IParraMartin/XLM-AgloBERTa-eu-hu-ner

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,75 @@
+---
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: XLM-AgloBERTa-eu-hu-ner
+  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. -->
+
+# XLM-AgloBERTa-eu-hu-ner
+
+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: 0.2582
+- Precision: 0.9039
+- Recall: 0.9209
+- F1: 0.9123
+- Accuracy: 0.9661
+
+## 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: 0.0001
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2501        | 1.0   | 1250  | 0.1919          | 0.8343    | 0.8634 | 0.8486 | 0.9456   |
+| 0.1718        | 2.0   | 2500  | 0.1631          | 0.8662    | 0.8793 | 0.8727 | 0.9540   |
+| 0.1269        | 3.0   | 3750  | 0.1743          | 0.8748    | 0.8913 | 0.8830 | 0.9571   |
+| 0.0899        | 4.0   | 5000  | 0.1642          | 0.8734    | 0.9083 | 0.8905 | 0.9587   |
+| 0.0578        | 5.0   | 6250  | 0.1958          | 0.8867    | 0.9000 | 0.8933 | 0.9599   |
+| 0.0474        | 6.0   | 7500  | 0.1823          | 0.9062    | 0.9069 | 0.9065 | 0.9647   |
+| 0.031         | 7.0   | 8750  | 0.1928          | 0.9007    | 0.9137 | 0.9071 | 0.9643   |
+| 0.0168        | 8.0   | 10000 | 0.2168          | 0.9042    | 0.9113 | 0.9077 | 0.9644   |
+| 0.0109        | 9.0   | 11250 | 0.2423          | 0.9028    | 0.9191 | 0.9108 | 0.9658   |
+| 0.0057        | 10.0  | 12500 | 0.2582          | 0.9039    | 0.9209 | 0.9123 | 0.9661   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

config.json

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+{
+  "_name_or_path": "xlm-roberta-base",
+  "architectures": [
+    "XLMRobertaForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
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+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6
+  },
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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runs/Jan04_22-30-56_5a44c94de183/events.out.tfevents.1704407514.5a44c94de183.1384.0

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special_tokens_map.json

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+}

tokenizer.json

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tokenizer_config.json

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+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
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+  "tokenizer_class": "XLMRobertaTokenizer",
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+}

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