trainer: training complete at 2024-02-19 21:07:39.915053.

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.8949278982249309
+      value: 0.8970593132847104
 ---
 
 <!-- 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: 0.2702
-- Claim: {'precision': 0.6345128453708192, 'recall': 0.6157102539981185, 'f1-score': 0.6249701599427071, 'support': 4252.0}
-- Majorclaim: {'precision': 0.8918156161806209, 'recall': 0.8689275893675527, 'f1-score': 0.8802228412256268, 'support': 2182.0}
-- O: {'precision': 1.0, 'recall': 0.9992100412534012, 'f1-score': 0.9996048645563507, 'support': 11393.0}
-- Premise: {'precision': 0.8856428052618837, 'recall': 0.8995081967213114, 'f1-score': 0.892521654263755, 'support': 12200.0}
-- Accuracy: 0.8949
-- Macro avg: {'precision': 0.8529928167033309, 'recall': 0.8458390203350961, 'f1-score': 0.84932987999711, 'support': 30027.0}
-- Weighted avg: {'precision': 0.8939198893401877, 'recall': 0.8949278982249309, 'f1-score': 0.8943711247723389, 'support': 30027.0}
+- Loss: 0.2727
+- Claim: {'precision': 0.6323296354992076, 'recall': 0.6568673565380997, 'f1-score': 0.6443649786595916, 'support': 4252.0}
+- Majorclaim: {'precision': 0.8782371649250341, 'recall': 0.885884509624198, 'f1-score': 0.8820442619210586, 'support': 2182.0}
+- O: {'precision': 1.0, 'recall': 0.9996489072237339, 'f1-score': 0.9998244227899218, 'support': 11393.0}
+- Premise: {'precision': 0.9002495840266223, 'recall': 0.8869672131147541, 'f1-score': 0.8935590421139553, 'support': 12200.0}
+- Accuracy: 0.8971
+- Macro avg: {'precision': 0.852704096112716, 'recall': 0.8573419966251964, 'f1-score': 0.8549481763711319, 'support': 30027.0}
+- Weighted avg: {'precision': 0.8985587647495203, 'recall': 0.8970593132847104, 'f1-score': 0.897754701815305, 'support': 30027.0}
 
 ## 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   | 0.3401          | {'precision': 0.5311909262759924, 'recall': 0.3304327375352775, 'f1-score': 0.40742351747136435, 'support': 4252.0} | {'precision': 0.6367229608336328, 'recall': 0.8120989917506874, 'f1-score': 0.7137965760322256, 'support': 2182.0} | {'precision': 0.9957709251101322, 'recall': 0.9920126393399455, 'f1-score': 0.9938882293452932, 'support': 11393.0} | {'precision': 0.842705109819609, 'recall': 0.9151639344262295, 'f1-score': 0.8774411568234507, 'support': 12200.0}  | 0.8540   | {'precision': 0.7515974805098417, 'recall': 0.7624270757630349, 'f1-score': 0.7481373699180834, 'support': 30027.0} | {'precision': 0.8417015955188155, 'recall': 0.8540313717654111, 'f1-score': 0.8431751302283804, 'support': 30027.0} |
-| No log        | 2.0   | 82   | 0.2705          | {'precision': 0.5723863918549789, 'recall': 0.5420978363123237, 'f1-score': 0.5568305350887789, 'support': 4252.0}  | {'precision': 0.8547959724430313, 'recall': 0.7392300641613199, 'f1-score': 0.7928237896289015, 'support': 2182.0} | {'precision': 0.9999120492524186, 'recall': 0.9978934433424033, 'f1-score': 0.9989017264859641, 'support': 11393.0} | {'precision': 0.8669073216667974, 'recall': 0.9054918032786885, 'f1-score': 0.8857795774365553, 'support': 12200.0} | 0.8770   | {'precision': 0.8235004338043066, 'recall': 0.7961782867736839, 'f1-score': 0.8085839071600499, 'support': 30027.0} | {'precision': 0.8747866603891763, 'recall': 0.8770106903786592, 'f1-score': 0.875364943509119, 'support': 30027.0}  |
-| No log        | 3.0   | 123  | 0.2601          | {'precision': 0.6572898799313893, 'recall': 0.4506114769520226, 'f1-score': 0.5346728059160039, 'support': 4252.0}  | {'precision': 0.7966573816155988, 'recall': 0.9175068744271311, 'f1-score': 0.8528221512247072, 'support': 2182.0} | {'precision': 0.9997364490907493, 'recall': 0.9988589484771351, 'f1-score': 0.9992975061468212, 'support': 11393.0} | {'precision': 0.8583535108958837, 'recall': 0.9298360655737705, 'f1-score': 0.8926660371419579, 'support': 12200.0} | 0.8873   | {'precision': 0.8280093053834052, 'recall': 0.8242033413575147, 'f1-score': 0.8198646251073725, 'support': 30027.0} | {'precision': 0.8790426340817996, 'recall': 0.8872681253538482, 'f1-score': 0.8795353796712885, 'support': 30027.0} |
-| No log        | 4.0   | 164  | 0.2546          | {'precision': 0.6344463971880492, 'recall': 0.5943085606773283, 'f1-score': 0.6137219186399513, 'support': 4252.0}  | {'precision': 0.8699369936993699, 'recall': 0.885884509624198, 'f1-score': 0.8778383287920073, 'support': 2182.0}  | {'precision': 1.0, 'recall': 0.9992978144474678, 'f1-score': 0.9996487839143033, 'support': 11393.0}                | {'precision': 0.8826083460641634, 'recall': 0.8997540983606558, 'f1-score': 0.8910987539067257, 'support': 12200.0} | 0.8933   | {'precision': 0.8467479342378956, 'recall': 0.8448112457774125, 'f1-score': 0.845576946313247, 'support': 30027.0}  | {'precision': 0.8910877018376263, 'recall': 0.8932627302094781, 'f1-score': 0.8920435682645267, 'support': 30027.0} |
-| No log        | 5.0   | 205  | 0.2814          | {'precision': 0.6258696212316414, 'recall': 0.5712605832549389, 'f1-score': 0.5973195622771426, 'support': 4252.0}  | {'precision': 0.868504292815183, 'recall': 0.8808432630614116, 'f1-score': 0.8746302616609783, 'support': 2182.0}  | {'precision': 1.0, 'recall': 0.9992100412534012, 'f1-score': 0.9996048645563507, 'support': 11393.0}                | {'precision': 0.8768826201290939, 'recall': 0.9019672131147541, 'f1-score': 0.8892480504262799, 'support': 12200.0} | 0.8905   | {'precision': 0.8428141335439796, 'recall': 0.8383202751711265, 'f1-score': 0.8402006847301878, 'support': 30027.0} | {'precision': 0.8874427003022152, 'recall': 0.8904985513038266, 'f1-score': 0.8887191676437134, 'support': 30027.0} |
-| No log        | 6.0   | 246  | 0.2702          | {'precision': 0.6345128453708192, 'recall': 0.6157102539981185, 'f1-score': 0.6249701599427071, 'support': 4252.0}  | {'precision': 0.8918156161806209, 'recall': 0.8689275893675527, 'f1-score': 0.8802228412256268, 'support': 2182.0} | {'precision': 1.0, 'recall': 0.9992100412534012, 'f1-score': 0.9996048645563507, 'support': 11393.0}                | {'precision': 0.8856428052618837, 'recall': 0.8995081967213114, 'f1-score': 0.892521654263755, 'support': 12200.0}  | 0.8949   | {'precision': 0.8529928167033309, 'recall': 0.8458390203350961, 'f1-score': 0.84932987999711, 'support': 30027.0}   | {'precision': 0.8939198893401877, 'recall': 0.8949278982249309, 'f1-score': 0.8943711247723389, 'support': 30027.0} |
+| No log        | 1.0   | 41   | 0.3545          | {'precision': 0.5265911072362686, 'recall': 0.28410159924741296, 'f1-score': 0.3690803544149099, 'support': 4252.0} | {'precision': 0.5903767014878126, 'recall': 0.8547204399633364, 'f1-score': 0.6983710915558883, 'support': 2182.0} | {'precision': 0.9956778689247596, 'recall': 0.9907838146230141, 'f1-score': 0.9932248130224374, 'support': 11393.0} | {'precision': 0.8420336934350684, 'recall': 0.9136065573770492, 'f1-score': 0.8763612061170736, 'support': 12200.0} | 0.8495   | {'precision': 0.7386698427709772, 'recall': 0.7608031028027031, 'f1-score': 0.7342593662775774, 'support': 30027.0} | {'precision': 0.837374242221422, 'recall': 0.8494688114030706, 'f1-score': 0.8359340726059904, 'support': 30027.0}  |
+| No log        | 2.0   | 82   | 0.2887          | {'precision': 0.5331588132635253, 'recall': 0.5747883349012229, 'f1-score': 0.5531914893617021, 'support': 4252.0}  | {'precision': 0.9024745269286754, 'recall': 0.5682859761686526, 'f1-score': 0.6974128233970753, 'support': 2182.0} | {'precision': 0.9994723419224343, 'recall': 0.997542350566137, 'f1-score': 0.9985064136355649, 'support': 11393.0}  | {'precision': 0.8662781540400063, 'recall': 0.9016393442622951, 'f1-score': 0.8836051088440838, 'support': 12200.0} | 0.8675   | {'precision': 0.8253459590386604, 'recall': 0.7605640014745769, 'f1-score': 0.7831789588096065, 'support': 30027.0} | {'precision': 0.8722740387839361, 'recall': 0.8675192326905785, 'f1-score': 0.8668828351772135, 'support': 30027.0} |
+| No log        | 3.0   | 123  | 0.2610          | {'precision': 0.6448462929475588, 'recall': 0.4193320790216369, 'f1-score': 0.5081943850648425, 'support': 4252.0}  | {'precision': 0.8409090909090909, 'recall': 0.847846012832264, 'f1-score': 0.8443633044272022, 'support': 2182.0}  | {'precision': 0.9999121959785758, 'recall': 0.9995611340296673, 'f1-score': 0.9997366341848828, 'support': 11393.0} | {'precision': 0.8441453960359834, 'recall': 0.9460655737704918, 'f1-score': 0.8922042283461523, 'support': 12200.0} | 0.8846   | {'precision': 0.8324532439678022, 'recall': 0.803201199913515, 'f1-score': 0.81112463800577, 'support': 30027.0}    | {'precision': 0.8747901406867009, 'recall': 0.8846371598894328, 'f1-score': 0.8751501753304457, 'support': 30027.0} |
+| No log        | 4.0   | 164  | 0.2530          | {'precision': 0.6281010374379793, 'recall': 0.6549858889934148, 'f1-score': 0.6412618005986644, 'support': 4252.0}  | {'precision': 0.8315485996705108, 'recall': 0.9252978918423465, 'f1-score': 0.8759219088937094, 'support': 2182.0} | {'precision': 1.0, 'recall': 0.9996489072237339, 'f1-score': 0.9998244227899218, 'support': 11393.0}                | {'precision': 0.9083729619565217, 'recall': 0.8768032786885246, 'f1-score': 0.8923089756423088, 'support': 12200.0} | 0.8955   | {'precision': 0.8420056497662529, 'recall': 0.8641839916870049, 'f1-score': 0.8523292769811512, 'support': 30027.0} | {'precision': 0.8978677454136914, 'recall': 0.8955273587104939, 'f1-score': 0.896362471543389, 'support': 30027.0}  |
+| No log        | 5.0   | 205  | 0.2707          | {'precision': 0.6235240690281563, 'recall': 0.6458137347130762, 'f1-score': 0.6344731977818855, 'support': 4252.0}  | {'precision': 0.873358348968105, 'recall': 0.8533455545371219, 'f1-score': 0.8632359758924432, 'support': 2182.0}  | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 11393.0}                                              | {'precision': 0.8975037196230782, 'recall': 0.89, 'f1-score': 0.8937361099678987, 'support': 12200.0}               | 0.8945   | {'precision': 0.848596534404835, 'recall': 0.8472898223125496, 'f1-score': 0.8478613209105568, 'support': 30027.0}  | {'precision': 0.8958416637811863, 'recall': 0.8944949545409132, 'f1-score': 0.8951257694066757, 'support': 30027.0} |
+| No log        | 6.0   | 246  | 0.2700          | {'precision': 0.631960692559663, 'recall': 0.6352304797742239, 'f1-score': 0.6335913675815154, 'support': 4252.0}   | {'precision': 0.885956644674835, 'recall': 0.8615948670944088, 'f1-score': 0.8736059479553903, 'support': 2182.0}  | {'precision': 1.0, 'recall': 0.9995611340296673, 'f1-score': 0.9997805188534304, 'support': 11393.0}                | {'precision': 0.8923466470636282, 'recall': 0.8954918032786885, 'f1-score': 0.8939164586998323, 'support': 12200.0} | 0.8957   | {'precision': 0.8525659960745315, 'recall': 0.8479695710442472, 'f1-score': 0.850223573272542, 'support': 30027.0}  | {'precision': 0.8958565077303907, 'recall': 0.8956605721517301, 'f1-score': 0.8957444606797332, 'support': 30027.0} |
+| No log        | 7.0   | 287  | 0.2727          | {'precision': 0.6323296354992076, 'recall': 0.6568673565380997, 'f1-score': 0.6443649786595916, 'support': 4252.0}  | {'precision': 0.8782371649250341, 'recall': 0.885884509624198, 'f1-score': 0.8820442619210586, 'support': 2182.0}  | {'precision': 1.0, 'recall': 0.9996489072237339, 'f1-score': 0.9998244227899218, 'support': 11393.0}                | {'precision': 0.9002495840266223, 'recall': 0.8869672131147541, 'f1-score': 0.8935590421139553, 'support': 12200.0} | 0.8971   | {'precision': 0.852704096112716, 'recall': 0.8573419966251964, 'f1-score': 0.8549481763711319, 'support': 30027.0}  | {'precision': 0.8985587647495203, 'recall': 0.8970593132847104, 'f1-score': 0.897754701815305, 'support': 30027.0}  |
 
 
 ### 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-sep_tok
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: essays_su_g
+      type: essays_su_g
+      config: sep_tok
+      split: test
+      args: sep_tok
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8970593132847104
+---
+
+<!-- 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-sep_tok
+
+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: 0.2727
+- Claim: {'precision': 0.6323296354992076, 'recall': 0.6568673565380997, 'f1-score': 0.6443649786595916, 'support': 4252.0}
+- Majorclaim: {'precision': 0.8782371649250341, 'recall': 0.885884509624198, 'f1-score': 0.8820442619210586, 'support': 2182.0}
+- O: {'precision': 1.0, 'recall': 0.9996489072237339, 'f1-score': 0.9998244227899218, 'support': 11393.0}
+- Premise: {'precision': 0.9002495840266223, 'recall': 0.8869672131147541, 'f1-score': 0.8935590421139553, 'support': 12200.0}
+- Accuracy: 0.8971
+- Macro avg: {'precision': 0.852704096112716, 'recall': 0.8573419966251964, 'f1-score': 0.8549481763711319, 'support': 30027.0}
+- Weighted avg: {'precision': 0.8985587647495203, 'recall': 0.8970593132847104, 'f1-score': 0.897754701815305, 'support': 30027.0}
+
+## 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   | 0.3545          | {'precision': 0.5265911072362686, 'recall': 0.28410159924741296, 'f1-score': 0.3690803544149099, 'support': 4252.0} | {'precision': 0.5903767014878126, 'recall': 0.8547204399633364, 'f1-score': 0.6983710915558883, 'support': 2182.0} | {'precision': 0.9956778689247596, 'recall': 0.9907838146230141, 'f1-score': 0.9932248130224374, 'support': 11393.0} | {'precision': 0.8420336934350684, 'recall': 0.9136065573770492, 'f1-score': 0.8763612061170736, 'support': 12200.0} | 0.8495   | {'precision': 0.7386698427709772, 'recall': 0.7608031028027031, 'f1-score': 0.7342593662775774, 'support': 30027.0} | {'precision': 0.837374242221422, 'recall': 0.8494688114030706, 'f1-score': 0.8359340726059904, 'support': 30027.0}  |
+| No log        | 2.0   | 82   | 0.2887          | {'precision': 0.5331588132635253, 'recall': 0.5747883349012229, 'f1-score': 0.5531914893617021, 'support': 4252.0}  | {'precision': 0.9024745269286754, 'recall': 0.5682859761686526, 'f1-score': 0.6974128233970753, 'support': 2182.0} | {'precision': 0.9994723419224343, 'recall': 0.997542350566137, 'f1-score': 0.9985064136355649, 'support': 11393.0}  | {'precision': 0.8662781540400063, 'recall': 0.9016393442622951, 'f1-score': 0.8836051088440838, 'support': 12200.0} | 0.8675   | {'precision': 0.8253459590386604, 'recall': 0.7605640014745769, 'f1-score': 0.7831789588096065, 'support': 30027.0} | {'precision': 0.8722740387839361, 'recall': 0.8675192326905785, 'f1-score': 0.8668828351772135, 'support': 30027.0} |
+| No log        | 3.0   | 123  | 0.2610          | {'precision': 0.6448462929475588, 'recall': 0.4193320790216369, 'f1-score': 0.5081943850648425, 'support': 4252.0}  | {'precision': 0.8409090909090909, 'recall': 0.847846012832264, 'f1-score': 0.8443633044272022, 'support': 2182.0}  | {'precision': 0.9999121959785758, 'recall': 0.9995611340296673, 'f1-score': 0.9997366341848828, 'support': 11393.0} | {'precision': 0.8441453960359834, 'recall': 0.9460655737704918, 'f1-score': 0.8922042283461523, 'support': 12200.0} | 0.8846   | {'precision': 0.8324532439678022, 'recall': 0.803201199913515, 'f1-score': 0.81112463800577, 'support': 30027.0}    | {'precision': 0.8747901406867009, 'recall': 0.8846371598894328, 'f1-score': 0.8751501753304457, 'support': 30027.0} |
+| No log        | 4.0   | 164  | 0.2530          | {'precision': 0.6281010374379793, 'recall': 0.6549858889934148, 'f1-score': 0.6412618005986644, 'support': 4252.0}  | {'precision': 0.8315485996705108, 'recall': 0.9252978918423465, 'f1-score': 0.8759219088937094, 'support': 2182.0} | {'precision': 1.0, 'recall': 0.9996489072237339, 'f1-score': 0.9998244227899218, 'support': 11393.0}                | {'precision': 0.9083729619565217, 'recall': 0.8768032786885246, 'f1-score': 0.8923089756423088, 'support': 12200.0} | 0.8955   | {'precision': 0.8420056497662529, 'recall': 0.8641839916870049, 'f1-score': 0.8523292769811512, 'support': 30027.0} | {'precision': 0.8978677454136914, 'recall': 0.8955273587104939, 'f1-score': 0.896362471543389, 'support': 30027.0}  |
+| No log        | 5.0   | 205  | 0.2707          | {'precision': 0.6235240690281563, 'recall': 0.6458137347130762, 'f1-score': 0.6344731977818855, 'support': 4252.0}  | {'precision': 0.873358348968105, 'recall': 0.8533455545371219, 'f1-score': 0.8632359758924432, 'support': 2182.0}  | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 11393.0}                                              | {'precision': 0.8975037196230782, 'recall': 0.89, 'f1-score': 0.8937361099678987, 'support': 12200.0}               | 0.8945   | {'precision': 0.848596534404835, 'recall': 0.8472898223125496, 'f1-score': 0.8478613209105568, 'support': 30027.0}  | {'precision': 0.8958416637811863, 'recall': 0.8944949545409132, 'f1-score': 0.8951257694066757, 'support': 30027.0} |
+| No log        | 6.0   | 246  | 0.2700          | {'precision': 0.631960692559663, 'recall': 0.6352304797742239, 'f1-score': 0.6335913675815154, 'support': 4252.0}   | {'precision': 0.885956644674835, 'recall': 0.8615948670944088, 'f1-score': 0.8736059479553903, 'support': 2182.0}  | {'precision': 1.0, 'recall': 0.9995611340296673, 'f1-score': 0.9997805188534304, 'support': 11393.0}                | {'precision': 0.8923466470636282, 'recall': 0.8954918032786885, 'f1-score': 0.8939164586998323, 'support': 12200.0} | 0.8957   | {'precision': 0.8525659960745315, 'recall': 0.8479695710442472, 'f1-score': 0.850223573272542, 'support': 30027.0}  | {'precision': 0.8958565077303907, 'recall': 0.8956605721517301, 'f1-score': 0.8957444606797332, 'support': 30027.0} |
+| No log        | 7.0   | 287  | 0.2727          | {'precision': 0.6323296354992076, 'recall': 0.6568673565380997, 'f1-score': 0.6443649786595916, 'support': 4252.0}  | {'precision': 0.8782371649250341, 'recall': 0.885884509624198, 'f1-score': 0.8820442619210586, 'support': 2182.0}  | {'precision': 1.0, 'recall': 0.9996489072237339, 'f1-score': 0.9998244227899218, 'support': 11393.0}                | {'precision': 0.9002495840266223, 'recall': 0.8869672131147541, 'f1-score': 0.8935590421139553, 'support': 12200.0} | 0.8971   | {'precision': 0.852704096112716, 'recall': 0.8573419966251964, 'f1-score': 0.8549481763711319, 'support': 30027.0}  | {'precision': 0.8985587647495203, 'recall': 0.8970593132847104, 'f1-score': 0.897754701815305, 'support': 30027.0}  |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

model.safetensors

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 size 592324828