### Fine tuned MT5 base model with Sinhala Wikipedia Dataset (Experimentally continues training)

This model is fine tuned with articles from Sinhala Wikipedia for article generation. Used around 10,000 articles for training and fine tuned arround 100 times.

### How to use

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

You can use this model with a pipeline for text generation.

First you might need to install required libraries and import them.
```py
!pip uninstall transformers -y
!pip install transformers

pip install tokenizers sentencepiece
```

Then we might need to restart the runtime either manually or use the below code to end it. 
```py
import os
os.kill(os.getpid(), 9)
```

Then we just have to import the tokenizer and run the pipeline:

```py
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('google/mt5-base')

from transformers import pipeline
generator = pipeline(model='Suchinthana/MT5-Sinhala-Wikigen-Experimental', tokenizer=tokenizer)
generator("writeWiki: මානව ආහාර", do_sample=True, max_length=180)
```
{"language": ["si"], "license": "apache-2.0", "datasets": ["wikipedia"], "widget": [{"text": "writeWiki: \u0db8\u0dcf\u0db1\u0dc0 \u0d86\u0dc4\u0dcf\u0dbb"}, {"text": "writeWiki: \u0d9c\u0ddd\u0dbd\u0dd3\u0dba\u0d9a\u0dbb\u0dab\u0dba"}, {"text": "writeWiki: \u0da2\u0d82\u0d9c\u0db8 \u0daf\u0dd4\u0dbb\u0d9a\u0dae\u0db1\u0dba"}, {"text": "writeWiki: \u0d87\u0dc3\u0dca\u0d9a\u0dd2\u0db8\u0ddd\u0dc0\u0dbb\u0dd4"}, {"text": "writeWiki: \u0d85\u0db1\u0dd4\u0dbb\u0dcf\u0db0\u0db4\u0dd4\u0dbb\u0dba"}]}
transformers pytorch safetensors mt5 text2text-generation si dataset:wikipedia license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #si #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

### Fine tuned MT5 base model with Sinhala Wikipedia Dataset (Experimentally continues training)

This model is fine tuned with articles from Sinhala Wikipedia for article generation. Used around 10,000 articles for training and fine tuned arround 100 times.

### How to use

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

You can use this model with a pipeline for text generation.

First you might need to install required libraries and import them.


Then we might need to restart the runtime either manually or use the below code to end it. 


Then we just have to import the tokenizer and run the pipeline:


### Fine tuned MT5 base model with Sinhala Wikipedia Dataset (Experimentally continues training)

This model is fine tuned with articles from Sinhala Wikipedia for article generation. Used around 10,000 articles for training and fine tuned arround 100 times. ### How to use

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

You can use this model with a pipeline for text generation.

First you might need to install required libraries and import them.


Then we might need to restart the runtime either manually or use the below code to end it. 


Then we just have to import the tokenizer and run the pipeline:
TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #si #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 ### Fine tuned MT5 base model with Sinhala Wikipedia Dataset (Experimentally continues training)

This model is fine tuned with articles from Sinhala Wikipedia for article generation. Used around 10,000 articles for training and fine tuned arround 100 times. ### How to use

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

You can use this model with a pipeline for text generation.

First you might need to install required libraries and import them.


Then we might need to restart the runtime either manually or use the below code to end it. 


Then we just have to import the tokenizer and run the pipeline:
TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #si #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
### Fine tuned MT5 base model with Sinhala Wikipedia Dataset (Experimentally continues training)

This model is fine tuned with articles from Sinhala Wikipedia for article generation. Used around 10,000 articles for training and fine tuned arround 100 times.### How to use

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

You can use this model with a pipeline for text generation.

First you might need to install required libraries and import them.


Then we might need to restart the runtime either manually or use the below code to end it. 


Then we just have to import the tokenizer and run the pipeline: