small tweaks

README.md

@@ -72,7 +72,7 @@ print(decoded_output)
 # Model Details
 
 ## Model Description
-The `t5-small-spoken-typo` model is specifically designed to tackle the challenges of text correction within user-generated content, particularly in short, conversation-like sentences. It corrects for missing spaces, removes unnecessary punctuation, introduces and then corrects typos, and normalizes text by replacing informal contractions and abbreviations with their full forms.
+The `t5-small-spoken-typo` model is specifically designed to tackle the challenges of text correction within user-generated content, particularly in short, conversation-like sentences. It corrects for missing spaces, removes unnecessary punctuation, corrects typos, and normalizes text by replacing informal contractions and abbreviations with their full forms.
 It has been training on
 - [BNC 2014 Spoken](http://cass.lancs.ac.uk/cass-projects/spoken-bnc2014/)
 - [Daily Dialog](https://huggingface.co/datasets/daily_dialog)
@@ -90,7 +90,7 @@ Then injecting  typos from a range of places
   - Find these resources [here](https://www.dcs.bbk.ac.uk/~ROGER/corpora.html).
 - **TOEFL Spell** A dataset of Spelling Annotations for English language learner essays written for TOEFL exams.
   - Find this [here](https://github.com/EducationalTestingService/TOEFL-Spell/tree/master) 
-- **Homonyms** We replace words in BNC and Dialy Dialog occasionally with homonyms from this list https://github.com/pimentel/homophones/
+- **Homonyms** We replace words in BNC and Dialy Dialog occasionally with homonyms from this list https://github.com/pimentel/homophones
 - **Our own typo augment function** This would make likely errors found in a English Qwerty layout as well as subsitutions, deletions etc
 
 And then compressing versions of the sentences (i.e. removing spaces)- both correct and typod we add to our dataset. (This is to solve a problem where some people write without spaces)
@@ -136,6 +136,14 @@ Users are encouraged to critically assess the model's output, especially when us
 
 # Training Details
 
+## System
+
+- System configuration: Linux-6.2.0-37-generic-x86_64-with-glibc2.35
+- Runtime: Python 3.10.12
+- Hardware: NVIDIA A10 GPU with 24GB GDDR6 dedicated memory
+- CPU Cores: 30 logical cores @ 2.59GHz
+- Disk Space: Approximately 1.3TB
+
 ## Training Data
 The model was trained on a curated subset of the DailyDialog and BNC corpora (2014 spoken), focusing on sentences 2-5 words in length, with manual introduction of typos and removal of spaces for robustness in text correction tasks.You can see the code to pre-process this [here](https://github.com/willwade/dailyDialogCorrections/tree/main)
 
@@ -264,9 +272,6 @@ We hope to build on this by further fine-tuning in time on real corpous of indvi
 ## Model Architecture and Objective
 The model follows the T5 architecture, fine-tuned for the specific task of text correction with a focus on typo correction and space insertion.
 
-## Compute Infrastructure
-- **Hardware**: T4 GPU (Google Colab)
-- **Software**: PyTorch 1.8.1 with Transformers 4.8.2
 
 # Citation