Text Classification
Transformers
TensorBoard
Safetensors
qwen2
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use JayHyeon/Qwen2-0.5B-Reward_1e-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JayHyeon/Qwen2-0.5B-Reward_1e-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JayHyeon/Qwen2-0.5B-Reward_1e-3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JayHyeon/Qwen2-0.5B-Reward_1e-3") model = AutoModelForSequenceClassification.from_pretrained("JayHyeon/Qwen2-0.5B-Reward_1e-3") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
all_results.json
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{
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"epoch": 1.0,
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"eval_accuracy": 0.64,
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"eval_loss": 0.6255366802215576,
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"eval_runtime": 35.9303,
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"eval_samples_per_second": 27.832,
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"eval_steps_per_second": 0.891
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}
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eval_results.json
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{
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"epoch": 1.0,
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"eval_accuracy": 0.64,
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"eval_loss": 0.6255366802215576,
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"eval_runtime": 35.9303,
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"eval_samples_per_second": 27.832,
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"eval_steps_per_second": 0.891
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}
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runs/Nov28_17-38-57_6696746798dd/events.out.tfevents.1732825551.6696746798dd.1299614.1
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version https://git-lfs.github.com/spec/v1
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oid sha256:d16a65aaf61a88b596cd1176861eb9584d044222ceee06944c02c7691cdf73e1
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size 363
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