transformers safetensors t5 text2text-generation generated_from_trainer summarization en dataset:samsum base_model:pszemraj/tFINE-base-300m base_model:finetune:pszemraj/tFINE-base-300m license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: pszemraj/tFINE-base-300m
datasets:
- samsum
language:
- en
library_name: transformers
license: apache-2.0
metrics:
- rouge
pipeline_tag: summarization
tags:
- generated_from_trainer
model-index:
- name: tFINE-base-300m-samsum
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: None
      args: samsum
    metrics:
    - type: rouge
      value: 42.3629
      name: Rouge1
---

# tFINE-base-300m-samsum

An example fine-tune of [pszemraj/tFINE-base-300m](https://hf.co/pszemraj/tFINE-base-300m) for summarization using the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9820
- Rouge1: 42.3629
- Rouge2: 18.4285
- Rougel: 34.6339
- Rougelsum: 38.7792
- Gen Len: 27.8033

> [!NOTE]
> The base model was pre-trained with CTX 1024 and fine-tuned on samsum with 1024 CTX inputs.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 16
- seed: 17868
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 4.0

### Training results

> keep epoch 3 checkpt as final


| Training Loss | Epoch  | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.9528        | 0.9989 | 115  | 1.9189          | 40.093  | 18.2018 | 33.9749 | 36.9071   | 29.3333 |
| 1.5346        | 1.9978 | 230  | 1.8827          | 41.4676 | 18.3467 | 34.1909 | 38.2131   | 27.6633 |
| 1.1696        | 2.9967 | 345  | 1.9820          | 42.3629 | 18.4285 | 34.6339 | 38.7792   | 27.8033 |
| 0.9359        | 3.9957 | 460  | 2.1588          | 41.2237 | 17.8161 | 33.7101 | 37.9569   | 30.18   |

