FrinzTheCoder/xlm-roberta-base-ptbr

README.md

@@ -20,13 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 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: 0.1249
-- Accuracy: 0.8305
-- F1 Micro: 0.8305
-- F1 Macro: 0.4537
-- F1 Weighted: 0.7536
-- Precision: 0.6897
-- Recall: 0.8305
+- Loss: 0.1723
+- Accuracy: 0.8531
+- F1 Binary: 0.0
+- Precision: 0.0
+- Recall: 0.0
 
 ## Model description
 
@@ -46,27 +44,27 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 3e-05
-- train_batch_size: 16
+- train_batch_size: 32
 - eval_batch_size: 8
 - seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 222
+- lr_scheduler_warmup_steps: 33
 - num_epochs: 4
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Micro | F1 Macro | F1 Weighted | Precision | Recall |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|:---------:|:------:|
-| 0.1522        | 1.0   | 557  | 0.1431          | 0.8305   | 0.8305   | 0.4537   | 0.7536      | 0.6897    | 0.8305 |
-| 0.1472        | 2.0   | 1114 | 0.1407          | 0.8305   | 0.8305   | 0.4537   | 0.7536      | 0.6897    | 0.8305 |
-| 0.1431        | 3.0   | 1671 | 0.1310          | 0.8305   | 0.8305   | 0.4537   | 0.7536      | 0.6897    | 0.8305 |
-| 0.1363        | 4.0   | 2228 | 0.1249          | 0.8305   | 0.8305   | 0.4537   | 0.7536      | 0.6897    | 0.8305 |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Binary | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:---------:|:------:|
+| No log        | 1.0   | 334  | 0.1746          | 0.1469   | 0.2561    | 0.1469    | 1.0    |
+| 0.1763        | 2.0   | 668  | 0.1699          | 0.8531   | 0.0       | 0.0       | 0.0    |
+| 0.1745        | 3.0   | 1002 | 0.1722          | 0.8531   | 0.0       | 0.0       | 0.0    |
+| 0.1745        | 4.0   | 1336 | 0.1723          | 0.8531   | 0.0       | 0.0       | 0.0    |
 
 
 ### Framework versions
 
-- Transformers 4.44.2
-- Pytorch 2.4.1+cu121
-- Datasets 3.2.0
-- Tokenizers 0.19.1
+- Transformers 4.48.0
+- Pytorch 2.5.1+cu124
+- Datasets 3.1.0
+- Tokenizers 0.21.0

config.json

@@ -10,14 +10,8 @@
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
-  "id2label": {
-    "0": "LABEL_0"
-  },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
-  "label2id": {
-    "LABEL_0": 0
-  },
   "layer_norm_eps": 1e-05,
   "max_position_embeddings": 514,
   "model_type": "xlm-roberta",
@@ -26,9 +20,8 @@
   "output_past": true,
   "pad_token_id": 1,
   "position_embedding_type": "absolute",
-  "problem_type": "regression",
   "torch_dtype": "float32",
-  "transformers_version": "4.44.2",
+  "transformers_version": "4.48.0",
   "type_vocab_size": 1,
   "use_cache": true,
   "vocab_size": 250002

model.safetensors

@@ -1,3 +1,3 @@
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tokenizer.json

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tokenizer_config.json

@@ -42,9 +42,10 @@
     }
   },
   "bos_token": "<s>",
-  "clean_up_tokenization_spaces": true,
+  "clean_up_tokenization_spaces": false,
   "cls_token": "<s>",
   "eos_token": "</s>",
+  "extra_special_tokens": {},
   "mask_token": "<mask>",
   "model_max_length": 512,
   "pad_token": "<pad>",

training_args.bin

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