Documentation Index
Fetch the complete documentation index at: https://developers.remeinium.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
Python example:from gradio_client import Client
client = Client("Remeinium/Embedding_Siyabasa")
result = client.predict(
sentence1="මම පාසලට යමි",
sentence2="ඔහු පාසලට යයි",
api_name="/sentence_similarity"
)
print(json.dumps(result, indent=4))
from gradio_client import Client
client = Client("Remeinium/Embedding_Siyabasa")
result = client.predict(
sentence1="මම පාසලට යමි",
sentence2="ඔහු පාසලට යයි",
api_name="/sentence_similarity"
)
print(result)
curl -X POST https://remeinium-embedding-siyabasa.hf.space/gradio_api/call/sentence_similarity -s -H "Content-Type: application/json" -d '{
"data": ["මම පාසලට යමි",
"ඔහු පාසලට යයි"
]}' \
| awk -F'"' '{ print $4}' \
| read EVENT_ID; curl -N https://remeinium-embedding-siyabasa.hf.space/gradio_api/call/sentence_similarity/$EVENT_ID
{
"sentence1": "මම පාසලට යමි",
"sentence2": "ඔහු පාසලට යයි",
"similarity": 0.734567,
"model": "UgannA_SiyabasaV2"
}
**\
Accepts 2 parameters:**
-
sentence1 : str *Required
The input value that is provided in the “Sentence A” Textbox component.
-
sentence2 : str *Required
The input value that is provided in the “Sentence B” Textbox component.
Returns 1 element
str | float | bool | list | dict
The output value that appears in the “Sentence Similarity” Json component.
