End of training

README.md

@@ -0,0 +1,93 @@
+---
+license: apache-2.0
+base_model: jinaai/jina-embeddings-v2-small-en
+tags:
+- generated_from_trainer
+datasets:
+- nyt_ingredients
+model-index:
+- name: nyt-ingredient-tagger-jina-embeddings-v2-small-en
+  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-jina-embeddings-v2-small-en
+
+This model is a fine-tuned version of [jinaai/jina-embeddings-v2-small-en](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) on the nyt_ingredients dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9890
+- Comment: {'precision': 0.4891238056515552, 'recall': 0.6700083542188805, 'f1': 0.5654524089306698, 'number': 7182}
+- Name: {'precision': 0.7393011781290907, 'recall': 0.7889533634214485, 'f1': 0.7633206840983521, 'number': 9306}
+- Qty: {'precision': 0.9253731343283582, 'recall': 0.9613688009624382, 'f1': 0.943027601127647, 'number': 7481}
+- Range End: {'precision': 0.5454545454545454, 'recall': 0.5121951219512195, 'f1': 0.5283018867924528, 'number': 82}
+- Unit: {'precision': 0.9031507061927674, 'recall': 0.9693486590038314, 'f1': 0.9350795436284751, 'number': 6003}
+- Overall Precision: 0.7401
+- Overall Recall: 0.8387
+- Overall F1: 0.7863
+- Overall Accuracy: 0.7817
+
+## 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: 5
+- label_smoothing_factor: 0.1
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Comment                                                                                                    | Name                                                                                                      | Qty                                                                                                       | Range End                                                                                                  | Unit                                                                                                      | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.1585        | 0.2   | 1000  | 1.1247          | {'precision': 0.38455309241826097, 'recall': 0.5557343475716794, 'f1': 0.454561770864493, 'number': 6836}  | {'precision': 0.6500338458563002, 'recall': 0.7587763855965685, 'f1': 0.7002083333333333, 'number': 8859} | {'precision': 0.8947789025039957, 'recall': 0.9468639887244539, 'f1': 0.9200849140587551, 'number': 7095} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 74}                                                 | {'precision': 0.8575376112987412, 'recall': 0.9760615062030403, 'f1': 0.912968864917872, 'number': 5723}  | 0.6666            | 0.7984         | 0.7266     | 0.7220           |
+| 1.1018        | 0.4   | 2000  | 1.0677          | {'precision': 0.3960273712222011, 'recall': 0.6095669982445875, 'f1': 0.48012443829934326, 'number': 6836} | {'precision': 0.7094423320659062, 'recall': 0.7582119878090078, 'f1': 0.733016860369946, 'number': 8859}  | {'precision': 0.8986009327115256, 'recall': 0.9505285412262157, 'f1': 0.9238356164383562, 'number': 7095} | {'precision': 0.2, 'recall': 0.02702702702702703, 'f1': 0.047619047619047616, 'number': 74}                | {'precision': 0.8794009877329935, 'recall': 0.9645290931329722, 'f1': 0.9199999999999999, 'number': 5723} | 0.6853            | 0.8098         | 0.7424     | 0.7415           |
+| 1.0676        | 0.59  | 3000  | 1.0472          | {'precision': 0.41734173417341736, 'recall': 0.6104447045055588, 'f1': 0.4957528957528958, 'number': 6836} | {'precision': 0.7021688219122288, 'recall': 0.7784174286036799, 'f1': 0.7383297644539616, 'number': 8859} | {'precision': 0.8949375410913872, 'recall': 0.9592670894996477, 'f1': 0.9259863945578231, 'number': 7095} | {'precision': 0.5161290322580645, 'recall': 0.21621621621621623, 'f1': 0.3047619047619048, 'number': 74}   | {'precision': 0.8788844621513944, 'recall': 0.9636554254761489, 'f1': 0.9193198866477745, 'number': 5723} | 0.6939            | 0.8188         | 0.7512     | 0.7541           |
+| 1.0613        | 0.79  | 4000  | 1.0459          | {'precision': 0.4413024850042845, 'recall': 0.6026916325336454, 'f1': 0.5095226317091268, 'number': 6836}  | {'precision': 0.7297499465697799, 'recall': 0.7708544982503669, 'f1': 0.7497392545424604, 'number': 8859} | {'precision': 0.9064651100013497, 'recall': 0.9465821000704722, 'f1': 0.9260893546607832, 'number': 7095} | {'precision': 0.34210526315789475, 'recall': 0.17567567567567569, 'f1': 0.23214285714285715, 'number': 74} | {'precision': 0.8965631196298744, 'recall': 0.9481041411846933, 'f1': 0.9216135881104034, 'number': 5723} | 0.7177            | 0.8082         | 0.7603     | 0.7502           |
+| 1.045         | 0.99  | 5000  | 1.0292          | {'precision': 0.43983577218654596, 'recall': 0.6111761263897015, 'f1': 0.5115396388123661, 'number': 6836} | {'precision': 0.7188987787207618, 'recall': 0.7840614064792866, 'f1': 0.7500674909562118, 'number': 8859} | {'precision': 0.886005680351149, 'recall': 0.9673009161381254, 'f1': 0.9248702917593155, 'number': 7095}  | {'precision': 0.3541666666666667, 'recall': 0.22972972972972974, 'f1': 0.27868852459016397, 'number': 74}  | {'precision': 0.8777340676632572, 'recall': 0.974663637952123, 'f1': 0.9236628580890875, 'number': 5723}  | 0.7080            | 0.8249         | 0.7620     | 0.7610           |
+| 1.0334        | 1.19  | 6000  | 1.0344          | {'precision': 0.47399084477736164, 'recall': 0.6664716208308953, 'f1': 0.5539883268482491, 'number': 6836} | {'precision': 0.7198329853862213, 'recall': 0.7784174286036799, 'f1': 0.7479798253701395, 'number': 8859} | {'precision': 0.9296510806611104, 'recall': 0.927554615926709, 'f1': 0.92860166502046, 'number': 7095}    | {'precision': 0.36666666666666664, 'recall': 0.2972972972972973, 'f1': 0.3283582089552239, 'number': 74}   | {'precision': 0.8982691051600261, 'recall': 0.9612091560370435, 'f1': 0.9286739258884106, 'number': 5723} | 0.7258            | 0.8240         | 0.7718     | 0.7595           |
+| 1.0187        | 1.39  | 7000  | 1.0210          | {'precision': 0.4423198816818086, 'recall': 0.6124926857811586, 'f1': 0.5136793031529874, 'number': 6836}  | {'precision': 0.7155410238070911, 'recall': 0.7904955412574782, 'f1': 0.751153062318996, 'number': 8859}  | {'precision': 0.8767850372804247, 'recall': 0.9778717406624383, 'f1': 0.9245735607675907, 'number': 7095} | {'precision': 0.37142857142857144, 'recall': 0.17567567567567569, 'f1': 0.23853211009174313, 'number': 74} | {'precision': 0.8888354957552459, 'recall': 0.9695963655425476, 'f1': 0.927461139896373, 'number': 5723}  | 0.7083            | 0.8287         | 0.7638     | 0.7651           |
+| 1.0319        | 1.58  | 8000  | 1.0136          | {'precision': 0.46955690149824675, 'recall': 0.6464306612053833, 'f1': 0.5439773496645535, 'number': 6836} | {'precision': 0.7399957428693061, 'recall': 0.7848515633818716, 'f1': 0.7617639003012875, 'number': 8859} | {'precision': 0.8963893249607535, 'recall': 0.9657505285412262, 'f1': 0.9297781396295542, 'number': 7095} | {'precision': 0.45098039215686275, 'recall': 0.3108108108108108, 'f1': 0.368, 'number': 74}                | {'precision': 0.8991981672394044, 'recall': 0.9601607548488555, 'f1': 0.9286800743620078, 'number': 5723} | 0.7280            | 0.8305         | 0.7759     | 0.7700           |
+| 1.0154        | 1.78  | 9000  | 1.0071          | {'precision': 0.47295907875796833, 'recall': 0.6729081334113517, 'f1': 0.5554884675763797, 'number': 6836} | {'precision': 0.7443054218800128, 'recall': 0.7856417202844564, 'f1': 0.7644151565074134, 'number': 8859} | {'precision': 0.9104236718224613, 'recall': 0.9540521494009866, 'f1': 0.9317274604267033, 'number': 7095} | {'precision': 0.4126984126984127, 'recall': 0.35135135135135137, 'f1': 0.3795620437956204, 'number': 74}   | {'precision': 0.8885522959183674, 'recall': 0.9737899702952997, 'f1': 0.9292205085452273, 'number': 5723} | 0.7285            | 0.8370         | 0.7790     | 0.7732           |
+| 1.011         | 1.98  | 10000 | 1.0127          | {'precision': 0.4703654417033473, 'recall': 0.6721767115272089, 'f1': 0.5534477566997892, 'number': 6836}  | {'precision': 0.742723104808615, 'recall': 0.7863189976295293, 'f1': 0.7638995503892971, 'number': 8859}  | {'precision': 0.915743991358358, 'recall': 0.9558844256518675, 'f1': 0.9353837666367836, 'number': 7095}  | {'precision': 0.39285714285714285, 'recall': 0.2972972972972973, 'f1': 0.3384615384615385, 'number': 74}   | {'precision': 0.901291060630822, 'recall': 0.9636554254761489, 'f1': 0.9314305016044586, 'number': 5723}  | 0.7296            | 0.8353         | 0.7789     | 0.7712           |
+| 0.9958        | 2.18  | 11000 | 1.0024          | {'precision': 0.4715127701375246, 'recall': 0.6670567583382094, 'f1': 0.5524928818077179, 'number': 6836}  | {'precision': 0.7483029845921776, 'recall': 0.7839485269217744, 'f1': 0.7657111356119073, 'number': 8859} | {'precision': 0.9206457791763579, 'recall': 0.9484143763213531, 'f1': 0.9343237989447376, 'number': 7095} | {'precision': 0.35, 'recall': 0.3783783783783784, 'f1': 0.36363636363636365, 'number': 74}                 | {'precision': 0.8947876447876448, 'recall': 0.9718679014502883, 'f1': 0.9317363263254879, 'number': 5723} | 0.7318            | 0.8334         | 0.7793     | 0.7763           |
+| 1.0042        | 2.38  | 12000 | 1.0007          | {'precision': 0.4789602641951635, 'recall': 0.657694558221182, 'f1': 0.5542747950440732, 'number': 6836}   | {'precision': 0.7298981923955953, 'recall': 0.7930917710802574, 'f1': 0.7601839329185826, 'number': 8859} | {'precision': 0.9267211525141986, 'recall': 0.9429175475687104, 'f1': 0.9347491965907503, 'number': 7095} | {'precision': 0.35555555555555557, 'recall': 0.43243243243243246, 'f1': 0.3902439024390244, 'number': 74}  | {'precision': 0.9089250165892502, 'recall': 0.9573650183470208, 'f1': 0.932516381584546, 'number': 5723}  | 0.7333            | 0.8299         | 0.7786     | 0.7769           |
+| 1.0048        | 2.57  | 13000 | 0.9943          | {'precision': 0.47168994262206343, 'recall': 0.6373610298420129, 'f1': 0.542151434082001, 'number': 6836}  | {'precision': 0.7342649994746243, 'recall': 0.7888023478947963, 'f1': 0.7605572485851111, 'number': 8859} | {'precision': 0.8968563263185243, 'recall': 0.9730796335447498, 'f1': 0.9334144527817211, 'number': 7095} | {'precision': 0.4307692307692308, 'recall': 0.3783783783783784, 'f1': 0.4028776978417266, 'number': 74}    | {'precision': 0.8915373540233562, 'recall': 0.9737899702952997, 'f1': 0.9308501753799899, 'number': 5723} | 0.7278            | 0.8343         | 0.7774     | 0.7787           |
+| 0.9911        | 2.77  | 14000 | 0.9951          | {'precision': 0.4768817204301075, 'recall': 0.6487712112346401, 'f1': 0.5497025285076846, 'number': 6836}  | {'precision': 0.729257190151045, 'recall': 0.7956880009030365, 'f1': 0.7610256410256411, 'number': 8859}  | {'precision': 0.9042483230303827, 'recall': 0.9689922480620154, 'f1': 0.9355014287658184, 'number': 7095} | {'precision': 0.5319148936170213, 'recall': 0.33783783783783783, 'f1': 0.4132231404958678, 'number': 74}   | {'precision': 0.8965628529933839, 'recall': 0.9708195002621003, 'f1': 0.9322147651006711, 'number': 5723} | 0.7296            | 0.8374         | 0.7798     | 0.7786           |
+| 0.9991        | 2.97  | 15000 | 0.9921          | {'precision': 0.4791033832617576, 'recall': 0.6691047396138092, 'f1': 0.5583836904107916, 'number': 6836}  | {'precision': 0.7481054541573273, 'recall': 0.7911728186025511, 'f1': 0.7690366469168313, 'number': 8859} | {'precision': 0.9137861466039005, 'recall': 0.9575757575757575, 'f1': 0.935168616655196, 'number': 7095}  | {'precision': 0.4166666666666667, 'recall': 0.40540540540540543, 'f1': 0.4109589041095891, 'number': 74}   | {'precision': 0.8894720101781171, 'recall': 0.977284640922593, 'f1': 0.9313129631171426, 'number': 5723}  | 0.7337            | 0.8395         | 0.7831     | 0.7807           |
+| 0.9805        | 3.17  | 16000 | 0.9880          | {'precision': 0.4859154929577465, 'recall': 0.6560854300760679, 'f1': 0.5583219220714553, 'number': 6836}  | {'precision': 0.7423922231614539, 'recall': 0.7930917710802574, 'f1': 0.7669049828084921, 'number': 8859} | {'precision': 0.9187102018696653, 'recall': 0.9557434813248766, 'f1': 0.9368610113290964, 'number': 7095} | {'precision': 0.4444444444444444, 'recall': 0.3783783783783784, 'f1': 0.4087591240875913, 'number': 74}    | {'precision': 0.9000486775920817, 'recall': 0.9692468984798183, 'f1': 0.9333669863705198, 'number': 5723} | 0.7389            | 0.8349         | 0.7840     | 0.7822           |
+| 0.9848        | 3.37  | 17000 | 0.9842          | {'precision': 0.48933174482833314, 'recall': 0.6609128145114102, 'f1': 0.5623249735515589, 'number': 6836} | {'precision': 0.7466623945316672, 'recall': 0.7891409865673327, 'f1': 0.7673142355394577, 'number': 8859} | {'precision': 0.9149737656397148, 'recall': 0.9585623678646934, 'f1': 0.936261013215859, 'number': 7095}  | {'precision': 0.4126984126984127, 'recall': 0.35135135135135137, 'f1': 0.3795620437956204, 'number': 74}   | {'precision': 0.899171943497321, 'recall': 0.9676742966975362, 'f1': 0.9321663019693655, 'number': 5723}  | 0.7403            | 0.8351         | 0.7848     | 0.7824           |
+| 0.9771        | 3.56  | 18000 | 0.9834          | {'precision': 0.4883396023643203, 'recall': 0.6647162083089526, 'f1': 0.5630382256365777, 'number': 6836}  | {'precision': 0.7373874816830647, 'recall': 0.795236482672988, 'f1': 0.7652202248411449, 'number': 8859}  | {'precision': 0.9162388543636855, 'recall': 0.9558844256518675, 'f1': 0.9356418569359177, 'number': 7095} | {'precision': 0.4583333333333333, 'recall': 0.44594594594594594, 'f1': 0.4520547945205479, 'number': 74}   | {'precision': 0.8992864093415505, 'recall': 0.968897431417089, 'f1': 0.9327950206072841, 'number': 5723}  | 0.7369            | 0.8378         | 0.7841     | 0.7837           |
+| 0.9787        | 3.76  | 19000 | 0.9832          | {'precision': 0.4892808110676946, 'recall': 0.677735517846694, 'f1': 0.5682919349892671, 'number': 6836}   | {'precision': 0.7466029723991507, 'recall': 0.7938819279828423, 'f1': 0.7695169319984682, 'number': 8859} | {'precision': 0.9206090266449157, 'recall': 0.9544749823819592, 'f1': 0.9372361774271676, 'number': 7095} | {'precision': 0.4189189189189189, 'recall': 0.4189189189189189, 'f1': 0.4189189189189189, 'number': 74}    | {'precision': 0.9048244174597965, 'recall': 0.9634806919447843, 'f1': 0.9332317847169331, 'number': 5723} | 0.7399            | 0.8389         | 0.7863     | 0.7844           |
+| 0.9746        | 3.96  | 20000 | 0.9827          | {'precision': 0.4950890447922288, 'recall': 0.6710064365125804, 'f1': 0.5697782746413266, 'number': 6836}  | {'precision': 0.7460368124268539, 'recall': 0.7915114572750874, 'f1': 0.768101654069449, 'number': 8859}  | {'precision': 0.9120629837203096, 'recall': 0.9633544749823819, 'f1': 0.9370073342929603, 'number': 7095} | {'precision': 0.40963855421686746, 'recall': 0.4594594594594595, 'f1': 0.43312101910828027, 'number': 74}  | {'precision': 0.9003893575600259, 'recall': 0.9697710990739122, 'f1': 0.9337932194834694, 'number': 5723} | 0.7412            | 0.8402         | 0.7876     | 0.7846           |
+| 0.976         | 4.16  | 21000 | 0.9836          | {'precision': 0.4884607241160279, 'recall': 0.6749561146869514, 'f1': 0.5667608401916225, 'number': 6836}  | {'precision': 0.7483774869666986, 'recall': 0.7939948075403545, 'f1': 0.7705115565779385, 'number': 8859} | {'precision': 0.9147193123824873, 'recall': 0.9599718111346018, 'f1': 0.936799394814662, 'number': 7095}  | {'precision': 0.4125, 'recall': 0.44594594594594594, 'f1': 0.42857142857142855, 'number': 74}              | {'precision': 0.9035317200784827, 'recall': 0.9655774943211602, 'f1': 0.9335247909451813, 'number': 5723} | 0.7393            | 0.8402         | 0.7865     | 0.7854           |
+| 0.9635        | 4.36  | 22000 | 0.9832          | {'precision': 0.49533612093920876, 'recall': 0.6758338209479228, 'f1': 0.5716760502381983, 'number': 6836} | {'precision': 0.7475583864118895, 'recall': 0.7948978440004515, 'f1': 0.7705016685814322, 'number': 8859} | {'precision': 0.9186408555570597, 'recall': 0.9564482029598309, 'f1': 0.9371633752244165, 'number': 7095} | {'precision': 0.391304347826087, 'recall': 0.4864864864864865, 'f1': 0.43373493975903615, 'number': 74}    | {'precision': 0.9026418786692759, 'recall': 0.9671500961034423, 'f1': 0.9337832138338253, 'number': 5723} | 0.7423            | 0.8402         | 0.7882     | 0.7851           |
+| 0.9688        | 4.55  | 23000 | 0.9836          | {'precision': 0.4930739135032251, 'recall': 0.6821240491515506, 'f1': 0.5723930522310194, 'number': 6836}  | {'precision': 0.7467897697124058, 'recall': 0.7943334462128908, 'f1': 0.7698282463625424, 'number': 8859} | {'precision': 0.9208593962469405, 'recall': 0.9544749823819592, 'f1': 0.9373659076752716, 'number': 7095} | {'precision': 0.35714285714285715, 'recall': 0.5405405405405406, 'f1': 0.4301075268817204, 'number': 74}   | {'precision': 0.9021013194331324, 'recall': 0.9676742966975362, 'f1': 0.9337379868487607, 'number': 5723} | 0.7403            | 0.8413         | 0.7876     | 0.7860           |
+| 0.9669        | 4.75  | 24000 | 0.9803          | {'precision': 0.4968897468897469, 'recall': 0.677735517846694, 'f1': 0.5733910891089109, 'number': 6836}   | {'precision': 0.7478168264110756, 'recall': 0.7926402528502088, 'f1': 0.7695764151460354, 'number': 8859} | {'precision': 0.9173631706659477, 'recall': 0.9591261451726568, 'f1': 0.9377799214497348, 'number': 7095} | {'precision': 0.4, 'recall': 0.4864864864864865, 'f1': 0.43902439024390244, 'number': 74}                  | {'precision': 0.8998864189518092, 'recall': 0.9690721649484536, 'f1': 0.9331987211845869, 'number': 5723} | 0.7424            | 0.8410         | 0.7886     | 0.7873           |
+| 0.9691        | 4.95  | 25000 | 0.9796          | {'precision': 0.4962978860392746, 'recall': 0.6765652428320655, 'f1': 0.5725781491798204, 'number': 6836}  | {'precision': 0.7474457215836526, 'recall': 0.7927531324077209, 'f1': 0.7694330320460149, 'number': 8859} | {'precision': 0.9183783783783783, 'recall': 0.9578576462297392, 'f1': 0.9377026560883062, 'number': 7095} | {'precision': 0.4065934065934066, 'recall': 0.5, 'f1': 0.4484848484848485, 'number': 74}                   | {'precision': 0.9002599090318388, 'recall': 0.968373230822995, 'f1': 0.933075174678003, 'number': 5723}   | 0.7423            | 0.8403         | 0.7883     | 0.7871           |
+
+
+### Framework versions
+
+- Transformers 4.34.1
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.14.1

all_results.json

@@ -0,0 +1,41 @@
+{
+    "epoch": 5.0,
+    "eval_COMMENT": {
+        "f1": 0.5654524089306698,
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