---
license: other
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: InstructTweetSummarizer
  results: []
language:
- en
pipeline_tag: summarization
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# InstructTweetSummarizer

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co./facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3548
- Rouge1: 47.5134
- Rouge2: 24.7121
- Rougel: 35.7366
- Rougelsum: 35.6499
- Gen Len: 111.96

## 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: 2e-05
- train_batch_size: 6
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log        | 1.0   | 417  | 0.3468          | 44.9326 | 22.3736 | 33.008  | 32.9247   | 116.43  |
| 0.5244        | 2.0   | 834  | 0.3440          | 46.9139 | 24.683  | 35.3699 | 35.333    | 119.65  |
| 0.2061        | 3.0   | 1251 | 0.3548          | 47.5134 | 24.7121 | 35.7366 | 35.6499   | 111.96  |

### How to use

Here is how to use this model with the [pipeline API](https://huggingface.co./transformers/main_classes/pipelines.html):

```python
from transformers import pipeline
summarizer = pipeline("summarization", model="Sidharthkr/InstructTweetSummarizer")
def summarymaker(instruction = "", tweets = ""):
    ARTICLE = f"""[INST] {instruction} [/INST] \\n [TWEETS] {tweets} [/TWEETS]"""
    out = summarizer(ARTICLE, max_length=130, min_length=10, do_sample=False)
    out = out[0]['summary_text'].split("[SUMMARY]")[-1].split("[/")[0].split("[via")[0].strip()
    return out

summarymaker(instruction = "Summarize the tweets for Stellantis in 100 words",
             tweets = """Stellantis - arch critic of Chinese EVs coming to Europe - is in talks with CATL to build a European plant. \n\nIt has concluded that cutting the price of EVs by using Chinese LFP batteries is more important.\n\n@FT story: \nhttps://t.co/l7nGggRFxH. State-of-the-art North America Battery Technology Centre begins to take shape at Stellantis' Automotive Research and Development Centre (ARDC) in Windsor, Ontario.\n\nhttps://t.co/04RO7CL1O5. RT @UAW: 🧵After the historic Stand Up Strike, UAW members at Ford, General Motors and Stellantis have voted to ratify their new contracts,…. RT @atorsoli: Stellantis and CATL are set to supply lower-cost EV batteries together for Europe, signaling automaker's efforts to tighten t…. RT @atorsoli: Stellantis and CATL are set to supply lower-cost EV batteries together for Europe, signaling automaker's efforts to tighten""")
>>> 'Stellantis is in talks with CATL to build a European plant, with a focus on cutting the price of EVs by using Chinese LFP batteries. The company is also developing a state-of-the-art North America Battery Technology Centre in Windsor, Ontario, and has ratified its new contracts with the UAW.'
```

### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.7
- Tokenizers 0.14.1