Instructions to use KostiuchenkoArtem/bart_large_small_modifications with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KostiuchenkoArtem/bart_large_small_modifications with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KostiuchenkoArtem/bart_large_small_modifications") model = AutoModelForSeq2SeqLM.from_pretrained("KostiuchenkoArtem/bart_large_small_modifications", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
d45b9e6
1
Parent(s): 28109c3
Update README.md
Browse files
README.md
CHANGED
|
@@ -20,15 +20,15 @@ It achieves the following results on the evaluation set:
|
|
| 20 |
|
| 21 |
## Model description
|
| 22 |
|
| 23 |
-
|
| 24 |
|
| 25 |
## Intended uses & limitations
|
| 26 |
|
| 27 |
-
|
| 28 |
|
| 29 |
## Training and evaluation data
|
| 30 |
|
| 31 |
-
|
| 32 |
|
| 33 |
## Training procedure
|
| 34 |
|
|
|
|
| 20 |
|
| 21 |
## Model description
|
| 22 |
|
| 23 |
+
|
| 24 |
|
| 25 |
## Intended uses & limitations
|
| 26 |
|
| 27 |
+
|
| 28 |
|
| 29 |
## Training and evaluation data
|
| 30 |
|
| 31 |
+
The dataset was split into 30% test data and 70% training data
|
| 32 |
|
| 33 |
## Training procedure
|
| 34 |
|