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bert-base-cased-finetuned-Stromberg_NLP_Twitter-PoS_v2

Token Classification
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - token-classification
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+ - generated_from_trainer
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+ datasets:
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+ - twitter_pos_vcb
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: bert-base-cased-finetuned-Stromberg_NLP_Twitter-PoS_v2
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: twitter_pos_vcb
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+ type: twitter_pos_vcb
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+ config: twitter-pos-vcb
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+ split: train
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+ args: twitter-pos-vcb
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9853480683735223
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # bert-base-cased-finetuned-Stromberg_NLP_Twitter-PoS_v2
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+
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+ This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the twitter_pos_vcb dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0502
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+ - $: {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 3}
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+ - '': {'precision': 0.9312320916905444, 'recall': 0.9530791788856305, 'f1-score': 0.9420289855072465, 'support': 341}
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+ - (: {'precision': 0.9791666666666666, 'recall': 0.9591836734693877, 'f1-score': 0.9690721649484536, 'support': 196}
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+ - ): {'precision': 0.960167714884696, 'recall': 0.9703389830508474, 'f1-score': 0.9652265542676501, 'support': 472}
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+ - ,: {'precision': 0.9988979501873485, 'recall': 0.9993384785005512, 'f1-score': 0.9991181657848325, 'support': 4535}
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+ - .: {'precision': 0.9839189708141322, 'recall': 0.9894762249577601, 'f1-score': 0.9866897730281368, 'support': 20715}
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+ - :: {'precision': 0.9926405887528997, 'recall': 0.9971072719967858, 'f1-score': 0.9948689168604183, 'support': 12445}
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+ - Cc: {'precision': 0.9991067440821796, 'recall': 0.9986607142857142, 'f1-score': 0.9988836793927215, 'support': 4480}
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+ - Cd: {'precision': 0.9903884661593912, 'recall': 0.9899919935948759, 'f1-score': 0.9901901901901902, 'support': 2498}
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+ - Dt: {'precision': 0.9981148589510537, 'recall': 0.9976446837146703, 'f1-score': 0.9978797159492478, 'support': 14860}
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+ - Ex: {'precision': 0.9142857142857143, 'recall': 0.9846153846153847, 'f1-score': 0.9481481481481482, 'support': 65}
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+ - Fw: {'precision': 1.0, 'recall': 0.1, 'f1-score': 0.18181818181818182, 'support': 10}
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+ - Ht: {'precision': 0.999877541023757, 'recall': 0.9997551120362435, 'f1-score': 0.9998163227820978, 'support': 8167}
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+ - In: {'precision': 0.9960399353003514, 'recall': 0.9954846981437092, 'f1-score': 0.9957622393219583, 'support': 17939}
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+ - Jj: {'precision': 0.9812470698546648, 'recall': 0.9834756049808129, 'f1-score': 0.9823600735322877, 'support': 12769}
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+ - Jjr: {'precision': 0.9304511278195489, 'recall': 0.9686888454011742, 'f1-score': 0.9491850431447747, 'support': 511}
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+ - Jjs: {'precision': 0.9578414839797639, 'recall': 0.9726027397260274, 'f1-score': 0.9651656754460493, 'support': 584}
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+ - Md: {'precision': 0.9901398761751892, 'recall': 0.9908214777420835, 'f1-score': 0.990480559697213, 'support': 4358}
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+ - Nn: {'precision': 0.9810285563194078, 'recall': 0.9819697621331922, 'f1-score': 0.9814989335846437, 'support': 30227}
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+ - Nnp: {'precision': 0.9609722697706266, 'recall': 0.9467116357504216, 'f1-score': 0.9537886510363575, 'support': 8895}
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+ - Nnps: {'precision': 1.0, 'recall': 0.037037037037037035, 'f1-score': 0.07142857142857142, 'support': 27}
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+ - Nns: {'precision': 0.9697771061579146, 'recall': 0.9776564681985528, 'f1-score': 0.9737008471361739, 'support': 7877}
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+ - Pos: {'precision': 0.9977272727272727, 'recall': 0.984304932735426, 'f1-score': 0.9909706546275394, 'support': 446}
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+ - Prp: {'precision': 0.9983503349829983, 'recall': 0.9985184187487373, 'f1-score': 0.9984343697917544, 'support': 29698}
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+ - Prp$: {'precision': 0.9974262182566919, 'recall': 0.9974262182566919, 'f1-score': 0.9974262182566919, 'support': 5828}
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+ - Rb: {'precision': 0.9939770374552983, 'recall': 0.9929802569727358, 'f1-score': 0.9934783971906942, 'support': 15955}
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+ - Rbr: {'precision': 0.9058823529411765, 'recall': 0.8191489361702128, 'f1-score': 0.8603351955307263, 'support': 94}
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+ - Rbs: {'precision': 0.92, 'recall': 1.0, 'f1-score': 0.9583333333333334, 'support': 69}
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+ - Rp: {'precision': 0.9802197802197802, 'recall': 0.9903774981495189, 'f1-score': 0.9852724594992636, 'support': 1351}
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+ - Rt: {'precision': 0.9995065383666419, 'recall': 0.9996298581122763, 'f1-score': 0.9995681944358769, 'support': 8105}
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+ - Sym: {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 9}
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+ - To: {'precision': 0.9984649496844619, 'recall': 0.9989761092150171, 'f1-score': 0.9987204640450398, 'support': 5860}
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+ - Uh: {'precision': 0.9614460148062687, 'recall': 0.9507510933637574, 'f1-score': 0.9560686457287633, 'support': 10518}
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+ - Url: {'precision': 1.0, 'recall': 0.9997242900468707, 'f1-score': 0.9998621260168207, 'support': 3627}
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+ - Usr: {'precision': 0.9999025388626285, 'recall': 1.0, 'f1-score': 0.9999512670565303, 'support': 20519}
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+ - Vb: {'precision': 0.9619302598929085, 'recall': 0.9570556133056133, 'f1-score': 0.9594867452615125, 'support': 15392}
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+ - Vbd: {'precision': 0.9592894152479645, 'recall': 0.9548719837907533, 'f1-score': 0.9570756023262255, 'support': 5429}
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+ - Vbg: {'precision': 0.9848831077518018, 'recall': 0.984191111891797, 'f1-score': 0.9845369882270251, 'support': 5693}
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+ - Vbn: {'precision': 0.9053408597481546, 'recall': 0.9164835164835164, 'f1-score': 0.910878112712975, 'support': 2275}
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+ - Vbp: {'precision': 0.963605718209626, 'recall': 0.9666228317364894, 'f1-score': 0.9651119169688633, 'support': 15969}
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+ - Vbz: {'precision': 0.9881780250347705, 'recall': 0.9861207494795281, 'f1-score': 0.9871483153872872, 'support': 5764}
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+ - Wdt: {'precision': 0.8666666666666667, 'recall': 0.9285714285714286, 'f1-score': 0.896551724137931, 'support': 14}
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+ - Wp: {'precision': 0.99125, 'recall': 0.993734335839599, 'f1-score': 0.9924906132665832, 'support': 1596}
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+ - Wrb: {'precision': 0.9963488843813387, 'recall': 0.9979683055668428, 'f1-score': 0.9971579374746244, 'support': 2461}
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+ - ``: {'precision': 0.9481865284974094, 'recall': 0.9786096256684492, 'f1-score': 0.963157894736842, 'support': 187}
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+ - Accuracy: 0.9853
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+ - Macro avg: {'precision': 0.9296417163691048, 'recall': 0.8931046018294694, 'f1-score': 0.8930917459781836, 'support': 308833}
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+ - Weighted avg: {'precision': 0.985306457604231, 'recall': 0.9853480683735223, 'f1-score': 0.9852689858931941, 'support': 308833}
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 2
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | $ | '' | ( | ) | , | . | : | Cc | Cd | Dt | Ex | Fw | Ht | In | Jj | Jjr | Jjs | Md | Nn | Nnp | Nnps | Nns | Pos | Prp | Prp$ | Rb | Rbr | Rbs | Rp | Rt | Sym | To | Uh | Url | Usr | Vb | Vbd | Vbg | Vbn | Vbp | Vbz | Wdt | Wp | Wrb | `` | Accuracy | Macro avg | Weighted avg |
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+ |:-------------:|:-----:|:-----:|:---------------:|:----------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|:--------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|
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+ | 0.0649 | 1.0 | 7477 | 0.0570 | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 3} | {'precision': 0.9257142857142857, 'recall': 0.9501466275659824, 'f1-score': 0.9377713458755427, 'support': 341} | {'precision': 0.9690721649484536, 'recall': 0.9591836734693877, 'f1-score': 0.964102564102564, 'support': 196} | {'precision': 0.9427402862985685, 'recall': 0.9766949152542372, 'f1-score': 0.959417273673257, 'support': 472} | {'precision': 0.998677831643896, 'recall': 0.9993384785005512, 'f1-score': 0.9990080458503251, 'support': 4535} | {'precision': 0.977118886452474, 'recall': 0.9895244991552016, 'f1-score': 0.9832825654186554, 'support': 20715} | {'precision': 0.9933418899406385, 'recall': 0.9950180795500201, 'f1-score': 0.9941792782305006, 'support': 12445} | {'precision': 0.9995530726256984, 'recall': 0.9984375, 'f1-score': 0.9989949748743719, 'support': 4480} | {'precision': 0.9856573705179282, 'recall': 0.9903923138510808, 'f1-score': 0.9880191693290733, 'support': 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+ | 0.0396 | 2.0 | 14954 | 0.0502 | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 3} | {'precision': 0.9312320916905444, 'recall': 0.9530791788856305, 'f1-score': 0.9420289855072465, 'support': 341} | {'precision': 0.9791666666666666, 'recall': 0.9591836734693877, 'f1-score': 0.9690721649484536, 'support': 196} | {'precision': 0.960167714884696, 'recall': 0.9703389830508474, 'f1-score': 0.9652265542676501, 'support': 472} | {'precision': 0.9988979501873485, 'recall': 0.9993384785005512, 'f1-score': 0.9991181657848325, 'support': 4535} | {'precision': 0.9839189708141322, 'recall': 0.9894762249577601, 'f1-score': 0.9866897730281368, 'support': 20715} | {'precision': 0.9926405887528997, 'recall': 0.9971072719967858, 'f1-score': 0.9948689168604183, 'support': 12445} | {'precision': 0.9991067440821796, 'recall': 0.9986607142857142, 'f1-score': 0.9988836793927215, 'support': 4480} | {'precision': 0.9903884661593912, 'recall': 0.9899919935948759, 'f1-score': 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{'precision': 0.9592894152479645, 'recall': 0.9548719837907533, 'f1-score': 0.9570756023262255, 'support': 5429} | {'precision': 0.9848831077518018, 'recall': 0.984191111891797, 'f1-score': 0.9845369882270251, 'support': 5693} | {'precision': 0.9053408597481546, 'recall': 0.9164835164835164, 'f1-score': 0.910878112712975, 'support': 2275} | {'precision': 0.963605718209626, 'recall': 0.9666228317364894, 'f1-score': 0.9651119169688633, 'support': 15969} | {'precision': 0.9881780250347705, 'recall': 0.9861207494795281, 'f1-score': 0.9871483153872872, 'support': 5764} | {'precision': 0.8666666666666667, 'recall': 0.9285714285714286, 'f1-score': 0.896551724137931, 'support': 14} | {'precision': 0.99125, 'recall': 0.993734335839599, 'f1-score': 0.9924906132665832, 'support': 1596} | {'precision': 0.9963488843813387, 'recall': 0.9979683055668428, 'f1-score': 0.9971579374746244, 'support': 2461} | {'precision': 0.9481865284974094, 'recall': 0.9786096256684492, 'f1-score': 0.963157894736842, 'support': 187} | 0.9853 | {'precision': 0.9296417163691048, 'recall': 0.8931046018294694, 'f1-score': 0.8930917459781836, 'support': 308833} | {'precision': 0.985306457604231, 'recall': 0.9853480683735223, 'f1-score': 0.9852689858931941, 'support': 308833} |
116
+
117
+
118
+ ### Framework versions
119
+
120
+ - Transformers 4.28.1
121
+ - Pytorch 2.0.0
122
+ - Datasets 2.11.0
123
+ - Tokenizers 0.13.3