Customer Service Empathy Model

Model Description

Empathy scoring model for customer service interactions. Predicts:

Empathy Score (0-5)
Active Listening Score (0-5)

This is part of a dual-model approach:

Intent model (available at raghavdw/cci-capstone-asapp)
This model (Empathy/Listening Scores)

Latest Training Info

{
  "training_args": {
    "output_dir": "models/test_run_20250110_162536/empathy_model",
    "overwrite_output_dir": false,
    "do_train": false,
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    "eval_strategy": "epoch",
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    "per_device_train_batch_size": 32,
    "per_device_eval_batch_size": 64,
    "per_gpu_train_batch_size": null,
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    "gradient_accumulation_steps": 1,
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    "learning_rate": 1e-05,
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    "adam_beta2": 0.999,
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    "num_train_epochs": 4,
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    "log_level": "passive",
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    "logging_dir": "models/test_run_20250110_162536/logs",
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    "logging_steps": 10,
    "logging_nan_inf_filter": true,
    "save_strategy": "epoch",
    "save_steps": 500,
    "save_total_limit": 2,
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    "save_only_model": false,
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    "use_mps_device": false,
    "seed": 42,
    "data_seed": null,
    "jit_mode_eval": false,
    "use_ipex": false,
    "bf16": false,
    "fp16": false,
    "fp16_opt_level": "O1",
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    "tf32": null,
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    "debug": [],
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    "load_best_model_at_end": true,
    "metric_for_best_model": "eval_loss",
    "greater_is_better": false,
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    "fsdp": [],
    "fsdp_min_num_params": 0,
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    "optim": "adamw_torch",
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    "length_column_name": "length",
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    "hub_strategy": "every_save",
    "hub_token": "<HUB_TOKEN>",
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    "include_inputs_for_metrics": false,
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    "fp16_backend": "auto",
    "evaluation_strategy": "epoch",
    "push_to_hub_model_id": null,
    "push_to_hub_organization": null,
    "push_to_hub_token": "<PUSH_TO_HUB_TOKEN>",
    "mp_parameters": "",
    "auto_find_batch_size": false,
    "full_determinism": false,
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    "ray_scope": "last",
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    "torch_compile": false,
    "torch_compile_backend": null,
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    "include_tokens_per_second": false,
    "include_num_input_tokens_seen": false,
    "neftune_noise_alpha": null,
    "optim_target_modules": null,
    "batch_eval_metrics": false,
    "eval_on_start": false,
    "use_liger_kernel": false,
    "eval_use_gather_object": false,
    "average_tokens_across_devices": false
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  "final_loss": 0.006501355601283602
}

Usage Example

from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load empathy model
model = AutoModelForSequenceClassification.from_pretrained("raghavdw/cci-capstone-asapp-empathy")
tokenizer = AutoTokenizer.from_pretrained("raghavdw/cci-capstone-asapp-empathy")

# Process text
text = "I understand this must be frustrating for you"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)  # Returns [empathy_score, listening_score]

Last Updated: 2025-01-10 19:11:35