---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: meeting-sensai-2
  results: []
---

<!-- 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. -->

# meeting-sensai-2

This model is a fine-tuned version of [raquelclemente/meeting-sensai](https://huggingface.co/raquelclemente/meeting-sensai) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7555
- Rouge1: 0.3039
- Rouge2: 0.1453
- Rougel: 0.2534
- Rougelsum: 0.2529

## 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: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log        | 0.09  | 30   | 3.1865          | 0.1936 | 0.0509 | 0.1606 | 0.1611    |
| No log        | 0.18  | 60   | 2.9928          | 0.2636 | 0.0961 | 0.1951 | 0.1969    |
| No log        | 0.28  | 90   | 2.9472          | 0.2667 | 0.0921 | 0.2097 | 0.2115    |
| No log        | 0.37  | 120  | 2.8506          | 0.2755 | 0.1151 | 0.2243 | 0.2243    |
| No log        | 0.46  | 150  | 2.8883          | 0.2505 | 0.1134 | 0.2057 | 0.2056    |
| No log        | 0.55  | 180  | 2.8464          | 0.2946 | 0.1229 | 0.2449 | 0.2446    |
| No log        | 0.65  | 210  | 2.8274          | 0.2668 | 0.1005 | 0.2112 | 0.2120    |
| No log        | 0.74  | 240  | 2.8153          | 0.2960 | 0.1268 | 0.2392 | 0.2384    |
| No log        | 0.83  | 270  | 2.7803          | 0.2940 | 0.1203 | 0.2323 | 0.2312    |
| No log        | 0.92  | 300  | 2.8129          | 0.2966 | 0.1083 | 0.2447 | 0.2444    |
| No log        | 1.02  | 330  | 2.7478          | 0.2977 | 0.1152 | 0.2388 | 0.2382    |
| No log        | 1.11  | 360  | 2.7482          | 0.2905 | 0.1135 | 0.2379 | 0.2369    |
| No log        | 1.2   | 390  | 2.7646          | 0.3215 | 0.1260 | 0.2590 | 0.2594    |
| No log        | 1.29  | 420  | 2.7763          | 0.3164 | 0.1273 | 0.2536 | 0.2524    |
| No log        | 1.38  | 450  | 2.8300          | 0.2867 | 0.1198 | 0.2182 | 0.2186    |
| No log        | 1.48  | 480  | 2.7683          | 0.3313 | 0.1567 | 0.2647 | 0.2643    |
| 2.7437        | 1.57  | 510  | 2.7669          | 0.3004 | 0.1313 | 0.2545 | 0.2541    |
| 2.7437        | 1.66  | 540  | 2.7242          | 0.2960 | 0.1394 | 0.2404 | 0.2409    |
| 2.7437        | 1.75  | 570  | 2.7565          | 0.3042 | 0.1172 | 0.2475 | 0.2463    |
| 2.7437        | 1.85  | 600  | 2.7866          | 0.2994 | 0.1300 | 0.2375 | 0.2373    |
| 2.7437        | 1.94  | 630  | 2.7293          | 0.3122 | 0.1306 | 0.2611 | 0.2581    |
| 2.7437        | 2.03  | 660  | 2.7398          | 0.3194 | 0.1314 | 0.2504 | 0.2506    |
| 2.7437        | 2.12  | 690  | 2.7183          | 0.3109 | 0.1374 | 0.2591 | 0.2588    |
| 2.7437        | 2.22  | 720  | 2.7929          | 0.3184 | 0.1454 | 0.2562 | 0.2562    |
| 2.7437        | 2.31  | 750  | 2.8156          | 0.3360 | 0.1566 | 0.2613 | 0.2611    |
| 2.7437        | 2.4   | 780  | 2.7750          | 0.3125 | 0.1364 | 0.2496 | 0.2494    |
| 2.7437        | 2.49  | 810  | 2.8071          | 0.2928 | 0.1434 | 0.2501 | 0.2491    |
| 2.7437        | 2.58  | 840  | 2.7322          | 0.3043 | 0.1403 | 0.2488 | 0.2484    |
| 2.7437        | 2.68  | 870  | 2.7449          | 0.3006 | 0.1437 | 0.2521 | 0.2516    |
| 2.7437        | 2.77  | 900  | 2.7425          | 0.3029 | 0.1479 | 0.2545 | 0.2543    |
| 2.7437        | 2.86  | 930  | 2.7242          | 0.3028 | 0.1355 | 0.2318 | 0.2307    |
| 2.7437        | 2.95  | 960  | 2.7232          | 0.3100 | 0.1474 | 0.2449 | 0.2443    |
| 2.7437        | 3.05  | 990  | 2.7787          | 0.3036 | 0.1457 | 0.2465 | 0.2467    |
| 2.1872        | 3.14  | 1020 | 2.7759          | 0.2957 | 0.1371 | 0.2394 | 0.2375    |
| 2.1872        | 3.23  | 1050 | 2.7896          | 0.3105 | 0.1391 | 0.2403 | 0.2375    |
| 2.1872        | 3.32  | 1080 | 2.7724          | 0.3121 | 0.1498 | 0.2452 | 0.2453    |
| 2.1872        | 3.42  | 1110 | 2.7639          | 0.3204 | 0.1534 | 0.2556 | 0.2563    |
| 2.1872        | 3.51  | 1140 | 2.7673          | 0.3103 | 0.1541 | 0.2529 | 0.2521    |
| 2.1872        | 3.6   | 1170 | 2.7644          | 0.3059 | 0.1399 | 0.2450 | 0.2448    |
| 2.1872        | 3.69  | 1200 | 2.7443          | 0.3186 | 0.1484 | 0.2554 | 0.2562    |
| 2.1872        | 3.78  | 1230 | 2.7517          | 0.3045 | 0.1444 | 0.2522 | 0.2519    |
| 2.1872        | 3.88  | 1260 | 2.7501          | 0.3039 | 0.1453 | 0.2534 | 0.2529    |
| 2.1872        | 3.97  | 1290 | 2.7555          | 0.3039 | 0.1453 | 0.2534 | 0.2529    |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2