Embedding Models Embedding models map high-dimensional data into a lower-dimensional space, which makes the data easier to process and analyze. Text Models Here is a leaderboard, https://huggingface.co/spaces/mteb/leaderboard The leaderboard above gives each model’s score, parameter count, memory usage, vector dimension, maximum tokens, and other information. Below are some commonly used open-source models:

This post uses Fluid 1.0. In later versions the configuration file paths changed, so adjust according to your actual situation. 1. Building the Image 1.1 fluid_config_init.py 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 #!

1. Jindo Mounting S3 Set the environment variables 1 2 3 4 export ENDPOINT=obs.ap-southeast-3.myhuaweicloud.com export BUCKET= export AK= export SK= Create the credentials 1 2 3 4 5 6 7 8 9 10 kubectl apply -f - <<EOF apiVersion: v1 kind: Secret metadata: name: mys3secret type: Opaque stringData: fs.s3.accessKeyId: ${AK} fs.

1. What TensorBoard Is TensorBoard is mainly used to monitor how a model’s various metrics change — accuracy, loss, the weight distributions of each layer, and so on. TensorBoard is a visualization tool from TensorFlow that supports visualizing scalar, text, image, audio, video, and embedding data, among other types. But PyTorch can use TensorBoard too.

1. Create the Training Script Create a training script mnist.py with the following contents: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36