End of training

.gitattributes

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

@@ -1,5 +1,6 @@
 ---
-base_model: lilyyellow/my_awesome_ner-token_classification_v1.0.7-7
+license: mit
+base_model: FacebookAI/xlm-roberta-base
 tags:
 - generated_from_trainer
 model-index:
@@ -12,25 +13,25 @@ should probably proofread and complete it, then remove this comment. -->
 
 # my_awesome_ner-token_classification_v1.0.7-7
 
-This model is a fine-tuned version of [lilyyellow/my_awesome_ner-token_classification_v1.0.7-7](https://huggingface.co/lilyyellow/my_awesome_ner-token_classification_v1.0.7-7) on an unknown dataset.
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4796
-- Age: {'precision': 0.9296875, 'recall': 0.8880597014925373, 'f1': 0.9083969465648856, 'number': 134}
-- Datetime: {'precision': 0.6636113657195234, 'recall': 0.7335359675785208, 'f1': 0.6968238691049086, 'number': 987}
-- Disease: {'precision': 0.6589403973509934, 'recall': 0.7595419847328244, 'f1': 0.7056737588652483, 'number': 262}
-- Event: {'precision': 0.2397094430992736, 'recall': 0.3535714285714286, 'f1': 0.28571428571428575, 'number': 280}
-- Gender: {'precision': 0.7741935483870968, 'recall': 0.8275862068965517, 'f1': 0.7999999999999999, 'number': 87}
-- Law: {'precision': 0.513595166163142, 'recall': 0.6666666666666666, 'f1': 0.5802047781569966, 'number': 255}
-- Location: {'precision': 0.6822724881641241, 'recall': 0.7225626740947075, 'f1': 0.7018398268398268, 'number': 1795}
-- Organization: {'precision': 0.6211683053788317, 'recall': 0.7098479841374752, 'f1': 0.662553979025293, 'number': 1513}
-- Person: {'precision': 0.6743572841133817, 'recall': 0.7359712230215827, 'f1': 0.7038183694530443, 'number': 1390}
-- Quantity: {'precision': 0.5043859649122807, 'recall': 0.6095406360424028, 'f1': 0.552, 'number': 566}
-- Role: {'precision': 0.4247391952309985, 'recall': 0.5210237659963437, 'f1': 0.4679802955665025, 'number': 547}
-- Transportation: {'precision': 0.45454545454545453, 'recall': 0.6086956521739131, 'f1': 0.5204460966542751, 'number': 115}
-- Overall Precision: 0.6076
-- Overall Recall: 0.6906
-- Overall F1: 0.6464
-- Overall Accuracy: 0.8885
+- Loss: 0.3063
+- Age: {'precision': 0.9090909090909091, 'recall': 0.916030534351145, 'f1': 0.9125475285171103, 'number': 131}
+- Datetime: {'precision': 0.6997055937193327, 'recall': 0.7396265560165975, 'f1': 0.719112455874937, 'number': 964}
+- Disease: {'precision': 0.6787003610108303, 'recall': 0.7258687258687259, 'f1': 0.7014925373134328, 'number': 259}
+- Event: {'precision': 0.2840466926070039, 'recall': 0.27137546468401486, 'f1': 0.27756653992395436, 'number': 269}
+- Gender: {'precision': 0.625, 'recall': 0.7471264367816092, 'f1': 0.6806282722513088, 'number': 87}
+- Law: {'precision': 0.5387205387205387, 'recall': 0.6808510638297872, 'f1': 0.6015037593984962, 'number': 235}
+- Location: {'precision': 0.6476527006562343, 'recall': 0.729806598407281, 'f1': 0.6862797539449051, 'number': 1758}
+- Organization: {'precision': 0.5866666666666667, 'recall': 0.697029702970297, 'f1': 0.63710407239819, 'number': 1515}
+- Person: {'precision': 0.7053824362606232, 'recall': 0.7238372093023255, 'f1': 0.7144906743185079, 'number': 1376}
+- Quantity: {'precision': 0.5528846153846154, 'recall': 0.6227436823104693, 'f1': 0.5857385398981324, 'number': 554}
+- Role: {'precision': 0.47495961227786754, 'recall': 0.5434380776340111, 'f1': 0.506896551724138, 'number': 541}
+- Transportation: {'precision': 0.48120300751879697, 'recall': 0.5614035087719298, 'f1': 0.5182186234817814, 'number': 114}
+- Overall Precision: 0.6189
+- Overall Recall: 0.6865
+- Overall F1: 0.6510
+- Overall Accuracy: 0.9023
 
 ## Model description
 
@@ -59,9 +60,9 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch  | Step | Validation Loss | Age                                                                                             | Datetime                                                                                                 | Disease                                                                                                  | Event                                                                                                     | Gender                                                                                                  | Law                                                                                                     | Location                                                                                                  | Organization                                                                                             | Person                                                                                                    | Quantity                                                                                    | Role                                                                                                     | Transportation                                                                                            | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:------:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.1263        | 1.9965 | 1156 | 0.4796          | {'precision': 0.9296875, 'recall': 0.8880597014925373, 'f1': 0.9083969465648856, 'number': 134} | {'precision': 0.6636113657195234, 'recall': 0.7335359675785208, 'f1': 0.6968238691049086, 'number': 987} | {'precision': 0.6589403973509934, 'recall': 0.7595419847328244, 'f1': 0.7056737588652483, 'number': 262} | {'precision': 0.2397094430992736, 'recall': 0.3535714285714286, 'f1': 0.28571428571428575, 'number': 280} | {'precision': 0.7741935483870968, 'recall': 0.8275862068965517, 'f1': 0.7999999999999999, 'number': 87} | {'precision': 0.513595166163142, 'recall': 0.6666666666666666, 'f1': 0.5802047781569966, 'number': 255} | {'precision': 0.6822724881641241, 'recall': 0.7225626740947075, 'f1': 0.7018398268398268, 'number': 1795} | {'precision': 0.6211683053788317, 'recall': 0.7098479841374752, 'f1': 0.662553979025293, 'number': 1513} | {'precision': 0.6743572841133817, 'recall': 0.7359712230215827, 'f1': 0.7038183694530443, 'number': 1390} | {'precision': 0.5043859649122807, 'recall': 0.6095406360424028, 'f1': 0.552, 'number': 566} | {'precision': 0.4247391952309985, 'recall': 0.5210237659963437, 'f1': 0.4679802955665025, 'number': 547} | {'precision': 0.45454545454545453, 'recall': 0.6086956521739131, 'f1': 0.5204460966542751, 'number': 115} | 0.6076            | 0.6906         | 0.6464     | 0.8885           |
+| Training Loss | Epoch  | Step | Validation Loss | Age                                                                                                     | Datetime                                                                                                | Disease                                                                                                  | Event                                                                                                      | Gender                                                                                     | Law                                                                                                      | Location                                                                                                 | Organization                                                                                           | Person                                                                                                    | Quantity                                                                                                 | Role                                                                                                     | Transportation                                                                                            | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.2811        | 1.9965 | 1156 | 0.3063          | {'precision': 0.9090909090909091, 'recall': 0.916030534351145, 'f1': 0.9125475285171103, 'number': 131} | {'precision': 0.6997055937193327, 'recall': 0.7396265560165975, 'f1': 0.719112455874937, 'number': 964} | {'precision': 0.6787003610108303, 'recall': 0.7258687258687259, 'f1': 0.7014925373134328, 'number': 259} | {'precision': 0.2840466926070039, 'recall': 0.27137546468401486, 'f1': 0.27756653992395436, 'number': 269} | {'precision': 0.625, 'recall': 0.7471264367816092, 'f1': 0.6806282722513088, 'number': 87} | {'precision': 0.5387205387205387, 'recall': 0.6808510638297872, 'f1': 0.6015037593984962, 'number': 235} | {'precision': 0.6476527006562343, 'recall': 0.729806598407281, 'f1': 0.6862797539449051, 'number': 1758} | {'precision': 0.5866666666666667, 'recall': 0.697029702970297, 'f1': 0.63710407239819, 'number': 1515} | {'precision': 0.7053824362606232, 'recall': 0.7238372093023255, 'f1': 0.7144906743185079, 'number': 1376} | {'precision': 0.5528846153846154, 'recall': 0.6227436823104693, 'f1': 0.5857385398981324, 'number': 554} | {'precision': 0.47495961227786754, 'recall': 0.5434380776340111, 'f1': 0.506896551724138, 'number': 541} | {'precision': 0.48120300751879697, 'recall': 0.5614035087719298, 'f1': 0.5182186234817814, 'number': 114} | 0.6189            | 0.6865         | 0.6510     | 0.9023           |
 
 
 ### Framework versions

config.json

@@ -1,12 +1,12 @@
 {
-  "_name_or_path": "lilyyellow/my_awesome_ner-token_classification_v1.0.7-7",
+  "_name_or_path": "FacebookAI/xlm-roberta-base",
   "architectures": [
-    "ElectraForTokenClassification"
+    "XLMRobertaForTokenClassification"
   ],
   "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
   "classifier_dropout": null,
-  "embedding_size": 768,
-  "finetuning_task": "ner",
+  "eos_token_id": 2,
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
@@ -66,20 +66,17 @@
     "I-TRANSPORTATION": 21,
     "O": 13
   },
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "electra",
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
   "num_attention_heads": 12,
   "num_hidden_layers": 12,
-  "pad_token_id": 0,
+  "output_past": true,
+  "pad_token_id": 1,
   "position_embedding_type": "absolute",
-  "summary_activation": "gelu",
-  "summary_last_dropout": 0.1,
-  "summary_type": "first",
-  "summary_use_proj": true,
   "torch_dtype": "float32",
   "transformers_version": "4.41.2",
-  "type_vocab_size": 2,
+  "type_vocab_size": 1,
   "use_cache": true,
-  "vocab_size": 62000
+  "vocab_size": 250002
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 532367844
+oid sha256:f665f1eaaf2b7ec290dae18852cafb3f63584af40fd6c7f469c5374f05c85d9c
+size 1109913172

sentencepiece.bpe.model

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special_tokens_map.json

@@ -1,37 +1,15 @@
 {
-  "cls_token": {
-    "content": "[CLS]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
   "mask_token": {
-    "content": "[MASK]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": {
-    "content": "[PAD]",
-    "lstrip": false,
+    "content": "<mask>",
+    "lstrip": true,
     "normalized": false,
     "rstrip": false,
     "single_word": false
   },
-  "sep_token": {
-    "content": "[SEP]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
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-  "unk_token": {
-    "content": "[UNK]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  }
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
 }

tokenizer_config.json

@@ -1,7 +1,7 @@
 {
   "added_tokens_decoder": {
     "0": {
-      "content": "[PAD]",
+      "content": "<s>",
       "lstrip": false,
       "normalized": false,
       "rstrip": false,
@@ -9,7 +9,7 @@
       "special": true
     },
     "1": {
-      "content": "[UNK]",
+      "content": "<pad>",
       "lstrip": false,
       "normalized": false,
       "rstrip": false,
@@ -17,7 +17,7 @@
       "special": true
     },
     "2": {
-      "content": "[CLS]",
+      "content": "</s>",
       "lstrip": false,
       "normalized": false,
       "rstrip": false,
@@ -25,40 +25,30 @@
       "special": true
     },
     "3": {
-      "content": "[SEP]",
+      "content": "<unk>",
       "lstrip": false,
       "normalized": false,
       "rstrip": false,
       "single_word": false,
       "special": true
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+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
       "normalized": false,
       "rstrip": false,
       "single_word": false,
       "special": true
     }
   },
+  "bos_token": "<s>",
   "clean_up_tokenization_spaces": true,
-  "cls_token": "[CLS]",
-  "do_basic_tokenize": true,
-  "do_lower_case": false,
-  "mask_token": "[MASK]",
-  "max_length": 256,
-  "model_max_length": 1000000000000000019884624838656,
-  "never_split": null,
-  "pad_to_multiple_of": null,
-  "pad_token": "[PAD]",
-  "pad_token_type_id": 0,
-  "padding_side": "right",
-  "sep_token": "[SEP]",
-  "stride": 0,
-  "strip_accents": null,
-  "tokenize_chinese_chars": true,
-  "tokenizer_class": "ElectraTokenizer",
-  "truncation_side": "right",
-  "truncation_strategy": "longest_first",
-  "unk_token": "[UNK]"
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
 }

training_args.bin

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