granite-embedding-107m-multilingual-chat-difficulty – AI Model by agentlans | AlphaNeural AI

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granite-embedding-107m-multilingual-chat-difficulty

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agentlans/chat-annotated

ibm-granite/granite-embedding-107m-multilingual

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granite-embedding-107m-multilingual-chat-difficulty

A fine-tuned model that estimates the difficulty of multilingual, multi-turn human–AI conversations based on reasoning complexity.

Input: A condensed conversation in the format <|user|>prompt<|assistant|>reply...
Output: A normalized difficulty score (lower scores indicate easier conversations)

Based on ibm-granite/granite-embedding-107m-multilingual.

Evaluation results:

Loss: 0.5663
MSE: 0.5663
Tokens processed: 51,173,120

Model description

This model maps multi-turn chat logs to a continuous difficulty representation, enabling comparison across languages and reasoning styles.

Use cases include:

Categorizing multilingual chat transcripts by reasoning depth.
Supporting dataset curation or curriculum design.
Serving as a difficulty scoring component in evaluation pipelines.

Intended uses and limitations

Use cases

Estimating reasoning difficulty in multilingual conversations.
Comparing dialogue complexity across datasets.
Benchmarking conversational reasoning.

Limitations

Not suitable for assessing factual accuracy, coherence, or sentiment.
May not generalize well to highly domain-specific data.
Produces relative difficulty scores, not absolute intelligence measures.

Training procedure

Hyperparameters

Parameter	Value
learning_rate	5e-5
train_batch_size	8
eval_batch_size	8
seed	42
optimizer	AdamW (fused), betas=(0.9, 0.999), epsilon=1e-8
lr_scheduler_type	linear
num_epochs	5.0

Results

Metric	Value
Training loss	0.5663
MSE	0.5663
Tokens processed	51,173,120

Framework versions

Transformers: 5.0.0.dev0
PyTorch: 2.9.1+cu128
Datasets: 4.4.1
Tokenizers: 0.22.1

See also

agentlans/bge-small-en-v1.5-prompt-difficulty for single-turn English conversations and prompts