| step | Training Loss | Validation Loss | Accuracy |
|------|---------------|-----------------|----------|
| 100  | 1.408000      | 2.027748        | 0.157143 |
| 200  | 3.115200      | 1.903195        | 0.264286 |
| 300  | 1.391100      | 1.747969        | 0.292857 |
| 400  | 1.939900      | 1.880145        | 0.260714 |
| 500  | 1.430700      | 1.627666        | 0.385714 |
| 600  | 2.042500      | 1.640029        | 0.378571 |
| 700  | 2.244100      | 1.665656        | 0.414286 |
| 800  | 1.382800      | 1.393861        | 0.442857 |
| 900  | 0.041200      | 1.501603        | 0.392857 |
| 1000 | 0.997600      | 1.547526        | 0.471429 |
| 1100 | 0.851200      | 1.427456        | 0.510714 |
| 1200 | 0.030400      | 1.371201        | 0.546429 |
| 1300 | 0.015900      | 1.251728        | 0.575000 |
| 1400 | 0.661500      | 1.452044        | 0.542857 |
| 1500 | 1.753200      | 1.673231        | 0.553571 |
| 1600 | 0.543100      | 1.374431        | 0.560714 |
| 1700 | 0.223200      | 1.466555        | 0.564286 |
| 1800 | 4.417000      | 2.020453        | 0.582143 |
| 1900 | 1.420700      | 1.788945        | 0.575000 |
| 2000 | 0.059900      | 1.279900        | 0.696429 |
| 2100 | 0.004900      | 1.520277        | 0.610714 |
| 2200 | 0.067500      | 1.465420        | 0.621429 |
| 2300 | 0.010000      | 1.283547        | 0.710714 |
| 2400 | 3.950700      | 1.684178        | 0.635714 |
| 2500 | 0.001800      | 1.535846        | 0.675000 |
| 2600 | 0.062600      | 1.461411        | 0.685714 |
| 2700 | 0.000800      | 1.647843        | 0.692857 |
| 2800 | 0.018900      | 1.565477        | 0.685714 |
| 2900 | 0.783800      | 1.612781        | 0.710714 |
| 3000 | 0.027400      | 1.620468        | 0.703571 |
| 3100 | 0.000300      | 1.756414        | 0.685714 |
| 3200 | 0.000700      | 1.941203        | 0.682143 |
| 3300 | 0.000200      | 1.673942        | 0.717857 |
| 3400 | 0.000300      | 1.576283        | 0.710714 |
| 3500 | 0.000400      | 1.698424        | 0.707143 |
| 3600 | 0.000800      | 1.714727        | 0.700000 |
| 3700 | 0.000400      | 1.731099        | 0.714286 |
| 3800 | 0.000200      | 1.784098        | 0.707143 |
| 3900 | 0.000400      | 1.781287        | 0.714286 |
| 4000 | 0.000600      | 1.686473        | 0.710714 |
| 4100 | 0.000600      | 1.682677        | 0.714286 |
| 4200 | 0.000200      | 1.679403        | 0.707143 |
| 4300 | 0.000300      | 1.664588        | 0.710714 |
| 4400 | 0.003700      | 1.596243        | 0.725000 |


|              | precision | recall | f1-score | support |
|--------------|-----------|--------|----------|---------|
| anger        | 0.97      | 0.86   | 0.92     | 44      |
| disgust      | 0.71      | 0.78   | 0.74     | 37      |
| enthusiasm   | 0.51      | 0.80   | 0.62     | 40      |
| fear         | 0.80      | 0.62   | 0.70     | 45      |
| happiness    | 0.66      | 0.70   | 0.68     | 44      |
| neutral      | 0.81      | 0.66   | 0.72     | 38      |
| sadness      | 0.79      | 0.59   | 0.68     | 32      |
| accuracy     | 0.72      | 280    | 0.442857 |         |
| macro avg    | 0.75      | 0.72   | 0.72     | 280     |
| weighted avg | 0.75      | 0.72   | 0.73     | 280     |