metadata

library_name: transformers
license: apache-2.0
base_model: google/flan-t5-base
tags:
  - generated_from_trainer
datasets:
  - samsum
metrics:
  - rouge
model-index:
  - name: flan-t5-base-samsum
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: samsum
          type: samsum
          config: samsum
          split: test
          args: samsum
        metrics:
          - name: Rouge1
            type: rouge
            value: 47.355
pipeline_tag: summarization

flan-t5-base-samsum

This model is a fine-tuned version of google/flan-t5-base on the samsum dataset. It achieves the following results on the evaluation set:

Loss: 1.3736
Rouge1: 47.355
Rouge2: 23.7601
Rougel: 39.8403
Rougelsum: 43.4718
Gen Len: 17.1575

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
1.3641	1.0	921	1.3780	47.4054	23.6308	39.8273	43.3697	17.3004
1.3074	2.0	1842	1.3736	47.355	23.7601	39.8403	43.4718	17.1575
1.2592	3.0	2763	1.3740	47.2208	23.4972	39.7293	43.2546	17.2320
1.2232	4.0	3684	1.3794	47.9156	24.2451	40.2628	43.9122	17.4017
1.2042	5.0	4605	1.3780	47.8982	24.1707	40.2955	43.8939	17.3712

Framework versions

Transformers 4.44.2
Pytorch 2.4.0
Datasets 3.0.0
Tokenizers 0.19.1

How to use

from transformers import pipeline

pipe = pipeline("summarization", model="sharmax-vikas/flan-t5-base-samsum")

res = pipe('''dialogue: 
Margaret: Hi, in December I'd like to meet on 4th and 11th around 10:00 or 11:00. 
Evans: Hi, 4th - we can meet at 10:00.
Evans: And 11th - at 11:00. 
Margaret: Okey. And what about 18th?
Evans: I'm not sure about 18th. 
Evans: I might not be in town. 
Margaret: Okey, so we'll see. 
Evans: Yes. And I'll let you know next week. 
Margaret: If it's not 18th, maybe we could meet on 17th?
Evans: If I go away, I won't also be 17th.
Margaret: Okey, I get it. 
Evans: But we could meet 14th, if you like?
Margaret: Hm, I'm not sure whether I'm avaliable. 
Evans: So let's set these dates later, ok?
Margaret: Okey and we see each other 4th 10:00. 
Evans: Yes!''')

print(f"flan-t5-base summary:\n{res[0]['summary_text']}")


#output : flan-t5-base summary:
Margaret and Evans will meet on the 4th and 11th of December. They will meet at 10:00 on the 18th and at 11:00 on the 17th. If it's not 18th, they can meet on 17th or 14th.