biomed.sm.mv-te-84m

100
19
license:apache-2.0
by
ibm-research
Language Model
OTHER
New
100 downloads
Early-stage
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Quick Summary

AI model with specialized capabilities.

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by biomed.sm.mv-te-84m with quality assessment

Specialized For

general
multilingual

Training Datasets (1)

c4
🔵 6/10
general
multilingual
Key Strengths
  • •Scale and Accessibility: 750GB of publicly available, filtered text
  • •Systematic Filtering: Documented heuristics enable reproducibility
  • •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • •English-Only: Limits multilingual applications
  • •Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Step 1: Set up the project directorybash
export ROOT_DIR=~/biomed-multiview
mkdir -p $ROOT_DIR
Step 2: Create and activate a Conda environmentbash
conda create -y python=3.11 --prefix $ROOT_DIR/envs/biomed-multiview
Step 4: Install package dependenciestext
Install additional requirements:
text
#### Step 5: macOS-Specific instructions (Apple Silicon)
If you are using a Mac with Apple Silicon (M1/M2/M3) and the zsh shell, you may need to disable globbing for the installation command:
text
Install macOS-specific requirements optimized for Apple’s Metal Performance Shaders (MPS):
text
#### Step 6:  Installation verification (optional)
Verify that the installation was successful by running unit tests
Step 6: Installation verification (optional)text
### Get embedding example

You can generate embeddings for a given molecule using the pretrained model with the following code.

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