Instructions to use VijayRam1812/content-classifier-gemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VijayRam1812/content-classifier-gemma with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VijayRam1812/content-classifier-gemma")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VijayRam1812/content-classifier-gemma") model = AutoModelForSequenceClassification.from_pretrained("VijayRam1812/content-classifier-gemma", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload Gemma3TextForSequenceClassification
Browse files- config.json +6 -1
- model.safetensors +2 -2
config.json
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"head_dim": 256,
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 1152,
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"initializer_range": 0.02,
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"intermediate_size": 6912,
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"layer_types": [
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"sliding_attention",
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"sliding_attention",
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"num_hidden_layers": 26,
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"num_key_value_heads": 1,
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"query_pre_attn_scalar": 256,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"head_dim": 256,
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 1152,
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"id2label": {
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"0": "LABEL_0"
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},
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"initializer_range": 0.02,
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"intermediate_size": 6912,
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"label2id": {
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"LABEL_0": 0
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},
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"layer_types": [
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"sliding_attention",
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"sliding_attention",
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"num_hidden_layers": 26,
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"num_key_value_heads": 1,
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"pad_token_id": 0,
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"query_pre_attn_scalar": 256,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:8b956981e832fc53f89357c7b9596dd18a1ae240ee175b8f059dfc3875265e67
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size 1999813600
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