Instructions to use castorini/afriteva_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use castorini/afriteva_base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("castorini/afriteva_base") model = AutoModelForSeq2SeqLM.from_pretrained("castorini/afriteva_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
4f358f7
1
Parent(s): c4136b8
Update README.md
Browse files
README.md
CHANGED
|
@@ -30,7 +30,7 @@ Afaan Oromoo(orm), Amharic(amh), Gahuza(gah), Hausa(hau), Igbo(igb), Nigerian Pi
|
|
| 30 |
|
| 31 |
### The model
|
| 32 |
|
| 33 |
-
-
|
| 34 |
- 12 layers, 12 attention heads and 512 token sequence length
|
| 35 |
|
| 36 |
### The dataset
|
|
|
|
| 30 |
|
| 31 |
### The model
|
| 32 |
|
| 33 |
+
- 229M parameters encoder-decoder architecture (T5-like)
|
| 34 |
- 12 layers, 12 attention heads and 512 token sequence length
|
| 35 |
|
| 36 |
### The dataset
|