update readme

README.md

@@ -20,7 +20,7 @@ language:
 
 ## Model desription
 
-AfriTeVa base is a sequence to sequence model pretrained on 10 African languages
+AfriTeVa large is a multilingual sequence to sequence model pretrained on 10 African languages
 
 ## Languages
 
@@ -30,7 +30,7 @@ Afaan Oromoo(orm), Amharic(amh), Gahuza(gah), Hausa(hau), Igbo(igb), Nigerian Pi
 
 ### The model
 
-- 229M parameters encoder-decoder architecture (T5-like)
+- 745M parameters encoder-decoder architecture (T5-like)
 - 12 layers, 12 attention heads and 512 token sequence length
 
 ### The dataset
@@ -39,6 +39,27 @@ Afaan Oromoo(orm), Amharic(amh), Gahuza(gah), Hausa(hau), Igbo(igb), Nigerian Pi
 - 143 Million Tokens (1GB of text data)
 - Tokenizer Vocabulary Size: 70,000 tokens
 
+
+## Intended uses & limitations 
+
+`afriteva_base` is pre-trained model and primarily aimed at being fine-tuned on multilingual sequence-to-sequence tasks. 
+
+```python
+>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+>>> tokenizer = AutoTokenizer.from_pretrained("castorini/afriteva_base")
+>>> model = AutoModelForSeq2SeqLM.from_pretrained("castorini/afriteva_base")
+
+>>> src_text = "Ó hùn ọ́ láti di ara wa bí?"
+>>> tgt_text =  "Would you like to be?"
+
+>>> model_inputs = tokenizer(src_text, return_tensors="pt")
+>>> with tokenizer.as_target_tokenizer():
+        labels = tokenizer(tgt_text, return_tensors="pt").input_ids
+
+>>> model(**model_inputs, labels=labels) # forward pass
+```
+
 ## Training Procedure
 
 For information on training procedures, please refer to the AfriTeVa [paper](#) or [repository](https://github.com/castorini/afriteva)