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_ingredients-crf-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_ingredients-crf-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: 1.6099
+- Comment: {'precision': 0.042328042328042326, 'recall': 0.015407896546980328, 'f1': 0.022592032274331823, 'number': 7269}
+- Name: {'precision': 0.1018981018981019, 'recall': 0.03297769156159069, 'f1': 0.049829018075232046, 'number': 9279}
+- Qty: {'precision': 0.15665304220758594, 'recall': 0.9842980705256155, 'f1': 0.27028903423831624, 'number': 7515}
+- Range End: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 90}
+- Unit: {'precision': 0.5, 'recall': 0.00016485328058028355, 'f1': 0.00032959789057350036, 'number': 6066}
+- Overall Precision: 0.1478
+- Overall Recall: 0.2586
+- Overall F1: 0.1881
+- Overall Accuracy: 0.1721
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 5.3925        | 0.19  | 1000  | 4.7348          | {'precision': 0.040214477211796246, 'recall': 0.010744985673352435, 'f1': 0.016958733747880157, 'number': 1396} | {'precision': 0.10684931506849316, 'recall': 0.04377104377104377, 'f1': 0.06210191082802548, 'number': 1782}  | {'precision': 0.15598917211820437, 'recall': 0.987152034261242, 'f1': 0.26940683744034283, 'number': 1401}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1093} | 0.1481            | 0.2595         | 0.1886     | 0.1706           |
+| 4.0783        | 0.38  | 2000  | 3.8413          | {'precision': 0.038560411311053984, 'recall': 0.010744985673352435, 'f1': 0.01680672268907563, 'number': 1396}  | {'precision': 0.10653409090909091, 'recall': 0.04208754208754209, 'f1': 0.06033789219629928, 'number': 1782}  | {'precision': 0.15589396503102088, 'recall': 0.9864382583868665, 'f1': 0.2692382622248198, 'number': 1401}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1093} | 0.1478            | 0.2588         | 0.1882     | 0.1701           |
+| 3.6055        | 0.57  | 3000  | 3.3592          | {'precision': 0.038461538461538464, 'recall': 0.012177650429799427, 'f1': 0.018498367791077257, 'number': 1396} | {'precision': 0.10407876230661041, 'recall': 0.04152637485970819, 'f1': 0.05936622543120738, 'number': 1782}  | {'precision': 0.1565937181086291, 'recall': 0.9857244825124911, 'f1': 0.2702544031311155, 'number': 1401}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1093} | 0.1476            | 0.2588         | 0.1880     | 0.1708           |
+| 3.2433        | 0.76  | 4000  | 3.0284          | {'precision': 0.03762376237623762, 'recall': 0.013610315186246419, 'f1': 0.019989479221462388, 'number': 1396}  | {'precision': 0.10084033613445378, 'recall': 0.04040404040404041, 'f1': 0.057692307692307696, 'number': 1782} | {'precision': 0.1578404401650619, 'recall': 0.9828693790149893, 'f1': 0.272, 'number': 1401}                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1093} | 0.1476            | 0.2581         | 0.1878     | 0.1722           |
+| 2.8283        | 0.95  | 5000  | 2.7775          | {'precision': 0.034545454545454546, 'recall': 0.013610315186246419, 'f1': 0.019527235354573486, 'number': 1396} | {'precision': 0.10086455331412104, 'recall': 0.03928170594837262, 'f1': 0.05654281098546042, 'number': 1782}  | {'precision': 0.1570031832651205, 'recall': 0.9857244825124911, 'f1': 0.2708639796018437, 'number': 1401}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1093} | 0.1464            | 0.2585         | 0.1869     | 0.1707           |
+| 2.5028        | 1.14  | 6000  | 2.4982          | {'precision': 0.03616636528028933, 'recall': 0.014326647564469915, 'f1': 0.02052334530528476, 'number': 1396}   | {'precision': 0.10099573257467995, 'recall': 0.03984287317620651, 'f1': 0.057142857142857134, 'number': 1782} | {'precision': 0.157134735645253, 'recall': 0.9864382583868665, 'f1': 0.2710867006669282, 'number': 1401}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1093} | 0.1466            | 0.2590         | 0.1872     | 0.1716           |
+| 2.3731        | 1.33  | 7000  | 2.3250          | {'precision': 0.037698412698412696, 'recall': 0.013610315186246419, 'f1': 0.02, 'number': 1396}                 | {'precision': 0.09957924263674614, 'recall': 0.03984287317620651, 'f1': 0.05691382765531061, 'number': 1782}  | {'precision': 0.15701254275940707, 'recall': 0.9828693790149893, 'f1': 0.27076983580768854, 'number': 1401} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1093} | 0.1469            | 0.2580         | 0.1872     | 0.1711           |
+| 2.1459        | 1.52  | 8000  | 2.1464          | {'precision': 0.04007285974499089, 'recall': 0.015759312320916905, 'f1': 0.02262210796915167, 'number': 1396}   | {'precision': 0.10235131396957123, 'recall': 0.04152637485970819, 'f1': 0.0590818363273453, 'number': 1782}   | {'precision': 0.15672235481304694, 'recall': 0.9842969307637401, 'f1': 0.2703921568627451, 'number': 1401}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1093} | 0.1465            | 0.2594         | 0.1872     | 0.1714           |
+| 1.9918        | 1.71  | 9000  | 1.9713          | {'precision': 0.04070796460176991, 'recall': 0.0164756446991404, 'f1': 0.023457419683834777, 'number': 1396}    | {'precision': 0.09900990099009901, 'recall': 0.03928170594837262, 'f1': 0.05624748895138609, 'number': 1782}  | {'precision': 0.15779205875602478, 'recall': 0.9814418272662384, 'f1': 0.2718734552644587, 'number': 1401}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1093} | 0.1470            | 0.2581         | 0.1873     | 0.1728           |
+| 1.954         | 1.9   | 10000 | 1.8380          | {'precision': 0.04, 'recall': 0.0164756446991404, 'f1': 0.02333840690005073, 'number': 1396}                    | {'precision': 0.10164835164835165, 'recall': 0.04152637485970819, 'f1': 0.058964143426294816, 'number': 1782} | {'precision': 0.1576777739608382, 'recall': 0.9828693790149893, 'f1': 0.27175843694493784, 'number': 1401}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1093} | 0.1469            | 0.2592         | 0.1875     | 0.1726           |
+| 1.6977        | 2.09  | 11000 | 1.7403          | {'precision': 0.04013377926421405, 'recall': 0.017191977077363897, 'f1': 0.02407221664994985, 'number': 1396}   | {'precision': 0.10339943342776203, 'recall': 0.0409652076318743, 'f1': 0.058681672025723476, 'number': 1782}  | {'precision': 0.15731749114589283, 'recall': 0.9828693790149893, 'f1': 0.27122316328540474, 'number': 1401} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1093} | 0.1466            | 0.2592         | 0.1872     | 0.1715           |
+| 1.5499        | 2.28  | 12000 | 1.6569          | {'precision': 0.03861788617886179, 'recall': 0.013610315186246419, 'f1': 0.0201271186440678, 'number': 1396}    | {'precision': 0.10152990264255911, 'recall': 0.0409652076318743, 'f1': 0.058376649340263896, 'number': 1782}  | {'precision': 0.15768394553152534, 'recall': 0.9835831548893648, 'f1': 0.27179487179487183, 'number': 1401} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1093} | 0.1477            | 0.2585         | 0.1880     | 0.1733           |
+| 1.5793        | 2.47  | 13000 | 1.5988          | {'precision': 0.033582089552238806, 'recall': 0.012893982808022923, 'f1': 0.018633540372670808, 'number': 1396} | {'precision': 0.10198300283286119, 'recall': 0.04040404040404041, 'f1': 0.057877813504823156, 'number': 1782} | {'precision': 0.1576962632841961, 'recall': 0.9850107066381156, 'f1': 0.27186761229314416, 'number': 1401}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1093} | 0.1471            | 0.2585         | 0.1875     | 0.1736           |
+| 1.405         | 2.66  | 14000 | 1.5497          | {'precision': 0.03512396694214876, 'recall': 0.012177650429799427, 'f1': 0.018085106382978725, 'number': 1396}  | {'precision': 0.10198300283286119, 'recall': 0.04040404040404041, 'f1': 0.057877813504823156, 'number': 1782} | {'precision': 0.15643407340280924, 'recall': 0.9857244825124911, 'f1': 0.2700166194153876, 'number': 1401}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1093} | 0.1467            | 0.2585         | 0.1872     | 0.1713           |
+| 1.4509        | 2.85  | 15000 | 1.5233          | {'precision': 0.03985507246376811, 'recall': 0.015759312320916905, 'f1': 0.022587268993839834, 'number': 1396}  | {'precision': 0.10198300283286119, 'recall': 0.04040404040404041, 'f1': 0.057877813504823156, 'number': 1782} | {'precision': 0.1578525641025641, 'recall': 0.9842969307637401, 'f1': 0.2720726053072901, 'number': 1401}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1093} | 0.1474            | 0.2590         | 0.1879     | 0.1727           |
+
+
+### 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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+{
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+  "[MASK]": 103,
+  "[PAD]": 0,
+  "[SEP]": 102,
+  "[UNK]": 100
+}

all_results.json

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+{
+    "epoch": 3.0,
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+        "number": 7269,
+        "precision": 0.042328042328042326,
+        "recall": 0.015407896546980328
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+    "eval_loss": 1.6098747253417969,
+    "eval_overall_accuracy": 0.17205696773030468,
+    "eval_overall_f1": 0.1881356136191313,
+    "eval_overall_precision": 0.14783431057310384,
+    "eval_overall_recall": 0.25864522320394456,
+    "eval_runtime": 13.1011,
+    "eval_samples_per_second": 683.452,
+    "eval_steps_per_second": 21.372
+}

config.json

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+{
+  "_name_or_path": "napsternxg/gte-small-L3-ingredient-v2",
+  "architectures": [
+    "PretrainedCRFModel"
+  ],
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+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
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+  "intermediate_size": 1536,
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+  "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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+size 69005087

special_tokens_map.json

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+{
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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

test_results.json

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+{
+    "epoch": 3.0,
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+    "eval_overall_recall": 0.25864522320394456,
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+}

tokenizer_config.json

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+{
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+}

train_results.json

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+{
+    "epoch": 3.0,
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+    "eval_steps_per_second": 25.622
+}

trainer_state.json

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+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 3.0,
+  "eval_steps": 1000,
+  "global_step": 15789,
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