Update README.md
Browse files
README.md
CHANGED
|
@@ -1,4 +1,6 @@
|
|
| 1 |
---
|
|
|
|
|
|
|
| 2 |
pipeline_tag: sentence-similarity
|
| 3 |
tags:
|
| 4 |
- sentence-transformers
|
|
@@ -7,7 +9,7 @@ tags:
|
|
| 7 |
- transformers
|
| 8 |
---
|
| 9 |
|
| 10 |
-
#
|
| 11 |
|
| 12 |
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
|
| 13 |
|
|
@@ -27,7 +29,7 @@ Then you can use the model like this:
|
|
| 27 |
from sentence_transformers import SentenceTransformer
|
| 28 |
sentences = ["This is an example sentence", "Each sentence is converted"]
|
| 29 |
|
| 30 |
-
model = SentenceTransformer('
|
| 31 |
embeddings = model.encode(sentences)
|
| 32 |
print(embeddings)
|
| 33 |
```
|
|
@@ -50,8 +52,8 @@ def cls_pooling(model_output, attention_mask):
|
|
| 50 |
sentences = ['This is an example sentence', 'Each sentence is converted']
|
| 51 |
|
| 52 |
# Load model from HuggingFace Hub
|
| 53 |
-
tokenizer = AutoTokenizer.from_pretrained('
|
| 54 |
-
model = AutoModel.from_pretrained('
|
| 55 |
|
| 56 |
# Tokenize sentences
|
| 57 |
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
|
|
@@ -93,7 +95,7 @@ The model was trained with the parameters:
|
|
| 93 |
Parameters of the fit()-Method:
|
| 94 |
```
|
| 95 |
{
|
| 96 |
-
"epochs":
|
| 97 |
"evaluation_steps": 0,
|
| 98 |
"evaluator": "NoneType",
|
| 99 |
"max_grad_norm": 1,
|
|
|
|
| 1 |
---
|
| 2 |
+
language:
|
| 3 |
+
- dv
|
| 4 |
pipeline_tag: sentence-similarity
|
| 5 |
tags:
|
| 6 |
- sentence-transformers
|
|
|
|
| 9 |
- transformers
|
| 10 |
---
|
| 11 |
|
| 12 |
+
# Dhivehi TSDAE News BERT
|
| 13 |
|
| 14 |
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
|
| 15 |
|
|
|
|
| 29 |
from sentence_transformers import SentenceTransformer
|
| 30 |
sentences = ["This is an example sentence", "Each sentence is converted"]
|
| 31 |
|
| 32 |
+
model = SentenceTransformer('ashraq/tsdae-bert-base-dv-news-title')
|
| 33 |
embeddings = model.encode(sentences)
|
| 34 |
print(embeddings)
|
| 35 |
```
|
|
|
|
| 52 |
sentences = ['This is an example sentence', 'Each sentence is converted']
|
| 53 |
|
| 54 |
# Load model from HuggingFace Hub
|
| 55 |
+
tokenizer = AutoTokenizer.from_pretrained('ashraq/tsdae-bert-base-dv-news-title')
|
| 56 |
+
model = AutoModel.from_pretrained('ashraq/tsdae-bert-base-dv-news-title')
|
| 57 |
|
| 58 |
# Tokenize sentences
|
| 59 |
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
|
|
|
|
| 95 |
Parameters of the fit()-Method:
|
| 96 |
```
|
| 97 |
{
|
| 98 |
+
"epochs": 3,
|
| 99 |
"evaluation_steps": 0,
|
| 100 |
"evaluator": "NoneType",
|
| 101 |
"max_grad_norm": 1,
|