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Theoreticallyhugo
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longformer-one-step

Token Classification
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PyTorch
Safetensors
longformer
Generated from Trainer
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Training in progress, epoch 1

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  1. README.md +15 -30
  2. config.json +12 -12
  3. model.safetensors +1 -1
  4. training_args.bin +1 -1
README.md CHANGED
@@ -3,26 +3,11 @@ license: apache-2.0
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  base_model: allenai/longformer-base-4096
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  tags:
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  - generated_from_trainer
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- datasets:
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- - fancy_dataset
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  metrics:
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  - accuracy
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  model-index:
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  - name: longformer-one-step
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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: fancy_dataset
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- type: fancy_dataset
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- config: full_labels
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- split: test
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- args: full_labels
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- metrics:
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- - name: Accuracy
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- type: accuracy
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- value: 0.8161524956107349
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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
@@ -30,16 +15,16 @@ should probably proofread and complete it, then remove this comment. -->
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  # longformer-one-step
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- This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the fancy_dataset dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5164
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- - Claim: {'precision': 0.5841029946823397, 'recall': 0.47910927456382, 'f1-score': 0.5264219952074664, 'support': 4356.0}
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- - Majorclaim: {'precision': 0.663898774219059, 'recall': 0.7694775435380385, 'f1-score': 0.7127998301846742, 'support': 2182.0}
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- - O: {'precision': 0.9219015280135824, 'recall': 0.8781671159029649, 'f1-score': 0.8995030369961348, 'support': 9275.0}
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- - Premise: {'precision': 0.8377274128893001, 'recall': 0.8983961640211641, 'f1-score': 0.8670017552257859, 'support': 12096.0}
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- - Accuracy: 0.8162
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- - Macro avg: {'precision': 0.7519076774510703, 'recall': 0.7562875245064969, 'f1-score': 0.7514316544035153, 'support': 27909.0}
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- - Weighted avg: {'precision': 0.8125252509519227, 'recall': 0.8161524956107349, 'f1-score': 0.8125897502575133, 'support': 27909.0}
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  ## Model description
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@@ -68,11 +53,11 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Claim | Majorclaim | O | Premise | Accuracy | Macro avg | Weighted avg |
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- |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
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- | No log | 1.0 | 41 | 0.7242 | {'precision': 0.4451114922813036, 'recall': 0.23829201101928374, 'f1-score': 0.3104066985645933, 'support': 4356.0} | {'precision': 0.6888297872340425, 'recall': 0.11869844179651695, 'f1-score': 0.20250195465207194, 'support': 2182.0} | {'precision': 0.7629536017331648, 'recall': 0.9112668463611859, 'f1-score': 0.8305409521937798, 'support': 9275.0} | {'precision': 0.7774552148976847, 'recall': 0.9077380952380952, 'f1-score': 0.83756054769442, 'support': 12096.0} | 0.7427 | {'precision': 0.6685875240365489, 'recall': 0.5439988486037705, 'f1-score': 0.5452525382762162, 'support': 27909.0} | {'precision': 0.7138351496506337, 'recall': 0.7427353183560859, 'f1-score': 0.7032996725252499, 'support': 27909.0} |
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- | No log | 2.0 | 82 | 0.5451 | {'precision': 0.5706823375775384, 'recall': 0.40128558310376494, 'f1-score': 0.47122253673001757, 'support': 4356.0} | {'precision': 0.6872317596566524, 'recall': 0.5870760769935839, 'f1-score': 0.6332179930795848, 'support': 2182.0} | {'precision': 0.8817295464179737, 'recall': 0.8970350404312668, 'f1-score': 0.8893164448720005, 'support': 9275.0} | {'precision': 0.819134799940942, 'recall': 0.9173280423280423, 'f1-score': 0.8654551127057172, 'support': 12096.0} | 0.8042 | {'precision': 0.7396946108982767, 'recall': 0.7006811857141645, 'f1-score': 0.71480302184683, 'support': 27909.0} | {'precision': 0.7908462519320261, 'recall': 0.8042208606542692, 'f1-score': 0.7936967322502336, 'support': 27909.0} |
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- | No log | 3.0 | 123 | 0.5164 | {'precision': 0.5841029946823397, 'recall': 0.47910927456382, 'f1-score': 0.5264219952074664, 'support': 4356.0} | {'precision': 0.663898774219059, 'recall': 0.7694775435380385, 'f1-score': 0.7127998301846742, 'support': 2182.0} | {'precision': 0.9219015280135824, 'recall': 0.8781671159029649, 'f1-score': 0.8995030369961348, 'support': 9275.0} | {'precision': 0.8377274128893001, 'recall': 0.8983961640211641, 'f1-score': 0.8670017552257859, 'support': 12096.0} | 0.8162 | {'precision': 0.7519076774510703, 'recall': 0.7562875245064969, 'f1-score': 0.7514316544035153, 'support': 27909.0} | {'precision': 0.8125252509519227, 'recall': 0.8161524956107349, 'f1-score': 0.8125897502575133, 'support': 27909.0} |
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  ### Framework versions
 
3
  base_model: allenai/longformer-base-4096
4
  tags:
5
  - generated_from_trainer
 
 
6
  metrics:
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  - accuracy
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  model-index:
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  - name: longformer-one-step
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+ results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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  # longformer-one-step
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+ This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5640
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+ - Claim: {'precision': 0.5519765739385066, 'recall': 0.32811140121845084, 'f1-score': 0.41157205240174677, 'support': 2298.0}
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+ - Majorclaim: {'precision': 0.5541490857946554, 'recall': 0.701067615658363, 'f1-score': 0.6190102120974077, 'support': 1124.0}
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+ - O: {'precision': 0.8899137758171245, 'recall': 0.8831840796019901, 'f1-score': 0.8865361566120655, 'support': 5025.0}
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+ - Premise: {'precision': 0.830119375573921, 'recall': 0.9103726082578046, 'f1-score': 0.8683957732949088, 'support': 6951.0}
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+ - Accuracy: 0.7993
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+ - Macro avg: {'precision': 0.7065397027810518, 'recall': 0.7056839261841521, 'f1-score': 0.6963785486015321, 'support': 15398.0}
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+ - Weighted avg: {'precision': 0.7879778050681424, 'recall': 0.7993245876087803, 'f1-score': 0.7879350085702844, 'support': 15398.0}
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Claim | Majorclaim | O | Premise | Accuracy | Macro avg | Weighted avg |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
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+ | No log | 1.0 | 36 | 0.7525 | {'precision': 0.41766381766381766, 'recall': 0.31897302001740646, 'f1-score': 0.36170737725141877, 'support': 2298.0} | {'precision': 0.43548387096774194, 'recall': 0.02402135231316726, 'f1-score': 0.045531197301854974, 'support': 1124.0} | {'precision': 0.7476681394207167, 'recall': 0.9092537313432836, 'f1-score': 0.8205818965517241, 'support': 5025.0} | {'precision': 0.8187416331994646, 'recall': 0.8798733995108617, 'f1-score': 0.8482074752097636, 'support': 6951.0} | 0.7433 | {'precision': 0.6048893653129352, 'recall': 0.5330303757961797, 'f1-score': 0.5190069865786904, 'support': 15398.0} | {'precision': 0.707714041883217, 'recall': 0.7432783478373814, 'f1-score': 0.7079942076273884, 'support': 15398.0} |
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+ | No log | 2.0 | 72 | 0.6577 | {'precision': 0.4793814432989691, 'recall': 0.3237597911227154, 'f1-score': 0.38649350649350644, 'support': 2298.0} | {'precision': 0.41677503250975295, 'recall': 0.5702846975088968, 'f1-score': 0.48159278737791134, 'support': 1124.0} | {'precision': 0.7966573816155988, 'recall': 0.9106467661691542, 'f1-score': 0.849846782431052, 'support': 5025.0} | {'precision': 0.8743144424131627, 'recall': 0.8256365990504964, 'f1-score': 0.8492785793562707, 'support': 6951.0} | 0.7598 | {'precision': 0.6417820749593709, 'recall': 0.6575819634628157, 'f1-score': 0.6418029139146851, 'support': 15398.0} | {'precision': 0.7566331163186304, 'recall': 0.7598389401220937, 'f1-score': 0.7535581151939423, 'support': 15398.0} |
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+ | No log | 3.0 | 108 | 0.5640 | {'precision': 0.5519765739385066, 'recall': 0.32811140121845084, 'f1-score': 0.41157205240174677, 'support': 2298.0} | {'precision': 0.5541490857946554, 'recall': 0.701067615658363, 'f1-score': 0.6190102120974077, 'support': 1124.0} | {'precision': 0.8899137758171245, 'recall': 0.8831840796019901, 'f1-score': 0.8865361566120655, 'support': 5025.0} | {'precision': 0.830119375573921, 'recall': 0.9103726082578046, 'f1-score': 0.8683957732949088, 'support': 6951.0} | 0.7993 | {'precision': 0.7065397027810518, 'recall': 0.7056839261841521, 'f1-score': 0.6963785486015321, 'support': 15398.0} | {'precision': 0.7879778050681424, 'recall': 0.7993245876087803, 'f1-score': 0.7879350085702844, 'support': 15398.0} |
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  ### Framework versions
config.json CHANGED
@@ -6,18 +6,18 @@
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  "attention_mode": "longformer",
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  "attention_probs_dropout_prob": 0.1,
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  "attention_window": [
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  "bos_token_id": 0,
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  "eos_token_id": 2,
 
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  "attention_mode": "longformer",
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  "attention_probs_dropout_prob": 0.1,
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  "attention_window": [
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