peft summarization en dataset:scientific_papers model-index region:us
---
language:
- en
tags:
- summarization
library_name: peft
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: flan-t5-base-finetuned-arxiv
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: scientific_papers
      type: scientific_papers
      args: arxiv
    metrics:
    - name: Rouge1
      type: rouge
      value: 12.032000
    - name: Rouge2
      type: rouge
      value: 4.384100
    - name: Rougel
      type: rouge
      value: 9.842600
    - name: Rougelsum
      type: rouge
      value: 11.139600

---

## flan-t5-base-finetuned-arxiv

This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the scientific_papers dataset.
It achieves the following results on the evaluation set:

- Loss: 2.485082
- Rouge1: 12.032000
- Rouge2: 4.38100
- Rougel: 9.842600
- Rougelsum: 11.139600
- Gen Len: 19.000000


## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16

The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-4
- weight_decay: 0.01
- train_batch_size: 32
- optimizer: paged_adamw_8bit (8-bit adam optimization)
- num_epochs: 4.47
- fp16: False

### Framework versions

- PEFT 0.5.0

- PEFT 0.5.0
- Transformers 4.35.0
- Pytorch 1.10.1+cu111
- Datasets 2.14.7
- Tokenizers 0.14.1
- bitsandbytes 0.41.2.post2
- accelerate 0.24.0
- evaluate 0.4.1
- rouge-score 0.1.2