

# flan-t5-base-cnn-samsum-lora

This model is a fine-tuned version of [braindao/flan-t5-cnn](https://huggingface.co/braindao/flan-t5-cnn) on the [samsum](https://huggingface.co/datasets/samsum) dataset.

The base model [braindao/flan-t5-cnn](https://huggingface.co/braindao/flan-t5-cnn) is a fine-tuned verstion of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the cnn_dailymail 3.0.0 dataset.

## Model API Spaces

Please visit HF Spaces [sooolee/summarize-transcripts-gradio](https://huggingface.co/spaces/sooolee/summarize-transcripts-gradio) for Gradio API.  This API takes YouTube 'Video_ID' as the input. 


## Model description

* This model further finetuned [braindao/flan-t5-cnn](https://huggingface.co/braindao/flan-t5-cnn) on the more conversational samsum dataset.  
* Huggingface [PEFT Library](https://github.com/huggingface/peft) LoRA (r = 16) and bitsandbytes int-8 was used to speed up training and reduce the model size.
* Only 1.7M parameters were trained (0.71% of original flan-t5-base 250M parameters).
* The model checkpoint is just 7MB.

## Intended uses & limitations

Summarize transcripts such as YouTube transcripts. 

## Training and evaluation data
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

- train_loss: 1.47

### How to use
Note 'max_new_tokens=60' is used in the below example to control the length of the summary. FLAN-T5 model has max generation length = 200  and min generation length = 20 (default).

```python
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load peft config for pre-trained checkpoint etc.
peft_model_id = "sooolee/flan-t5-base-cnn-samsum-lora"
config = PeftConfig.from_pretrained(peft_model_id)

# load base LLM model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, device_map='auto') # load_in_8bit=True, 
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id, device_map='auto')

# Tokenize the text inputs
texts = "<e.g. Part of YouTube Transcript>"
inputs = tokenizer(texts, return_tensors="pt", padding=True, ) # truncation=True

# Make inferences
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
with torch.no_grad():    
    output = self.model.generate(input_ids=inputs["input_ids"].to(device), max_new_tokens=60, do_sample=True, top_p=0.9)
    summary = self.tokenizer.batch_decode(output.detach().cpu().numpy(), skip_special_tokens=True)

summary
```

### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.3

## Other
Please check out the BART-Large-CNN-Samsum model fine-tuned for the same purpose: [sooolee/bart-large-cnn-finetuned-samsum-lora](https://huggingface.co/sooolee/bart-large-cnn-finetuned-samsum-lora)
{"language": ["en"], "license": "apache-2.0", "library_name": "adapter-transformers", "tags": ["generated_from_trainer"], "datasets": ["samsum"], "metrics": ["rouge"], "pipeline_tag": "summarization", "inference": true, "base_model": "braindao/flan-t5-cnn", "model-index": [{"name": "flan-t5-base", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "samsum", "type": "samsum", "split": "validation"}, "metrics": [{"type": "rogue1", "value": "46.819522%"}, {"type": "rouge2", "value": "20.898074%"}, {"type": "rougeL", "value": "37.300937%"}, {"type": "rougeLsum", "value": "37.271341%"}]}]}]}
adapter-transformers generated_from_trainer summarization en dataset:samsum base_model:braindao/flan-t5-cnn license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#adapter-transformers #generated_from_trainer #summarization #en #dataset-samsum #base_model-braindao/flan-t5-cnn #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 



# flan-t5-base-cnn-samsum-lora

This model is a fine-tuned version of braindao/flan-t5-cnn on the samsum dataset.

The base model braindao/flan-t5-cnn is a fine-tuned verstion of google/flan-t5-base on the cnn_dailymail 3.0.0 dataset.

## Model API Spaces

Please visit HF Spaces sooolee/summarize-transcripts-gradio for Gradio API.  This API takes YouTube 'Video_ID' as the input. 


## Model description

* This model further finetuned braindao/flan-t5-cnn on the more conversational samsum dataset.  
* Huggingface PEFT Library LoRA (r = 16) and bitsandbytes int-8 was used to speed up training and reduce the model size.
* Only 1.7M parameters were trained (0.71% of original flan-t5-base 250M parameters).
* The model checkpoint is just 7MB.

## Intended uses & limitations

Summarize transcripts such as YouTube transcripts. 

## Training and evaluation data
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

- train_loss: 1.47

### How to use
Note 'max_new_tokens=60' is used in the below example to control the length of the summary. FLAN-T5 model has max generation length = 200  and min generation length = 20 (default).



### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.3

## Other
Please check out the BART-Large-CNN-Samsum model fine-tuned for the same purpose: sooolee/bart-large-cnn-finetuned-samsum-lora
# flan-t5-base-cnn-samsum-lora

This model is a fine-tuned version of braindao/flan-t5-cnn on the samsum dataset.

The base model braindao/flan-t5-cnn is a fine-tuned verstion of google/flan-t5-base on the cnn_dailymail 3.0.0 dataset. ## Model API Spaces

Please visit HF Spaces sooolee/summarize-transcripts-gradio for Gradio API.  This API takes YouTube 'Video_ID' as the input. ## Model description

* This model further finetuned braindao/flan-t5-cnn on the more conversational samsum dataset.  
* Huggingface PEFT Library LoRA (r = 16) and bitsandbytes int-8 was used to speed up training and reduce the model size.
* Only 1.7M parameters were trained (0.71% of original flan-t5-base 250M parameters).
* The model checkpoint is just 7MB. ## Intended uses & limitations

Summarize transcripts such as YouTube transcripts. ## Training and evaluation data ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Training results

- train_loss: 1.47 ### How to use
Note 'max_new_tokens=60' is used in the below example to control the length of the summary. FLAN-T5 model has max generation length = 200  and min generation length = 20 (default). ### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.3 ## Other
Please check out the BART-Large-CNN-Samsum model fine-tuned for the same purpose: sooolee/bart-large-cnn-finetuned-samsum-lora
TAGS
#adapter-transformers #generated_from_trainer #summarization #en #dataset-samsum #base_model-braindao/flan-t5-cnn #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 # flan-t5-base-cnn-samsum-lora

This model is a fine-tuned version of braindao/flan-t5-cnn on the samsum dataset.

The base model braindao/flan-t5-cnn is a fine-tuned verstion of google/flan-t5-base on the cnn_dailymail 3.0.0 dataset. ## Model API Spaces

Please visit HF Spaces sooolee/summarize-transcripts-gradio for Gradio API.  This API takes YouTube 'Video_ID' as the input. ## Model description

* This model further finetuned braindao/flan-t5-cnn on the more conversational samsum dataset.  
* Huggingface PEFT Library LoRA (r = 16) and bitsandbytes int-8 was used to speed up training and reduce the model size.
* Only 1.7M parameters were trained (0.71% of original flan-t5-base 250M parameters).
* The model checkpoint is just 7MB. ## Intended uses & limitations

Summarize transcripts such as YouTube transcripts. ## Training and evaluation data ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Training results

- train_loss: 1.47 ### How to use
Note 'max_new_tokens=60' is used in the below example to control the length of the summary. FLAN-T5 model has max generation length = 200  and min generation length = 20 (default). ### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.3 ## Other
Please check out the BART-Large-CNN-Samsum model fine-tuned for the same purpose: sooolee/bart-large-cnn-finetuned-samsum-lora
TAGS
#adapter-transformers #generated_from_trainer #summarization #en #dataset-samsum #base_model-braindao/flan-t5-cnn #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
# flan-t5-base-cnn-samsum-lora

This model is a fine-tuned version of braindao/flan-t5-cnn on the samsum dataset.

The base model braindao/flan-t5-cnn is a fine-tuned verstion of google/flan-t5-base on the cnn_dailymail 3.0.0 dataset.## Model API Spaces

Please visit HF Spaces sooolee/summarize-transcripts-gradio for Gradio API.  This API takes YouTube 'Video_ID' as the input.## Model description

* This model further finetuned braindao/flan-t5-cnn on the more conversational samsum dataset.  
* Huggingface PEFT Library LoRA (r = 16) and bitsandbytes int-8 was used to speed up training and reduce the model size.
* Only 1.7M parameters were trained (0.71% of original flan-t5-base 250M parameters).
* The model checkpoint is just 7MB.## Intended uses & limitations

Summarize transcripts such as YouTube transcripts.## Training and evaluation data### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5### Training results

- train_loss: 1.47### How to use
Note 'max_new_tokens=60' is used in the below example to control the length of the summary. FLAN-T5 model has max generation length = 200  and min generation length = 20 (default).### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.3## Other
Please check out the BART-Large-CNN-Samsum model fine-tuned for the same purpose: sooolee/bart-large-cnn-finetuned-samsum-lora