Update README.md

README.md

@@ -1,78 +1,47 @@
 ---
-tags:
-- generated_from_trainer
-base_model: jq/nllb-1.3B-many-to-many-step-2k
-datasets:
-- generator
+base_model: facebook/nllb-200-1.3B
 model-index:
-- name: nllb-1.3B-many-to-many-pronouncorrection-charaug
+- name: translate-nllb-1.3b-salt
   results: []
+datasets:
+- Sunbird/salt
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
-
-# nllb-1.3B-many-to-many-pronouncorrection-charaug
-
-This model is a fine-tuned version of [jq/nllb-1.3B-many-to-many-step-2k](https://huggingface.co/jq/nllb-1.3B-many-to-many-step-2k) on the generator dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.2075
-- Bleu Ach Eng: 28.371
-- Bleu Lgg Eng: 30.45
-- Bleu Lug Eng: 41.978
-- Bleu Nyn Eng: 32.296
-- Bleu Teo Eng: 30.422
-- Bleu Eng Ach: 20.972
-- Bleu Eng Lgg: 22.362
-- Bleu Eng Lug: 30.359
-- Bleu Eng Nyn: 15.305
-- Bleu Eng Teo: 21.391
-- Bleu Mean: 27.391
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.0003
-- train_batch_size: 25
-- eval_batch_size: 25
-- seed: 42
-- gradient_accumulation_steps: 120
-- total_train_batch_size: 3000
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- training_steps: 1500
-- mixed_precision_training: Native AMP
-
-### Training results
-
-| Training Loss | Epoch  | Step | Validation Loss | Bleu Ach Eng | Bleu Lgg Eng | Bleu Lug Eng | Bleu Nyn Eng | Bleu Teo Eng | Bleu Eng Ach | Bleu Eng Lgg | Bleu Eng Lug | Bleu Eng Nyn | Bleu Eng Teo | Bleu Mean |
-|:-------------:|:------:|:----:|:---------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:---------:|
-| No log        | 0.0667 | 100  | 1.1541          | 29.033       | 31.47        | 41.596       | 34.169       | 32.442       | 19.677       | 19.657       | 27.889       | 14.554       | 19.143       | 26.963    |
-| No log        | 1.0301 | 200  | 1.1570          | 27.473       | 31.853       | 41.934       | 32.575       | 31.606       | 20.25        | 20.634       | 28.592       | 13.672       | 19.997       | 26.859    |
-| No log        | 1.0968 | 300  | 1.1288          | 29.086       | 33.257       | 43.387       | 33.678       | 33.579       | 20.377       | 20.91        | 28.906       | 14.992       | 21.013       | 27.919    |
-| No log        | 2.0603 | 400  | 1.1620          | 28.122       | 31.46        | 42.491       | 33.304       | 32.331       | 20.282       | 21.604       | 29.577       | 14.961       | 20.94        | 27.507    |
-| 0.7273        | 3.0237 | 500  | 1.1661          | 28.311       | 32.122       | 42.825       | 32.333       | 32.415       | 19.799       | 22.287       | 29.558       | 15.708       | 21.948       | 27.731    |
-| 0.7273        | 3.0904 | 600  | 1.1652          | 28.593       | 30.62        | 41.964       | 33.383       | 32.08        | 21.142       | 21.8         | 30.215       | 14.717       | 21.744       | 27.626    |
-| 0.7273        | 4.0538 | 700  | 1.2075          | 28.371       | 30.45        | 41.978       | 32.296       | 30.422       | 20.972       | 22.362       | 30.359       | 15.305       | 21.391       | 27.391    |
-
-
-### Framework versions
-
-- Transformers 4.40.1
-- Pytorch 2.2.0
-- Datasets 2.19.0
-- Tokenizers 0.19.1
+# Model details
+
+This machine translation model can convert single sentences from and to any of the following languages:
+
+| ISO 693-3 | Language name |
+| --- | --- |
+| eng | English |
+| ach | Acholi |
+| lgg | Lugbara |
+| lug | Luganda |
+| nyn | Runyankole |
+| teo | Ateso |
+
+It was trained on the [SALT](http://huggingface.co/datasets/Sunbird/salt) dataset and a variety of
+additional external data resources, including back-translated news articles, FLORES-200, MT560 and LAFAND-MT.
+The base model was [facebok/nllb-200-1.3B](https://huggingface.co/facebook/nllb-200-1.3B),
+with tokens adapted to add support for languages not originally included.
+
+# Usage 
+
+# Evaluation metrics
+
+Results on salt-dev:
+
+| Source language | Target language | BLEU |
+| --- | --- | --- |
+| ach | eng | 28.371 |
+| lgg | eng | 30.45 |
+| lug | eng | 41.978 |
+| nyn | eng |32.296 |
+| teo | eng | 30.422 |
+| eng | ach | 20.972 |
+| eng | lgg | 22.362 |
+| eng | lug | 30.359 |
+| eng | nyn | 15.305 |
+| eng | teo | 21.391 |