
#### Pre-trained BART Model fine-tune on WikiLingua dataset
The repository for the fine-tuned BART model (by sshleifer) using the **wiki_lingua** dataset (English)

**Purpose:** Examine the performance of a fine-tuned model research purposes

**Observation:**
- Pre-trained model was trained on the XSum dataset, which summarize a not-too-long documents into one-liner summary
- Fine-tuning this model using WikiLingua is appropriate since the summaries for that dataset are also short
- In the end, however, the model cannot capture much clearer key points, but instead it mostly extracts the opening sentence
- Some data pre-processing and models' hyperparameter are also need to be tuned more properly.
{"language": ["en"], "license": "mit", "tags": ["summarization"], "datasets": ["wiki_lingua"], "metrics": ["rouge"]}
transformers pytorch bart text2text-generation summarization en dataset:wiki_lingua license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-wiki_lingua #license-mit #autotrain_compatible #endpoints_compatible #region-us 


#### Pre-trained BART Model fine-tune on WikiLingua dataset
The repository for the fine-tuned BART model (by sshleifer) using the wiki_lingua dataset (English)

Purpose: Examine the performance of a fine-tuned model research purposes

Observation:
- Pre-trained model was trained on the XSum dataset, which summarize a not-too-long documents into one-liner summary
- Fine-tuning this model using WikiLingua is appropriate since the summaries for that dataset are also short
- In the end, however, the model cannot capture much clearer key points, but instead it mostly extracts the opening sentence
- Some data pre-processing and models' hyperparameter are also need to be tuned more properly.
#### Pre-trained BART Model fine-tune on WikiLingua dataset
The repository for the fine-tuned BART model (by sshleifer) using the wiki_lingua dataset (English)

Purpose: Examine the performance of a fine-tuned model research purposes

Observation:
- Pre-trained model was trained on the XSum dataset, which summarize a not-too-long documents into one-liner summary
- Fine-tuning this model using WikiLingua is appropriate since the summaries for that dataset are also short
- In the end, however, the model cannot capture much clearer key points, but instead it mostly extracts the opening sentence
- Some data pre-processing and models' hyperparameter are also need to be tuned more properly.
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-wiki_lingua #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 #### Pre-trained BART Model fine-tune on WikiLingua dataset
The repository for the fine-tuned BART model (by sshleifer) using the wiki_lingua dataset (English)

Purpose: Examine the performance of a fine-tuned model research purposes

Observation:
- Pre-trained model was trained on the XSum dataset, which summarize a not-too-long documents into one-liner summary
- Fine-tuning this model using WikiLingua is appropriate since the summaries for that dataset are also short
- In the end, however, the model cannot capture much clearer key points, but instead it mostly extracts the opening sentence
- Some data pre-processing and models' hyperparameter are also need to be tuned more properly.
passage: TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-wiki_lingua #license-mit #autotrain_compatible #endpoints_compatible #region-us 
#### Pre-trained BART Model fine-tune on WikiLingua dataset
The repository for the fine-tuned BART model (by sshleifer) using the wiki_lingua dataset (English)

Purpose: Examine the performance of a fine-tuned model research purposes

Observation:
- Pre-trained model was trained on the XSum dataset, which summarize a not-too-long documents into one-liner summary
- Fine-tuning this model using WikiLingua is appropriate since the summaries for that dataset are also short
- In the end, however, the model cannot capture much clearer key points, but instead it mostly extracts the opening sentence
- Some data pre-processing and models' hyperparameter are also need to be tuned more properly.