README.md and .gitattributes after training

README.md

@@ -21,61 +21,19 @@ It achieves the following results on the evaluation set:
 - Loss: 0.1385
 - Accuracy: 0.9962
 
-MatsRooth/down_on is the part of [superb ks](https://huggingface.co/datasets/superb)
-with the labels *down* and *on*.
-Superb ks is in turn derived from (Speech Commands dataset v1.0)[https://www.tensorflow.org/datasets/catalog/speech_commands].
-Train/validation/test splits are as in superb ks.
+## Model description
 
-## Intended uses 
+More information needed
 
-MatsRooth/down_on and this model exercise methodology for creating an audio classification dataset from
-local directory structures and audio files, and check whether fine tuning wav2vec2 classification with two labels works
-well.
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
 
 ## Training procedure
-Training used 'sbatch' on a cluster and the program [run_audio_classification.py](https://github.com/huggingface/transformers).
-'down_on.sub' is below, start it with 'sbatch down_on.sub'.
-
-'''
-#!/bin/bash
-#SBATCH -J down_on                         # Job name
-#SBATCH -o down_on_%j.out                  # Name of stdout output log file (%j expands to jobID)
-#SBATCH -e down_on_%j.err                  # Name of stderr output log file (%j expands to jobID)
-#SBATCH -N 1                                 # Total number of nodes requested
-#SBATCH -n 1                                 # Total number of cores requested
-#SBATCH --mem=5000                          # Total amount of (real) memory requested (per node)
-#SBATCH -t 10:00:00                          # Time limit (hh:mm:ss)
-#SBATCH --partition=gpu              # Request partition for resource allocation
-#SBATCH --gres=gpu:1                         # Specify a list of generic consumable resources (per node)
-
-cd ~/ac_h
-/home/mr249/env/hugh/bin/python run_audio_classification.py \
-    --model_name_or_path facebook/wav2vec2-base \
-    --dataset_name MatsRooth/down_on \
-    --output_dir wav2vec2-base_down_on \
-    --overwrite_output_dir \
-    --remove_unused_columns False \
-    --do_train \
-    --do_eval \
-    --fp16 \
-    --learning_rate 3e-5 \
-    --max_length_seconds 1 \
-    --attention_mask False \
-    --warmup_ratio 0.1 \
-    --num_train_epochs 5 \
-    --per_device_train_batch_size 32 \
-    --gradient_accumulation_steps 4 \
-    --per_device_eval_batch_size 32 \
-    --dataloader_num_workers 1 \
-    --logging_strategy steps \
-    --logging_steps 10 \
-    --evaluation_strategy epoch \
-    --save_strategy epoch \
-    --load_best_model_at_end True \
-    --metric_for_best_model accuracy \
-    --save_total_limit 3 \
-    --seed 0
-'''
 
 ### Training hyperparameters
 
@@ -96,10 +54,10 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
 | 0.6089        | 1.0   | 29   | 0.1385          | 0.9962   |
-| 0.1297        | 2.0   | 58   | 0.0513          | 0.9962   |
-| 0.0835        | 3.0   | 87   | 0.0389          | 0.9885   |
-| 0.058         | 4.0   | 116  | 0.0302          | 0.9923   |
-| 0.0481        | 5.0   | 145  | 0.0245          | 0.9942   |
+| 0.1289        | 2.0   | 58   | 0.0510          | 0.9962   |
+| 0.0835        | 3.0   | 87   | 0.0433          | 0.9885   |
+| 0.0605        | 4.0   | 116  | 0.0330          | 0.9923   |
+| 0.0479        | 5.0   | 145  | 0.0273          | 0.9904   |
 
 
 ### Framework versions