OSainz/mdt-ie-re-baseline

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,72 @@
+---
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+model-index:
+- name: tmp
+  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. -->
+
+# tmp
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4127
+- Precision: 0.3197
+- Recall: 0.2438
+- F1: 0.2766
+
+## 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: 1e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: constant
+- num_epochs: 3
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
+| 0.8182        | 0.35  | 500  | 0.5251          | 0.0       | 0.0    | 0.0    |
+| 0.6835        | 0.7   | 1000 | 0.4857          | 0.0       | 0.0    | 0.0    |
+| 0.6643        | 1.04  | 1500 | 0.4691          | 0.0       | 0.0    | 0.0    |
+| 0.6403        | 1.39  | 2000 | 0.4580          | 0.4531    | 0.0349 | 0.0647 |
+| 0.5617        | 1.74  | 2500 | 0.4528          | 0.3373    | 0.0673 | 0.1122 |
+| 0.4896        | 2.09  | 3000 | 0.4265          | 0.3268    | 0.1611 | 0.2158 |
+| 0.4451        | 2.43  | 3500 | 0.4087          | 0.3860    | 0.1791 | 0.2447 |
+| 0.416         | 2.78  | 4000 | 0.4222          | 0.2937    | 0.2224 | 0.2531 |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

added_tokens.json

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+}

config.json

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+  "_name_or_path": "xlm-roberta-base",
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
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+  "hidden_act": "gelu",
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+  "hidden_size": 768,
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+    "1": "causes",
+    "2": "domain",
+    "3": "entails",
+    "4": "has-part",
+    "5": "has-property",
+    "6": "in-context",
+    "7": "in-place",
+    "8": "in-time",
+    "9": "is-a",
+    "10": "no_relation",
+    "11": "part-of",
+    "12": "subject",
+    "13": "target"
+  },
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+  "intermediate_size": 3072,
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+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "tail_token": "[E2]",
+  "tail_token_id": 250004,
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250006
+}

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special_tokens_map.json

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