End of training

README.md

@@ -0,0 +1,81 @@
+---
+base_model: napsternxg/gte-small-L3-ingredient-v2
+tags:
+- generated_from_trainer
+datasets:
+- nyt_ingredients
+model-index:
+- name: nyt-ingredient-tagger-gte-small-L3-ingredient-v2
+  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. -->
+
+# nyt-ingredient-tagger-gte-small-L3-ingredient-v2
+
+This model is a fine-tuned version of [napsternxg/gte-small-L3-ingredient-v2](https://huggingface.co/napsternxg/gte-small-L3-ingredient-v2) on the nyt_ingredients dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4618
+- Comment: {'precision': 0.639661138288987, 'recall': 0.7582743988684583, 'f1': 0.6939356675943305, 'number': 7070}
+- Name: {'precision': 0.7939526184538653, 'recall': 0.8216129032258065, 'f1': 0.8075459733671528, 'number': 9300}
+- Qty: {'precision': 0.9855801031882524, 'recall': 0.9875397667020148, 'f1': 0.9865589617956697, 'number': 7544}
+- Range End: {'precision': 0.6176470588235294, 'recall': 0.875, 'f1': 0.7241379310344829, 'number': 96}
+- Unit: {'precision': 0.9226940426193809, 'recall': 0.9844009293063392, 'f1': 0.9525491770373343, 'number': 6026}
+- Overall Precision: 0.8238
+- Overall Recall: 0.8812
+- Overall F1: 0.8515
+- Overall Accuracy: 0.8364
+
+## 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: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Comment                                                                                                   | Name                                                                                                      | Qty                                                                                                       | Range End                                                                                               | Unit                                                                                                      | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.6038        | 0.2   | 1000  | 0.5654          | {'precision': 0.5337459131247081, 'recall': 0.662463768115942, 'f1': 0.5911795137092601, 'number': 6900}  | {'precision': 0.7791639308382579, 'recall': 0.8054298642533937, 'f1': 0.7920792079207921, 'number': 8840} | {'precision': 0.9738444505950733, 'recall': 0.9815873901520435, 'f1': 0.9777005904828066, 'number': 7169} | {'precision': 0.5523809523809524, 'recall': 0.6170212765957447, 'f1': 0.5829145728643217, 'number': 94} | {'precision': 0.918520942408377, 'recall': 0.9716164762893735, 'f1': 0.9443229604709841, 'number': 5778}  | 0.7834            | 0.8478         | 0.8143     | 0.7991           |
+| 0.5583        | 0.4   | 2000  | 0.5386          | {'precision': 0.575587084148728, 'recall': 0.6820289855072463, 'f1': 0.6243035287874767, 'number': 6900}  | {'precision': 0.7881196864303853, 'recall': 0.8074660633484163, 'f1': 0.7976755880873889, 'number': 8840} | {'precision': 0.982230306422275, 'recall': 0.9792160691867764, 'f1': 0.9807208717518859, 'number': 7169}  | {'precision': 0.5246913580246914, 'recall': 0.9042553191489362, 'f1': 0.6640625, 'number': 94}          | {'precision': 0.9144843194309732, 'recall': 0.9790584977500866, 'f1': 0.9456703443664326, 'number': 5778} | 0.8008            | 0.8549         | 0.8270     | 0.8086           |
+| 0.5351        | 0.59  | 3000  | 0.5205          | {'precision': 0.5827500296595088, 'recall': 0.7118840579710145, 'f1': 0.6408767695218214, 'number': 6900} | {'precision': 0.7792900696864111, 'recall': 0.8096153846153846, 'f1': 0.7941633377718598, 'number': 8840} | {'precision': 0.974776016540317, 'recall': 0.9864695215511229, 'f1': 0.9805879090404881, 'number': 7169}  | {'precision': 0.6027397260273972, 'recall': 0.9361702127659575, 'f1': 0.7333333333333333, 'number': 94} | {'precision': 0.9150242326332795, 'recall': 0.980269989615784, 'f1': 0.946524064171123, 'number': 5778}   | 0.7978            | 0.8649         | 0.8300     | 0.8152           |
+| 0.5238        | 0.79  | 4000  | 0.5070          | {'precision': 0.592994874298267, 'recall': 0.7042028985507246, 'f1': 0.64383198621969, 'number': 6900}    | {'precision': 0.7898789878987899, 'recall': 0.8122171945701357, 'f1': 0.8008923591745678, 'number': 8840} | {'precision': 0.9833984375, 'recall': 0.983261263774585, 'f1': 0.9833298458533863, 'number': 7169}        | {'precision': 0.6277372262773723, 'recall': 0.9148936170212766, 'f1': 0.7445887445887446, 'number': 94} | {'precision': 0.9206581948517433, 'recall': 0.9780200761509172, 'f1': 0.948472641826116, 'number': 5778}  | 0.8079            | 0.8625         | 0.8343     | 0.8203           |
+| 0.5134        | 0.99  | 5000  | 0.4994          | {'precision': 0.6057287278854254, 'recall': 0.7294202898550725, 'f1': 0.6618449602209219, 'number': 6900} | {'precision': 0.7922092132618448, 'recall': 0.8190045248868778, 'f1': 0.8053840591801545, 'number': 8840} | {'precision': 0.980102252314495, 'recall': 0.9893988003905705, 'f1': 0.9847285853116757, 'number': 7169}  | {'precision': 0.6776859504132231, 'recall': 0.8723404255319149, 'f1': 0.7627906976744186, 'number': 94} | {'precision': 0.9163571774584208, 'recall': 0.9821737625475944, 'f1': 0.9481246345334559, 'number': 5778} | 0.8104            | 0.8729         | 0.8405     | 0.8230           |
+| 0.4954        | 1.19  | 6000  | 0.5022          | {'precision': 0.6057512759865554, 'recall': 0.7052173913043478, 'f1': 0.6517109756914216, 'number': 6900} | {'precision': 0.7810020649929356, 'recall': 0.81289592760181, 'f1': 0.7966298985643812, 'number': 8840}   | {'precision': 0.9804736186123806, 'recall': 0.9875854372994839, 'f1': 0.9840166782487838, 'number': 7169} | {'precision': 0.7024793388429752, 'recall': 0.9042553191489362, 'f1': 0.7906976744186046, 'number': 94} | {'precision': 0.9155247460906013, 'recall': 0.9828660436137072, 'f1': 0.9480010015858442, 'number': 5778} | 0.8089            | 0.8650         | 0.8360     | 0.8203           |
+| 0.4998        | 1.39  | 7000  | 0.4921          | {'precision': 0.6202128961213753, 'recall': 0.7346376811594203, 'f1': 0.672593378889405, 'number': 6900}  | {'precision': 0.7895139268159476, 'recall': 0.8176470588235294, 'f1': 0.8033342595165324, 'number': 8840} | {'precision': 0.9836797321802204, 'recall': 0.9836797321802204, 'f1': 0.9836797321802204, 'number': 7169} | {'precision': 0.6397058823529411, 'recall': 0.925531914893617, 'f1': 0.7565217391304346, 'number': 94}  | {'precision': 0.9238952536824877, 'recall': 0.976981654551748, 'f1': 0.9496971736204576, 'number': 5778}  | 0.8158            | 0.8714         | 0.8427     | 0.8252           |
+| 0.4912        | 1.58  | 8000  | 0.4944          | {'precision': 0.6245536264006896, 'recall': 0.735072463768116, 'f1': 0.6753212169629186, 'number': 6900}  | {'precision': 0.7958374628344896, 'recall': 0.8175339366515837, 'f1': 0.8065398136264716, 'number': 8840} | {'precision': 0.9819494584837545, 'recall': 0.9864695215511229, 'f1': 0.9842043003270474, 'number': 7169} | {'precision': 0.6541353383458647, 'recall': 0.925531914893617, 'f1': 0.7665198237885463, 'number': 94}  | {'precision': 0.9229010127409344, 'recall': 0.9778470058843891, 'f1': 0.9495798319327731, 'number': 5778} | 0.8189            | 0.8724         | 0.8448     | 0.8256           |
+| 0.4974        | 1.78  | 9000  | 0.4864          | {'precision': 0.6223597960670065, 'recall': 0.7430434782608696, 'f1': 0.6773682124455014, 'number': 6900} | {'precision': 0.7926226808650785, 'recall': 0.8167420814479638, 'f1': 0.8045016435456014, 'number': 8840} | {'precision': 0.9842442833240379, 'recall': 0.9846561584600363, 'f1': 0.984450177811868, 'number': 7169}  | {'precision': 0.6206896551724138, 'recall': 0.9574468085106383, 'f1': 0.7531380753138076, 'number': 94} | {'precision': 0.9221351616062684, 'recall': 0.9776739356178609, 'f1': 0.949092741935484, 'number': 5778}  | 0.8167            | 0.8737         | 0.8442     | 0.8258           |
+| 0.4872        | 1.98  | 10000 | 0.4848          | {'precision': 0.6306733167082295, 'recall': 0.7330434782608696, 'f1': 0.6780160857908847, 'number': 6900} | {'precision': 0.7935724470768893, 'recall': 0.8184389140271493, 'f1': 0.8058138887341983, 'number': 8840} | {'precision': 0.9848147116188354, 'recall': 0.9860510531454875, 'f1': 0.9854324945981738, 'number': 7169} | {'precision': 0.6641221374045801, 'recall': 0.925531914893617, 'f1': 0.7733333333333333, 'number': 94}  | {'precision': 0.922813315926893, 'recall': 0.9787123572170301, 'f1': 0.949941206114564, 'number': 5778}   | 0.8211            | 0.8722         | 0.8459     | 0.8277           |
+| 0.4777        | 2.18  | 11000 | 0.4820          | {'precision': 0.6294968475707752, 'recall': 0.7379710144927536, 'f1': 0.6794315831609847, 'number': 6900} | {'precision': 0.7957142857142857, 'recall': 0.8191176470588235, 'f1': 0.8072463768115943, 'number': 8840} | {'precision': 0.9861557824080548, 'recall': 0.9836797321802204, 'f1': 0.9849162011173185, 'number': 7169} | {'precision': 0.6796875, 'recall': 0.925531914893617, 'f1': 0.7837837837837838, 'number': 94}           | {'precision': 0.9237939493049877, 'recall': 0.9776739356178609, 'f1': 0.949970570924073, 'number': 5778}  | 0.8214            | 0.8728         | 0.8463     | 0.8307           |
+| 0.4781        | 2.38  | 12000 | 0.4821          | {'precision': 0.6305942773294204, 'recall': 0.7473913043478261, 'f1': 0.6840429765220851, 'number': 6900} | {'precision': 0.7970950704225352, 'recall': 0.8194570135746606, 'f1': 0.8081213743864346, 'number': 8840} | {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 7169} | {'precision': 0.6496350364963503, 'recall': 0.9468085106382979, 'f1': 0.7705627705627707, 'number': 94} | {'precision': 0.923453566182471, 'recall': 0.9792315680166147, 'f1': 0.9505249895002099, 'number': 5778}  | 0.8212            | 0.8759         | 0.8477     | 0.8304           |
+| 0.4804        | 2.57  | 13000 | 0.4809          | {'precision': 0.6338080495356038, 'recall': 0.7417391304347826, 'f1': 0.6835392320534225, 'number': 6900} | {'precision': 0.7985173710998008, 'recall': 0.8164027149321267, 'f1': 0.8073610023492561, 'number': 8840} | {'precision': 0.9853617733166039, 'recall': 0.9859115636769424, 'f1': 0.9856365918281969, 'number': 7169} | {'precision': 0.6382978723404256, 'recall': 0.9574468085106383, 'f1': 0.7659574468085107, 'number': 94} | {'precision': 0.9213263979193758, 'recall': 0.9809622706818969, 'f1': 0.9502095557418274, 'number': 5778} | 0.8228            | 0.8742         | 0.8477     | 0.8298           |
+| 0.4721        | 2.77  | 14000 | 0.4799          | {'precision': 0.6337924249877029, 'recall': 0.7469565217391304, 'f1': 0.6857370941990419, 'number': 6900} | {'precision': 0.795054945054945, 'recall': 0.8184389140271493, 'f1': 0.8065774804905238, 'number': 8840}  | {'precision': 0.9849372384937238, 'recall': 0.9850746268656716, 'f1': 0.9850059278889741, 'number': 7169} | {'precision': 0.6616541353383458, 'recall': 0.9361702127659575, 'f1': 0.7753303964757708, 'number': 94} | {'precision': 0.9225008140670792, 'recall': 0.9806161301488404, 'f1': 0.9506711409395974, 'number': 5778} | 0.8216            | 0.8758         | 0.8478     | 0.8302           |
+| 0.4792        | 2.97  | 15000 | 0.4793          | {'precision': 0.6364423552696685, 'recall': 0.7456521739130435, 'f1': 0.6867325146823279, 'number': 6900} | {'precision': 0.7941402392186986, 'recall': 0.8186651583710407, 'f1': 0.8062162312705399, 'number': 8840} | {'precision': 0.9848062447727907, 'recall': 0.985493095271307, 'f1': 0.9851495503032839, 'number': 7169}  | {'precision': 0.6616541353383458, 'recall': 0.9361702127659575, 'f1': 0.7753303964757708, 'number': 94} | {'precision': 0.921151032352463, 'recall': 0.9806161301488404, 'f1': 0.9499538938720764, 'number': 5778}  | 0.8221            | 0.8756         | 0.8480     | 0.8304           |
+
+
+### Framework versions
+
+- Transformers 4.34.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.14.1

added_tokens.json

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+{
+  "[CLS]": 101,
+  "[MASK]": 103,
+  "[PAD]": 0,
+  "[SEP]": 102,
+  "[UNK]": 100
+}

all_results.json

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+{
+    "epoch": 3.0,
+    "eval_COMMENT": {
+        "f1": 0.6939356675943305,
+        "number": 7070,
+        "precision": 0.639661138288987,
+        "recall": 0.7582743988684583
+    },
+    "eval_NAME": {
+        "f1": 0.8075459733671528,
+        "number": 9300,
+        "precision": 0.7939526184538653,
+        "recall": 0.8216129032258065
+    },
+    "eval_QTY": {
+        "f1": 0.9865589617956697,
+        "number": 7544,
+        "precision": 0.9855801031882524,
+        "recall": 0.9875397667020148
+    },
+    "eval_RANGE_END": {
+        "f1": 0.7241379310344829,
+        "number": 96,
+        "precision": 0.6176470588235294,
+        "recall": 0.875
+    },
+    "eval_UNIT": {
+        "f1": 0.9525491770373343,
+        "number": 6026,
+        "precision": 0.9226940426193809,
+        "recall": 0.9844009293063392
+    },
+    "eval_loss": 0.4618041217327118,
+    "eval_overall_accuracy": 0.8363606371084514,
+    "eval_overall_f1": 0.8515402557709322,
+    "eval_overall_precision": 0.8238040399638955,
+    "eval_overall_recall": 0.8812092156079372,
+    "eval_runtime": 10.2148,
+    "eval_samples_per_second": 876.573,
+    "eval_steps_per_second": 27.411
+}

config.json

@@ -0,0 +1,51 @@
+{
+  "_name_or_path": "napsternxg/gte-small-L3-ingredient-v2",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "O",
+    "1": "B-COMMENT",
+    "2": "I-COMMENT",
+    "3": "B-NAME",
+    "4": "I-NAME",
+    "5": "B-RANGE_END",
+    "6": "I-RANGE_END",
+    "7": "B-QTY",
+    "8": "I-QTY",
+    "9": "B-UNIT",
+    "10": "I-UNIT"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "B-COMMENT": 1,
+    "B-NAME": 3,
+    "B-QTY": 7,
+    "B-RANGE_END": 5,
+    "B-UNIT": 9,
+    "I-COMMENT": 2,
+    "I-NAME": 4,
+    "I-QTY": 8,
+    "I-RANGE_END": 6,
+    "I-UNIT": 10,
+    "O": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 3,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.34.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f100c7e490090038c2c704f22879e3779272bb7a7c44986d1aebeee90e527dfe
+size 69003222

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

test_results.json

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+{
+    "epoch": 3.0,
+    "eval_COMMENT": {
+        "f1": 0.6939356675943305,
+        "number": 7070,
+        "precision": 0.639661138288987,
+        "recall": 0.7582743988684583
+    },
+    "eval_NAME": {
+        "f1": 0.8075459733671528,
+        "number": 9300,
+        "precision": 0.7939526184538653,
+        "recall": 0.8216129032258065
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+        "precision": 0.9855801031882524,
+        "recall": 0.9875397667020148
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+        "f1": 0.7241379310344829,
+        "number": 96,
+        "precision": 0.6176470588235294,
+        "recall": 0.875
+    },
+    "eval_UNIT": {
+        "f1": 0.9525491770373343,
+        "number": 6026,
+        "precision": 0.9226940426193809,
+        "recall": 0.9844009293063392
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+    "eval_loss": 0.4618041217327118,
+    "eval_overall_accuracy": 0.8363606371084514,
+    "eval_overall_f1": 0.8515402557709322,
+    "eval_overall_precision": 0.8238040399638955,
+    "eval_overall_recall": 0.8812092156079372,
+    "eval_runtime": 10.2148,
+    "eval_samples_per_second": 876.573,
+    "eval_steps_per_second": 27.411
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
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+      "rstrip": false,
+      "single_word": false,
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "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": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}

train_results.json

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+{
+    "epoch": 3.0,
+    "eval_COMMENT": {
+        "f1": 0.707197550855535,
+        "number": 129818,
+        "precision": 0.654796821584877,
+        "recall": 0.768714662065353
+    },
+    "eval_NAME": {
+        "f1": 0.8149847409187171,
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