InstructTweetSummarizer
This model is a fine-tuned version of 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:
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
- Downloads last month
- 20
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for Sidharthkr/InstructTweetSummarizer
Base model
facebook/bart-large-cnn