | 0.5742          | 0.745    | 0.7469 | 0.7513    | 0.745  |
| 0.7055        | 0.75  | 690  | 0.5907          | 0.731    | 0.7313 | 0.7462    | 0.731  |
| 0.7055        | 0.75  | 695  | 0.6017          | 0.734    | 0.7340 | 0.7555    | 0.734  |
| 0.7055        | 0.76  | 700  | 0.5767          | 0.746    | 0.7477 | 0.7599    | 0.746  |
| 0.7055        | 0.76  | 705  | 0.5859          | 0.747    | 0.7510 | 0.7676    | 0.747  |
| 0.7055        | 0.77  | 710  | 0.6001          | 0.747    | 0.7518 | 0.7690    | 0.747  |
| 0.7055        | 0.78  | 715  | 0.6427          | 0.719    | 0.7233 | 0.7541    | 0.719  |
| 0.7055        | 0.78  | 720  | 0.6600          | 0.72     | 0.7247 | 0.7556    | 0.72   |
| 0.7055        | 0.79  | 725  | 0.6365          | 0.744    | 0.7468 | 0.7640    | 0.744  |
| 0.7055        | 0.79  | 730  | 0.6089          | 0.754    | 0.7555 | 0.7596    | 0.754  |
| 0.7055        | 0.8   | 735  | 0.6050          | 0.749    | 0.7484 | 0.7494    | 0.749  |
| 0.7055        | 0.8   | 740  | 0.6120          | 0.745    | 0.7442 | 0.7518    | 0.745  |
| 0.7055        | 0.81  | 745  | 0.6205          | 0.736    | 0.7356 | 0.7490    | 0.736  |
| 0.7055        | 0.81  | 750  | 0.6174          | 0.737    | 0.7376 | 0.7544    | 0.737  |
| 0.7055        | 0.82  | 755  | 0.6222          | 0.733    | 0.7358 | 0.7585    | 0.733  |
| 0.7055        | 0.82  | 760  | 0.6216          | 0.737    | 0.7428 | 0.7636    | 0.737  |
| 0.7055        | 0.83  | 765  | 0.6138          | 0.749    | 0.7548 | 0.7691    | 0.749  |
| 0.7055        | 0.84  | 770  | 0.5977          | 0.76     | 0.7628 | 0.7682    | 0.76   |
| 0.7055        | 0.84  | 775  | 0.5930          | 0.762    | 0.7639 | 0.7671    | 0.762  |
| 0.7055        | 0.85  | 780  | 0.6002          | 0.762    | 0.7632 | 0.7682    | 0.762  |
| 0.7055        | 0.85  | 785  | 0.6029          | 0.76     | 0.7621 | 0.7676    | 0.76   |
| 0.7055        | 0.86  | 790  | 0.6068          | 0.751    | 0.7544 | 0.7615    | 0.751  |
| 0.7055        | 0.86  | 795  | 0.6188          | 0.746    | 0.7508 | 0.7615    | 0.746  |
| 0.7055        | 0.87  | 800  | 0.6398          | 0.725    | 0.7300 | 0.7486    | 0.725  |
| 0.7055        | 0.87  | 805  | 0.6555          | 0.717    | 0.7205 | 0.7461    | 0.717  |
| 0.7055        | 0.88  | 810  | 0.6550          | 0.726    | 0.7282 | 0.7578    | 0.726  |
| 0.7055        | 0.88  | 815  | 0.6376          | 0.726    | 0.7283 | 0.7474    | 0.726  |
| 0.7055        | 0.89  | 820  | 0.6115          | 0.741    | 0.7436 | 0.7524    | 0.741  |
| 0.7055        | 0.89  | 825  | 0.6048          | 0.756    | 0.7583 | 0.7638    | 0.756  |
| 0.7055        | 0.9   | 830  | 0.6039          | 0.753    | 0.7548 | 0.7591    | 0.753  |
| 0.7055        | 0.91  | 835  | 0.6018          | 0.754    | 0.7559 | 0.7605    | 0.754  |
| 0.7055        | 0.91  | 840  | 0.5967          | 0.757    | 0.7597 | 0.7653    | 0.757  |
| 0.7055        | 0.92  | 845  | 0.5937          | 0.766    | 0.7687 | 0.7738    | 0.766  |
| 0.7055        | 0.92  | 850  | 0.5945          | 0.766    | 0.7689 | 0.7740    | 0.766  |
| 0.7055        | 0.93  | 855  | 0.5951          | 0.764    | 0.7669 | 0.7722    | 0.764  |
| 0.7055        | 0.93  | 860  | 0.5953          | 0.761    | 0.7640 | 0.7699    | 0.761  |
| 0.7055        | 0.94  | 865  | 0.5977          | 0.762    | 0.7651 | 0.7726    | 0.762  |
| 0.7055        | 0.94  | 870  | 0.5969          | 0.763    | 0.7659 | 0.7733    | 0.763  |
| 0.7055        | 0.95  | 875  | 0.5957          | 0.764    | 0.7667 | 0.7740    | 0.764  |
| 0.7055        | 0.95  | 880  | 0.5927          | 0.762    | 0.7650 | 0.7717    | 0.762  |
| 0.7055        | 0.96  | 885  | 0.5916          | 0.763    | 0.7660 | 0.7715    | 0.763  |
| 0.7055        | 0.97  | 890  | 0.5935          | 0.762    | 0.7654 | 0.7717    | 0.762  |
| 0.7055        | 0.97  | 895  | 0.5934          | 0.759    | 0.7625 | 0.7689    | 0.759  |
| 0.7055        | 0.98  | 900  | 0.5919          | 0.763    | 0.7660 | 0.7715    | 0.763  |
| 0.7055        | 0.98  | 905  | 0.5913          | 0.762    | 0.7650 | 0.7705    | 0.762  |
| 0.7055        | 0.99  | 910  | 0.5916          | 0.764    | 0.7671 | 0.7726    | 0.764  |
| 0.7055        | 0.99  | 915  | 0.5916          | 0.762    | 0.7650 | 0.7706    | 0.762  |
| 0.7055        | 1.0   | 920  | 0.5913          | 0.762    | 0.7650 | 0.7706    | 0.762  |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"tags": ["generated_from_trainer"], "datasets": ["daily_dialog"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "cardiffnlp/twitter-roberta-base-sentiment-latest", "model-index": [{"name": "text-message-analyzer-finetuned", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "daily_dialog", "type": "daily_dialog", "config": "default", "split": "validation", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.762, "name": "Accuracy"}, {"type": "f1", "value": 0.7650409655164931, "name": "F1"}, {"type": "precision", "value": 0.7705665981905709, "name": "Precision"}, {"type": "recall", "value": 0.762, "name": "Recall"}]}]}]}
transformers tensorboard safetensors roberta text-classification generated_from_trainer dataset:daily_dialog base_model:cardiffnlp/twitter-roberta-base-sentiment-latest model-index autotrain_compatible endpoints_compatible region:us