IParraMartin/XLM-AgloBERTa-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-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-hu-ner
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2705
+- Precision: 0.9070
+- Recall: 0.9256
+- F1: 0.9162
+- Accuracy: 0.9671
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0861        | 1.0   | 1250  | 0.2330          | 0.8811    | 0.9005 | 0.8907 | 0.9574   |
+| 0.0666        | 2.0   | 2500  | 0.1955          | 0.8842    | 0.9113 | 0.8975 | 0.9597   |
+| 0.0613        | 3.0   | 3750  | 0.2059          | 0.8941    | 0.9035 | 0.8988 | 0.9603   |
+| 0.0408        | 4.0   | 5000  | 0.2386          | 0.9021    | 0.9023 | 0.9022 | 0.9616   |
+| 0.0327        | 5.0   | 6250  | 0.2314          | 0.8892    | 0.9188 | 0.9038 | 0.9621   |
+| 0.0222        | 6.0   | 7500  | 0.2574          | 0.9015    | 0.9108 | 0.9061 | 0.9631   |
+| 0.0143        | 7.0   | 8750  | 0.2482          | 0.9070    | 0.9192 | 0.9131 | 0.9657   |
+| 0.0093        | 8.0   | 10000 | 0.2570          | 0.9092    | 0.9206 | 0.9149 | 0.9664   |
+| 0.0044        | 9.0   | 11250 | 0.2697          | 0.9055    | 0.9199 | 0.9126 | 0.9660   |
+| 0.0026        | 10.0  | 12500 | 0.2705          | 0.9070    | 0.9256 | 0.9162 | 0.9671   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

config.json

@@ -0,0 +1,46 @@
+{
+  "_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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special_tokens_map.json

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tokenizer_config.json

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+  "tokenizer_class": "XLMRobertaTokenizer",
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+}

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