FrinzTheCoder/xlm-roberta-base-amh

README.md

@@ -6,7 +6,8 @@ tags:
 - generated_from_trainer
 metrics:
 - accuracy
-- f1
+- precision
+- recall
 model-index:
 - name: xlm-roberta-base-amh
   results: []
@@ -19,9 +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: 0.1320
-- Accuracy: 0.8439
-- F1: 0.7725
+- Loss: 0.1441
+- Accuracy: 0.7585
+- F1 Binary: 0.4666
+- Precision: 0.4014
+- Recall: 0.5569
 
 ## Model description
 
@@ -40,27 +43,28 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 0.0003
-- train_batch_size: 16
+- learning_rate: 3e-05
+- 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: 53
 - num_epochs: 4
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
-| 0.1515        | 1.0   | 888  | 0.1611          | 0.8439   | 0.7725 |
-| 0.1378        | 2.0   | 1776 | 0.1318          | 0.8439   | 0.7725 |
-| 0.133         | 3.0   | 2664 | 0.1378          | 0.8439   | 0.7725 |
-| 0.134         | 4.0   | 3552 | 0.1320          | 0.8439   | 0.7725 |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Binary | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:---------:|:------:|
+| 0.1629        | 1.0   | 533  | 0.1619          | 0.6829   | 0.4559    | 0.3379    | 0.7005 |
+| 0.1525        | 2.0   | 1066 | 0.1641          | 0.4427   | 0.3290    | 0.2132    | 0.7203 |
+| 0.1487        | 3.0   | 1599 | 0.1486          | 0.7272   | 0.4498    | 0.3643    | 0.5879 |
+| 0.137         | 4.0   | 2132 | 0.1441          | 0.7585   | 0.4666    | 0.4014    | 0.5569 |
 
 
 ### 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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+size 1112205008

tokenizer.json

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+size 17082987

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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