transformers pytorch safetensors t5 text2text-generation generated_from_trainer summarization en dataset:cnn_dailymail dataset:xsum dataset:samsum dataset:billsum dataset:lytang/MeetingBank-transcript license:mit model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- cnn_dailymail
- xsum
- samsum
- billsum
- lytang/MeetingBank-transcript
language:
- en
library_name: transformers
license: mit
metrics:
- rouge
pipeline_tag: summarization
tags:
- generated_from_trainer
model-index:
- name: t5_xsum_samsum_billsum_cnn_dailymail
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: cnn_dailymail
      type: cnn_dailymail
      config: 3.0.0
      split: train
      args: 3.0.0
    metrics:
    - type: rouge
      value: 0.2373
      name: Rouge1
---

# t5_xsum_samsum_billsum_cnn_dailymail

The `t5_xsum_samsum_billsum_cnn_dailymail` model is a text summarization model fine-tuned on the `t5-base` architecture, which is a versatile text-to-text transfer transformer. This powerful model excels at generating abstractive summaries from input text. It has been fine-tuned on multiple datasets, including CNN/Daily Mail (cnn_dailymail), XSum (xsum), SamSum (samsum), BillSum (billsum), and the MeetingBank-transcript dataset by lytang.

## Intended Uses & Limitations

### Intended Uses

- Document summarization: The model is well-suited for summarizing lengthy documents or articles, making it valuable for content curation and information extraction tasks.
- Content generation: It can be used to generate concise summaries from input text, which is useful for creating short and informative snippets.

### Limitations

- Model size: The model's size may require significant computational resources for deployment, limiting its use in resource-constrained environments.
- Domain-specific content: While it performs well on general text summarization tasks, its performance may vary when applied to domain-specific content.

## Training and Evaluation Data

The model has been trained on a diverse set of datasets, including CNN/Daily Mail, XSum, SamSum, BillSum, and the MeetingBank-transcript dataset. These datasets provide a wide range of text summarization examples, enabling the model to generalize across various domains and styles of text.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

#### samsum

| Rouge1  | Rouge2  | RougeL  | RougeLsum |
|:-------:|:-------:|:-------:|:---------:|
| 0.0138  | 0.0002  | 0.0138  | 0.0138    |


#### CNN_Dailymail

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.8486        | 1.0   | 32300 | 1.6478          | 0.2373 | 0.1086 | 0.1972 | 0.1971    | 18.9674 |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3