transformers safetensors bart text2text-generation generated_from_trainer base_model:facebook/bart-large license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: cnn_dailymail_726_bart-large
  results: []
---

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# cnn_dailymail_726_bart-large

This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8412
- Rouge1: 0.2469
- Rouge2: 0.1266
- Rougel: 0.2074
- Rougelsum: 0.2332
- Gen Len: 20.0

## 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: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.9706        | 0.22  | 500   | 0.9015          | 0.237  | 0.1181 | 0.1979 | 0.2232    | 19.9999 |
| 0.9212        | 0.45  | 1000  | 0.8771          | 0.237  | 0.1193 | 0.199  | 0.2233    | 20.0    |
| 0.8991        | 0.67  | 1500  | 0.8572          | 0.2443 | 0.1238 | 0.2045 | 0.2304    | 20.0    |
| 0.9085        | 0.89  | 2000  | 0.8519          | 0.2404 | 0.1227 | 0.2022 | 0.2269    | 20.0    |
| 0.8494        | 1.11  | 2500  | 0.8471          | 0.2437 | 0.1233 | 0.2041 | 0.2298    | 20.0    |
| 0.832         | 1.34  | 3000  | 0.8400          | 0.2438 | 0.1248 | 0.2055 | 0.2301    | 20.0    |
| 0.8522        | 1.56  | 3500  | 0.8393          | 0.2417 | 0.1242 | 0.2043 | 0.2283    | 20.0    |
| 0.8494        | 1.78  | 4000  | 0.8338          | 0.2436 | 0.1239 | 0.2047 | 0.23      | 19.9999 |
| 0.7729        | 2.01  | 4500  | 0.8332          | 0.2431 | 0.1253 | 0.2048 | 0.2298    | 20.0    |
| 0.7761        | 2.23  | 5000  | 0.8323          | 0.2477 | 0.1264 | 0.207  | 0.2335    | 19.9994 |
| 0.7788        | 2.45  | 5500  | 0.8277          | 0.2473 | 0.1259 | 0.2068 | 0.2333    | 20.0    |
| 0.7832        | 2.67  | 6000  | 0.8251          | 0.2453 | 0.126  | 0.2061 | 0.2317    | 20.0    |
| 0.7888        | 2.9   | 6500  | 0.8239          | 0.242  | 0.1241 | 0.2037 | 0.2287    | 20.0    |
| 0.7413        | 3.12  | 7000  | 0.8360          | 0.2394 | 0.1228 | 0.2017 | 0.2258    | 20.0    |
| 0.7438        | 3.34  | 7500  | 0.8283          | 0.2462 | 0.1267 | 0.2072 | 0.2326    | 19.9999 |
| 0.7271        | 3.57  | 8000  | 0.8275          | 0.2406 | 0.1235 | 0.2028 | 0.2276    | 20.0    |
| 0.7435        | 3.79  | 8500  | 0.8221          | 0.2451 | 0.1254 | 0.2055 | 0.2311    | 19.9998 |
| 0.7072        | 4.01  | 9000  | 0.8277          | 0.2437 | 0.1251 | 0.2049 | 0.2301    | 19.9999 |
| 0.708         | 4.24  | 9500  | 0.8270          | 0.2465 | 0.1263 | 0.2067 | 0.2325    | 19.9999 |
| 0.7058        | 4.46  | 10000 | 0.8279          | 0.2424 | 0.1249 | 0.2045 | 0.229     | 19.9999 |
| 0.6918        | 4.68  | 10500 | 0.8248          | 0.246  | 0.1259 | 0.2063 | 0.232     | 19.9998 |
| 0.7121        | 4.9   | 11000 | 0.8231          | 0.2457 | 0.126  | 0.2058 | 0.232     | 19.9999 |
| 0.6667        | 5.13  | 11500 | 0.8297          | 0.2458 | 0.1262 | 0.2066 | 0.2323    | 19.9996 |
| 0.6767        | 5.35  | 12000 | 0.8309          | 0.2469 | 0.1269 | 0.2071 | 0.2332    | 19.9996 |
| 0.6961        | 5.57  | 12500 | 0.8299          | 0.247  | 0.1271 | 0.2074 | 0.2333    | 20.0    |
| 0.6842        | 5.8   | 13000 | 0.8333          | 0.2473 | 0.127  | 0.2077 | 0.2336    | 19.9996 |
| 0.6485        | 6.02  | 13500 | 0.8360          | 0.2454 | 0.1259 | 0.2061 | 0.2316    | 19.9998 |
| 0.6651        | 6.24  | 14000 | 0.8349          | 0.2454 | 0.126  | 0.2062 | 0.2314    | 20.0    |
| 0.6483        | 6.46  | 14500 | 0.8331          | 0.2454 | 0.1258 | 0.2058 | 0.2316    | 20.0    |
| 0.6626        | 6.69  | 15000 | 0.8309          | 0.2468 | 0.127  | 0.2069 | 0.2328    | 19.9996 |
| 0.6675        | 6.91  | 15500 | 0.8337          | 0.2448 | 0.1255 | 0.2056 | 0.231     | 19.9999 |
| 0.6479        | 7.13  | 16000 | 0.8387          | 0.2471 | 0.1267 | 0.2074 | 0.2333    | 19.9999 |
| 0.6506        | 7.36  | 16500 | 0.8377          | 0.2474 | 0.1264 | 0.2071 | 0.2335    | 19.9999 |
| 0.643         | 7.58  | 17000 | 0.8369          | 0.2454 | 0.1259 | 0.2059 | 0.2318    | 20.0    |
| 0.6262        | 7.8   | 17500 | 0.8378          | 0.2466 | 0.1269 | 0.2071 | 0.233     | 19.9997 |
| 0.6235        | 8.02  | 18000 | 0.8415          | 0.2458 | 0.1266 | 0.2065 | 0.2321    | 20.0    |
| 0.6081        | 8.25  | 18500 | 0.8421          | 0.2465 | 0.1267 | 0.2069 | 0.2326    | 19.9997 |
| 0.6257        | 8.47  | 19000 | 0.8409          | 0.2477 | 0.1267 | 0.2075 | 0.2337    | 19.9999 |
| 0.6187        | 8.69  | 19500 | 0.8381          | 0.2459 | 0.1264 | 0.2066 | 0.2321    | 19.9997 |
| 0.6178        | 8.92  | 20000 | 0.8384          | 0.248  | 0.1273 | 0.2079 | 0.2339    | 19.9996 |
| 0.6018        | 9.14  | 20500 | 0.8432          | 0.2468 | 0.1265 | 0.2071 | 0.2329    | 20.0    |
| 0.6235        | 9.36  | 21000 | 0.8418          | 0.2469 | 0.1265 | 0.207  | 0.233     | 20.0    |
| 0.606         | 9.58  | 21500 | 0.8418          | 0.2464 | 0.1264 | 0.207  | 0.2327    | 19.9999 |
| 0.6016        | 9.81  | 22000 | 0.8412          | 0.2469 | 0.1266 | 0.2074 | 0.2332    | 20.0    |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
