Pushing of the new best model checkpoint

README.md

@@ -4,16 +4,14 @@ license: mit
 base_model: deepset/gbert-large
 tags:
 - generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
 model-index:
 - name: german-zeroshot
   results: []
-datasets:
-- facebook/xnli
-language:
-- de
-metrics:
-- accuracy
-pipeline_tag: zero-shot-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -21,60 +19,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # german-zeroshot
 
-This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on facebook/xnli de dataset.
+This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- eval_loss: 0.5051
-- eval_accuracy: 0.8096
-- eval_f1: 0.8102
-- eval_precision: 0.8131
-- eval_recall: 0.8096
-- eval_runtime: 5.9824
-- eval_samples_per_second: 416.224
-- eval_steps_per_second: 13.038
-- epoch: 0.4889
-- step: 3000
-
-```python
-# Use a pipeline as a high-level helper
-
-pipe = pipeline(
-    "zero-shot-classification",
-    model="kaixkhazaki/german-zeroshot",
-    tokenizer="kaixkhazaki/german-zeroshot",
-    device=0 if torch.cuda.is_available() else -1  # Use GPU if available
-)
-
-#Enter your text and possible candidates of classification
-sequence = "Können Sie mir die Schritte zur Konfiguration eines VPN auf einem Linux-Server erklären?"
-candidate_labels = [
-    "Technische Dokumentation", 
-    "IT-Support", 
-    "Netzwerkadministration", 
-    "Linux-Konfiguration", 
-    "VPN-Setup"
-]
-pipe(sequence,candidate_labels)
->>
-{'sequence': 'Können Sie mir die Schritte zur Konfiguration eines VPN auf einem Linux-Server erklären?',
-'labels': ['VPN-Setup', 'Linux-Konfiguration', 'Netzwerkadministration', 'IT-Support', 'Technische Dokumentation'],
-'scores': [0.3245040476322174, 0.32373329997062683, 0.16423103213310242, 0.09850211441516876, 0.08902951329946518]}
-
-#example 2
-sequence = "Können Sie mir die Schritte zur Konfiguration eines VPN auf einem Linux-Server erklären?"
-candidate_labels = [
-    "Technische Dokumentation", 
-    "IT-Support", 
-    "Netzwerkadministration", 
-    "Linux-Konfiguration", 
-    "VPN-Setup"
-]
-pipe(sequence,candidate_labels)
->>
-{'sequence': 'Wie lautet die Garantiezeit für dieses Produkt?',
-'labels': ['Garantiebedingungen', 'Produktdetails', 'Reklamation', 'Kundendienst', 'Kaufberatung'],
-'scores': [0.4313304126262665, 0.2905466556549072, 0.10058070719242096, 0.09384352713823318, 0.08369863778352737]}
-
-```
+- Loss: 0.4592
+- Accuracy: 0.8486
+- F1: 0.8487
+- Precision: 0.8505
+- Recall: 0.8486
 
 ## Model description
 
@@ -102,9 +53,33 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_steps: 500
 - num_epochs: 3
 
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.6429        | 0.1630 | 1000  | 0.5203          | 0.8004   | 0.8006 | 0.8009    | 0.8004 |
+| 0.5715        | 0.3259 | 2000  | 0.5209          | 0.7964   | 0.7968 | 0.8005    | 0.7964 |
+| 0.5897        | 0.4889 | 3000  | 0.5435          | 0.7924   | 0.7940 | 0.8039    | 0.7924 |
+| 0.5701        | 0.6519 | 4000  | 0.5242          | 0.7880   | 0.7884 | 0.8078    | 0.7880 |
+| 0.5238        | 0.8149 | 5000  | 0.4816          | 0.8233   | 0.8226 | 0.8263    | 0.8233 |
+| 0.5285        | 0.9778 | 6000  | 0.4483          | 0.8265   | 0.8273 | 0.8303    | 0.8265 |
+| 0.4302        | 1.1408 | 7000  | 0.4751          | 0.8209   | 0.8214 | 0.8277    | 0.8209 |
+| 0.4163        | 1.3038 | 8000  | 0.4560          | 0.8285   | 0.8289 | 0.8344    | 0.8285 |
+| 0.3942        | 1.4668 | 9000  | 0.4330          | 0.8414   | 0.8422 | 0.8454    | 0.8414 |
+| 0.3875        | 1.6297 | 10000 | 0.4171          | 0.8430   | 0.8432 | 0.8455    | 0.8430 |
+| 0.3639        | 1.7927 | 11000 | 0.4194          | 0.8442   | 0.8447 | 0.8487    | 0.8442 |
+| 0.3768        | 1.9557 | 12000 | 0.4215          | 0.8474   | 0.8477 | 0.8492    | 0.8474 |
+| 0.2443        | 2.1186 | 13000 | 0.4750          | 0.8390   | 0.8398 | 0.8452    | 0.8390 |
+| 0.2404        | 2.2816 | 14000 | 0.4592          | 0.8486   | 0.8487 | 0.8505    | 0.8486 |
+| 0.2154        | 2.4446 | 15000 | 0.4914          | 0.8418   | 0.8424 | 0.8466    | 0.8418 |
+| 0.2157        | 2.6076 | 16000 | 0.4804          | 0.8454   | 0.8458 | 0.8488    | 0.8454 |
+| 0.2249        | 2.7705 | 17000 | 0.4809          | 0.8466   | 0.8471 | 0.8507    | 0.8466 |
+| 0.2204        | 2.9335 | 18000 | 0.4777          | 0.8466   | 0.8470 | 0.8502    | 0.8466 |
+
+
 ### Framework versions
 
 - Transformers 4.48.0.dev0
 - Pytorch 2.4.1+cu121
 - Datasets 3.1.0
-- Tokenizers 0.21.0
+- Tokenizers 0.21.0

model.safetensors

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tokenizer.json

@@ -1,21 +1,7 @@
 {
   "version": "1.0",
-  "truncation": {
-    "direction": "Right",
-    "max_length": 128,
-    "strategy": "LongestFirst",
-    "stride": 0
-  },
-  "padding": {
-    "strategy": {
-      "Fixed": 128
-    },
-    "direction": "Right",
-    "pad_to_multiple_of": null,
-    "pad_id": 0,
-    "pad_type_id": 0,
-    "pad_token": "[PAD]"
-  },
+  "truncation": null,
+  "padding": null,
   "added_tokens": [
     {
       "id": 0,

training_args.bin

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