
### Fine tuned T5 base model with Simple English Wikipedia Dataset

This model is fine tuned with articles from Simple English Wikipedia for article generation. Used around 25,000 articles for training.

### How to use

We have to use **"writeWiki: "** part at the begining of each prompt.

You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:

```py
>>> from transformers import pipeline
>>> generator = pipeline('text2text-generation', model='Suchinthana/T5-Base-Wikigen')
>>> generator("writeWiki: Microcontroller", do_sample=True, max_length=250)
```
{"language": ["en"], "license": "mit", "datasets": ["wikipedia"], "widget": [{"text": "writeWiki: Jupiter"}, {"text": "writeWiki: Sri Lanka"}, {"text": "writeWiki: Language Model"}]}
transformers pytorch safetensors t5 text2text-generation en dataset:wikipedia license:mit autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


### Fine tuned T5 base model with Simple English Wikipedia Dataset

This model is fine tuned with articles from Simple English Wikipedia for article generation. Used around 25,000 articles for training.

### How to use

We have to use "writeWiki: " part at the begining of each prompt.

You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:


### Fine tuned T5 base model with Simple English Wikipedia Dataset

This model is fine tuned with articles from Simple English Wikipedia for article generation. Used around 25,000 articles for training. ### How to use

We have to use "writeWiki: " part at the begining of each prompt.

You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 ### Fine tuned T5 base model with Simple English Wikipedia Dataset

This model is fine tuned with articles from Simple English Wikipedia for article generation. Used around 25,000 articles for training. ### How to use

We have to use "writeWiki: " part at the begining of each prompt.

You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
### Fine tuned T5 base model with Simple English Wikipedia Dataset

This model is fine tuned with articles from Simple English Wikipedia for article generation. Used around 25,000 articles for training.### How to use

We have to use "writeWiki: " part at the begining of each prompt.

You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: