---
base_model: Qwen/Qwen-14B
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: final_cnn_dailymail
  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. -->

# final_cnn_dailymail

This model is a fine-tuned version of [Qwen/Qwen-14B](https://huggingface.co/Qwen/Qwen-14B) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2127

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.01
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9757        | 0.02  | 100  | 1.9261          |
| 1.9258        | 0.04  | 200  | 1.8833          |
| 1.8977        | 0.06  | 300  | 1.8657          |
| 1.8903        | 0.08  | 400  | 1.8630          |
| 1.8858        | 0.1   | 500  | 1.8638          |
| 1.89          | 0.12  | 600  | 1.8636          |
| 1.873         | 0.14  | 700  | 1.8637          |
| 1.8908        | 0.16  | 800  | 1.8637          |
| 1.8791        | 0.18  | 900  | 1.8626          |
| 1.8851        | 0.2   | 1000 | 1.8634          |
| 1.89          | 0.22  | 1100 | 1.8651          |
| 1.8889        | 0.24  | 1200 | 1.8681          |
| 1.8896        | 0.26  | 1300 | 1.8708          |
| 1.8817        | 0.28  | 1400 | 1.8739          |
| 1.9003        | 0.3   | 1500 | 1.8791          |
| 1.9005        | 0.32  | 1600 | 1.8825          |
| 1.9024        | 0.34  | 1700 | 1.8864          |
| 1.9204        | 0.36  | 1800 | 1.8929          |
| 1.9182        | 0.38  | 1900 | 1.8955          |
| 1.9289        | 0.4   | 2000 | 1.9035          |
| 1.9348        | 0.42  | 2100 | 1.9157          |
| 1.9453        | 0.44  | 2200 | 1.9277          |
| 1.9689        | 0.46  | 2300 | 1.9457          |
| 1.9829        | 0.48  | 2400 | 1.9596          |
| 1.9874        | 0.5   | 2500 | 1.9803          |
| 2.0148        | 0.52  | 2600 | 1.9991          |
| 2.0391        | 0.54  | 2700 | 2.0249          |
| 2.0619        | 0.56  | 2800 | 2.0477          |
| 2.0736        | 0.58  | 2900 | 2.0678          |
| 2.0957        | 0.6   | 3000 | 2.0825          |
| 2.1223        | 0.62  | 3100 | 2.1097          |
| 2.1357        | 0.64  | 3200 | 2.1164          |
| 2.1759        | 0.66  | 3300 | 2.1524          |
| 2.168         | 0.68  | 3400 | 2.1650          |
| 2.1842        | 0.7   | 3500 | 2.1637          |
| 2.1956        | 0.72  | 3600 | 2.1775          |
| 2.2131        | 0.74  | 3700 | 2.1888          |
| 2.198         | 0.76  | 3800 | 2.1953          |
| 2.2231        | 0.78  | 3900 | 2.1994          |
| 2.2292        | 0.8   | 4000 | 2.2080          |
| 2.2343        | 0.82  | 4100 | 2.2093          |
| 2.2261        | 0.84  | 4200 | 2.2009          |
| 2.2104        | 0.86  | 4300 | 2.2015          |
| 2.2255        | 0.88  | 4400 | 2.2077          |
| 2.2299        | 0.9   | 4500 | 2.2099          |
| 2.2253        | 0.92  | 4600 | 2.2100          |
| 2.2239        | 0.94  | 4700 | 2.2116          |
| 2.2322        | 0.96  | 4800 | 2.2122          |
| 2.2457        | 0.98  | 4900 | 2.2127          |
| 2.2325        | 1.0   | 5000 | 2.2127          |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.1.0
- Datasets 2.14.7
- Tokenizers 0.13.3