---
base_model: haryoaw/scenario-MDBT-TCR_data-cl-massive_all_1_1
datasets:
- massive
library_name: transformers
license: mit
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: scenario-KD-SCR-MSV-CL-D2_data-cl-massive_all_1_166
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# scenario-KD-SCR-MSV-CL-D2_data-cl-massive_all_1_166

This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR_data-cl-massive_all_1_1](https://huggingface.co/haryoaw/scenario-MDBT-TCR_data-cl-massive_all_1_1) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 153.5196
- Accuracy: 0.2013
- F1: 0.2077

## 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: 8
- eval_batch_size: 32
- seed: 66
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch   | Step   | Validation Loss | Accuracy | F1     |
|:-------------:|:-------:|:------:|:---------------:|:--------:|:------:|
| 216.5718      | 0.5558  | 5000   | 278.3075        | 0.0831   | 0.0143 |
| 96.6856       | 1.1117  | 10000  | 166.4707        | 0.1225   | 0.0520 |
| 76.1691       | 1.6675  | 15000  | 148.1709        | 0.1620   | 0.1031 |
| 68.2425       | 2.2233  | 20000  | 143.5187        | 0.1716   | 0.1399 |
| 64.4004       | 2.7792  | 25000  | 142.7946        | 0.1949   | 0.1573 |
| 60.4123       | 3.3350  | 30000  | 143.0922        | 0.2043   | 0.1841 |
| 58.3257       | 3.8908  | 35000  | 139.9617        | 0.1968   | 0.1831 |
| 55.7519       | 4.4467  | 40000  | 142.7401        | 0.1894   | 0.1785 |
| 54.1464       | 5.0025  | 45000  | 142.8607        | 0.2036   | 0.1990 |
| 52.1675       | 5.5583  | 50000  | 144.5067        | 0.2011   | 0.2008 |
| 50.2095       | 6.1142  | 55000  | 143.3653        | 0.2015   | 0.2047 |
| 49.9481       | 6.6700  | 60000  | 146.0492        | 0.2070   | 0.2100 |
| 48.5061       | 7.2258  | 65000  | 145.7824        | 0.2060   | 0.2071 |
| 47.8933       | 7.7817  | 70000  | 144.2163        | 0.2035   | 0.2029 |
| 46.6586       | 8.3375  | 75000  | 145.5161        | 0.2011   | 0.1895 |
| 46.4338       | 8.8933  | 80000  | 145.8758        | 0.2069   | 0.2087 |
| 45.3242       | 9.4492  | 85000  | 148.9746        | 0.2062   | 0.2001 |
| 44.8473       | 10.0050 | 90000  | 146.6478        | 0.2048   | 0.2094 |
| 44.0707       | 10.5608 | 95000  | 147.1517        | 0.2069   | 0.2087 |
| 43.2148       | 11.1167 | 100000 | 147.0233        | 0.2000   | 0.2018 |
| 43.2003       | 11.6725 | 105000 | 150.5463        | 0.1995   | 0.2018 |
| 42.3747       | 12.2283 | 110000 | 149.6212        | 0.2031   | 0.2138 |
| 42.4209       | 12.7842 | 115000 | 150.6038        | 0.2070   | 0.2057 |
| 41.8157       | 13.3400 | 120000 | 149.7630        | 0.2098   | 0.2109 |
| 41.4871       | 13.8958 | 125000 | 148.6580        | 0.1977   | 0.2088 |
| 41.1541       | 14.4517 | 130000 | 149.6882        | 0.2003   | 0.2043 |
| 40.8411       | 15.0075 | 135000 | 150.0774        | 0.1982   | 0.2045 |
| 40.4155       | 15.5633 | 140000 | 149.2371        | 0.1960   | 0.1992 |
| 39.9237       | 16.1192 | 145000 | 154.0368        | 0.2009   | 0.2069 |
| 39.9569       | 16.6750 | 150000 | 150.4786        | 0.1917   | 0.2061 |
| 39.2982       | 17.2308 | 155000 | 150.7963        | 0.2006   | 0.2038 |
| 39.3455       | 17.7867 | 160000 | 149.2980        | 0.1968   | 0.2061 |
| 38.8963       | 18.3425 | 165000 | 152.4315        | 0.2014   | 0.2079 |
| 38.9814       | 18.8983 | 170000 | 149.7322        | 0.2008   | 0.2035 |
| 38.4953       | 19.4542 | 175000 | 150.7164        | 0.2019   | 0.1999 |
| 38.4956       | 20.0100 | 180000 | 151.1443        | 0.2032   | 0.2064 |
| 38.1569       | 20.5658 | 185000 | 151.4858        | 0.2004   | 0.2041 |
| 37.8998       | 21.1217 | 190000 | 150.7066        | 0.2019   | 0.2058 |
| 37.9294       | 21.6775 | 195000 | 153.0376        | 0.2020   | 0.2056 |
| 37.6479       | 22.2333 | 200000 | 153.4656        | 0.2067   | 0.2097 |
| 37.6083       | 22.7892 | 205000 | 153.5261        | 0.1996   | 0.2062 |
| 37.2665       | 23.3450 | 210000 | 152.2416        | 0.2014   | 0.2077 |
| 37.3541       | 23.9008 | 215000 | 152.2836        | 0.1960   | 0.2081 |
| 36.9886       | 24.4567 | 220000 | 152.6591        | 0.1998   | 0.2045 |
| 36.9563       | 25.0125 | 225000 | 152.0537        | 0.2015   | 0.2083 |
| 36.8296       | 25.5683 | 230000 | 153.0621        | 0.1974   | 0.2032 |
| 36.5423       | 26.1242 | 235000 | 153.1660        | 0.2043   | 0.2088 |
| 36.6304       | 26.6800 | 240000 | 152.7207        | 0.1997   | 0.2062 |
| 36.497        | 27.2358 | 245000 | 152.6269        | 0.2006   | 0.2056 |
| 36.6385       | 27.7917 | 250000 | 152.8843        | 0.2002   | 0.2072 |
| 36.3263       | 28.3475 | 255000 | 153.2631        | 0.2029   | 0.2085 |
| 36.3559       | 28.9033 | 260000 | 152.8185        | 0.2019   | 0.2097 |
| 36.1499       | 29.4592 | 265000 | 153.5196        | 0.2013   | 0.2077 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1