transformers tensorboard safetensors bart text2text-generation generated_from_trainer base_model:facebook/bart-large-cnn license:mit autotrain_compatible endpoints_compatible region:us
---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
model-index:
- name: bart-large-cnn-prompt_generation-2.0
  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. -->

# bart-large-cnn-prompt_generation-2.0

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6403
- Actual score: 0.8766
- Predction score: 0.5039
- Score difference: 0.3727

## 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-07
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 75
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Actual score | Predction score | Score difference |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:---------------:|:----------------:|
| No log        | 1.0   | 8    | 3.6549          | 0.8766       | -0.2093         | 1.0859           |
| No log        | 2.0   | 16   | 3.6012          | 0.8766       | -0.1961         | 1.0728           |
| No log        | 3.0   | 24   | 3.5331          | 0.8766       | -0.1613         | 1.0379           |
| No log        | 4.0   | 32   | 3.4417          | 0.8766       | -0.1132         | 0.9899           |
| No log        | 5.0   | 40   | 3.3501          | 0.8766       | -0.1821         | 1.0587           |
| No log        | 6.0   | 48   | 3.2904          | 0.8766       | -0.1653         | 1.0419           |
| No log        | 7.0   | 56   | 3.2418          | 0.8766       | -0.4566         | 1.3332           |
| No log        | 8.0   | 64   | 3.1620          | 0.8766       | -0.2897         | 1.1663           |
| No log        | 9.0   | 72   | 3.0925          | 0.8766       | -0.5185         | 1.3951           |
| No log        | 10.0  | 80   | 3.0442          | 0.8766       | -0.7127         | 1.5893           |
| No log        | 11.0  | 88   | 3.0064          | 0.8766       | -0.4893         | 1.3659           |
| No log        | 12.0  | 96   | 2.9742          | 0.8766       | -0.6391         | 1.5157           |
| No log        | 13.0  | 104  | 2.9475          | 0.8766       | -0.4873         | 1.3640           |
| No log        | 14.0  | 112  | 2.9254          | 0.8766       | -0.2786         | 1.1552           |
| No log        | 15.0  | 120  | 2.9061          | 0.8766       | -0.1893         | 1.0660           |
| No log        | 16.0  | 128  | 2.8887          | 0.8766       | -0.2202         | 1.0968           |
| No log        | 17.0  | 136  | 2.8730          | 0.8766       | -0.2009         | 1.0775           |
| No log        | 18.0  | 144  | 2.8588          | 0.8766       | -0.2101         | 1.0867           |
| No log        | 19.0  | 152  | 2.8461          | 0.8766       | -0.3374         | 1.2140           |
| No log        | 20.0  | 160  | 2.8337          | 0.8766       | -0.2005         | 1.0772           |
| No log        | 21.0  | 168  | 2.8216          | 0.8766       | -0.2570         | 1.1336           |
| No log        | 22.0  | 176  | 2.8104          | 0.8766       | -0.3601         | 1.2367           |
| No log        | 23.0  | 184  | 2.7996          | 0.8766       | -0.4823         | 1.3589           |
| No log        | 24.0  | 192  | 2.7895          | 0.8766       | -0.4451         | 1.3217           |
| No log        | 25.0  | 200  | 2.7798          | 0.8766       | -0.3621         | 1.2388           |
| No log        | 26.0  | 208  | 2.7706          | 0.8766       | -0.4108         | 1.2874           |
| No log        | 27.0  | 216  | 2.7625          | 0.8766       | -0.4750         | 1.3517           |
| No log        | 28.0  | 224  | 2.7547          | 0.8766       | -0.4004         | 1.2771           |
| No log        | 29.0  | 232  | 2.7471          | 0.8766       | -0.4535         | 1.3301           |
| No log        | 30.0  | 240  | 2.7393          | 0.8766       | -0.5414         | 1.4180           |
| No log        | 31.0  | 248  | 2.7328          | 0.8766       | -0.5666         | 1.4433           |
| No log        | 32.0  | 256  | 2.7268          | 0.8766       | -0.6630         | 1.5396           |
| No log        | 33.0  | 264  | 2.7211          | 0.8766       | -0.4073         | 1.2839           |
| No log        | 34.0  | 272  | 2.7160          | 0.8766       | -0.5464         | 1.4230           |
| No log        | 35.0  | 280  | 2.7113          | 0.8766       | -0.3629         | 1.2396           |
| No log        | 36.0  | 288  | 2.7065          | 0.8766       | -0.2926         | 1.1692           |
| No log        | 37.0  | 296  | 2.7025          | 0.8766       | -0.2596         | 1.1362           |
| No log        | 38.0  | 304  | 2.6981          | 0.8766       | -0.1478         | 1.0244           |
| No log        | 39.0  | 312  | 2.6939          | 0.8766       | -0.2252         | 1.1018           |
| No log        | 40.0  | 320  | 2.6901          | 0.8766       | -0.2750         | 1.1516           |
| No log        | 41.0  | 328  | 2.6867          | 0.8766       | -0.0900         | 0.9667           |
| No log        | 42.0  | 336  | 2.6836          | 0.8766       | -0.2377         | 1.1144           |
| No log        | 43.0  | 344  | 2.6804          | 0.8766       | -0.3135         | 1.1901           |
| No log        | 44.0  | 352  | 2.6774          | 0.8766       | -0.1023         | 0.9789           |
| No log        | 45.0  | 360  | 2.6745          | 0.8766       | -0.0386         | 0.9152           |
| No log        | 46.0  | 368  | 2.6714          | 0.8766       | 0.1602          | 0.7164           |
| No log        | 47.0  | 376  | 2.6689          | 0.8766       | 0.2508          | 0.6258           |
| No log        | 48.0  | 384  | 2.6668          | 0.8766       | 0.1577          | 0.7190           |
| No log        | 49.0  | 392  | 2.6648          | 0.8766       | 0.0565          | 0.8201           |
| No log        | 50.0  | 400  | 2.6627          | 0.8766       | 0.2379          | 0.6387           |
| No log        | 51.0  | 408  | 2.6607          | 0.8766       | 0.2343          | 0.6423           |
| No log        | 52.0  | 416  | 2.6588          | 0.8766       | 0.2719          | 0.6048           |
| No log        | 53.0  | 424  | 2.6570          | 0.8766       | 0.2214          | 0.6552           |
| No log        | 54.0  | 432  | 2.6555          | 0.8766       | 0.2729          | 0.6037           |
| No log        | 55.0  | 440  | 2.6541          | 0.8766       | 0.2798          | 0.5968           |
| No log        | 56.0  | 448  | 2.6528          | 0.8766       | 0.0662          | 0.8104           |
| No log        | 57.0  | 456  | 2.6514          | 0.8766       | 0.0377          | 0.8390           |
| No log        | 58.0  | 464  | 2.6502          | 0.8766       | 0.2886          | 0.5880           |
| No log        | 59.0  | 472  | 2.6491          | 0.8766       | 0.2257          | 0.6509           |
| No log        | 60.0  | 480  | 2.6481          | 0.8766       | 0.2561          | 0.6206           |
| No log        | 61.0  | 488  | 2.6471          | 0.8766       | 0.2683          | 0.6083           |
| No log        | 62.0  | 496  | 2.6461          | 0.8766       | 0.2897          | 0.5869           |
| 2.5848        | 63.0  | 504  | 2.6453          | 0.8766       | 0.2974          | 0.5793           |
| 2.5848        | 64.0  | 512  | 2.6445          | 0.8766       | 0.2946          | 0.5820           |
| 2.5848        | 65.0  | 520  | 2.6438          | 0.8766       | 0.3021          | 0.5745           |
| 2.5848        | 66.0  | 528  | 2.6433          | 0.8766       | 0.2679          | 0.6087           |
| 2.5848        | 67.0  | 536  | 2.6428          | 0.8766       | 0.3133          | 0.5633           |
| 2.5848        | 68.0  | 544  | 2.6423          | 0.8766       | 0.3398          | 0.5368           |
| 2.5848        | 69.0  | 552  | 2.6418          | 0.8766       | 0.4149          | 0.4617           |
| 2.5848        | 70.0  | 560  | 2.6413          | 0.8766       | 0.4674          | 0.4092           |
| 2.5848        | 71.0  | 568  | 2.6410          | 0.8766       | 0.4929          | 0.3838           |
| 2.5848        | 72.0  | 576  | 2.6407          | 0.8766       | 0.4974          | 0.3793           |
| 2.5848        | 73.0  | 584  | 2.6406          | 0.8766       | 0.4948          | 0.3818           |
| 2.5848        | 74.0  | 592  | 2.6404          | 0.8766       | 0.4623          | 0.4143           |
| 2.5848        | 75.0  | 600  | 2.6403          | 0.8766       | 0.5039          | 0.3727           |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
