Text Classification
TensorRT
Core ML
ONNX
Safetensors
OpenVINO
code
multilingual
xlm-roberta
knowledge-graph
relation-extraction
relation-classification
cross-encoder
knowledge-distillation
Instructions to use faxenoff/code-daemon-relation-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TensorRT
How to use faxenoff/code-daemon-relation-v1 with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
compiled artefacts from mac_arm64 (cpu)
Browse files- .gitattributes +2 -0
- manifest.json +3 -4
- model_gpu_mlx0.22/config.json +0 -45
.gitattributes
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@@ -39,3 +39,5 @@ code-daemon-relation-v1-m_ov2026.4_cpu_fp16_b16_s128.bin filter=lfs diff=lfs mer
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code-daemon-relation-v1-m_ov2026.4_igpu_fp16_b16_s128.bin filter=lfs diff=lfs merge=lfs -text
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code-daemon-relation-v1-s_ov2026.4_cpu_fp16_b16_s64.bin filter=lfs diff=lfs merge=lfs -text
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code-daemon-relation-v1-s_ov2026.4_igpu_fp16_b16_s64.bin filter=lfs diff=lfs merge=lfs -text
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code-daemon-relation-v1-m_ov2026.4_igpu_fp16_b16_s128.bin filter=lfs diff=lfs merge=lfs -text
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code-daemon-relation-v1-s_ov2026.4_cpu_fp16_b16_s64.bin filter=lfs diff=lfs merge=lfs -text
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code-daemon-relation-v1-s_ov2026.4_igpu_fp16_b16_s64.bin filter=lfs diff=lfs merge=lfs -text
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model.onnx filter=lfs diff=lfs merge=lfs -text
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model.onnx.data filter=lfs diff=lfs merge=lfs -text
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manifest.json
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"max_tokens": 256,
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"quantization": "fp16",
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"targets": [
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"openvino"
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"tensorrt"
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],
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"runtime_tags": {
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"trt": "trt11.0",
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"ov": "ov2026.4",
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"tvm": "tvm0.25",
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"mlx_dir": "model_gpu_mlx0.
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},
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"compiled_at": "2026-09-
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"compiled_by": "models/_compile"
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}
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"max_tokens": 256,
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"quantization": "fp16",
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"targets": [
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"openvino"
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],
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"runtime_tags": {
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"trt": "trt11.0",
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"ov": "ov2026.4",
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"tvm": "tvm0.25",
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"mlx_dir": "model_gpu_mlx0.32"
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},
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"compiled_at": "2026-09-17T13:20:22Z",
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"compiled_by": "models/_compile"
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}
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model_gpu_mlx0.22/config.json
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{
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"add_cross_attention": false,
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"architectures": [
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"XLMRobertaForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"custom_head": "entity_start_marker_meanmask (concat E1,E2 -> Linear); [CLS] is not read",
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"dtype": "float32",
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"id2label": {
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"0": "NO_RELATION",
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"1": "semantically_similar_to",
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"2": "invalidates_with",
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"3": "depends_on"
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},
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"is_decoder": false,
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"label2id": {
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"NO_RELATION": 0,
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"depends_on": 3,
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"invalidates_with": 2,
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"semantically_similar_to": 1
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "xlm-roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"num_labels": 4,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"sbert_ce_default_activation_function": "torch.nn.modules.linear.Identity",
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"tie_word_embeddings": true,
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"transformers_version": "5.1.0",
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"type_vocab_size": 1,
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"ultracode_note": "Inference artefact is model.onnx (inputs: input_ids, attention_mask; output: logits[batch, 4]). Feed the pair as (empty query, marked passage).",
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"use_cache": true,
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"vocab_size": 250006
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}
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