Model save

README.md

@@ -0,0 +1,70 @@
+---
+base_model: scales-okn/docket-language-model
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: ontology-answer-test
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# ontology-answer-test
+
+This model is a fine-tuned version of [scales-okn/docket-language-model](https://huggingface.co/scales-okn/docket-language-model) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0009
+- Accuracy: 1.0
+- F1: 1.0
+- Precision: 1.0
+- Recall: 1.0
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 8
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.06
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.0233        | 1.2903 | 100  | 0.0434          | 0.9862   | 0.9720 | 0.9630    | 0.9811 |
+| 0.0007        | 2.5806 | 200  | 0.0072          | 0.9954   | 0.9905 | 1.0       | 0.9811 |
+| 0.0003        | 3.8710 | 300  | 0.0009          | 1.0      | 1.0    | 1.0       | 1.0    |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.1.2+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

added_tokens.json

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+}

config.json

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+{
+  "_name_or_path": "scales-okn/docket-language-model",
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 1024,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 0,
+  "vocab_size": 128001
+}

model.safetensors

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runs/events.out.tfevents.1718056601.delilah.39183.0

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runs/events.out.tfevents.1718056997.delilah.39183.1

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special_tokens_map.json

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tokenizer_config.json

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+  "truncation_strategy": "longest_first",
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+  "vocab_type": "spm"
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training_args.bin

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