trainer: training complete at 2024-02-19 21:00:31.064789.

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.836576014905586
+      value: 0.8374001218245011
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,14 +32,14 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4521
-- Claim: {'precision': 0.5926012643409038, 'recall': 0.5952492944496708, 'f1-score': 0.5939223278188431, 'support': 4252.0}
-- Majorclaim: {'precision': 0.746797608881298, 'recall': 0.8015582034830431, 'f1-score': 0.773209549071618, 'support': 2182.0}
-- O: {'precision': 0.9330482727579611, 'recall': 0.8940161725067386, 'f1-score': 0.9131152956722828, 'support': 9275.0}
-- Premise: {'precision': 0.8684019663147715, 'recall': 0.8832786885245901, 'f1-score': 0.8757771546995001, 'support': 12200.0}
-- Accuracy: 0.8366
-- Macro avg: {'precision': 0.7852122780737336, 'recall': 0.7935255897410106, 'f1-score': 0.789006081815561, 'support': 27909.0}
-- Weighted avg: {'precision': 0.8383596573659686, 'recall': 0.836576014905586, 'f1-score': 0.837225505344309, 'support': 27909.0}
+- Loss: 0.4624
+- Claim: {'precision': 0.5906025179856115, 'recall': 0.617826904985889, 'f1-score': 0.6039080459770114, 'support': 4252.0}
+- Majorclaim: {'precision': 0.7631810193321616, 'recall': 0.7960586617781852, 'f1-score': 0.7792732166890982, 'support': 2182.0}
+- O: {'precision': 0.9296403841858387, 'recall': 0.897466307277628, 'f1-score': 0.913270064183444, 'support': 9275.0}
+- Premise: {'precision': 0.8734363502575423, 'recall': 0.875655737704918, 'f1-score': 0.8745446359133887, 'support': 12200.0}
+- Accuracy: 0.8374
+- Macro avg: {'precision': 0.7892150679402886, 'recall': 0.7967519029366551, 'f1-score': 0.7927489906907357, 'support': 27909.0}
+- Weighted avg: {'precision': 0.8404042039171331, 'recall': 0.8374001218245011, 'f1-score': 0.8387335832080923, 'support': 27909.0}
 
 ## Model description
 
@@ -64,18 +64,19 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 6
+- num_epochs: 7
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                                 | Majorclaim                                                                                                         | O                                                                                                                  | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
-|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 41   | 0.5884          | {'precision': 0.49868766404199477, 'recall': 0.22342427093132644, 'f1-score': 0.30859184667857725, 'support': 4252.0} | {'precision': 0.6308376575240919, 'recall': 0.3900091659028414, 'f1-score': 0.4820164259416595, 'support': 2182.0} | {'precision': 0.817888247382327, 'recall': 0.9011320754716982, 'f1-score': 0.8574946137273007, 'support': 9275.0}  | {'precision': 0.7873372125242449, 'recall': 0.9316393442622951, 'f1-score': 0.8534314461630875, 'support': 12200.0} | 0.7713   | {'precision': 0.6836876953681646, 'recall': 0.6115512141420403, 'f1-score': 0.6253835831276563, 'support': 27909.0} | {'precision': 0.7412782687839407, 'recall': 0.7712565838976674, 'f1-score': 0.7427359833384354, 'support': 27909.0} |
-| No log        | 2.0   | 82   | 0.4638          | {'precision': 0.5763888888888888, 'recall': 0.5075258701787394, 'f1-score': 0.5397698849424711, 'support': 4252.0}    | {'precision': 0.6741528762805359, 'recall': 0.7841429880843263, 'f1-score': 0.725, 'support': 2182.0}              | {'precision': 0.9210763341589732, 'recall': 0.8820485175202156, 'f1-score': 0.9011400561766811, 'support': 9275.0} | {'precision': 0.8506865437426442, 'recall': 0.8886885245901639, 'f1-score': 0.8692723992784124, 'support': 12200.0} | 0.8202   | {'precision': 0.7555761607677606, 'recall': 0.7656014750933613, 'f1-score': 0.7587955850993913, 'support': 27909.0} | {'precision': 0.8184874400582042, 'recall': 0.8202371994697051, 'f1-score': 0.8183829174463697, 'support': 27909.0} |
-| No log        | 3.0   | 123  | 0.4497          | {'precision': 0.6111299626739056, 'recall': 0.4235653809971778, 'f1-score': 0.5003472704542298, 'support': 4252.0}    | {'precision': 0.7032967032967034, 'recall': 0.8212648945921174, 'f1-score': 0.7577167019027485, 'support': 2182.0} | {'precision': 0.9438293905139261, 'recall': 0.8732075471698113, 'f1-score': 0.9071460573476703, 'support': 9275.0} | {'precision': 0.8196342080532061, 'recall': 0.9293442622950819, 'f1-score': 0.8710482848692045, 'support': 12200.0} | 0.8252   | {'precision': 0.7694725661344353, 'recall': 0.7618455212635471, 'f1-score': 0.7590645786434633, 'support': 27909.0} | {'precision': 0.8200463271041109, 'recall': 0.8251818409831954, 'f1-score': 0.8177069473942856, 'support': 27909.0} |
-| No log        | 4.0   | 164  | 0.4504          | {'precision': 0.5816213828142257, 'recall': 0.6192380056444027, 'f1-score': 0.5998405285340016, 'support': 4252.0}    | {'precision': 0.6949866054343666, 'recall': 0.8322639780018332, 'f1-score': 0.7574556830031283, 'support': 2182.0} | {'precision': 0.9409930715935335, 'recall': 0.8785983827493261, 'f1-score': 0.908725954836911, 'support': 9275.0}  | {'precision': 0.8776116937814848, 'recall': 0.8710655737704918, 'f1-score': 0.8743263811756962, 'support': 12200.0} | 0.8322   | {'precision': 0.7738031884059027, 'recall': 0.8002914850415135, 'f1-score': 0.7850871368874343, 'support': 27909.0} | {'precision': 0.8393023145203343, 'recall': 0.8321688344261707, 'f1-score': 0.8348025837219264, 'support': 27909.0} |
-| No log        | 5.0   | 205  | 0.4540          | {'precision': 0.5803511891531451, 'recall': 0.6140639698965192, 'f1-score': 0.5967318020797622, 'support': 4252.0}    | {'precision': 0.7292703150912107, 'recall': 0.806141154903758, 'f1-score': 0.7657814540705268, 'support': 2182.0}  | {'precision': 0.9338842975206612, 'recall': 0.8893800539083558, 'f1-score': 0.9110890214269937, 'support': 9275.0} | {'precision': 0.8739005343197699, 'recall': 0.8713934426229508, 'f1-score': 0.8726451877693413, 'support': 12200.0} | 0.8331   | {'precision': 0.7793515840211966, 'recall': 0.795244655332896, 'f1-score': 0.7865618663366559, 'support': 27909.0}  | {'precision': 0.8378044523993523, 'recall': 0.8330646028162958, 'f1-score': 0.8350303027606281, 'support': 27909.0} |
-| No log        | 6.0   | 246  | 0.4521          | {'precision': 0.5926012643409038, 'recall': 0.5952492944496708, 'f1-score': 0.5939223278188431, 'support': 4252.0}    | {'precision': 0.746797608881298, 'recall': 0.8015582034830431, 'f1-score': 0.773209549071618, 'support': 2182.0}   | {'precision': 0.9330482727579611, 'recall': 0.8940161725067386, 'f1-score': 0.9131152956722828, 'support': 9275.0} | {'precision': 0.8684019663147715, 'recall': 0.8832786885245901, 'f1-score': 0.8757771546995001, 'support': 12200.0} | 0.8366   | {'precision': 0.7852122780737336, 'recall': 0.7935255897410106, 'f1-score': 0.789006081815561, 'support': 27909.0}  | {'precision': 0.8383596573659686, 'recall': 0.836576014905586, 'f1-score': 0.837225505344309, 'support': 27909.0}   |
+| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                              | Majorclaim                                                                                                          | O                                                                                                                  | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.5869          | {'precision': 0.50130958617077, 'recall': 0.2250705550329257, 'f1-score': 0.310663853270573, 'support': 4252.0}    | {'precision': 0.6411985018726591, 'recall': 0.3923006416131989, 'f1-score': 0.48677850440716514, 'support': 2182.0} | {'precision': 0.8172767203513909, 'recall': 0.9027493261455526, 'f1-score': 0.8578893442622951, 'support': 9275.0} | {'precision': 0.7879334257975035, 'recall': 0.931311475409836, 'f1-score': 0.8536438767843726, 'support': 12200.0}  | 0.7721   | {'precision': 0.6869295585480809, 'recall': 0.6128579995503783, 'f1-score': 0.6272438946811014, 'support': 27909.0} | {'precision': 0.7425451598936884, 'recall': 0.7720806908165825, 'f1-score': 0.7436480119504476, 'support': 27909.0} |
+| No log        | 2.0   | 82   | 0.4605          | {'precision': 0.5800683670786222, 'recall': 0.5188146754468486, 'f1-score': 0.5477343265052762, 'support': 4252.0} | {'precision': 0.679080824088748, 'recall': 0.7855178735105408, 'f1-score': 0.7284317892052699, 'support': 2182.0}   | {'precision': 0.9250369696280286, 'recall': 0.8767654986522911, 'f1-score': 0.900254621941769, 'support': 9275.0}  | {'precision': 0.8497380970995231, 'recall': 0.8909016393442623, 'f1-score': 0.8698331399303749, 'support': 12200.0} | 0.8213   | {'precision': 0.7584810644737304, 'recall': 0.7679999217384857, 'f1-score': 0.7615634693956725, 'support': 27909.0} | {'precision': 0.8203349361458346, 'recall': 0.8212762908022502, 'f1-score': 0.8198154876923865, 'support': 27909.0} |
+| No log        | 3.0   | 123  | 0.4587          | {'precision': 0.6081277213352685, 'recall': 0.39416745061147695, 'f1-score': 0.478310502283105, 'support': 4252.0} | {'precision': 0.7005473025801408, 'recall': 0.8212648945921174, 'f1-score': 0.7561181434599156, 'support': 2182.0}  | {'precision': 0.9445551517993201, 'recall': 0.8687870619946092, 'f1-score': 0.905088172526115, 'support': 9275.0}  | {'precision': 0.8125, 'recall': 0.9366393442622951, 'f1-score': 0.8701644837039293, 'support': 12200.0}             | 0.8224   | {'precision': 0.7664325439286823, 'recall': 0.7552146878651247, 'f1-score': 0.7524203254932662, 'support': 27909.0} | {'precision': 0.8164965537384401, 'recall': 0.8224228743416102, 'f1-score': 0.8131543783763286, 'support': 27909.0} |
+| No log        | 4.0   | 164  | 0.4491          | {'precision': 0.5829145728643216, 'recall': 0.6274694261523989, 'f1-score': 0.6043719560539133, 'support': 4252.0} | {'precision': 0.7112758486149044, 'recall': 0.8354720439963337, 'f1-score': 0.7683877766069548, 'support': 2182.0}  | {'precision': 0.9357652656621729, 'recall': 0.8905660377358491, 'f1-score': 0.9126063418406806, 'support': 9275.0} | {'precision': 0.881426896667225, 'recall': 0.8627868852459016, 'f1-score': 0.8720072901996521, 'support': 12200.0}  | 0.8340   | {'precision': 0.7778456459521561, 'recall': 0.8040735982826209, 'f1-score': 0.7893433411753001, 'support': 27909.0} | {'precision': 0.8407032729174679, 'recall': 0.8340320326776308, 'f1-score': 0.8366234708053201, 'support': 27909.0} |
+| No log        | 5.0   | 205  | 0.4611          | {'precision': 0.5805860805860806, 'recall': 0.5964252116650988, 'f1-score': 0.588399071925754, 'support': 4252.0}  | {'precision': 0.7489102005231038, 'recall': 0.7873510540788268, 'f1-score': 0.7676496872207329, 'support': 2182.0}  | {'precision': 0.9323308270676691, 'recall': 0.8957412398921832, 'f1-score': 0.9136698559331353, 'support': 9275.0} | {'precision': 0.8673800259403373, 'recall': 0.8770491803278688, 'f1-score': 0.8721878056732963, 'support': 12200.0} | 0.8335   | {'precision': 0.7823017835292977, 'recall': 0.7891416714909945, 'f1-score': 0.7854766051882296, 'support': 27909.0} | {'precision': 0.8360091300196414, 'recall': 0.8334945716435559, 'f1-score': 0.8345646069131102, 'support': 27909.0} |
+| No log        | 6.0   | 246  | 0.4642          | {'precision': 0.5962333486449242, 'recall': 0.6105362182502352, 'f1-score': 0.6033000232396003, 'support': 4252.0} | {'precision': 0.7385488447507094, 'recall': 0.8350137488542622, 'f1-score': 0.783824478382448, 'support': 2182.0}   | {'precision': 0.9409678526484384, 'recall': 0.8867924528301887, 'f1-score': 0.9130772646536413, 'support': 9275.0} | {'precision': 0.8715477443913501, 'recall': 0.8820491803278688, 'f1-score': 0.8767670183729173, 'support': 12200.0} | 0.8386   | {'precision': 0.7868244476088555, 'recall': 0.8035979000656387, 'f1-score': 0.7942421961621517, 'support': 27909.0} | {'precision': 0.8422751475356695, 'recall': 0.8385825360994661, 'f1-score': 0.8399041873394746, 'support': 27909.0} |
+| No log        | 7.0   | 287  | 0.4624          | {'precision': 0.5906025179856115, 'recall': 0.617826904985889, 'f1-score': 0.6039080459770114, 'support': 4252.0}  | {'precision': 0.7631810193321616, 'recall': 0.7960586617781852, 'f1-score': 0.7792732166890982, 'support': 2182.0}  | {'precision': 0.9296403841858387, 'recall': 0.897466307277628, 'f1-score': 0.913270064183444, 'support': 9275.0}   | {'precision': 0.8734363502575423, 'recall': 0.875655737704918, 'f1-score': 0.8745446359133887, 'support': 12200.0}  | 0.8374   | {'precision': 0.7892150679402886, 'recall': 0.7967519029366551, 'f1-score': 0.7927489906907357, 'support': 27909.0} | {'precision': 0.8404042039171331, 'recall': 0.8374001218245011, 'f1-score': 0.8387335832080923, 'support': 27909.0} |
 
 
 ### Framework versions

meta_data/README_s42_e7.md

@@ -0,0 +1,87 @@
+---
+license: apache-2.0
+base_model: allenai/longformer-base-4096
+tags:
+- generated_from_trainer
+datasets:
+- essays_su_g
+metrics:
+- accuracy
+model-index:
+- name: longformer-simple
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: essays_su_g
+      type: essays_su_g
+      config: simple
+      split: test
+      args: simple
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8374001218245011
+---
+
+<!-- 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. -->
+
+# longformer-simple
+
+This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4624
+- Claim: {'precision': 0.5906025179856115, 'recall': 0.617826904985889, 'f1-score': 0.6039080459770114, 'support': 4252.0}
+- Majorclaim: {'precision': 0.7631810193321616, 'recall': 0.7960586617781852, 'f1-score': 0.7792732166890982, 'support': 2182.0}
+- O: {'precision': 0.9296403841858387, 'recall': 0.897466307277628, 'f1-score': 0.913270064183444, 'support': 9275.0}
+- Premise: {'precision': 0.8734363502575423, 'recall': 0.875655737704918, 'f1-score': 0.8745446359133887, 'support': 12200.0}
+- Accuracy: 0.8374
+- Macro avg: {'precision': 0.7892150679402886, 'recall': 0.7967519029366551, 'f1-score': 0.7927489906907357, 'support': 27909.0}
+- Weighted avg: {'precision': 0.8404042039171331, 'recall': 0.8374001218245011, 'f1-score': 0.8387335832080923, 'support': 27909.0}
+
+## 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: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 7
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                              | Majorclaim                                                                                                          | O                                                                                                                  | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.5869          | {'precision': 0.50130958617077, 'recall': 0.2250705550329257, 'f1-score': 0.310663853270573, 'support': 4252.0}    | {'precision': 0.6411985018726591, 'recall': 0.3923006416131989, 'f1-score': 0.48677850440716514, 'support': 2182.0} | {'precision': 0.8172767203513909, 'recall': 0.9027493261455526, 'f1-score': 0.8578893442622951, 'support': 9275.0} | {'precision': 0.7879334257975035, 'recall': 0.931311475409836, 'f1-score': 0.8536438767843726, 'support': 12200.0}  | 0.7721   | {'precision': 0.6869295585480809, 'recall': 0.6128579995503783, 'f1-score': 0.6272438946811014, 'support': 27909.0} | {'precision': 0.7425451598936884, 'recall': 0.7720806908165825, 'f1-score': 0.7436480119504476, 'support': 27909.0} |
+| No log        | 2.0   | 82   | 0.4605          | {'precision': 0.5800683670786222, 'recall': 0.5188146754468486, 'f1-score': 0.5477343265052762, 'support': 4252.0} | {'precision': 0.679080824088748, 'recall': 0.7855178735105408, 'f1-score': 0.7284317892052699, 'support': 2182.0}   | {'precision': 0.9250369696280286, 'recall': 0.8767654986522911, 'f1-score': 0.900254621941769, 'support': 9275.0}  | {'precision': 0.8497380970995231, 'recall': 0.8909016393442623, 'f1-score': 0.8698331399303749, 'support': 12200.0} | 0.8213   | {'precision': 0.7584810644737304, 'recall': 0.7679999217384857, 'f1-score': 0.7615634693956725, 'support': 27909.0} | {'precision': 0.8203349361458346, 'recall': 0.8212762908022502, 'f1-score': 0.8198154876923865, 'support': 27909.0} |
+| No log        | 3.0   | 123  | 0.4587          | {'precision': 0.6081277213352685, 'recall': 0.39416745061147695, 'f1-score': 0.478310502283105, 'support': 4252.0} | {'precision': 0.7005473025801408, 'recall': 0.8212648945921174, 'f1-score': 0.7561181434599156, 'support': 2182.0}  | {'precision': 0.9445551517993201, 'recall': 0.8687870619946092, 'f1-score': 0.905088172526115, 'support': 9275.0}  | {'precision': 0.8125, 'recall': 0.9366393442622951, 'f1-score': 0.8701644837039293, 'support': 12200.0}             | 0.8224   | {'precision': 0.7664325439286823, 'recall': 0.7552146878651247, 'f1-score': 0.7524203254932662, 'support': 27909.0} | {'precision': 0.8164965537384401, 'recall': 0.8224228743416102, 'f1-score': 0.8131543783763286, 'support': 27909.0} |
+| No log        | 4.0   | 164  | 0.4491          | {'precision': 0.5829145728643216, 'recall': 0.6274694261523989, 'f1-score': 0.6043719560539133, 'support': 4252.0} | {'precision': 0.7112758486149044, 'recall': 0.8354720439963337, 'f1-score': 0.7683877766069548, 'support': 2182.0}  | {'precision': 0.9357652656621729, 'recall': 0.8905660377358491, 'f1-score': 0.9126063418406806, 'support': 9275.0} | {'precision': 0.881426896667225, 'recall': 0.8627868852459016, 'f1-score': 0.8720072901996521, 'support': 12200.0}  | 0.8340   | {'precision': 0.7778456459521561, 'recall': 0.8040735982826209, 'f1-score': 0.7893433411753001, 'support': 27909.0} | {'precision': 0.8407032729174679, 'recall': 0.8340320326776308, 'f1-score': 0.8366234708053201, 'support': 27909.0} |
+| No log        | 5.0   | 205  | 0.4611          | {'precision': 0.5805860805860806, 'recall': 0.5964252116650988, 'f1-score': 0.588399071925754, 'support': 4252.0}  | {'precision': 0.7489102005231038, 'recall': 0.7873510540788268, 'f1-score': 0.7676496872207329, 'support': 2182.0}  | {'precision': 0.9323308270676691, 'recall': 0.8957412398921832, 'f1-score': 0.9136698559331353, 'support': 9275.0} | {'precision': 0.8673800259403373, 'recall': 0.8770491803278688, 'f1-score': 0.8721878056732963, 'support': 12200.0} | 0.8335   | {'precision': 0.7823017835292977, 'recall': 0.7891416714909945, 'f1-score': 0.7854766051882296, 'support': 27909.0} | {'precision': 0.8360091300196414, 'recall': 0.8334945716435559, 'f1-score': 0.8345646069131102, 'support': 27909.0} |
+| No log        | 6.0   | 246  | 0.4642          | {'precision': 0.5962333486449242, 'recall': 0.6105362182502352, 'f1-score': 0.6033000232396003, 'support': 4252.0} | {'precision': 0.7385488447507094, 'recall': 0.8350137488542622, 'f1-score': 0.783824478382448, 'support': 2182.0}   | {'precision': 0.9409678526484384, 'recall': 0.8867924528301887, 'f1-score': 0.9130772646536413, 'support': 9275.0} | {'precision': 0.8715477443913501, 'recall': 0.8820491803278688, 'f1-score': 0.8767670183729173, 'support': 12200.0} | 0.8386   | {'precision': 0.7868244476088555, 'recall': 0.8035979000656387, 'f1-score': 0.7942421961621517, 'support': 27909.0} | {'precision': 0.8422751475356695, 'recall': 0.8385825360994661, 'f1-score': 0.8399041873394746, 'support': 27909.0} |
+| No log        | 7.0   | 287  | 0.4624          | {'precision': 0.5906025179856115, 'recall': 0.617826904985889, 'f1-score': 0.6039080459770114, 'support': 4252.0}  | {'precision': 0.7631810193321616, 'recall': 0.7960586617781852, 'f1-score': 0.7792732166890982, 'support': 2182.0}  | {'precision': 0.9296403841858387, 'recall': 0.897466307277628, 'f1-score': 0.913270064183444, 'support': 9275.0}   | {'precision': 0.8734363502575423, 'recall': 0.875655737704918, 'f1-score': 0.8745446359133887, 'support': 12200.0}  | 0.8374   | {'precision': 0.7892150679402886, 'recall': 0.7967519029366551, 'f1-score': 0.7927489906907357, 'support': 27909.0} | {'precision': 0.8404042039171331, 'recall': 0.8374001218245011, 'f1-score': 0.8387335832080923, 'support': 27909.0} |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

model.safetensors

@@ -1,3 +1,3 @@
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