Instructions to use HiAmNear/cafebert-ViFE-task-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HiAmNear/cafebert-ViFE-task-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HiAmNear/cafebert-ViFE-task-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HiAmNear/cafebert-ViFE-task-1") model = AutoModelForSequenceClassification.from_pretrained("HiAmNear/cafebert-ViFE-task-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 4, checkpoint
Browse files
last-checkpoint/model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 2239622772
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2c95f094b1849ad37adb524b2b32fe0ed35369a86f735f5527bb5834d420c931
|
| 3 |
size 2239622772
|
last-checkpoint/optimizer.pt
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 4479480977
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:488c218548f6a272b1f03cbd8262d072956f965fbae3dc63d3ff4b7670976bb6
|
| 3 |
size 4479480977
|
last-checkpoint/rng_state.pth
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 14244
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c1d199696fa260379e7e66bd8e81ab7711f1d9ddff0db691d968dbd82daf5acc
|
| 3 |
size 14244
|
last-checkpoint/scheduler.pt
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1064
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6d45b5ae0bbbda8e986dabb22da819431f04bf448a3f66c2e021aa9732f02406
|
| 3 |
size 1064
|
last-checkpoint/trainer_state.json
CHANGED
|
@@ -1,9 +1,9 @@
|
|
| 1 |
{
|
| 2 |
-
"best_metric": 0.
|
| 3 |
-
"best_model_checkpoint": "../../modelParams/TrainingArguments_output/checkpoint-
|
| 4 |
-
"epoch":
|
| 5 |
"eval_steps": 500,
|
| 6 |
-
"global_step":
|
| 7 |
"is_hyper_param_search": false,
|
| 8 |
"is_local_process_zero": true,
|
| 9 |
"is_world_process_zero": true,
|
|
@@ -43,6 +43,15 @@
|
|
| 43 |
"eval_samples_per_second": 37.68,
|
| 44 |
"eval_steps_per_second": 4.725,
|
| 45 |
"step": 156
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 46 |
}
|
| 47 |
],
|
| 48 |
"logging_steps": 500,
|
|
@@ -62,7 +71,7 @@
|
|
| 62 |
"attributes": {}
|
| 63 |
}
|
| 64 |
},
|
| 65 |
-
"total_flos":
|
| 66 |
"train_batch_size": 8,
|
| 67 |
"trial_name": null,
|
| 68 |
"trial_params": null
|
|
|
|
| 1 |
{
|
| 2 |
+
"best_metric": 0.8170926517571885,
|
| 3 |
+
"best_model_checkpoint": "../../modelParams/TrainingArguments_output/checkpoint-195",
|
| 4 |
+
"epoch": 4.984025559105431,
|
| 5 |
"eval_steps": 500,
|
| 6 |
+
"global_step": 195,
|
| 7 |
"is_hyper_param_search": false,
|
| 8 |
"is_local_process_zero": true,
|
| 9 |
"is_world_process_zero": true,
|
|
|
|
| 43 |
"eval_samples_per_second": 37.68,
|
| 44 |
"eval_steps_per_second": 4.725,
|
| 45 |
"step": 156
|
| 46 |
+
},
|
| 47 |
+
{
|
| 48 |
+
"epoch": 4.984025559105431,
|
| 49 |
+
"eval_accuracy": 0.8170926517571885,
|
| 50 |
+
"eval_loss": 0.4626227915287018,
|
| 51 |
+
"eval_runtime": 33.1556,
|
| 52 |
+
"eval_samples_per_second": 37.761,
|
| 53 |
+
"eval_steps_per_second": 4.735,
|
| 54 |
+
"step": 195
|
| 55 |
}
|
| 56 |
],
|
| 57 |
"logging_steps": 500,
|
|
|
|
| 71 |
"attributes": {}
|
| 72 |
}
|
| 73 |
},
|
| 74 |
+
"total_flos": 1.161992193425145e+16,
|
| 75 |
"train_batch_size": 8,
|
| 76 |
"trial_name": null,
|
| 77 |
"trial_params": null
|